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import ipyvuetify as v
import traitlets as tr
from .common import load_template, reload_module
import ipywidgets as ipw
import time
class AppLoader(v.VuetifyTemplate):
template = tr.Unicode(load_template("vue-templates/app-loader.vue")).tag(sync=True)
apps = tr.List(["widget1", "widget2"]).tag(sync=True)
selected_app = tr.Unicode("").tag(sync=True)
info = tr.Unicode("").tag(sync=True)
app_dialog = tr.Bool(False).tag(sync=True)
wsz = tr.Dict({}).tag(sync=True)
output_dialog = tr.Bool(False).tag(sync=True)
loading_app = tr.Bool(False).tag(sync=True)
loading_app_result = tr.Unicode("").tag(sync=True)
def __init__(self, appcfg, *ag, **kargs):
super().__init__(*ag, **kargs)
self.appcfg = appcfg
self.app_box = ipw.VBox([])
self.app_output = ipw.Output()
self.components = {"mycontent": self.app_box, "app-output": self.app_output}
self.apps = self.appcfg["apps"]
self.selected_app = self.apps[0]
def vue_clear_output(self, data):
self.app_output.clear_output()
def vue_reload(self, data):
try:
self.app_output.clear_output()
self.loading_app_result = ""
self.loading_app = True
with self.app_output:
try:
t0 = time.time()
print(f"Loading {self.selected_app}")
self.info = ""
mod = reload_module(self.selected_app)
self.app = mod.new(output=self.app_output)
self.app_box.children = (self.app,)
self.loading_app_result = "success"
t1 = time.time()
print(f"Loaded {self.selected_app} in {t1-t0:.1f} s")
except Exception as e:
self.loading_app_result = "error"
self.info = f"Error: check out the output"
raise e
finally:
self.loading_app = False
except Exception as e:
self.info = f"{e} "
finally:
self.loading_app = False
def vue_pressed_r(self, data):
self.selected_app = "Pressed R" + str(data)
if data == "h":
self.m.center = [-70, 10]
| energy-sdk-l2rpn-master | nvgridui/nvapp/appLoader.py |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import ipywidgets as ipw
import ipyvuetify as v
import traitlets as tr
from .common import load_template
import matplotlib
# matplotlib.use("WebAgg")
class PlotsComponent(v.VuetifyTemplate):
template = tr.Unicode(load_template("vue-templates/plots-component.vue")).tag(
sync=True
)
gby = tr.Unicode("").tag(sync=True)
showfig = tr.Bool(True).tag(sync=True)
def __init__(self, gridcontrol, *ag, app=None, **kargs):
super().__init__(*ag, **kargs)
self.app = app
self.gc = gridcontrol
self.outputwidget = ipw.Output()
with self.outputwidget:
plt.close("all")
f = plt.figure(figsize=(5, 3))
self.figure = f
self.ax = plt.gca()
self.components = {"plt-figure": self.outputwidget}
self.gby = "all"
def vue_set_plot_gby(self, gby):
with self.app.output:
self.gby = gby
self.update()
def vue_update_plot(self, data):
self.update()
def update(self):
with self.app.output:
self.gc.steps_data
ts = [e["ts"] for e in self.gc.steps_data]
maxrhos = [e["rho"].max() * 100 for e in self.gc.steps_data]
env = self.gc.gm.env
gent = {gt: [] for gt in env.gen_type}
for e in self.gc.steps_data:
d = {}
for gt, gv in zip(env.gen_type, e["gen_p"]):
if gt not in d:
d[gt] = 0
d[gt] += gv
for gt, gv in d.items():
gent[gt] += [gv]
gen_p = [e["gen_p"].sum() for e in self.gc.steps_data]
load_p = [e["load_p"].sum() for e in self.gc.steps_data]
plt.clf()
plt.subplot(2, 1, 1)
df = pd.DataFrame({"max(ρ)": maxrhos}, index=ts)
df.plot(ax=plt.gca(), style=".-")
plt.ylabel("max(ρ) (%)")
legend = plt.legend("off")
legend.remove()
plt.axhline(100, lw=1, color="red")
plt.axhline(0, lw=1, color="black")
plt.subplot(2, 1, 2)
gby = self.gby
if gby == "all":
df = pd.DataFrame({"gen_p": gen_p, "load_p": load_p}, index=ts)
df.plot(ax=plt.gca())
plt.ylabel("P MW")
if gby == "gen type":
dfg = pd.DataFrame(gent, index=ts)
ax = plt.gca()
dfg.plot.area(ax=ax, stacked=True)
plt.ylabel("P MW")
plt.legend(
ncol=5,
loc="lower center",
fontsize="small",
bbox_to_anchor=(0.5, 1),
)
plt.title(" ")
if gby == "gen type (100%)":
dfg = pd.DataFrame(gent, index=ts)
ax = plt.gca()
dfg.apply(lambda x: x * 100 / sum(x), axis=1).plot.area(
ax=ax, stacked=True
)
plt.ylabel("%")
plt.legend(
ncol=5,
loc="lower center",
fontsize="small",
bbox_to_anchor=(0.5, 1.0),
)
plt.title(" ")
plt.tight_layout()
| energy-sdk-l2rpn-master | nvgridui/nvgrid/plotscomponent.py |
import ipywidgets as ipw
import ipyvuetify as v
import time
import traitlets as tr
import numpy as np
from .common import load_template, rho2color, AllTopoActions
import heapq
def list_els_from_action(a):
topo = a.impact_on_objects()["topology"]["bus_switch"]
subs = set()
gens = set()
loads = set()
lines = set()
for ev in topo:
subs.add(ev["substation"])
if "gener" in ev["object_type"]:
gens.add(ev["object_id"])
if "load" in ev["object_type"]:
loads.add(ev["object_id"])
if "line" in ev["object_type"]:
lines.add(ev["object_id"])
return subs, gens, loads, lines
class ActionRecommender(v.VuetifyTemplate):
template = tr.Unicode(load_template("vue-templates/action-recommender.vue")).tag(
sync=True
)
nactions = tr.Int(0).tag(sync=True)
act_search_status = tr.Unicode("").tag(sync=True)
recommended_acts = tr.List(
[
{"aid": 1, "rho": "54.2%", "color": "green", "info": "lots of text here"},
{"aid": 10, "rho": "154.2%", "color": "red", "info": "lots more text here"},
]
).tag(sync=True)
randomize = tr.Bool(False).tag(sync=True)
hot_subs = tr.List([]).tag(sync=True)
def __init__(self, gridcontrol, *ag, app=None, **kargs):
super().__init__(*ag, **kargs)
self.gc = gridcontrol
self.gm = gridcontrol.gm
self.app = app
def reset(self):
self.nactions = 0
self.alltopoactions = None
self.recommended_acts = []
def update(self):
obs = self.gm.env.get_obs()
self.hot_subs = [
(subid, cdt)
for subid, cdt in enumerate(obs.time_before_cooldown_sub)
if cdt > 0
]
def vue_unhighlight_elements(self, data):
self.gm.unhighlight_elements()
def vue_highlight_elements(self, aid):
with self.app.output:
action = self.alltopoactions.get_action(aid)
subs, gens, loads, lines = list_els_from_action(action)
self.gm.highlight_elements(
{"subs": subs, "lines": lines, "gens": gens, "loads": loads}
)
def vue_take_action(self, aid):
with self.app.output:
print("taking action")
action = self.alltopoactions.get_action(aid)
self.gc.take_action(action)
def vue_search_actions(self, perc):
with self.app.output:
if self.alltopoactions is None:
self.act_search_status = "Calculating all possible actions..."
env = self.gm.env
self.alltopoactions = AllTopoActions(env)
# self.actions = env.action_space.get_all_unitary_topologies_change(
# env.action_space
# )
self.nactions = self.alltopoactions.nactions
self.act_search_status = (
f"Calculating all possible actions... {self.nactions:,}"
)
perc = int(perc)
env = self.gm.env
obs = self.gm.env.get_obs()
# allacts = [(aid, a) for aid, a in enumerate(self.actions)]
allacts = self.alltopoactions.get_actions()
if self.randomize:
np.random.shuffle(allacts)
acts = allacts[: int(self.nactions * (perc / 100))]
print("nacts all", len(allacts))
nacts = len(acts)
prefix = f"Searching among {nacts} actions... "
self.act_search_status = prefix
current_rho = obs.rho.max()
selected_acts = []
self.recommended_acts = []
t0 = time.time()
for ai, (actid, act) in enumerate(acts):
if not env._game_rules(act, env):
continue
sobs, reward, done, info = obs.simulate(act)
rho = sobs.rho.max()
if not done and rho < current_rho:
item = (rho, actid, act)
heapq.heappush(selected_acts, item)
selected_acts = heapq.nsmallest(3, selected_acts)
l = []
for mrho, aid, action in selected_acts:
l += [
{
"aid": aid,
"rho": f"{mrho*100:.1f}%",
"color": rho2color(mrho),
"info": str(action),
}
]
self.recommended_acts = l
if ai % 50 == 0:
itspersec = (ai + 1) / (time.time() - t0)
self.act_search_status = (
prefix + f"{ai+1} of {nacts} [{itspersec:,.1f} acts/s]"
)
self.act_search_status = (
prefix + f"{ai+1} of {nacts} [{itspersec:,.1f} acts/s]"
)
| energy-sdk-l2rpn-master | nvgridui/nvgrid/actionrecommender.py |
import ipywidgets as ipw
import ipyvuetify as v
import time
import traitlets as tr
import ipyleaflet as ipl
import numpy as np
from .common import load_template, rho2color
from grid2op.MakeEnv import make
from lightsim2grid import LightSimBackend as BACKEND
PATH2ICONS = "/nvgrid/assets/"
def rho2delay(rho):
d = max((900 - 10 * 100 * np.abs(rho)), 50)
return int(d)
class GridMap(v.VuetifyTemplate):
template = tr.Unicode(load_template("vue-templates/grid-map.vue")).tag(sync=True)
wsz = tr.Dict({}).tag(sync=True)
line_weight = tr.Unicode("3").tag(sync=True)
animate_lines = tr.Bool(False)
selected_line = tr.Int(-1).tag(sync=True)
selected_gen = tr.Int(-1).tag(sync=True)
def __init__(self, *ag, app=None, **kargs):
super().__init__(*ag, **kargs)
self.app = app
self.lm = ipl.Map(
layers=[],
crs=ipl.projections.Simple,
scroll_wheel_zoom=True,
zoom_control=False,
attribution_control=False,
zoom=2,
)
self._lg = ipl.LayerGroup()
self.lm.add_layer(self._lg)
self._lg2 = ipl.LayerGroup()
self.lm.add_layer(self._lg2)
# self.lm.add_control(ipl.FullScreenControl())
self.status_html = ipw.HTML("status")
self.lm.add_control(
ipl.WidgetControl(widget=self.status_html, position="bottomright")
)
self.lm_box = ipw.VBox([self.lm])
self.components = {"grid-map": self.lm_box}
self.observe(lambda a: self.on_app_resize(), "wsz")
def handle_line_anim(a):
if self.animate_lines:
self._make_lines_animated()
else:
# self._make_lines()
self.lgs["lines-animated"].layers = []
self.observe(handle_line_anim, "animate_lines")
# display config
# self.animate_lines = False # True
# load environment
# self.load_env()
# self.reset()
def unhighlight_elements(self):
self._lg2.layers = []
def highlight_elements(self, els):
features = []
fc = {"type": "FeatureCollection", "features": features}
for subid in els.get("subs", []):
subn = f"sub_{subid}"
f = self.el2feature["subs"][subn]
f["properties"]["style"]["weight"] = 5
features += [f]
for etype in ["lines", "gens", "loads"]:
for lid in els.get(etype, []):
f = self.el2feature[etype][lid]
f["properties"]["style"]["weight"] = 5
features += [f]
gj = ipl.GeoJSON(data=fc)
self._lg2.layers = [gj]
pass
def center_map(self):
self.lm.center = [0, 0]
def on_app_resize(self):
self.lm.layout.height = f"{int(self.wsz['y']-120)}px"
self.lm_box.children = []
self.lm_box.children = [self.lm]
def vue_lc_key(self, key):
self.lm.center = [10, -60]
def load_env(self, dataset="l2rpn_icaps_2021_large"):
self.env = make(dataset, backend=BACKEND()) # make("l2rpn_icaps_2021_large")
self.obs = self.env.get_obs()
self.subs = {subn: loc for subn, loc in self.env.grid_layout.items()}
def reset(self, update_icons=True):
self._make_elements(update_icons=update_icons)
self.selected_line = -1
def redraw(self):
self._make_lines()
def _update_lgs(self, key, newlayers):
# self._lg2.layers = []
# self._lg2.layers = newlayers
# time.sleep(0.05)
self.lgs[key].layers = newlayers
self._lg2.layers = []
def _make_elements(self, update_icons=True):
if update_icons or not hasattr(self, "lgs"):
self.el2feature = {}
self.lgs = {
lgn: ipl.LayerGroup()
for lgn in ["subs", "lines", "lines-animated", "gens", "loads"]
}
self._lg.layers = list(self.lgs.values())
self.sub2pos = {}
self._make_subs()
self._make_lines()
self._make_gens(update_icons=update_icons)
self._make_loads(update_icons=update_icons)
def _deselect_lines(self):
for pl, ap in self.lid2ap.values():
pl.weight = 1
def _select_line(self, lid):
self._deselect_all()
pl, ap = self.lid2ap[lid]
pl.weight = 7
def _deselect_subs(self):
for sr in self.subn2rect.values():
sr.weight = 1
def _deselect_all(self):
self._deselect_subs()
self._deselect_lines()
def _select_sub(self, subn):
self._deselect_all()
sr = self.subn2rect[subn]
sr.weight = 5
def _on_hover_sub(self, subn):
# gen = -1
# s = f"[{subn}] gen={gen:.1f}Mw"
# self.status_html.value = s
obs = self.env.get_obs()
locs = f"{self.sub2pos[subn]}"
subid = int(subn.split("_")[1])
genids = np.where(obs.gen_to_subid == subid)[0]
gen = np.sum(obs.gen_p[genids]) if len(genids) > 0 else 0
loadids = np.where(obs.load_to_subid == subid)[0]
load = np.sum(obs.load_p[loadids]) if len(loadids) > 0 else 0
s = f"[{subn}] gen={gen:.1f}Mw | load={load:.1f}Mw | gen-load={gen-load:.1f} Mw"
self.status_html.value = s
# self._select_sub(subn)
def _on_hover_line(self, lid):
obs = self.env.get_obs()
rho = obs.rho[lid]
p_or = obs.p_or[lid]
q_or = obs.q_or[lid]
s2s = f" | {obs.line_or_to_subid[lid]}->{obs.line_ex_to_subid[lid]}"
self.status_html.value = (
f"PL {lid}: ρ={rho*100:.1f}% | P={p_or:.1f} MW | Q={q_or:.1f} MW" + s2s
)
# self._select_line(lid)
def _on_hover_load(self, lid):
# gen = -1
# s = f"[{subn}] gen={gen:.1f}Mw"
obs = self.env.get_obs()
self.status_html.value = (
f"[Load {lid}] P={obs.load_p[lid]:.1f} | Q={obs.load_q[lid]:.1f}"
)
def _on_hover_gen(self, lid):
# gen = -1
# s = f"[{subn}] gen={gen:.1f}Mw"
obs = self.env.get_obs()
self.status_html.value = f"[Gen {lid} - {obs.gen_type[lid]}] P={obs.gen_p[lid]:.1f} ≤ {obs.gen_pmax[lid]:.1f} | max Δ = (-{obs.gen_max_ramp_down[lid]:.1f}, +{obs.gen_max_ramp_up[lid]:.1f}) | Q={obs.gen_q[lid]:.1f} | redispatchable={obs.gen_redispatchable[lid]}"
def _scale(self, loc, s=0.1):
return loc[0] * s, loc[1] * s
# return loc[1] * s, loc[0] * s
def _make_subs(self):
scale = self._scale
# scale = lambda p: self._scale([p[1], p[0]])
scale = lambda p: (p[1], p[0])
dx = 15
bb = 15
markl = []
self.subn2rect = {}
for sn, loc in self.subs.items():
l1 = loc[0] - dx, loc[1]
l2 = loc[0] + dx, loc[1]
self.sub2pos[sn] = {
-1: loc,
1: l1,
2: l2,
}
# self.lgs["subs"].layers = [self.make_subs_fc(self.sub2pos)]
self._deselect_subs()
newlayers = [self.make_subs_fc(self.sub2pos)]
self._update_lgs("subs", newlayers)
def make_subs_fc(self, sub2pos):
if "subs" not in self.el2feature:
self.el2feature["subs"] = {}
scale = self._scale
# scale = lambda p: (p[1], p[0])
obs = self.env.get_obs()
rhos = obs.rho
features = []
fc = {"type": "FeatureCollection", "features": features}
for lid, pos in sub2pos.items():
bb = 15
l1, l2 = pos[1], pos[2]
locs = [
scale([l1[0] - bb, l1[1] - bb]),
scale([l1[0] - bb, l2[1] + bb]),
scale([l2[0] + bb, l2[1] + bb]),
scale([l2[0] + bb, l1[1] - bb]),
scale([l1[0] - bb, l1[1] - bb]),
]
f = make_polygon_feature(locs, fid=lid)
features += [f]
self.el2feature["subs"][lid] = f
for p in [pos[1], pos[2]]:
f = {
"type": "Feature",
"properties": {
"ftype": "sub",
"fid": lid,
"style": {"weight": 10},
},
}
f["geometry"] = {
"type": "LineString",
"coordinates": [
scale(p),
scale(p),
],
}
features += [f]
hover_style = {"weight": 5}
def oh(**a):
props = a["feature"]["properties"]
lid = props["fid"]
self._on_hover_sub(lid)
gj = ipl.GeoJSON(data=fc, hover_style=hover_style)
gj.on_hover(oh)
return gj
def _is_line_disconnected(self, lid):
obs = self.env.get_obs()
return not obs.line_status[lid]
def lm_bounds(self):
arr = []
for locs in self.lid2locs.values():
arr += locs
arr = np.array(arr)
mn = np.min(arr[:, 0]), np.min(arr[:, 1])
mx = np.max(arr[:, 0]), np.max(arr[:, 1])
return [mn, mx]
def center_map(self):
lm = self.lm
lm.center = [0, 0]
lmb = lm.bounds
gb = self.lm_bounds()
# if all(np.array(lmb[0])<np.array(gb[0])) and all(np.array(lmb[1])>np.array(gb[1])):
# lm.zoom += 1
gx = max(np.abs(gb[0][0]), np.abs(gb[1][0]))
lmx = max(np.abs(lmb[0][0]), np.abs(lmb[1][0]))
xzoom = np.round(np.log(lmx / gx) / np.log(2))
gx = max(np.abs(gb[0][1]), np.abs(gb[1][1]))
lmx = max(np.abs(lmb[0][1]), np.abs(lmb[1][1]))
yzoom = np.round(np.log(lmx / gx) / np.log(2))
lm.zoom += min(xzoom, yzoom)
def _make_lines(self):
obs = self.env.get_obs()
sub2pos = self.sub2pos
scale = self._scale # lambda p: self._scale([p[1], p[0]])
# scale = lambda p: (p[1], p[0])
lid2ap = {}
locs2ap = {}
for lid, (sor, sex) in enumerate(
zip(obs.line_or_to_subid, obs.line_ex_to_subid)
):
exb = obs.line_ex_bus[lid]
orb = obs.line_or_bus[lid]
locs = (
scale(sub2pos[f"sub_{sor}"][orb]),
scale(sub2pos[f"sub_{sex}"][exb]),
)
if locs not in locs2ap:
locs2ap[locs] = []
locs2ap[locs] += [lid]
# fix geometry of lines that run parallel to one another
lid2locs = {}
for locs, aps in locs2ap.items():
if len(aps) == 2:
p1, p2 = locs
p1, p2 = np.array(p1), np.array(p2)
tvec = p1 - p2
nvec = np.array([tvec[1], -tvec[0]])
nvec = nvec / np.linalg.norm(nvec)
sign = 1
for lid in aps:
sign = -sign
pm = p2 + tvec / 2 + sign * nvec
lid2locs[lid] = np.array([p1, pm, p2]).tolist()
else:
for lid in aps:
lid2locs[lid] = locs
# finally, update the layer
# make GeoJSON layer
self.lid2ap = {}
self.lid2locs = lid2locs
# self.lgs["lines"].layers = [self.make_lines_fc(lid2locs)]
self._update_lgs("lines", [self.make_lines_fc(lid2locs)])
def make_lines_fc(self, lid2locs):
if "lines" not in self.el2feature:
self.el2feature["lines"] = {}
obs = self.env.get_obs()
rhos = obs.rho
features = []
fc = {"type": "FeatureCollection", "features": features}
for lid, locs in lid2locs.items():
f = {
"type": "Feature",
"properties": {
"ftype": "line",
"fid": lid,
"style": {"color": rho2color(rhos[lid]), "weight": 3},
},
}
f["geometry"] = {"type": "LineString", "coordinates": locs}
if self._is_line_disconnected(lid):
f["properties"]["style"]["dashArray"] = "5"
f["properties"]["style"]["color"] = "black"
features += [f]
self.el2feature["lines"][lid] = f
hover_style = {"weight": 7}
def oh(**a):
props = a["feature"]["properties"]
lid = props["fid"]
self._on_hover_line(lid)
def oc(**a):
with self.app.output:
if a["event"] == "click":
self.selected_line = a["feature"]["properties"]["fid"]
gj = ipl.GeoJSON(data=fc, hover_style=hover_style)
gj.on_hover(oh)
gj.on_click(oc)
return gj
def _make_lines_animated(self):
obs = self.env.get_obs()
sub2pos = self.sub2pos
scale = lambda p: self._scale([p[1], p[0]])
lid2ap = {}
self.lid2ap = lid2ap
rhos = obs.rho
locs2ap = {}
for lid, (sor, sex) in enumerate(
zip(obs.line_or_to_subid, obs.line_ex_to_subid)
):
if self._is_line_disconnected(lid):
continue
exb = obs.line_ex_bus[lid]
orb = obs.line_or_bus[lid]
locs = [
scale(sub2pos[f"sub_{sor}"][orb]),
scale(sub2pos[f"sub_{sex}"][exb]),
]
if obs.p_or[lid] < 0:
locs = list(reversed(locs))
with self.app.output:
ap = ipl.AntPath(
locations=locs, # if else reversed(locs),
delay=rho2delay(rhos[lid]),
paused=(not self.animate_lines)
or bool(np.round(obs.p_or[lid] * 100) == 0),
weight=int(self.line_weight),
color=rho2color(obs.rho[lid]),
)
pl = ipl.Polyline(locations=locs, weight=1, fill=False)
lid2ap[lid] = (pl, ap)
locsk = str(locs)
if locsk not in locs2ap:
locs2ap[locsk] = []
locs2ap[locsk] += [(pl, ap)]
def mkomo(lid):
def omo(**a):
self._on_hover_line(lid)
return omo
ap.on_mouseover(mkomo(lid))
# fix geometry of lines that run parallel to one another
for locs, aps in locs2ap.items():
if len(aps) == 2:
p1, p2 = eval(locs)
p1, p2 = np.array(p1), np.array(p2)
tvec = p1 - p2
nvec = np.array([tvec[1], -tvec[0]])
nvec = nvec / np.linalg.norm(nvec)
pm = p2 + tvec / 2 + nvec
aps[0][0].locations = np.array([p1, pm, p2]).tolist()
aps[0][1].locations = np.array([p1, pm, p2]).tolist()
pm = p2 + tvec / 2 - nvec
aps[1][0].locations = np.array([p1, pm, p2]).tolist()
aps[1][1].locations = np.array([p1, pm, p2]).tolist()
l = [e[0] for e in lid2ap.values()] + [e[1] for e in lid2ap.values()]
self.lgs["lines-animated"].layers = l # list(lid2ap.values())
def _make_gens(self, update_icons=True):
obs = self.env.get_obs()
sub2pos = self.sub2pos
scale = self._scale # lambda p: self._scale([p[1], p[0]])
gtype2icon = {
"nuclear": "radioactive",
"thermal": "fire",
"solar": "solar-panel",
"wind": "wind-turbine",
"hydro": "hydro-power",
}
gen2loc = {}
self.gen2loc = gen2loc
r = 50
if update_icons:
self.gen_icons = []
for lid, sid in enumerate(obs.gen_to_subid):
sloc = sub2pos[f"sub_{sid}"][obs.gen_bus[lid]]
sl = np.array(sloc)
a = 2 * 3.1415 / 16 * obs.gen_to_sub_pos[lid]
x = sl[0] + r * np.cos(a)
y = sl[1] + r * np.sin(a)
lloc = np.array([x, y]).tolist()
gen2loc[lid] = [
scale(lloc),
scale(sloc),
] # {"loc": (x, y), "sub_loc": sloc}
gicon = gtype2icon[obs.gen_type[lid]]
# icon = ipl.Icon(
# icon_url=f"/files/nvgrid/assets/{gicon}.svg", icon_size=[30, 30]
# )
# marker = ipl.Marker(location=scale([lloc[1], lloc[0]]), icon=icon)
if update_icons:
imo = place_image(
scale([lloc[1], lloc[0]]), 1, f"{PATH2ICONS}{gicon}.svg"
)
self.gen_icons += [imo]
gj = self.make_gen_fc()
# self.lgs["gens"].layers = self.gen_icons + [gj]
newlayers = [gj] + self.gen_icons
self._update_lgs("gens", newlayers)
def make_gen_fc(self):
if "gens" not in self.el2feature:
self.el2feature["gens"] = {}
obs = self.env.get_obs()
features = []
fc = {"type": "FeatureCollection", "features": features}
for lid, locs in self.gen2loc.items():
f = {
"type": "Feature",
"properties": {
"ftype": "gen",
"fid": lid,
"style": {"color": "yellow", "weight": 3},
},
}
t = np.array(locs[1]) - np.array(locs[0])
t /= np.linalg.norm(t)
p0 = (np.array(locs[0]) + t * 3 / 2).tolist()
nlocs = [p0, locs[1]]
f["geometry"] = {"type": "LineString", "coordinates": nlocs}
# features += [f]
col = "green" if obs.gen_p[lid] > 0 else "black"
fpoly = make_reg_poly(locs[0], 3 / 2, 6, fid=lid, color=col)
f = merge_feature_geometries(fpoly, f)
features += [f]
self.el2feature["gens"][lid] = f
hover_style = {"weight": 7}
def oh(**a):
props = a["feature"]["properties"]
lid = props["fid"]
self._on_hover_gen(lid)
def oc(**a):
with self.app.output:
if a["event"] == "click":
self.selected_gen = a["feature"]["properties"]["fid"]
gj = ipl.GeoJSON(data=fc, hover_style=hover_style)
gj.on_hover(oh)
gj.on_click(oc)
return gj
def _make_loads(self, update_icons=True):
obs = self.env.get_obs()
sub2pos = self.sub2pos
scale = self._scale # lambda p: self._scale([p[1], p[0]])
load2loc = {}
self.load2loc = load2loc
r = 50
# markers = []
if update_icons:
self.load_icons = []
for lid, sid in enumerate(obs.load_to_subid):
sloc = sub2pos[f"sub_{sid}"][obs.load_bus[lid]]
sl = np.array(sloc)
a = 2 * 3.1415 / 16 * obs.load_to_sub_pos[lid]
x = sl[0] + r * np.cos(a)
y = sl[1] + r * np.sin(a)
lloc = np.array([x, y]).tolist()
load2loc[lid] = [
scale(lloc),
scale(sloc),
] # {"loc": (x, y), "sub_loc": sloc}
# icon = ipl.Icon(
# icon_url="/files/nvgrid/assets/power-plug.svg", icon_size=[30, 30]
# )
# marker = ipl.Marker(location=scale([lloc[1], lloc[0]]), icon=icon)
if update_icons:
imo = place_image(
scale([lloc[1], lloc[0]]), 1, f"{PATH2ICONS}/power-plug.svg"
)
self.load_icons += [imo]
gj = self.make_load_fc()
# self.lgs["loads"].layers =
newlayers = [gj] + self.load_icons
self._update_lgs("loads", newlayers)
def make_load_fc(self):
if "loads" not in self.el2feature:
self.el2feature["loads"] = {}
obs = self.env.get_obs()
features = []
fc = {"type": "FeatureCollection", "features": features}
for lid, locs in self.load2loc.items():
f = {
"type": "Feature",
"properties": {
"ftype": "load",
"fid": lid,
"style": {"color": "black", "weight": 3},
},
}
t = np.array(locs[1]) - np.array(locs[0])
t /= np.linalg.norm(t)
p0 = (np.array(locs[0]) + t * 3 / 2).tolist()
nlocs = [p0, locs[1]]
f["geometry"] = {"type": "LineString", "coordinates": nlocs}
# features += [f]
col = "black"
if obs.load_p[lid] > 0:
col = "orange"
elif obs.load_p[lid] < 0:
col = "green"
fpoly = make_reg_poly(locs[0], 3 / 2, 4, fid=lid, color=col)
f = merge_feature_geometries(fpoly, f)
features += [f]
self.el2feature["loads"][lid] = f
hover_style = {"weight": 7}
def oh(**a):
props = a["feature"]["properties"]
lid = props["fid"]
self._on_hover_load(lid)
gj = ipl.GeoJSON(data=fc, hover_style=hover_style)
gj.on_hover(oh)
return gj
#####
# GEoJSON utilities
def make_polygon_feature(locs, fid="", color="black", weight=1):
f = {
"type": "Feature",
"properties": {
"ftype": "gen",
"fid": fid,
"style": {"color": color, "weight": weight},
},
}
f["geometry"] = {"type": "Polygon", "coordinates": [locs]}
return f
def make_reg_poly(loc, r, n, fid="", color="black", weight="1"):
cx, cy = loc
locs = []
for i in range(n + 1):
a = 2 * 3.1415 / n * i
x = cx + r * np.cos(a)
y = cy + r * np.sin(a)
locs += [[x, y]]
return make_polygon_feature(locs, fid=fid, color=color, weight=weight)
def place_image(loc, r, fname):
x, y = loc
bounds = [[x - r, y - r], [x + r, y + r]]
imo = ipl.ImageOverlay(url=fname, bounds=bounds)
return imo
def merge_feature_geometries(f1, f2):
geom = {
"type": "GeometryCollection",
"geometries": [f["geometry"] for f in [f1, f2]],
}
f = {k: v for k, v in f1.items()}
f["geometry"] = geom
return f
| energy-sdk-l2rpn-master | nvgridui/nvgrid/gridmap.py |
from .app import new
| energy-sdk-l2rpn-master | nvgridui/nvgrid/__init__.py |
import os
def load_template(filename):
with open(os.path.join(os.path.dirname(__file__), filename)) as f:
return f.read()
def rho2color(rho):
color = "black"
color = "green"
if rho > 0.70:
color = "#888800"
if rho > 0.80:
color = "orange"
if rho > 0.99:
color = "red"
return color
def dict2pretty_string(d):
from pprint import pprint
from io import StringIO
o = StringIO()
pprint(d, o, width=30)
o.seek(0)
return o.read()
class AllTopoActions:
def __init__(self, env):
self.env = env
obs = env.get_obs()
self.subid2actids = {}
self.nsubs = len(obs.time_before_cooldown_sub)
self.actions = []
for subid, cdt in enumerate(obs.time_before_cooldown_sub):
acts = env.action_space.get_all_unitary_topologies_change(
env.action_space, sub_id=subid
)
n = len(self.actions)
self.actions += acts
self.subid2actids[subid] = list(range(n, len(self.actions)))
self.nactions = len(self.actions)
def get_actions(self, exclude_subs=[]):
obs = self.env.get_obs()
excludesubs = exclude_subs + [
subid for subid, cdt in enumerate(obs.time_before_cooldown_sub) if cdt > 0
]
print("excludesubs", excludesubs)
acts = []
for subid in range(self.nsubs):
if subid not in excludesubs:
acts += [(aid, self.actions[aid]) for aid in self.subid2actids[subid]]
return acts
def get_action(self, aid):
return self.actions[aid]
| energy-sdk-l2rpn-master | nvgridui/nvgrid/common.py |
import ipywidgets as ipw
import ipyvuetify as v
import time
import traitlets as tr
import ipyleaflet as ipl
from .common import load_template
from .gridmap import GridMap
from .gridcontrol import GridControl
class App(v.VuetifyTemplate):
template = tr.Unicode(load_template("vue-templates/app.vue")).tag(sync=True)
wsz = tr.Dict({}).tag(sync=True)
output_dialog = tr.Bool(False).tag(sync=True)
def __init__(self, output, *ag, **kargs):
super().__init__(*ag, **kargs)
self.output = output
self.gm = ipw.HTML("GridMap")
self.gm = GridMap(app=self)
self.gc = GridControl(self.gm, app=self)
self.lc_box = ipw.VBox([self.gm])
self.rc_box = ipw.VBox([self.gc])
self.components = {
"left-card": self.lc_box,
"right-card": self.rc_box,
"app-output": self.output,
}
def vue_clear_output(self, key):
self.output.clear_output()
def new(output=None):
"""Creates a new app"""
return App(output)
| energy-sdk-l2rpn-master | nvgridui/nvgrid/app.py |
import ipywidgets as ipw
import ipyvuetify as v
import time
import traitlets as tr
import numpy as np
import grid2op
from .common import load_template, rho2color, dict2pretty_string
from .actionrecommender import ActionRecommender
from .plotscomponent import PlotsComponent
class GridControl(v.VuetifyTemplate):
template = tr.Unicode(load_template("vue-templates/grid-control.vue")).tag(
sync=True
)
date = tr.Unicode(f"{time.ctime(time.time())}").tag(sync=True)
steps = tr.Unicode("").tag(sync=True)
maxrho = tr.Unicode(f"{.65132*100:.1f}%").tag(sync=True)
maxrho_color = tr.Unicode("black").tag(sync=True)
steps2attempt = tr.Unicode("0").tag(sync=True)
scenario = tr.Unicode("scenario").tag(sync=True)
step_progress = tr.Int(0).tag(sync=True)
animated_lines = tr.Bool(False).tag(sync=True)
animation_loading = tr.Bool(False).tag(sync=True)
nsubs = tr.Int(0).tag(sync=True)
nlines = tr.Int(0).tag(sync=True)
selected_gen = tr.Dict(
{"gid": 0, "redispatchable": True, "maxup": 1, "maxdown": -1, "info": ""}
).tag(sync=True)
disconnected_lines = tr.List([{"lid": 1, "cdt": 0}, {"lid": 10, "cdt": 3}]).tag(
sync=True
)
selected_lines = tr.List([{"lid": 1}, {"lid": 10}]).tag(sync=True)
logs = tr.List([]).tag(sync=True)
show_nlogs = tr.Int(1).tag(sync=True)
environments = tr.List(
[{"text": e, "value": e} for e in grid2op.list_available_local_env()]
+ [
{"text": e + " [remote]", "value": e}
for e in grid2op.list_available_remote_env()
]
).tag(sync=True)
env_name = tr.Unicode("").tag(sync=True)
env_loading = tr.Bool(False).tag(sync=True)
def select_gen(self, gid):
obs = self.gm.env.get_obs()
lid = gid
self.selected_gen = {
"gid": gid,
"redispatchable": bool(obs.gen_redispatchable[gid]),
"maxup": int(100 * obs.gen_max_ramp_up[gid]),
"maxdown": int(100 * obs.gen_max_ramp_down[gid]),
"info": f"Gen {lid} - {obs.gen_type[lid]} | P={obs.gen_p[lid]:.1f} ≤ {obs.gen_pmax[lid]:.1f} | Q={obs.gen_q[lid]:.1f}",
}
def _mk_redispatch_act(self, gid, amnt):
with self.app.output:
env = self.gm.env
act = env.action_space()
act.redispatch = (int(gid), float(amnt))
print(act)
return act
def vue_redispatch(self, data):
with self.app.output:
gid, amnt = data
action = self._mk_redispatch_act(gid, amnt)
self.take_action(action)
def vue_reconnect_line(self, lid):
self.reconnect_line(lid)
def vue_sim_disconnect_line(self, lid):
self.sim_disconnect_line(lid)
def vue_disconnect_line(self, lid):
self.disconnect_line(lid)
def vue_unhighlight_elements(self, data):
self.gm.unhighlight_elements()
def vue_highlight_elements(self, els):
with self.app.output:
self.gm.highlight_elements(els)
def vue_load_env(self, data):
self.env_loading = True
try:
with self.app.output:
if self.env_name not in grid2op.list_available_local_env():
self.app.output_dialog = True
print(f"Loading environment [{self.env_name}] ")
t0 = time.time()
self.gm.load_env(self.env_name)
self.init_game()
self.nlines = len(self.gm.lid2locs)
self.nsubs = len(self.gm.sub2pos)
print(f"Loaded environment [{self.env_name}] in {time.time()-t0:.1f} s")
finally:
self.env_loading = False
def vue_restart(self, data):
self.init_game()
def vue_attempt_nsteps(self, data):
self.step(int(self.steps2attempt))
self.animated_lines = False
self.gm.animate_lines = self.animated_lines
def vue_animate_lines(self, data):
self.animation_loading = True
self.animated_lines = not self.animated_lines
self.gm.animate_lines = self.animated_lines
self.animation_loading = False
def vue_center_map(self, data):
self.gm.center_map()
def __init__(self, gridmap, *ag, app=None, **kargs):
super().__init__(*ag, **kargs)
self.app = app
self.gm = gridmap
self.pc = PlotsComponent(self, app=self.app)
def osl(a):
if self.gm.selected_line >= 0:
self.selected_lines = [{"lid": self.gm.selected_line, "info": ""}]
self.gm.observe(osl, "selected_line")
def osg(a):
with self.app.output:
if self.gm.selected_gen >= 0:
self.select_gen(self.gm.selected_gen)
self.gm.observe(osg, "selected_gen")
self.ar = ActionRecommender(self, app=self.app)
self.components = {"action-recommender": self.ar, "plots-control": self.pc}
self.init_game()
def _update_info(self, done=False):
with self.app.output:
self.logs = [dict2pretty_string(log) for log in self.rets[-10:]]
minperstep = 5
days = self.nsteps * minperstep / 60 / 24
self.steps = f"{self.nsteps:,} steps [{days:.1f} days]"
if done:
self.steps = "Success!!! " + self.steps
obs = self.gm.env.get_obs()
rho = obs.rho.max()
self.maxrho = f"{rho*100:.1f}%"
self.maxrho_color = rho2color(rho)
self.date = str(np.datetime64(self.gm.env.time_stamp, "m"))
self.scenario = self.gm.env.chronics_handler.get_name()
self.update_disconnected_lines(obs=obs)
self.ar.update()
self.pc.update()
def reconnect_line(self, lid):
with self.app.output:
obs = self.gm.env.get_obs()
connected = obs.line_status[lid]
cdt = obs.time_before_cooldown_line[lid]
if not connected and cdt == 0:
a = self.gm.env.action_space()
a.line_set_status = [(lid, 1)]
self.take_action(a)
def disconnect_line(self, lid):
with self.app.output:
obs = self.gm.env.get_obs()
connected = obs.line_status[lid]
cdt = obs.time_before_cooldown_line[lid]
if connected and cdt == 0:
a = self.gm.env.action_space()
a.line_set_status = [(lid, -1)]
self.take_action(a)
def sim_disconnect_line(self, lid):
with self.app.output:
obs = self.gm.env.get_obs()
connected = obs.line_status[lid]
cdt = obs.time_before_cooldown_line[lid]
if connected and cdt == 0:
a = self.gm.env.action_space()
a.line_set_status = [(lid, -1)]
sobs, reward, done, info = obs.simulate(a)
s = f"ρ={sobs.rho.max()*100:.1f}%\n" + str(info)
maxrho = f"ρ={sobs.rho.max()*100:.1f}%"
d = {
"maxrho": maxrho,
"step-id": self.nsteps,
"done": done,
"reward": reward,
"info": info,
}
self.selected_lines = [{"lid": lid, "info": dict2pretty_string(d)}]
def update_disconnected_lines(self, obs=None):
if obs is None:
obs = self.env.get_obs()
l = []
for lid, connected in enumerate(obs.line_status):
cdt = obs.time_before_cooldown_line[lid]
if not connected:
e = {"lid": lid, "cdt": cdt}
l += [e]
self.disconnected_lines = l
def init_game(self):
if hasattr(self.gm, "env"):
self.nsteps = 0
self.gm.env.reset()
self._update_info()
self.animated_lines = False
self.animation_loading = False
self.gm.animate_lines = self.animated_lines
self.selected_lines = []
self.gm.reset()
self.ar.reset()
self.rets = []
self.steps_data = []
def _make_step(self, action):
ret = self.gm.env.step(action)
self.nsteps += 1
obs, reward, done, info = ret
maxrho = f"ρ={obs.rho.max()*100:.1f}%"
d = {
"maxrho": maxrho,
"step-id": self.nsteps,
"done": done,
"reward": reward,
"info": info,
}
self.rets += [d]
datum = {
"ts": np.datetime64(self.gm.env.time_stamp),
"rho": obs.rho.copy(),
"gen_p": obs.gen_p,
"load_p": obs.load_p,
}
self.steps_data += [datum]
return ret
def take_action(self, action):
self._make_step(action)
self._update_info()
self.gm.reset(update_icons=False)
def step(self, nsteps=1):
self.step_progress = 0
obs = self.gm.env.get_obs()
ndisconnected = sum(obs.line_status == False)
for stepi in range(nsteps):
a = self.gm.env.action_space({})
obs, reward, done, info = self._make_step(a)
self.step_progress = int(((stepi + 1) / nsteps) * 100)
if (
obs.rho.max() > 1
or done
or ndisconnected < sum(obs.line_status == False)
):
break
self._update_info(done=done)
self.gm.reset(update_icons=False)
| energy-sdk-l2rpn-master | nvgridui/nvgrid/gridcontrol.py |
#!/usr/bin/env python3
#
# SPDX-License-Identifier: BSD-3-Clause
# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
# Author: John Linford <[email protected]>
#
import sys
import argparse
KERNEL_FILE_TEMPLATE = """
//
// SPDX-License-Identifier: BSD-3-Clause
// Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
// Author: John Linford <[email protected]>
//
// This file was autogenerated by kgen.py
//
%(headers)s
const char * description = "%(descr)s";
unsigned long block_inst = %(block_inst)s;
unsigned long block_ops = %(block_ops)s;
unsigned long unroll = %(unroll)s;
void kernel(unsigned long iters)
{
for (unsigned long i=0; i<iters; ++i) {
asm volatile (
%(body)s
: /* no input */
: /* no output */
: %(clobber)s
);
}
}
"""
def usage_error(*args, **kwargs):
print(*args, file=sys.stderr, **kwargs)
sys.exit(100)
def generate_block(lines, clobber, block_ops, blk):
def ignore_register(x):
return x[0] not in ("x", "w", "v", "z", "d", "s", "h")
indent = 8*" "
count = blk.count
opcode = blk.opcode
formats = blk.operand[0::2]
values = []
for val in blk.operand[1::2]:
try:
evaluated = eval(val)
except SyntaxError as err:
usage_error("Syntax error in operand range value: %s" % err.text)
if (evaluated is not None) and (len(evaluated) != count):
usage_error("Invalid length %d of operand range value '%s' (expected %d)" % (len(evaluated), val, count))
values.append(evaluated)
for i in range(count):
operands = [fmt % val[i] if val is not None else fmt for (fmt, val) in zip(formats, values)]
clobber |= set([x.split(".")[0] for x in operands if not ignore_register(x)])
lines.append('%s"%s %s \\n\\t"' % (indent, opcode, ", ".join(operands)))
if blk.isa == "SCALAR":
lanes = "1"
elif blk.isa == "NEON":
lanes = "(128/%s)" % blk.typebits
elif blk.isa == "SVE":
lanes = "(8*svcntb()/%s)" % blk.typebits
block_ops.append("(%s*(%s*%s))" % (blk.count, blk.laneops, lanes))
def describe(unroll, blocks):
parts = ["%d(" % unroll]
for blk in blocks:
parts.append("%d(%s_%s_%db)" % (blk.count, blk.isa, blk.opcode.upper(), blk.typebits))
parts.append(")")
return " ".join(parts)
def generate(unroll, blocks):
lines = []
clobber = set()
headers = set()
block_ops = []
for blk in blocks:
generate_block(lines, clobber, block_ops, blk)
if blk.isa == "SVE":
headers.add("arm_sve.h")
block_ops = "+".join(block_ops)
block_inst = str(len(lines))
lines *= unroll
headers = "\n".join(["#include <%s>" % x for x in headers])
body = "\n".join(lines)
descr = describe(unroll, blocks)
clobber = ", ".join(sorted(['"%s"' % x for x in clobber]))
print(KERNEL_FILE_TEMPLATE % {
"headers": headers,
"descr": descr,
"block_inst": block_inst,
"block_ops": block_ops,
"unroll": unroll,
"body": body,
"clobber": clobber})
def parse_args(args):
block_parser = argparse.ArgumentParser(prog="", add_help=False)
block_parser.add_argument("isa", choices=["SCALAR", "SVE", "NEON"], help="Instruction ISA")
block_parser.add_argument("typebits", type=int, help="Size of the operation datatype in bits")
block_parser.add_argument("laneops", type=int, help="Operations performed per lane")
block_parser.add_argument("count", type=int, help="Instructions in the block")
block_parser.add_argument("opcode", help="Instruction opcode")
block_parser.add_argument("operand", nargs="+", help="Instruction operands")
block_help = block_parser.format_usage().replace("usage: ", "")
parser = argparse.ArgumentParser()
parser.add_argument("-u", "--unroll", type=int, help="Number of times to unroll the loop", default=4)
parser.add_argument("-b", required=True, nargs="+", metavar="block_template", dest="blocks", action="append", help=block_help)
parsed = parser.parse_args(args)
parsed.blocks = [block_parser.parse_args(blk) for blk in parsed.blocks]
return parsed
def main(*args, **kwargs):
parsed = parse_args(*args)
generate(parsed.unroll, parsed.blocks)
if __name__ == "__main__":
main(sys.argv[1:])
| arm-kernels-main | kgen.py |
#!/usr/bin/python3
import os, re, sys
from io import open
def list_whence():
with open('WHENCE', encoding='utf-8') as whence:
for line in whence:
match = re.match(r'(?:File|Source):\s*"(.*)"', line)
if match:
yield match.group(1)
continue
match = re.match(r'(?:File|Source):\s*(\S*)', line)
if match:
yield match.group(1)
continue
match = re.match(r'Licen[cs]e: (?:.*\bSee (.*) for details\.?|(\S*))\n',
line)
if match:
if match.group(1):
for name in re.split(r', | and ', match.group(1)):
yield name
continue
if match.group(2):
# Just one word - may or may not be a filename
if not re.search(r'unknown|distributable', match.group(2),
re.IGNORECASE):
yield match.group(2)
continue
def list_git():
with os.popen('git ls-files') as git_files:
for line in git_files:
yield line.rstrip('\n')
def main():
ret = 0
whence_list = list(list_whence())
known_files = set(name for name in whence_list if not name.endswith('/')) | \
set(['check_whence.py', 'configure', 'Makefile',
'README', 'copy-firmware.sh', 'WHENCE'])
known_prefixes = set(name for name in whence_list if name.endswith('/'))
git_files = set(list_git())
for name in sorted(list(known_files - git_files)):
sys.stderr.write('E: %s listed in WHENCE does not exist\n' % name)
ret = 1
for name in sorted(list(git_files - known_files)):
# Ignore subdirectory changelogs and GPG detached signatures
if (name.endswith('/ChangeLog') or
(name.endswith('.asc') and name[:-4] in known_files)):
continue
# Ignore unknown files in known directories
for prefix in known_prefixes:
if name.startswith(prefix):
break
else:
sys.stderr.write('E: %s not listed in WHENCE\n' % name)
ret = 1
return ret
if __name__ == '__main__':
sys.exit(main())
| linux-firmware-nouveau | check_whence.py |
#!/usr/bin/env python3
import argparse
import os
import platform
import subprocess
# This list contains symbols that _might_ be exported for some platforms
PLATFORM_SYMBOLS = [
'__bss_end__',
'__bss_start__',
'__bss_start',
'__cxa_guard_abort',
'__cxa_guard_acquire',
'__cxa_guard_release',
'__cxa_allocate_dependent_exception',
'__cxa_allocate_exception',
'__cxa_begin_catch',
'__cxa_call_unexpected',
'__cxa_current_exception_type',
'__cxa_current_primary_exception',
'__cxa_decrement_exception_refcount',
'__cxa_deleted_virtual',
'__cxa_demangle',
'__cxa_end_catch',
'__cxa_free_dependent_exception',
'__cxa_free_exception',
'__cxa_get_exception_ptr',
'__cxa_get_globals',
'__cxa_get_globals_fast',
'__cxa_increment_exception_refcount',
'__cxa_new_handler',
'__cxa_pure_virtual',
'__cxa_rethrow',
'__cxa_rethrow_primary_exception',
'__cxa_terminate_handler',
'__cxa_throw',
'__cxa_uncaught_exception',
'__cxa_uncaught_exceptions',
'__cxa_unexpected_handler',
'__dynamic_cast',
'__emutls_get_address',
'__gxx_personality_v0',
'__end__',
'__odr_asan._glapi_Context',
'__odr_asan._glapi_Dispatch',
'_bss_end__',
'_edata',
'_end',
'_fini',
'_init',
'_fbss',
'_fdata',
'_ftext',
'_gSharedObjectHaikuABI',
'_gSharedObjectHaikuVersion',
]
def get_symbols_nm(nm, lib):
'''
List all the (non platform-specific) symbols exported by the library
using `nm`
'''
symbols = []
platform_name = platform.system()
output = subprocess.check_output([nm, '-gP', lib],
stderr=open(os.devnull, 'w')).decode("ascii")
for line in output.splitlines():
fields = line.split()
if len(fields) == 2 or fields[1] == 'U':
continue
symbol_name = fields[0]
if platform_name in ['Linux', 'GNU', 'Haiku'] or platform_name.startswith('GNU/'):
if symbol_name in PLATFORM_SYMBOLS:
continue
elif platform_name == 'Darwin':
assert symbol_name[0] == '_'
symbol_name = symbol_name[1:]
symbols.append(symbol_name)
return symbols
def get_symbols_dumpbin(dumpbin, lib):
'''
List all the (non platform-specific) symbols exported by the library
using `dumpbin`
'''
symbols = []
output = subprocess.check_output([dumpbin, '/exports', lib],
stderr=open(os.devnull, 'w')).decode("ascii")
for line in output.splitlines():
fields = line.split()
# The lines with the symbols are made of at least 4 columns; see details below
if len(fields) < 4:
continue
try:
# Making sure the first 3 columns are a dec counter, a hex counter
# and a hex address
_ = int(fields[0], 10)
_ = int(fields[1], 16)
_ = int(fields[2], 16)
except ValueError:
continue
symbol_name = fields[3]
# De-mangle symbols
if symbol_name[0] == '_' and '@' in symbol_name:
symbol_name = symbol_name[1:].split('@')[0]
symbols.append(symbol_name)
return symbols
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--symbols-file',
action='store',
required=True,
help='path to file containing symbols')
parser.add_argument('--lib',
action='store',
required=True,
help='path to library')
parser.add_argument('--nm',
action='store',
help='path to binary (or name in $PATH)')
parser.add_argument('--dumpbin',
action='store',
help='path to binary (or name in $PATH)')
parser.add_argument('--ignore-symbol',
action='append',
help='do not process this symbol')
args = parser.parse_args()
try:
if platform.system() == 'Windows':
if not args.dumpbin:
parser.error('--dumpbin is mandatory')
lib_symbols = get_symbols_dumpbin(args.dumpbin, args.lib)
else:
if not args.nm:
parser.error('--nm is mandatory')
lib_symbols = get_symbols_nm(args.nm, args.lib)
except:
# We can't run this test, but we haven't technically failed it either
# Return the GNU "skip" error code
exit(77)
mandatory_symbols = []
optional_symbols = []
with open(args.symbols_file) as symbols_file:
qualifier_optional = '(optional)'
for line in symbols_file.readlines():
# Strip comments
line = line.split('#')[0]
line = line.strip()
if not line:
continue
# Line format:
# [qualifier] symbol
qualifier = None
symbol = None
fields = line.split()
if len(fields) == 1:
symbol = fields[0]
elif len(fields) == 2:
qualifier = fields[0]
symbol = fields[1]
else:
print(args.symbols_file + ': invalid format: ' + line)
exit(1)
# The only supported qualifier is 'optional', which means the
# symbol doesn't have to be exported by the library
if qualifier and not qualifier == qualifier_optional:
print(args.symbols_file + ': invalid qualifier: ' + qualifier)
exit(1)
if qualifier == qualifier_optional:
optional_symbols.append(symbol)
else:
mandatory_symbols.append(symbol)
unknown_symbols = []
for symbol in lib_symbols:
if symbol in mandatory_symbols:
continue
if symbol in optional_symbols:
continue
if args.ignore_symbol and symbol in args.ignore_symbol:
continue
if symbol[:2] == '_Z':
# As ajax found out, the compiler intentionally exports symbols
# that we explicitely asked it not to export, and we can't do
# anything about it:
# https://gcc.gnu.org/bugzilla/show_bug.cgi?id=36022#c4
continue
unknown_symbols.append(symbol)
missing_symbols = [
sym for sym in mandatory_symbols if sym not in lib_symbols
]
for symbol in unknown_symbols:
print(args.lib + ': unknown symbol exported: ' + symbol)
for symbol in missing_symbols:
print(args.lib + ': missing symbol: ' + symbol)
if unknown_symbols or missing_symbols:
exit(1)
exit(0)
if __name__ == '__main__':
main()
| libglvnd-master | bin/symbols-check.py |
#!/usr/bin/env python
# (C) Copyright 2015, NVIDIA CORPORATION.
# All Rights Reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# on the rights to use, copy, modify, merge, publish, distribute, sub
# license, and/or sell copies of the Software, and to permit persons to whom
# the Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice (including the next
# paragraph) shall be included in all copies or substantial portions of the
# Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL
# IBM AND/OR ITS SUPPLIERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
#
# Authors:
# Kyle Brenneman <[email protected]>
"""
Generates src/GL/g_libglglxwrapper.c from Khronos's glx.xml file.
This script generates stubs for every known extension function as well.
"""
import sys
import genCommon
_LIBGLX_FUNCTIONS = frozenset((
"glXChooseVisual",
"glXCopyContext",
"glXCreateContext",
"glXCreateGLXPixmap",
"glXDestroyContext",
"glXDestroyGLXPixmap",
"glXGetConfig",
"glXIsDirect",
"glXMakeCurrent",
"glXSwapBuffers",
"glXUseXFont",
"glXWaitGL",
"glXWaitX",
"glXQueryServerString",
"glXGetClientString",
"glXQueryExtensionsString",
"glXChooseFBConfig",
"glXCreateNewContext",
"glXCreatePbuffer",
"glXCreatePixmap",
"glXCreateWindow",
"glXDestroyPbuffer",
"glXDestroyPixmap",
"glXDestroyWindow",
"glXGetFBConfigAttrib",
"glXGetFBConfigs",
"glXGetSelectedEvent",
"glXGetVisualFromFBConfig",
"glXMakeContextCurrent",
"glXQueryContext",
"glXQueryDrawable",
"glXSelectEvent",
"glXGetCurrentContext",
"glXGetCurrentDrawable",
"glXGetCurrentReadDrawable",
"glXGetProcAddress",
"glXGetProcAddressARB",
"glXQueryExtension",
"glXQueryVersion",
))
# These are functions to skip when we generate the entrypoint stubs. They
# require some additional typedefs that probably won't be available.
_SKIP_GLX_FUNCTIONS = frozenset((
"glXAssociateDMPbufferSGIX",
"glXCreateGLXVideoSourceSGIX",
"glXDestroyGLXVideoSourceSGIX",
))
def generateGLXExtensionStubFunction(func):
text = ""
text += "typedef {f.rt} (*fn_{f.name}_ptr)({f.decArgs});\n"
text += "static fn_{f.name}_ptr __real_{f.name};\n"
text += "static glvnd_mutex_t __mutex_{f.name} = GLVND_MUTEX_INITIALIZER;\n"
text += "PUBLIC {f.rt} {f.name}({f.decArgs})\n"
text += "{{\n"
text += " fn_{f.name}_ptr _real = "
text += "(fn_{f.name}_ptr) LOAD_GLX_FUNC({f.name});\n"
text += " if(_real != NULL) {{\n"
if (func.hasReturn()):
text += " return _real({f.callArgs});\n"
text += " }} else {{\n"
text += " return {retVal};\n"
else:
text += " _real({f.callArgs});\n"
text += " }}\n"
text += "}}\n\n"
return text.format(f=func, retVal=getDefaultReturnValue(func))
def generateGLXCoreStubFunction(func):
text = "PUBLIC {f.rt} {f.name}({f.decArgs})\n"
text += "{{\n"
text += " "
if (func.hasReturn()):
text += "return "
text += "__GLXGL_CORE_FUNCTIONS.ptr_{f.name}({f.callArgs});\n"
text += "}}\n\n"
return text.format(f=func)
def generateLibGLXStubs(functions):
text = r"""
/*
* THIS FILE IS AUTOMATICALLY GENERATED BY gen_noop.pl
* DO NOT EDIT!!
*/
#include <X11/Xlib.h>
#include <GL/glx.h>
#include "compiler.h"
#include "libglxgl.h"
#include "glvnd_pthread.h"
""".lstrip("\n")
text += "#define LOAD_GLX_FUNC(name) __glXGLLoadGLXFunction(#name, (__GLXextFuncPtr *) &__real_##name, &__mutex_##name)\n\n"
for func in functions:
if (func.name in _LIBGLX_FUNCTIONS):
text += generateGLXCoreStubFunction(func)
else:
text += generateGLXExtensionStubFunction(func)
return text
def getDefaultReturnValue(func):
POINTER_TYPE_NAMES = frozenset((
"__GLXextFuncPtr",
"GLXFBConfig", "GLXFBConfigSGIX",
"GLXContext",
))
XID_TYPE_NAMES = frozenset((
"GLXContextID",
"GLXWindow",
"GLXPbuffer",
"GLXPixmap",
"GLXDrawable",
"GLXFBConfigID",
"GLXContextID",
"GLXWindow",
"GLXPbuffer",
"GLXPbufferSGIX",
"GLXVideoSourceSGIX",
))
if (not func.hasReturn()):
return ""
if (func.rt.endswith("*")):
return "NULL"
if (func.rt in POINTER_TYPE_NAMES):
return "NULL"
if (func.rt in XID_TYPE_NAMES):
return "None"
if (func.rt == "Bool"):
return "False"
if (func.rt.startswith("GLX")):
raise ValueError("Unknown GLX typedef: %r" % (func.rt,))
return "0"
def _main():
functions = genCommon.getFunctions(sys.argv[1:])
functions = [f for f in functions if(f.name not in _SKIP_GLX_FUNCTIONS)]
sys.stdout.write(generateLibGLXStubs(functions))
if (__name__ == "__main__"):
_main()
| libglvnd-master | src/generate/gen_libgl_glxstubs.py |
#!/usr/bin/env python
# (C) Copyright 2015, NVIDIA CORPORATION.
# All Rights Reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# on the rights to use, copy, modify, merge, publish, distribute, sub
# license, and/or sell copies of the Software, and to permit persons to whom
# the Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice (including the next
# paragraph) shall be included in all copies or substantial portions of the
# Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL
# IBM AND/OR ITS SUPPLIERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
#
# Authors:
# Kyle Brenneman <[email protected]>
import sys
import collections
import re
import xml.etree.ElementTree as etree
MAPI_TABLE_NUM_DYNAMIC = 4096
_LIBRARY_FEATURE_NAMES = {
# libGL and libGLdiapatch both include every function.
"gldispatch" : None,
"opengl" : frozenset(( "GL_VERSION_1_0", "GL_VERSION_1_1",
"GL_VERSION_1_2", "GL_VERSION_1_3", "GL_VERSION_1_4", "GL_VERSION_1_5",
"GL_VERSION_2_0", "GL_VERSION_2_1", "GL_VERSION_3_0", "GL_VERSION_3_1",
"GL_VERSION_3_2", "GL_VERSION_3_3", "GL_VERSION_4_0", "GL_VERSION_4_1",
"GL_VERSION_4_2", "GL_VERSION_4_3", "GL_VERSION_4_4", "GL_VERSION_4_5",
)),
"glesv1" : frozenset(("GL_VERSION_ES_CM_1_0", "GL_OES_point_size_array")),
"glesv2" : frozenset(("GL_ES_VERSION_2_0", "GL_ES_VERSION_3_0",
"GL_ES_VERSION_3_1", "GL_ES_VERSION_3_2",
)),
}
def getFunctions(xmlFiles):
"""
Reads an XML file and returns all of the functions defined in it.
xmlFile should be the path to Khronos's gl.xml file. The return value is a
sequence of FunctionDesc objects, ordered by slot number.
"""
roots = [ etree.parse(xmlFile).getroot() for xmlFile in xmlFiles ]
return getFunctionsFromRoots(roots)
def getFunctionsFromRoots(roots):
functions = {}
for root in roots:
for func in _getFunctionList(root):
functions[func.name] = func
functions = functions.values()
# Sort the function list by name.
functions = sorted(functions, key=lambda f: f.name)
# Assign a slot number to each function. This isn't strictly necessary,
# since you can just look at the index in the list, but it makes it easier
# to include the slot when formatting output.
for i in range(len(functions)):
functions[i] = functions[i]._replace(slot=i)
return functions
def readSymbolsFile(symbols_file):
"""
Returns the set of function names based on a list of symbols in a text
file.
"""
symbols = set()
with open(symbols_file) as f:
qualifier_optional = '(optional)'
for line in f.readlines():
# Strip comments
line = line.split('#')[0]
line = line.strip()
if not line:
continue
# Line format:
# [qualifier] symbol
qualifier = None
symbol = None
fields = line.split()
if len(fields) == 1:
symbol = fields[0]
elif len(fields) == 2:
qualifier = fields[0]
symbol = fields[1]
else:
raise ValueError(symbols_file + ': invalid format: ' + line)
# The only supported qualifier is 'optional', which means the
# symbol doesn't have to be exported by the library
if qualifier and not qualifier == qualifier_optional:
raise ValueError(symbols_file + ': invalid qualifier: ' + qualifier)
# For our purposes here, we expect generated functions to be
# mandatory symbols.
if qualifier != qualifier_optional:
symbols.add(symbol)
return symbols
def getExportNamesFromRoots(target, roots):
"""
Goes through the <feature> tags from gl.xml and returns a set of OpenGL
functions that a library should export.
target should be one of "gl", "gldispatch", "opengl", "glesv1", or
"glesv2".
"""
featureNames = _LIBRARY_FEATURE_NAMES[target]
if (featureNames == None):
return set(func.name for func in getFunctionsFromRoots(roots))
names = set()
for root in roots:
features = []
for featElem in root.findall("feature"):
if (featElem.get("name") in featureNames):
features.append(featElem)
for featElem in root.findall("extensions/extension"):
if (featElem.get("name") in featureNames):
features.append(featElem)
for featElem in features:
for commandElem in featElem.findall("require/command"):
names.add(commandElem.get("name"))
return names
class FunctionArg(collections.namedtuple("FunctionArg", "type name")):
@property
def dec(self):
"""
Returns a "TYPE NAME" string, suitable for a function prototype.
"""
rv = str(self.type)
if(not rv.endswith("*")):
rv += " "
rv += self.name
return rv
class FunctionDesc(collections.namedtuple("FunctionDesc", "name rt args slot")):
def hasReturn(self):
"""
Returns true if the function returns a value.
"""
return (self.rt != "void")
@property
def decArgs(self):
"""
Returns a string with the types and names of the arguments, as you
would use in a function declaration.
"""
if(len(self.args) == 0):
return "void"
else:
return ", ".join(arg.dec for arg in self.args)
@property
def callArgs(self):
"""
Returns a string with the names of the arguments, as you would use in a
function call.
"""
return ", ".join(arg.name for arg in self.args)
@property
def basename(self):
assert(self.name.startswith("gl"))
return self.name[2:]
def _getFunctionList(root):
for elem in root.findall("commands/command"):
yield _parseCommandElem(elem)
def _parseCommandElem(elem):
protoElem = elem.find("proto")
(rt, name) = _parseProtoElem(protoElem)
args = []
for ch in elem.findall("param"):
# <param> tags have the same format as a <proto> tag.
args.append(FunctionArg(*_parseProtoElem(ch)))
func = FunctionDesc(name, rt, tuple(args), slot=None)
return func
def _parseProtoElem(elem):
# If I just remove the tags and string the text together, I'll get valid C code.
text = _flattenText(elem)
text = text.strip()
m = re.match(r"^(.+)\b(\w+)(?:\s*\[\s*(\d*)\s*\])?$", text, re.S)
if (m):
typename = _fixupTypeName(m.group(1))
name = m.group(2)
if (m.group(3)):
# HACK: glPathGlyphIndexRangeNV defines an argument like this:
# GLuint baseAndCount[2]
# Convert it to a pointer and hope for the best.
typename += "*"
return (typename, name)
else:
raise ValueError("Can't parse element %r -> %r" % (elem, text))
def _flattenText(elem):
"""
Returns the text in an element and all child elements, with the tags
removed.
"""
text = ""
if(elem.text != None):
text = elem.text
for ch in elem:
text += _flattenText(ch)
if(ch.tail != None):
text += ch.tail
return text
def _fixupTypeName(typeName):
"""
Converts a typename into a more consistent format.
"""
rv = typeName.strip()
# Replace "GLvoid" with just plain "void".
rv = re.sub(r"\bGLvoid\b", "void", rv)
# Remove the vendor suffixes from types that have a suffix-less version.
rv = re.sub(r"\b(GLhalf|GLintptr|GLsizeiptr|GLint64|GLuint64)(?:ARB|EXT|NV|ATI)\b", r"\1", rv)
rv = re.sub(r"\bGLvoid\b", "void", rv)
# Clear out any leading and trailing whitespace.
rv = rv.strip()
# Remove any whitespace before a '*'
rv = re.sub(r"\s+\*", r"*", rv)
# Change "foo*" to "foo *"
rv = re.sub(r"([^\*])\*", r"\1 *", rv)
# Condense all whitespace into a single space.
rv = re.sub(r"\s+", " ", rv)
return rv
| libglvnd-master | src/generate/genCommon.py |
#!/usr/bin/env python
"""
Generates dispatch functions for EGL.
The list of functions and arguments is read from the Khronos's XML files, with
additional information defined in the module eglFunctionList.
"""
import argparse
import collections
import sys
import textwrap
import eglFunctionList
import genCommon
def main():
parser = argparse.ArgumentParser()
parser.add_argument("target", choices=("header", "source"),
help="Whether to build the source or header file.")
parser.add_argument("xml_files", nargs="+",
help="The XML files with the EGL function lists.")
args = parser.parse_args()
xmlFunctions = genCommon.getFunctions(args.xml_files)
xmlByName = dict((f.name, f) for f in xmlFunctions)
functions = []
for (name, eglFunc) in eglFunctionList.EGL_FUNCTIONS:
func = xmlByName[name]
eglFunc = fixupEglFunc(func, eglFunc)
functions.append((func, eglFunc))
# Sort the function list by name.
functions = sorted(functions, key=lambda f: f[0].name)
if args.target == "header":
text = generateHeader(functions)
elif args.target == "source":
text = generateSource(functions)
sys.stdout.write(text)
def fixupEglFunc(func, eglFunc):
result = dict(eglFunc)
if result.get("prefix") is None:
result["prefix"] = ""
if result.get("extension") is not None:
text = "defined(" + result["extension"] + ")"
result["extension"] = text
if result["method"] in ("none", "custom"):
return result
if result["method"] not in ("display", "device", "current"):
raise ValueError("Invalid dispatch method %r for function %r" % (result["method"], func.name))
if func.hasReturn():
if result.get("retval") is None:
result["retval"] = getDefaultReturnValue(func.rt)
return result
def generateHeader(functions):
text = textwrap.dedent(r"""
#ifndef G_EGLDISPATCH_STUBS_H
#define G_EGLDISPATCH_STUBS_H
#ifdef __cplusplus
extern "C" {
#endif
#include <EGL/egl.h>
#include <EGL/eglext.h>
#include "glvnd/libeglabi.h"
""".lstrip("\n"))
text += "enum {\n"
for (func, eglFunc) in functions:
text += generateGuardBegin(func, eglFunc)
text += " __EGL_DISPATCH_" + func.name + ",\n"
text += generateGuardEnd(func, eglFunc)
text += " __EGL_DISPATCH_COUNT\n"
text += "};\n"
for (func, eglFunc) in functions:
if eglFunc["inheader"]:
text += generateGuardBegin(func, eglFunc)
text += "{f.rt} EGLAPIENTRY {ex[prefix]}{f.name}({f.decArgs});\n".format(f=func, ex=eglFunc)
text += generateGuardEnd(func, eglFunc)
text += textwrap.dedent(r"""
#ifdef __cplusplus
}
#endif
#endif // G_EGLDISPATCH_STUBS_H
""")
return text
def generateSource(functions):
# First, sort the function list by name.
text = ""
text += '#include "egldispatchstubs.h"\n'
text += '#include "g_egldispatchstubs.h"\n'
text += '#include <stddef.h>\n'
text += "\n"
for (func, eglFunc) in functions:
if eglFunc["method"] not in ("custom", "none"):
text += generateGuardBegin(func, eglFunc)
text += generateDispatchFunc(func, eglFunc)
text += generateGuardEnd(func, eglFunc)
text += "\n"
text += "const char * const __EGL_DISPATCH_FUNC_NAMES[__EGL_DISPATCH_COUNT + 1] = {\n"
for (func, eglFunc) in functions:
text += generateGuardBegin(func, eglFunc)
text += ' "' + func.name + '",\n'
text += generateGuardEnd(func, eglFunc)
text += " NULL\n"
text += "};\n"
text += "const __eglMustCastToProperFunctionPointerType __EGL_DISPATCH_FUNCS[__EGL_DISPATCH_COUNT + 1] = {\n"
for (func, eglFunc) in functions:
text += generateGuardBegin(func, eglFunc)
if eglFunc["method"] != "none":
text += " (__eglMustCastToProperFunctionPointerType) " + eglFunc.get("prefix", "") + func.name + ",\n"
else:
text += " NULL, // " + func.name + "\n"
text += generateGuardEnd(func, eglFunc)
text += " NULL\n"
text += "};\n"
return text
def generateGuardBegin(func, eglFunc):
ext = eglFunc.get("extension")
if ext is not None:
return "#if " + ext + "\n"
else:
return ""
def generateGuardEnd(func, eglFunc):
if eglFunc.get("extension") is not None:
return "#endif\n"
else:
return ""
def generateDispatchFunc(func, eglFunc):
text = ""
if eglFunc.get("static"):
text += "static "
elif eglFunc.get("public"):
text += "PUBLIC "
text += textwrap.dedent(
r"""
{f.rt} EGLAPIENTRY {ef[prefix]}{f.name}({f.decArgs})
{{
typedef {f.rt} EGLAPIENTRY (* _pfn_{f.name})({f.decArgs});
""").lstrip("\n").format(f=func, ef=eglFunc)
if func.hasReturn():
text += " {f.rt} _ret = {ef[retval]};\n".format(f=func, ef=eglFunc)
text += " _pfn_{f.name} _ptr_{f.name} = (_pfn_{f.name}) ".format(f=func)
if eglFunc["method"] == "current":
text += "__eglDispatchFetchByCurrent(__EGL_DISPATCH_{f.name});\n".format(f=func)
elif eglFunc["method"] in ("display", "device"):
if eglFunc["method"] == "display":
lookupFunc = "__eglDispatchFetchByDisplay"
lookupType = "EGLDisplay"
else:
assert eglFunc["method"] == "device"
lookupFunc = "__eglDispatchFetchByDevice"
lookupType = "EGLDeviceEXT"
lookupArg = None
for arg in func.args:
if arg.type == lookupType:
lookupArg = arg.name
break
if lookupArg is None:
raise ValueError("Can't find %s argument for function %s" % (lookupType, func.name,))
text += "{lookupFunc}({lookupArg}, __EGL_DISPATCH_{f.name});\n".format(
f=func, lookupFunc=lookupFunc, lookupArg=lookupArg)
else:
raise ValueError("Unknown dispatch method: %r" % (eglFunc["method"],))
text += " if(_ptr_{f.name} != NULL) {{\n".format(f=func)
text += " "
if func.hasReturn():
text += "_ret = "
text += "_ptr_{f.name}({f.callArgs});\n".format(f=func)
text += " }\n"
if func.hasReturn():
text += " return _ret;\n"
text += "}\n"
return text
def getDefaultReturnValue(typename):
if typename.endswith("*"):
return "NULL"
elif typename == "EGLDisplay":
return "EGL_NO_DISPLAY"
elif typename == "EGLContext":
return "EGL_NO_CONTEXT"
elif typename == "EGLSurface":
return "EGL_NO_SURFACE"
elif typename == "EGLBoolean":
return "EGL_FALSE";
return "0"
if __name__ == "__main__":
main()
| libglvnd-master | src/generate/gen_egl_dispatch.py |
#!/usr/bin/env python
# (C) Copyright 2015, NVIDIA CORPORATION.
# All Rights Reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# on the rights to use, copy, modify, merge, publish, distribute, sub
# license, and/or sell copies of the Software, and to permit persons to whom
# the Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice (including the next
# paragraph) shall be included in all copies or substantial portions of the
# Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL
# IBM AND/OR ITS SUPPLIERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
#
# Authors:
# Kyle Brenneman <[email protected]>
"""
Generates the list of functions that should be exported from libOpenGL.so.
"""
import sys
import xml.etree.ElementTree as etree
import genCommon
def _main():
target = sys.argv[1]
xmlFiles = sys.argv[2:]
roots = [ etree.parse(filename).getroot() for filename in xmlFiles ]
names = genCommon.getExportNamesFromRoots(target, roots)
for name in sorted(names):
print(name)
if (__name__ == "__main__"):
_main()
| libglvnd-master | src/generate/gen_libOpenGL_exports.py |
"""
Contains a list of EGL functions to generate dispatch functions for.
This is used from gen_egl_dispatch.py.
EGL_FUNCTIONS is a sequence of (name, eglData) pairs, where name is the name
of the function, and eglData is a dictionary containing data about that
function.
The values in the eglData dictionary are:
- method (string):
How to select a vendor library. See "Method values" below.
- prefix (string):
This string is prepended to the name of the dispatch function. If
unspecified, the default is "" (an empty string).
- static (boolean)
If True, this function should be declared static.
- "public" (boolean)
If True, the function should be exported from the library. Vendor libraries
generally should not use this.
- extension (string):
If specified, this is the name of a macro to check for before defining a
function. Used for checking for extension macros and such.
- retval (string):
If specified, this is a C expression with the default value to return if we
can't find a function to call. By default, it will try to guess from the
return type: EGL_NO_whatever for the various handle types, NULL for
pointers, and zero for everything else.
method values:
- "custom"
The dispatch stub will be hand-written instead of generated.
- "none"
No dispatch function exists at all, but the function should still have an
entry in the index array. This is for other functions that a stub may need
to call that are implemented in libEGL itself.
- "display"
Select a vendor from an EGLDisplay argument.
- "device"
Select a vendor from an EGLDeviceEXT argument.
- "current"
Select the vendor that owns the current context.
"""
def _eglFunc(name, method, inheader, static=False, public=False, prefix="", extension=None, retval=None):
"""
A convenience function to define an entry in the EGL function list.
"""
values = {
"method" : method,
"prefix" : prefix,
"extension" : extension,
"retval" : retval,
"static" : static,
"public" : public,
"inheader" : inheader,
}
return (name, values)
def _eglCore(name, method, **kwargs):
return _eglFunc(name, method, public=True, inheader=False, **kwargs)
def _eglExt(name, method, static=None, **kwargs):
if (static is None):
static = (method != "custom")
inheader = not static
return _eglFunc(name, method, static=static, inheader=inheader, public=False, **kwargs)
EGL_FUNCTIONS = (
# EGL_VERSION_1_0
_eglCore("eglChooseConfig", "display"),
_eglCore("eglCopyBuffers", "display"),
_eglCore("eglCreateContext", "display"),
_eglCore("eglCreatePbufferSurface", "display"),
_eglCore("eglCreatePixmapSurface", "display"),
_eglCore("eglCreateWindowSurface", "display"),
_eglCore("eglDestroyContext", "display"),
_eglCore("eglDestroySurface", "display"),
_eglCore("eglGetConfigAttrib", "display"),
_eglCore("eglGetConfigs", "display"),
_eglCore("eglQueryContext", "display"),
_eglCore("eglQuerySurface", "display"),
_eglCore("eglSwapBuffers", "display"),
_eglCore("eglWaitGL", "current", retval="EGL_TRUE"),
_eglCore("eglWaitNative", "current", retval="EGL_TRUE"),
_eglCore("eglTerminate", "display"),
_eglCore("eglInitialize", "display"),
_eglCore("eglGetCurrentDisplay", "custom"),
_eglCore("eglGetCurrentSurface", "custom"),
_eglCore("eglGetDisplay", "custom"),
_eglCore("eglGetError", "custom"),
_eglCore("eglGetProcAddress", "custom"),
_eglCore("eglMakeCurrent", "custom"),
_eglCore("eglQueryString", "custom"),
# EGL_VERSION_1_1
_eglCore("eglBindTexImage", "display"),
_eglCore("eglReleaseTexImage", "display"),
_eglCore("eglSurfaceAttrib", "display"),
_eglCore("eglSwapInterval", "display"),
# EGL_VERSION_1_2
_eglCore("eglCreatePbufferFromClientBuffer", "display"),
_eglCore("eglWaitClient", "current", retval="EGL_TRUE"),
_eglCore("eglBindAPI", "custom"),
_eglCore("eglQueryAPI", "custom"),
_eglCore("eglReleaseThread", "custom"),
# EGL_VERSION_1_4
_eglCore("eglGetCurrentContext", "custom"),
# EGL_VERSION_1_5
_eglCore("eglCreateSync", "display"),
_eglCore("eglDestroySync", "display"),
_eglCore("eglClientWaitSync", "display"),
_eglCore("eglGetSyncAttrib", "display"),
_eglCore("eglCreateImage", "display"),
_eglCore("eglDestroyImage", "display"),
_eglCore("eglCreatePlatformWindowSurface", "display"),
_eglCore("eglCreatePlatformPixmapSurface", "display"),
_eglCore("eglWaitSync", "display"),
_eglCore("eglGetPlatformDisplay", "custom"),
# EGL_EXT_platform_base
_eglExt("eglCreatePlatformWindowSurfaceEXT", "display"),
_eglExt("eglCreatePlatformPixmapSurfaceEXT", "display"),
_eglExt("eglGetPlatformDisplayEXT", "custom"),
# EGL_EXT_device_enumeration
_eglExt("eglQueryDevicesEXT", "custom"),
# EGL_EXT_device_query
_eglExt("eglQueryDisplayAttribEXT", "custom"),
# EGL_KHR_display_reference
_eglExt("eglQueryDisplayAttribKHR", "custom"),
# EGL_NV_stream_metadata
_eglExt("eglQueryDisplayAttribNV", "custom"),
# EGL_KHR_debug
_eglExt("eglDebugMessageControlKHR", "custom"),
_eglExt("eglQueryDebugKHR", "custom"),
_eglExt("eglLabelObjectKHR", "custom"),
)
| libglvnd-master | src/generate/eglFunctionList.py |
#!/usr/bin/env python
# Copyright (C) 2010 LunarG Inc.
# (C) Copyright 2015, NVIDIA CORPORATION.
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction, including without limitation
# the rights to use, copy, modify, merge, publish, distribute, sublicense,
# and/or sell copies of the Software, and to permit persons to whom the
# Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
#
# Authors:
# Kyle Brenneman <[email protected]>
#
# Based on code ogiginally by:
# Chia-I Wu <[email protected]>
"""
Generates the glapi_mapi_tmp.h header file from Khronos's XML file.
"""
import sys
import xml.etree.ElementTree as etree
import os.path
import genCommon
def _main():
target = sys.argv[1]
xmlFiles = sys.argv[2:]
roots = [ etree.parse(filename).getroot() for filename in xmlFiles ]
allFunctions = genCommon.getFunctionsFromRoots(roots)
# Technically, libGL.so is only supposed to export OpenGL 1.2 plus
# ARB_multitexture, but in the past, implementations exported everything
# and many applications rely on that. Here, we read a list of symbols from
# a file instead of exporting everything -- buggy applications won't break,
# but libGL.so won't get any *more* symbols added to it.
if os.path.isfile(target):
names = genCommon.readSymbolsFile(target)
else:
names = genCommon.getExportNamesFromRoots(target, roots)
functions = [f for f in allFunctions if(f.name in names)]
if (target in ("gl", "gldispatch")):
assert(len(functions) == len(allFunctions))
assert(all(functions[i] == allFunctions[i] for i in range(len(functions))))
assert(all(functions[i].slot == i for i in range(len(functions))))
print(r"""
/* This file is automatically generated by gen_gldispatch_mapi.py. Do not modify. */
#ifndef _GLAPI_TMP_H_
#define _GLAPI_TMP_H_
typedef int GLclampx;
typedef void (APIENTRY *GLDEBUGPROCKHR)(GLenum source,GLenum type,GLuint id,GLenum severity,GLsizei length,const GLchar *message,const void *userParam);
#endif /* _GLAPI_TMP_H_ */
""".lstrip("\n"))
print(generate_defines(functions))
print(generate_table(functions, allFunctions))
print(generate_noop_array(functions))
print(generate_public_stubs(functions))
print(generate_public_entries(functions))
print(generate_stub_asm_gcc(functions, (target == "gldispatch")))
def generate_defines(functions):
text = r"""
#ifdef MAPI_TMP_DEFINES
#define GL_GLEXT_PROTOTYPES
#include "GL/gl.h"
#include "GL/glext.h"
""".lstrip("\n")
for func in functions:
text += "GLAPI {f.rt} APIENTRY {f.name}({f.decArgs});\n".format(f=func)
text += "#undef MAPI_TMP_DEFINES\n"
text += "#endif /* MAPI_TMP_DEFINES */\n"
return text
def generate_table(functions, allFunctions):
text = "#ifdef MAPI_TMP_TABLE\n"
text += "#define MAPI_TABLE_NUM_STATIC %d\n" % (len(allFunctions))
text += "#define MAPI_TABLE_NUM_DYNAMIC %d\n" % (genCommon.MAPI_TABLE_NUM_DYNAMIC,)
text += "#undef MAPI_TMP_TABLE\n"
text += "#endif /* MAPI_TMP_TABLE */\n"
return text
def generate_noop_array(functions):
text = "#ifdef MAPI_TMP_NOOP_ARRAY\n"
text += "#ifdef DEBUG\n\n"
for func in functions:
text += "static {f.rt} APIENTRY noop{f.basename}({f.decArgs})\n".format(f=func)
text += "{\n"
if (len(func.args) > 0):
text += " "
for arg in func.args:
text += " (void) {a.name};".format(a=arg)
text += "\n"
text += " noop_warn(\"{f.name}\");\n".format(f=func)
if (func.hasReturn()):
text += " return ({f.rt}) 0;\n".format(f=func)
text += "}\n\n"
text += "const mapi_func table_noop_array[] = {\n"
for func in functions:
text += " (mapi_func) noop{f.basename},\n".format(f=func)
for i in range(genCommon.MAPI_TABLE_NUM_DYNAMIC - 1):
text += " (mapi_func) noop_generic,\n"
text += " (mapi_func) noop_generic\n"
text += "};\n\n"
text += "#else /* DEBUG */\n\n"
text += "const mapi_func table_noop_array[] = {\n"
for i in range(len(functions) + genCommon.MAPI_TABLE_NUM_DYNAMIC - 1):
text += " (mapi_func) noop_generic,\n"
text += " (mapi_func) noop_generic\n"
text += "};\n\n"
text += "#endif /* DEBUG */\n"
text += "#undef MAPI_TMP_NOOP_ARRAY\n"
text += "#endif /* MAPI_TMP_NOOP_ARRAY */\n"
return text
def generate_public_stubs(functions):
text = "#ifdef MAPI_TMP_PUBLIC_STUBS\n"
text += "static const struct mapi_stub public_stubs[] = {\n"
for func in functions:
text += " { \"%s\", %d },\n" % (func.name, func.slot)
text += "};\n"
text += "#undef MAPI_TMP_PUBLIC_STUBS\n"
text += "#endif /* MAPI_TMP_PUBLIC_STUBS */\n"
return text
def generate_public_entries(functions):
text = "#ifdef MAPI_TMP_PUBLIC_ENTRIES\n"
for func in functions:
retStr = ("return " if func.hasReturn() else "")
text += r"""
GLAPI {f.rt} APIENTRY {f.name}({f.decArgs})
{{
const struct _glapi_table *_tbl = entry_current_get();
mapi_func _func = ((const mapi_func *) _tbl)[{f.slot}];
{retStr}(({f.rt} (APIENTRY *)({f.decArgs})) _func)({f.callArgs});
}}
""".lstrip("\n").format(f=func, retStr=retStr)
text += "\n"
text += "static const mapi_func public_entries[] = {\n"
for func in functions:
text += " (mapi_func) %s,\n" % (func.name,)
text += "};\n"
text += "#undef MAPI_TMP_PUBLIC_ENTRIES\n"
text += "#endif /* MAPI_TMP_PUBLIC_ENTRIES */\n"
return text
def generate_stub_asm_gcc(functions, includeDynamic):
text = "#ifdef MAPI_TMP_STUB_ASM_GCC\n"
text += "__asm__(\n"
for func in functions:
text += 'STUB_ASM_ENTRY("%s")"\\n"\n' % (func.name,)
text += '"\\t"STUB_ASM_CODE("%d")"\\n"\n\n' % (func.slot,)
if (includeDynamic):
for i in range(genCommon.MAPI_TABLE_NUM_DYNAMIC):
text += 'STUB_ASM_ENTRY("dynamic_%04d")"\\n"\n' % (i,)
text += '"\\t"STUB_ASM_CODE("%d")"\\n"\n\n' % (len(functions) + i)
text += ");\n"
text += "#undef MAPI_TMP_STUB_ASM_GCC\n"
text += "#endif /* MAPI_TMP_STUB_ASM_GCC */\n"
return text
if (__name__ == "__main__"):
_main()
| libglvnd-master | src/generate/gen_gldispatch_mapi.py |
#!/usr/bin/env python
import sys
GENERATED_ENTRYPOINT_MAX = 4096
def main():
text = ""
text += "#if defined(GLX_STUBS_COUNT)\n"
text += "#define GENERATED_ENTRYPOINT_MAX %d\n" % (GENERATED_ENTRYPOINT_MAX)
text += "#undef GLX_STUBS_COUNT\n"
text += "#endif\n\n"
text += "#if defined(GLX_STUBS_ASM)\n"
for i in range(GENERATED_ENTRYPOINT_MAX):
text += "STUB_ASM(\"%d\")\n" % (i)
text += "#undef GLX_STUBS_ASM\n"
text += "#endif\n"
sys.stdout.write(text)
if (__name__ == "__main__"):
main()
| libglvnd-master | src/GLX/gen_glx_stubs.py |
"""
This is an object detection finetuning example. We finetune a Faster R-CNN
model pretrained on COCO to detect pedestrians in the relatively small PennFudan
dataset.
Useful References:
https://docs.determined.ai/latest/reference/api/pytorch.html
https://www.cis.upenn.edu/~jshi/ped_html/
Based on: https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html
"""
import copy
import os
from typing import Any, Dict, Sequence, Union
import torch
import torchvision
from torch import nn
from torchvision.models.detection import fasterrcnn_resnet50_fpn
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from torch.utils.tensorboard import SummaryWriter
import determined as det
from determined.pytorch import DataLoader, LRScheduler, PyTorchTrial
from support.data import get_transform, collate_fn, PennFudanDataset
TorchData = Union[Dict[str, torch.Tensor], Sequence[torch.Tensor], torch.Tensor]
class ObjectDetectionModel(PyTorchTrial):
def __init__(self, context: det.TrialContext) -> None:
self.context = context
self.current_step = context.env.first_step()
self.model = self.context.wrap_model(self._build_model())
self.opt = self.context.wrap_optimizer(self._build_optimizer(self.model))
self.lr_scheduler = self.context.wrap_lr_scheduler(
self._build_lr_scheduler(self.opt),
step_mode=LRScheduler.StepMode.STEP_EVERY_EPOCH
)
def build_training_data_loader(self) -> DataLoader:
# Create a unique download directory for each rank so they don't
# overwrite each other.
self.download_directory = f"/tmp/data-rank{self.context.distributed.get_rank()}"
#download_data(
# download_directory=self.download_directory, data_config=self.context.get_data_config(),
#)
os.environ['TORCH_HOME'] = self.download_directory
dataset = PennFudanDataset(self.download_directory + "/PennFudanPed", get_transform())
# Split 80/20 into training and validation datasets.
train_size = int(0.8 * len(dataset))
test_size = len(dataset) - train_size
self.dataset_train, self.dataset_val = torch.utils.data.random_split(
dataset, [train_size, test_size]
)
return DataLoader(
self.dataset_train,
batch_size=self.context.get_per_slot_batch_size(),
collate_fn=collate_fn,
)
def build_validation_data_loader(self) -> DataLoader:
return DataLoader(
self.dataset_val,
batch_size=self.context.get_per_slot_batch_size(),
collate_fn=collate_fn,
)
def _build_model(self) -> nn.Module:
model = fasterrcnn_resnet50_fpn(pretrained=True)
# Replace the classifier with a new two-class classifier. There are
# only two "classes": pedestrian and background.
num_classes = 2
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
return model
def _build_optimizer(self, model: nn.Module) -> torch.optim.Optimizer:
optimizer = torch.optim.SGD(
model.parameters(),
lr=self.context.get_hparam("learning_rate"),
momentum=self.context.get_hparam("momentum"),
weight_decay=self.context.get_hparam("weight_decay"),
)
return optimizer
def _build_lr_scheduler(self, optimizer):
return torch.optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.1)
def train_batch(
self, batch: TorchData, epoch_idx: int, batch_idx: int
) -> Dict[str, torch.Tensor]:
images, targets = batch
loss_dict = self.model(list(images), list(targets))
total_loss = sum([loss_dict[l] for l in loss_dict])
self.context.backward(total_loss)
self.context.step_optimizer(self.opt)
# Set current step based on 100 batches / step
self.current_step = batch_idx // 20
return {"loss": total_loss}
def evaluate_batch(self, batch: TorchData) -> Dict[str, Any]:
images, targets = batch
output = self.model(list(images), copy.deepcopy(list(targets)))
sum_iou = 0
num_boxes = 0
# Instantiate the Tensorboard writer and set the log_dir to /tmp/tensorboard where Determined looks for events
writer = SummaryWriter(log_dir="/tmp/tensorboard")
# Our eval metric is the average best IoU (across all predicted
# pedestrian bounding boxes) per target pedestrian. Given predicted
# and target bounding boxes, IoU is the area of the intersection over
# the area of the union.
for idx, target in enumerate(targets):
# Filter out overlapping bounding box predictions based on
# non-maximum suppression (NMS)
predicted_boxes = output[idx]["boxes"]
prediction_scores = output[idx]["scores"]
keep_indices = torchvision.ops.nms(predicted_boxes, prediction_scores, 0.1)
predicted_boxes = torch.index_select(predicted_boxes, 0, keep_indices)
prediction_scores = torch.index_select(prediction_scores, 0, keep_indices)
# Tally IoU with respect to the ground truth target boxes
target_boxes = target["boxes"]
boxes_iou = torchvision.ops.box_iou(target_boxes, predicted_boxes)
sum_iou += sum(max(iou_result) for iou_result in boxes_iou)
num_boxes += len(target_boxes)
# boxes are ordered by confidence, so get the top 5 bounding boxes and write out to Tensorboard
# new_predicted_boxes = output[idx]["boxes"][:5]
threshold = 0.7
cutoff = 0
for i, score in enumerate(prediction_scores):
if score < threshold:
break
cutoff = i
new_predicted_boxes = output[idx]["boxes"][:cutoff]
writer.add_image_with_boxes("step_"+str(self.current_step), images[idx], predicted_boxes)
writer.close()
return {"val_avg_iou": sum_iou / num_boxes}
| data-science-stack-master | examples/object_detection/experiments/model_def.py |
import torch
from determined.experimental import Determined
from .data import load_and_transform_image, draw_example
def predict(experiment_id, file, det_master=None):
if det_master:
checkpoint = Determined(master=det_master).get_experiment(experiment_id).top_checkpoint()
else:
checkpoint = Determined().get_experiment(experiment_id).top_checkpoint()
model = checkpoint.load().model
run_predict(model, file)
def filter_boxes(boxes, scores, threshold):
cutoff = 0
for i, score in enumerate(scores):
if score < threshold:
break
cutoff = i
# slicing excludes end, so we add 1 in the general case, and set to None if all
# boxes are to be returned
cutoff = None if cutoff == len(scores) - 1 else cutoff + 1
return boxes[:cutoff]
def run_predict(model, file, threshold=0.7):
model.eval()
test_image = load_and_transform_image(file)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
model.cuda()
with torch.no_grad():
outputs = model(test_image.unsqueeze(0).to(device))[0]
if len(outputs['boxes']) > 0:
boxes = filter_boxes(outputs['boxes'], outputs['scores'], threshold)
draw_example(test_image.permute(1,2,0).cpu().numpy(), {'boxes': boxes.cpu()}, title="Predictions")
else:
print("No objects detected!") | data-science-stack-master | examples/object_detection/experiments/support/helper.py |
import os
import shutil
from typing import Any, Dict
from urllib.parse import urlparse
from urllib.request import urlretrieve
import numpy as np
import torch
from torchvision.transforms import Compose, ToTensor
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from PIL import Image
def download_data(download_directory: str, data_config: Dict[str, Any]) -> str:
if not os.path.exists(download_directory):
os.makedirs(download_directory, exist_ok=True)
url = data_config["url"]
filename = os.path.basename(urlparse(url).path)
filepath = os.path.join(download_directory, filename)
if not os.path.exists(filepath):
urlretrieve(url, filename=filepath)
shutil.unpack_archive(filepath, download_directory)
def collate_fn(batch):
return tuple(zip(*batch))
def get_transform():
transforms = []
transforms.append(ToTensor())
return Compose(transforms)
def load_and_transform_image(img_path):
image = Image.open(img_path).convert("RGB")
image = get_transform()(image)
return image
def draw_example(image, labels, title=None):
fig,ax = plt.subplots(1)
plt.title(title)
ax.imshow(image)
boxes = labels['boxes'].numpy()
boxes = np.vsplit(boxes, boxes.shape[0])
for box in boxes:
box = np.squeeze(box)
bottom, left = box[0], box[1]
width = box[2] - box[0]
height = box[3] - box[1]
rect = patches.Rectangle((bottom,left),width,height,linewidth=2,edgecolor='r',facecolor='none')
# # Add the patch to the Axes
ax.add_patch(rect)
plt.axis('off')
plt.show()
# Custom dataset for PennFudan based on:
# https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html
class PennFudanDataset(object):
def __init__(self, root, transforms, device=None):
self.root = root
self.transforms = transforms
# load all image files, sorting them to
# ensure that they are aligned
self.imgs = sorted(os.listdir(os.path.join(root, "PNGImages")))
self.masks = sorted(os.listdir(os.path.join(root, "PedMasks")))
self.device = device
def __getitem__(self, idx):
# load images ad masks
img_path = os.path.join(self.root, "PNGImages", self.imgs[idx])
mask_path = os.path.join(self.root, "PedMasks", self.masks[idx])
img = Image.open(img_path).convert("RGB")
# note that we haven't converted the mask to RGB,
# because each color corresponds to a different instance
# with 0 being background
mask = Image.open(mask_path)
mask = np.array(mask)
# instances are encoded as different colors
obj_ids = np.unique(mask)
# first id is the background, so remove it
obj_ids = obj_ids[1:]
# split the color-encoded mask into a set of binary masks
masks = mask == obj_ids[:, np.newaxis, np.newaxis]
# get bounding box coordinates for each mask
num_objs = len(obj_ids)
boxes = []
for i in range(num_objs):
pos = np.where(masks[i])
xmin = np.min(pos[1])
xmax = np.max(pos[1])
ymin = np.min(pos[0])
ymax = np.max(pos[0])
boxes.append([xmin, ymin, xmax, ymax])
boxes = torch.as_tensor(boxes, dtype=torch.float32)
# there is only one class
labels = torch.ones((num_objs,), dtype=torch.int64)
masks = torch.as_tensor(masks, dtype=torch.uint8).to(self.device)
image_id = torch.tensor([idx]).to(self.device)
area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])
# suppose all instances are not crowd
iscrowd = torch.zeros((num_objs,), dtype=torch.int64).to(self.device)
if self.transforms is not None:
img = self.transforms(img)
if self.device:
boxes = boxes.to(self.device)
labels = labels.to(self.device)
masks = masks.to(self.device)
image_id = image_id.to(self.device)
iscrowd = iscrowd.to(self.device)
img = img.to(self.device)
target = {}
target["boxes"] = boxes
target["labels"] = labels
target["masks"] = masks
target["image_id"] = image_id
target["area"] = area
target["iscrowd"] = iscrowd
return img.to(self.device), target
def __len__(self):
return len(self.imgs)
| data-science-stack-master | examples/object_detection/experiments/support/data.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
from Cython import Tempita
from Cython.Build import cythonize
from distutils.sysconfig import get_python_lib
import os
import platform
from pyclibrary import CParser
import sys
import sysconfig
from setuptools import find_packages, setup
from setuptools.extension import Extension
from setuptools.command.build_ext import build_ext
import versioneer
# ----------------------------------------------------------------------
# Fetch configuration options
CUDA_HOME = os.environ.get("CUDA_HOME")
if not CUDA_HOME:
CUDA_HOME = os.environ.get("CUDA_PATH")
if not CUDA_HOME:
raise RuntimeError('Environment variable CUDA_HOME or CUDA_PATH is not set')
nthreads = int(os.environ.get("PARALLEL_LEVEL", "0") or "0")
PARSER_CACHING = os.environ.get("CUDA_PYTHON_PARSER_CACHING", False)
PARSER_CACHING = True if PARSER_CACHING else False
# ----------------------------------------------------------------------
# Parse user-provided CUDA headers
header_dict = {
'driver' : ['cuda.h',
'cudaProfiler.h',
'cudaEGL.h',
'cudaGL.h',
'cudaVDPAU.h'],
'runtime' : ['driver_types.h',
'vector_types.h',
'cuda_runtime.h',
'surface_types.h',
'texture_types.h',
'library_types.h',
'cuda_runtime_api.h',
'device_types.h',
'driver_functions.h',
'cuda_profiler_api.h',
'cuda_egl_interop.h',
'cuda_gl_interop.h',
'cuda_vdpau_interop.h'],
'nvrtc' : ['nvrtc.h']}
replace = {' __device_builtin__ ':' ',
'CUDARTAPI ':' ',
'typedef __device_builtin__ enum cudaError cudaError_t;' : 'typedef cudaError cudaError_t;',
'typedef __device_builtin__ enum cudaOutputMode cudaOutputMode_t;' : 'typedef cudaOutputMode cudaOutputMode_t;',
'typedef enum cudaError cudaError_t;' : 'typedef cudaError cudaError_t;',
'typedef enum cudaOutputMode cudaOutputMode_t;' : 'typedef cudaOutputMode cudaOutputMode_t;',
'typedef enum cudaDataType_t cudaDataType_t;' : '',
'typedef enum libraryPropertyType_t libraryPropertyType_t;' : '',
' enum ' : ' ',
', enum ' : ', ',
'\(enum ' : '(',}
found_types = []
found_structs = {}
found_unions = {}
found_functions = []
found_values = []
include_path = os.path.join(CUDA_HOME, 'include')
print(f'Parsing headers in "{include_path}" (Caching {PARSER_CACHING})')
for library, header_list in header_dict.items():
header_paths = []
for header in header_list:
path = os.path.join(include_path, header)
if not os.path.exists(path):
print(f'Missing header {header}')
continue
header_paths += [path]
print(f'Parsing {library} headers')
parser = CParser(header_paths,
cache='./cache_{}'.format(library.split('.')[0]) if PARSER_CACHING else None,
replace=replace)
# Combine types with others since they sometimes get tangled
found_types += {key for key in parser.defs['types']}
found_types += {key for key in parser.defs['structs']}
found_structs.update(parser.defs['structs'])
found_types += {key for key in parser.defs['unions']}
found_unions.update(parser.defs['unions'])
found_types += {key for key in parser.defs['enums']}
found_functions += {key for key in parser.defs['functions']}
found_values += {key for key in parser.defs['values']}
if len(found_functions) == 0:
raise RuntimeError(f'Parser found no functions. Is CUDA_HOME setup correctly? (CUDA_HOME="{CUDA_HOME}")')
# Unwrap struct and union members
def unwrapMembers(found_dict):
for key in found_dict:
members = [var for var, _, _ in found_dict[key]['members']]
found_dict[key]['members'] = members
unwrapMembers(found_structs)
unwrapMembers(found_unions)
# ----------------------------------------------------------------------
# Generate
def fetch_input_files(path):
return [os.path.join(path, f) for f in os.listdir(path) if f.endswith('.in')]
def generate_output(infile, local):
assert infile.endswith('.in')
outfile = infile[:-3]
with open(infile) as f:
pxdcontent = Tempita.Template(f.read()).substitute(local)
if os.path.exists(outfile):
with open(outfile) as f:
if f.read() == pxdcontent:
print(f'Skipping {infile} (No change)')
return
with open(outfile, "w") as f:
print(f'Generating {infile}')
f.write(pxdcontent)
path_list = [os.path.join('cuda'),
os.path.join('cuda', '_cuda'),
os.path.join('cuda', '_lib'),
os.path.join('cuda', '_lib', 'ccudart')]
input_files = []
for path in path_list:
input_files += fetch_input_files(path)
for file in input_files:
generate_output(file, locals())
# ----------------------------------------------------------------------
# Prepare compile arguments
# For Cython
include_dirs = [
os.path.dirname(sysconfig.get_path("include")),
f'{CUDA_HOME}/include',
]
library_dirs = [get_python_lib(), os.path.join(os.sys.prefix, "lib")]
extra_compile_args = []
extra_cythonize_kwargs = {}
if sys.platform != 'win32':
extra_compile_args += [
'-std=c++14',
'-fpermissive',
'-Wno-deprecated-declarations',
'-D _GLIBCXX_ASSERTIONS',
'-fno-var-tracking-assignments'
]
if '--debug' in sys.argv:
extra_cythonize_kwargs['gdb_debug'] = True
extra_compile_args += ['-g', '-O0']
else:
extra_compile_args += ['-O3']
# For Setup
if sys.platform == 'win32':
from distutils import _msvccompiler
_msvccompiler.PLAT_TO_VCVARS['win-amd64'] = 'amd64'
setup_requires = ["cython"]
install_requires = ["cython"]
extensions = []
cmdclass = {}
# ----------------------------------------------------------------------
# Cythonize
def do_cythonize(sources):
return cythonize(
[
Extension(
"*",
sources=sources,
include_dirs=include_dirs,
library_dirs=library_dirs,
runtime_library_dirs=[],
libraries=[],
language="c++",
extra_compile_args=extra_compile_args,
)
],
nthreads=nthreads,
compiler_directives=dict(
profile=True, language_level=3, embedsignature=True, binding=True
),
**extra_cythonize_kwargs)
sources_list = [
# private
["cuda/_cuda/*.pyx", "cuda/_cuda/loader.cpp"],
# utils
["cuda/_lib/*.pyx", "cuda/_lib/param_packer.cpp"],
["cuda/_lib/ccudart/*.pyx"],
# public
["cuda/*.pyx"],
# tests
["cuda/tests/*.pyx"]]
for sources in sources_list:
extensions += do_cythonize(sources)
# ---------------------------------------------------------------------
# Custom build_ext command
# Files are build in two steps:
# 1) Cythonized (in the do_cythonize() command)
# 2) Compiled to .o files as part of build_ext
# This class is solely for passing the value of nthreads to build_ext
class ParallelBuildExtensions(build_ext):
def initialize_options(self):
build_ext.initialize_options(self)
if nthreads > 0:
self.parallel = nthreads
def finalize_options(self):
build_ext.finalize_options(self)
cmdclass = {"build_ext": ParallelBuildExtensions}
cmdclass = versioneer.get_cmdclass(cmdclass)
# ----------------------------------------------------------------------
# Setup
setup(
name="cuda-python",
version=versioneer.get_version(),
description="Python bindings for CUDA",
url="https://github.com/NVIDIA/cuda-python",
author="NVIDIA Corporation",
author_email="[email protected]",
license="NVIDIA Proprietary License",
license_files = ('LICENSE',),
classifiers=[
"Intended Audience :: Developers",
"Topic :: Database",
"Topic :: Scientific/Engineering",
"License :: Other/Proprietary License",
"Programming Language :: Python",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Environment :: GPU :: NVIDIA CUDA",
],
# Include the separately-compiled shared library
setup_requires=setup_requires,
ext_modules=extensions,
packages=find_packages(include=["cuda", "cuda.*"]),
package_data=dict.fromkeys(
find_packages(include=["cuda", "cuda.*"]),
["*.pxd", "*.pyx", "*.h", "*.cpp"],
),
cmdclass=cmdclass,
install_requires=install_requires,
zip_safe=False,
)
| cuda-python-main | setup.py |
# Version: 0.20
"""The Versioneer - like a rocketeer, but for versions.
The Versioneer
==============
* like a rocketeer, but for versions!
* https://github.com/python-versioneer/python-versioneer
* Brian Warner
* License: Public Domain
* Compatible with: Python 3.6, 3.7, 3.8, 3.9 and pypy3
* [![Latest Version][pypi-image]][pypi-url]
* [![Build Status][travis-image]][travis-url]
This is a tool for managing a recorded version number in distutils-based
python projects. The goal is to remove the tedious and error-prone "update
the embedded version string" step from your release process. Making a new
release should be as easy as recording a new tag in your version-control
system, and maybe making new tarballs.
## Quick Install
* `pip install versioneer` to somewhere in your $PATH
* add a `[versioneer]` section to your setup.cfg (see [Install](INSTALL.md))
* run `versioneer install` in your source tree, commit the results
* Verify version information with `python setup.py version`
## Version Identifiers
Source trees come from a variety of places:
* a version-control system checkout (mostly used by developers)
* a nightly tarball, produced by build automation
* a snapshot tarball, produced by a web-based VCS browser, like github's
"tarball from tag" feature
* a release tarball, produced by "setup.py sdist", distributed through PyPI
Within each source tree, the version identifier (either a string or a number,
this tool is format-agnostic) can come from a variety of places:
* ask the VCS tool itself, e.g. "git describe" (for checkouts), which knows
about recent "tags" and an absolute revision-id
* the name of the directory into which the tarball was unpacked
* an expanded VCS keyword ($Id$, etc)
* a `_version.py` created by some earlier build step
For released software, the version identifier is closely related to a VCS
tag. Some projects use tag names that include more than just the version
string (e.g. "myproject-1.2" instead of just "1.2"), in which case the tool
needs to strip the tag prefix to extract the version identifier. For
unreleased software (between tags), the version identifier should provide
enough information to help developers recreate the same tree, while also
giving them an idea of roughly how old the tree is (after version 1.2, before
version 1.3). Many VCS systems can report a description that captures this,
for example `git describe --tags --dirty --always` reports things like
"0.7-1-g574ab98-dirty" to indicate that the checkout is one revision past the
0.7 tag, has a unique revision id of "574ab98", and is "dirty" (it has
uncommitted changes).
The version identifier is used for multiple purposes:
* to allow the module to self-identify its version: `myproject.__version__`
* to choose a name and prefix for a 'setup.py sdist' tarball
## Theory of Operation
Versioneer works by adding a special `_version.py` file into your source
tree, where your `__init__.py` can import it. This `_version.py` knows how to
dynamically ask the VCS tool for version information at import time.
`_version.py` also contains `$Revision$` markers, and the installation
process marks `_version.py` to have this marker rewritten with a tag name
during the `git archive` command. As a result, generated tarballs will
contain enough information to get the proper version.
To allow `setup.py` to compute a version too, a `versioneer.py` is added to
the top level of your source tree, next to `setup.py` and the `setup.cfg`
that configures it. This overrides several distutils/setuptools commands to
compute the version when invoked, and changes `setup.py build` and `setup.py
sdist` to replace `_version.py` with a small static file that contains just
the generated version data.
## Installation
See [INSTALL.md](./INSTALL.md) for detailed installation instructions.
## Version-String Flavors
Code which uses Versioneer can learn about its version string at runtime by
importing `_version` from your main `__init__.py` file and running the
`get_versions()` function. From the "outside" (e.g. in `setup.py`), you can
import the top-level `versioneer.py` and run `get_versions()`.
Both functions return a dictionary with different flavors of version
information:
* `['version']`: A condensed version string, rendered using the selected
style. This is the most commonly used value for the project's version
string. The default "pep440" style yields strings like `0.11`,
`0.11+2.g1076c97`, or `0.11+2.g1076c97.dirty`. See the "Styles" section
below for alternative styles.
* `['full-revisionid']`: detailed revision identifier. For Git, this is the
full SHA1 commit id, e.g. "1076c978a8d3cfc70f408fe5974aa6c092c949ac".
* `['date']`: Date and time of the latest `HEAD` commit. For Git, it is the
commit date in ISO 8601 format. This will be None if the date is not
available.
* `['dirty']`: a boolean, True if the tree has uncommitted changes. Note that
this is only accurate if run in a VCS checkout, otherwise it is likely to
be False or None
* `['error']`: if the version string could not be computed, this will be set
to a string describing the problem, otherwise it will be None. It may be
useful to throw an exception in setup.py if this is set, to avoid e.g.
creating tarballs with a version string of "unknown".
Some variants are more useful than others. Including `full-revisionid` in a
bug report should allow developers to reconstruct the exact code being tested
(or indicate the presence of local changes that should be shared with the
developers). `version` is suitable for display in an "about" box or a CLI
`--version` output: it can be easily compared against release notes and lists
of bugs fixed in various releases.
The installer adds the following text to your `__init__.py` to place a basic
version in `YOURPROJECT.__version__`:
from ._version import get_versions
__version__ = get_versions()['version']
del get_versions
## Styles
The setup.cfg `style=` configuration controls how the VCS information is
rendered into a version string.
The default style, "pep440", produces a PEP440-compliant string, equal to the
un-prefixed tag name for actual releases, and containing an additional "local
version" section with more detail for in-between builds. For Git, this is
TAG[+DISTANCE.gHEX[.dirty]] , using information from `git describe --tags
--dirty --always`. For example "0.11+2.g1076c97.dirty" indicates that the
tree is like the "1076c97" commit but has uncommitted changes (".dirty"), and
that this commit is two revisions ("+2") beyond the "0.11" tag. For released
software (exactly equal to a known tag), the identifier will only contain the
stripped tag, e.g. "0.11".
Other styles are available. See [details.md](details.md) in the Versioneer
source tree for descriptions.
## Debugging
Versioneer tries to avoid fatal errors: if something goes wrong, it will tend
to return a version of "0+unknown". To investigate the problem, run `setup.py
version`, which will run the version-lookup code in a verbose mode, and will
display the full contents of `get_versions()` (including the `error` string,
which may help identify what went wrong).
## Known Limitations
Some situations are known to cause problems for Versioneer. This details the
most significant ones. More can be found on Github
[issues page](https://github.com/python-versioneer/python-versioneer/issues).
### Subprojects
Versioneer has limited support for source trees in which `setup.py` is not in
the root directory (e.g. `setup.py` and `.git/` are *not* siblings). The are
two common reasons why `setup.py` might not be in the root:
* Source trees which contain multiple subprojects, such as
[Buildbot](https://github.com/buildbot/buildbot), which contains both
"master" and "slave" subprojects, each with their own `setup.py`,
`setup.cfg`, and `tox.ini`. Projects like these produce multiple PyPI
distributions (and upload multiple independently-installable tarballs).
* Source trees whose main purpose is to contain a C library, but which also
provide bindings to Python (and perhaps other languages) in subdirectories.
Versioneer will look for `.git` in parent directories, and most operations
should get the right version string. However `pip` and `setuptools` have bugs
and implementation details which frequently cause `pip install .` from a
subproject directory to fail to find a correct version string (so it usually
defaults to `0+unknown`).
`pip install --editable .` should work correctly. `setup.py install` might
work too.
Pip-8.1.1 is known to have this problem, but hopefully it will get fixed in
some later version.
[Bug #38](https://github.com/python-versioneer/python-versioneer/issues/38) is tracking
this issue. The discussion in
[PR #61](https://github.com/python-versioneer/python-versioneer/pull/61) describes the
issue from the Versioneer side in more detail.
[pip PR#3176](https://github.com/pypa/pip/pull/3176) and
[pip PR#3615](https://github.com/pypa/pip/pull/3615) contain work to improve
pip to let Versioneer work correctly.
Versioneer-0.16 and earlier only looked for a `.git` directory next to the
`setup.cfg`, so subprojects were completely unsupported with those releases.
### Editable installs with setuptools <= 18.5
`setup.py develop` and `pip install --editable .` allow you to install a
project into a virtualenv once, then continue editing the source code (and
test) without re-installing after every change.
"Entry-point scripts" (`setup(entry_points={"console_scripts": ..})`) are a
convenient way to specify executable scripts that should be installed along
with the python package.
These both work as expected when using modern setuptools. When using
setuptools-18.5 or earlier, however, certain operations will cause
`pkg_resources.DistributionNotFound` errors when running the entrypoint
script, which must be resolved by re-installing the package. This happens
when the install happens with one version, then the egg_info data is
regenerated while a different version is checked out. Many setup.py commands
cause egg_info to be rebuilt (including `sdist`, `wheel`, and installing into
a different virtualenv), so this can be surprising.
[Bug #83](https://github.com/python-versioneer/python-versioneer/issues/83) describes
this one, but upgrading to a newer version of setuptools should probably
resolve it.
## Updating Versioneer
To upgrade your project to a new release of Versioneer, do the following:
* install the new Versioneer (`pip install -U versioneer` or equivalent)
* edit `setup.cfg`, if necessary, to include any new configuration settings
indicated by the release notes. See [UPGRADING](./UPGRADING.md) for details.
* re-run `versioneer install` in your source tree, to replace
`SRC/_version.py`
* commit any changed files
## Future Directions
This tool is designed to make it easily extended to other version-control
systems: all VCS-specific components are in separate directories like
src/git/ . The top-level `versioneer.py` script is assembled from these
components by running make-versioneer.py . In the future, make-versioneer.py
will take a VCS name as an argument, and will construct a version of
`versioneer.py` that is specific to the given VCS. It might also take the
configuration arguments that are currently provided manually during
installation by editing setup.py . Alternatively, it might go the other
direction and include code from all supported VCS systems, reducing the
number of intermediate scripts.
## Similar projects
* [setuptools_scm](https://github.com/pypa/setuptools_scm/) - a non-vendored build-time
dependency
* [minver](https://github.com/jbweston/miniver) - a lightweight reimplementation of
versioneer
* [versioningit](https://github.com/jwodder/versioningit) - a PEP 518-based setuptools
plugin
## License
To make Versioneer easier to embed, all its code is dedicated to the public
domain. The `_version.py` that it creates is also in the public domain.
Specifically, both are released under the Creative Commons "Public Domain
Dedication" license (CC0-1.0), as described in
https://creativecommons.org/publicdomain/zero/1.0/ .
[pypi-image]: https://img.shields.io/pypi/v/versioneer.svg
[pypi-url]: https://pypi.python.org/pypi/versioneer/
[travis-image]:
https://img.shields.io/travis/com/python-versioneer/python-versioneer.svg
[travis-url]: https://travis-ci.com/github/python-versioneer/python-versioneer
"""
import configparser
import errno
import json
import os
import re
import subprocess
import sys
class VersioneerConfig: # pylint: disable=too-few-public-methods # noqa
"""Container for Versioneer configuration parameters."""
def get_root():
"""Get the project root directory.
We require that all commands are run from the project root, i.e. the
directory that contains setup.py, setup.cfg, and versioneer.py .
"""
root = os.path.realpath(os.path.abspath(os.getcwd()))
setup_py = os.path.join(root, "setup.py")
versioneer_py = os.path.join(root, "versioneer.py")
if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):
# allow 'python path/to/setup.py COMMAND'
root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0])))
setup_py = os.path.join(root, "setup.py")
versioneer_py = os.path.join(root, "versioneer.py")
if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):
err = ("Versioneer was unable to run the project root directory. "
"Versioneer requires setup.py to be executed from "
"its immediate directory (like 'python setup.py COMMAND'), "
"or in a way that lets it use sys.argv[0] to find the root "
"(like 'python path/to/setup.py COMMAND').")
raise VersioneerBadRootError(err)
try:
# Certain runtime workflows (setup.py install/develop in a setuptools
# tree) execute all dependencies in a single python process, so
# "versioneer" may be imported multiple times, and python's shared
# module-import table will cache the first one. So we can't use
# os.path.dirname(__file__), as that will find whichever
# versioneer.py was first imported, even in later projects.
my_path = os.path.realpath(os.path.abspath(__file__))
me_dir = os.path.normcase(os.path.splitext(my_path)[0])
vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0])
if me_dir != vsr_dir:
print("Warning: build in %s is using versioneer.py from %s"
% (os.path.dirname(my_path), versioneer_py))
except NameError:
pass
return root
def get_config_from_root(root):
"""Read the project setup.cfg file to determine Versioneer config."""
# This might raise EnvironmentError (if setup.cfg is missing), or
# configparser.NoSectionError (if it lacks a [versioneer] section), or
# configparser.NoOptionError (if it lacks "VCS="). See the docstring at
# the top of versioneer.py for instructions on writing your setup.cfg .
setup_cfg = os.path.join(root, "setup.cfg")
parser = configparser.ConfigParser()
with open(setup_cfg, "r") as cfg_file:
parser.read_file(cfg_file)
VCS = parser.get("versioneer", "VCS") # mandatory
# Dict-like interface for non-mandatory entries
section = parser["versioneer"]
# pylint:disable=attribute-defined-outside-init # noqa
cfg = VersioneerConfig()
cfg.VCS = VCS
cfg.style = section.get("style", "")
cfg.versionfile_source = section.get("versionfile_source")
cfg.versionfile_build = section.get("versionfile_build")
cfg.tag_prefix = section.get("tag_prefix")
if cfg.tag_prefix in ("''", '""'):
cfg.tag_prefix = ""
cfg.parentdir_prefix = section.get("parentdir_prefix")
cfg.verbose = section.get("verbose")
return cfg
class NotThisMethod(Exception):
"""Exception raised if a method is not valid for the current scenario."""
# these dictionaries contain VCS-specific tools
LONG_VERSION_PY = {}
HANDLERS = {}
def register_vcs_handler(vcs, method): # decorator
"""Create decorator to mark a method as the handler of a VCS."""
def decorate(f):
"""Store f in HANDLERS[vcs][method]."""
HANDLERS.setdefault(vcs, {})[method] = f
return f
return decorate
# pylint:disable=too-many-arguments,consider-using-with # noqa
def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,
env=None):
"""Call the given command(s)."""
assert isinstance(commands, list)
process = None
for command in commands:
try:
dispcmd = str([command] + args)
# remember shell=False, so use git.cmd on windows, not just git
process = subprocess.Popen([command] + args, cwd=cwd, env=env,
stdout=subprocess.PIPE,
stderr=(subprocess.PIPE if hide_stderr
else None))
break
except EnvironmentError:
e = sys.exc_info()[1]
if e.errno == errno.ENOENT:
continue
if verbose:
print("unable to run %s" % dispcmd)
print(e)
return None, None
else:
if verbose:
print("unable to find command, tried %s" % (commands,))
return None, None
stdout = process.communicate()[0].strip().decode()
if process.returncode != 0:
if verbose:
print("unable to run %s (error)" % dispcmd)
print("stdout was %s" % stdout)
return None, process.returncode
return stdout, process.returncode
LONG_VERSION_PY['git'] = r'''
# This file helps to compute a version number in source trees obtained from
# git-archive tarball (such as those provided by githubs download-from-tag
# feature). Distribution tarballs (built by setup.py sdist) and build
# directories (produced by setup.py build) will contain a much shorter file
# that just contains the computed version number.
# This file is released into the public domain. Generated by
# versioneer-0.20 (https://github.com/python-versioneer/python-versioneer)
"""Git implementation of _version.py."""
import errno
import os
import re
import subprocess
import sys
def get_keywords():
"""Get the keywords needed to look up the version information."""
# these strings will be replaced by git during git-archive.
# setup.py/versioneer.py will grep for the variable names, so they must
# each be defined on a line of their own. _version.py will just call
# get_keywords().
git_refnames = "%(DOLLAR)sFormat:%%d%(DOLLAR)s"
git_full = "%(DOLLAR)sFormat:%%H%(DOLLAR)s"
git_date = "%(DOLLAR)sFormat:%%ci%(DOLLAR)s"
keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
return keywords
class VersioneerConfig: # pylint: disable=too-few-public-methods
"""Container for Versioneer configuration parameters."""
def get_config():
"""Create, populate and return the VersioneerConfig() object."""
# these strings are filled in when 'setup.py versioneer' creates
# _version.py
cfg = VersioneerConfig()
cfg.VCS = "git"
cfg.style = "%(STYLE)s"
cfg.tag_prefix = "%(TAG_PREFIX)s"
cfg.parentdir_prefix = "%(PARENTDIR_PREFIX)s"
cfg.versionfile_source = "%(VERSIONFILE_SOURCE)s"
cfg.verbose = False
return cfg
class NotThisMethod(Exception):
"""Exception raised if a method is not valid for the current scenario."""
LONG_VERSION_PY = {}
HANDLERS = {}
def register_vcs_handler(vcs, method): # decorator
"""Create decorator to mark a method as the handler of a VCS."""
def decorate(f):
"""Store f in HANDLERS[vcs][method]."""
if vcs not in HANDLERS:
HANDLERS[vcs] = {}
HANDLERS[vcs][method] = f
return f
return decorate
# pylint:disable=too-many-arguments,consider-using-with # noqa
def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,
env=None):
"""Call the given command(s)."""
assert isinstance(commands, list)
process = None
for command in commands:
try:
dispcmd = str([command] + args)
# remember shell=False, so use git.cmd on windows, not just git
process = subprocess.Popen([command] + args, cwd=cwd, env=env,
stdout=subprocess.PIPE,
stderr=(subprocess.PIPE if hide_stderr
else None))
break
except EnvironmentError:
e = sys.exc_info()[1]
if e.errno == errno.ENOENT:
continue
if verbose:
print("unable to run %%s" %% dispcmd)
print(e)
return None, None
else:
if verbose:
print("unable to find command, tried %%s" %% (commands,))
return None, None
stdout = process.communicate()[0].strip().decode()
if process.returncode != 0:
if verbose:
print("unable to run %%s (error)" %% dispcmd)
print("stdout was %%s" %% stdout)
return None, process.returncode
return stdout, process.returncode
def versions_from_parentdir(parentdir_prefix, root, verbose):
"""Try to determine the version from the parent directory name.
Source tarballs conventionally unpack into a directory that includes both
the project name and a version string. We will also support searching up
two directory levels for an appropriately named parent directory
"""
rootdirs = []
for _ in range(3):
dirname = os.path.basename(root)
if dirname.startswith(parentdir_prefix):
return {"version": dirname[len(parentdir_prefix):],
"full-revisionid": None,
"dirty": False, "error": None, "date": None}
rootdirs.append(root)
root = os.path.dirname(root) # up a level
if verbose:
print("Tried directories %%s but none started with prefix %%s" %%
(str(rootdirs), parentdir_prefix))
raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
@register_vcs_handler("git", "get_keywords")
def git_get_keywords(versionfile_abs):
"""Extract version information from the given file."""
# the code embedded in _version.py can just fetch the value of these
# keywords. When used from setup.py, we don't want to import _version.py,
# so we do it with a regexp instead. This function is not used from
# _version.py.
keywords = {}
try:
with open(versionfile_abs, "r") as fobj:
for line in fobj:
if line.strip().startswith("git_refnames ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["refnames"] = mo.group(1)
if line.strip().startswith("git_full ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["full"] = mo.group(1)
if line.strip().startswith("git_date ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["date"] = mo.group(1)
except EnvironmentError:
pass
return keywords
@register_vcs_handler("git", "keywords")
def git_versions_from_keywords(keywords, tag_prefix, verbose):
"""Get version information from git keywords."""
if "refnames" not in keywords:
raise NotThisMethod("Short version file found")
date = keywords.get("date")
if date is not None:
# Use only the last line. Previous lines may contain GPG signature
# information.
date = date.splitlines()[-1]
# git-2.2.0 added "%%cI", which expands to an ISO-8601 -compliant
# datestamp. However we prefer "%%ci" (which expands to an "ISO-8601
# -like" string, which we must then edit to make compliant), because
# it's been around since git-1.5.3, and it's too difficult to
# discover which version we're using, or to work around using an
# older one.
date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
refnames = keywords["refnames"].strip()
if refnames.startswith("$Format"):
if verbose:
print("keywords are unexpanded, not using")
raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
refs = {r.strip() for r in refnames.strip("()").split(",")}
# starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
# just "foo-1.0". If we see a "tag: " prefix, prefer those.
TAG = "tag: "
tags = {r[len(TAG):] for r in refs if r.startswith(TAG)}
if not tags:
# Either we're using git < 1.8.3, or there really are no tags. We use
# a heuristic: assume all version tags have a digit. The old git %%d
# expansion behaves like git log --decorate=short and strips out the
# refs/heads/ and refs/tags/ prefixes that would let us distinguish
# between branches and tags. By ignoring refnames without digits, we
# filter out many common branch names like "release" and
# "stabilization", as well as "HEAD" and "master".
tags = {r for r in refs if re.search(r'\d', r)}
if verbose:
print("discarding '%%s', no digits" %% ",".join(refs - tags))
if verbose:
print("likely tags: %%s" %% ",".join(sorted(tags)))
for ref in sorted(tags):
# sorting will prefer e.g. "2.0" over "2.0rc1"
if ref.startswith(tag_prefix):
r = ref[len(tag_prefix):]
# Filter out refs that exactly match prefix or that don't start
# with a number once the prefix is stripped (mostly a concern
# when prefix is '')
if not re.match(r'\d', r):
continue
if verbose:
print("picking %%s" %% r)
return {"version": r,
"full-revisionid": keywords["full"].strip(),
"dirty": False, "error": None,
"date": date}
# no suitable tags, so version is "0+unknown", but full hex is still there
if verbose:
print("no suitable tags, using unknown + full revision id")
return {"version": "0+unknown",
"full-revisionid": keywords["full"].strip(),
"dirty": False, "error": "no suitable tags", "date": None}
@register_vcs_handler("git", "pieces_from_vcs")
def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command):
"""Get version from 'git describe' in the root of the source tree.
This only gets called if the git-archive 'subst' keywords were *not*
expanded, and _version.py hasn't already been rewritten with a short
version string, meaning we're inside a checked out source tree.
"""
GITS = ["git"]
if sys.platform == "win32":
GITS = ["git.cmd", "git.exe"]
_, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root,
hide_stderr=True)
if rc != 0:
if verbose:
print("Directory %%s not under git control" %% root)
raise NotThisMethod("'git rev-parse --git-dir' returned error")
# if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
# if there isn't one, this yields HEX[-dirty] (no NUM)
describe_out, rc = runner(GITS, ["describe", "--tags", "--dirty",
"--always", "--long",
"--match", "%%s*" %% tag_prefix],
cwd=root)
# --long was added in git-1.5.5
if describe_out is None:
raise NotThisMethod("'git describe' failed")
describe_out = describe_out.strip()
full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root)
if full_out is None:
raise NotThisMethod("'git rev-parse' failed")
full_out = full_out.strip()
pieces = {}
pieces["long"] = full_out
pieces["short"] = full_out[:7] # maybe improved later
pieces["error"] = None
branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"],
cwd=root)
# --abbrev-ref was added in git-1.6.3
if rc != 0 or branch_name is None:
raise NotThisMethod("'git rev-parse --abbrev-ref' returned error")
branch_name = branch_name.strip()
if branch_name == "HEAD":
# If we aren't exactly on a branch, pick a branch which represents
# the current commit. If all else fails, we are on a branchless
# commit.
branches, rc = runner(GITS, ["branch", "--contains"], cwd=root)
# --contains was added in git-1.5.4
if rc != 0 or branches is None:
raise NotThisMethod("'git branch --contains' returned error")
branches = branches.split("\n")
# Remove the first line if we're running detached
if "(" in branches[0]:
branches.pop(0)
# Strip off the leading "* " from the list of branches.
branches = [branch[2:] for branch in branches]
if "master" in branches:
branch_name = "master"
elif not branches:
branch_name = None
else:
# Pick the first branch that is returned. Good or bad.
branch_name = branches[0]
pieces["branch"] = branch_name
# parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
# TAG might have hyphens.
git_describe = describe_out
# look for -dirty suffix
dirty = git_describe.endswith("-dirty")
pieces["dirty"] = dirty
if dirty:
git_describe = git_describe[:git_describe.rindex("-dirty")]
# now we have TAG-NUM-gHEX or HEX
if "-" in git_describe:
# TAG-NUM-gHEX
mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
if not mo:
# unparseable. Maybe git-describe is misbehaving?
pieces["error"] = ("unable to parse git-describe output: '%%s'"
%% describe_out)
return pieces
# tag
full_tag = mo.group(1)
if not full_tag.startswith(tag_prefix):
if verbose:
fmt = "tag '%%s' doesn't start with prefix '%%s'"
print(fmt %% (full_tag, tag_prefix))
pieces["error"] = ("tag '%%s' doesn't start with prefix '%%s'"
%% (full_tag, tag_prefix))
return pieces
pieces["closest-tag"] = full_tag[len(tag_prefix):]
# distance: number of commits since tag
pieces["distance"] = int(mo.group(2))
# commit: short hex revision ID
pieces["short"] = mo.group(3)
else:
# HEX: no tags
pieces["closest-tag"] = None
count_out, rc = runner(GITS, ["rev-list", "HEAD", "--count"], cwd=root)
pieces["distance"] = int(count_out) # total number of commits
# commit date: see ISO-8601 comment in git_versions_from_keywords()
date = runner(GITS, ["show", "-s", "--format=%%ci", "HEAD"], cwd=root)[0].strip()
# Use only the last line. Previous lines may contain GPG signature
# information.
date = date.splitlines()[-1]
pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
return pieces
def plus_or_dot(pieces):
"""Return a + if we don't already have one, else return a ."""
if "+" in pieces.get("closest-tag", ""):
return "."
return "+"
def render_pep440(pieces):
"""Build up version string, with post-release "local version identifier".
Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
Exceptions:
1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += plus_or_dot(pieces)
rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
else:
# exception #1
rendered = "0+untagged.%%d.g%%s" %% (pieces["distance"],
pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
return rendered
def render_pep440_branch(pieces):
"""TAG[[.dev0]+DISTANCE.gHEX[.dirty]] .
The ".dev0" means not master branch. Note that .dev0 sorts backwards
(a feature branch will appear "older" than the master branch).
Exceptions:
1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
if pieces["branch"] != "master":
rendered += ".dev0"
rendered += plus_or_dot(pieces)
rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
else:
# exception #1
rendered = "0"
if pieces["branch"] != "master":
rendered += ".dev0"
rendered += "+untagged.%%d.g%%s" %% (pieces["distance"],
pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
return rendered
def render_pep440_pre(pieces):
"""TAG[.post0.devDISTANCE] -- No -dirty.
Exceptions:
1: no tags. 0.post0.devDISTANCE
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"]:
rendered += ".post0.dev%%d" %% pieces["distance"]
else:
# exception #1
rendered = "0.post0.dev%%d" %% pieces["distance"]
return rendered
def render_pep440_post(pieces):
"""TAG[.postDISTANCE[.dev0]+gHEX] .
The ".dev0" means dirty. Note that .dev0 sorts backwards
(a dirty tree will appear "older" than the corresponding clean one),
but you shouldn't be releasing software with -dirty anyways.
Exceptions:
1: no tags. 0.postDISTANCE[.dev0]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += ".post%%d" %% pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
rendered += plus_or_dot(pieces)
rendered += "g%%s" %% pieces["short"]
else:
# exception #1
rendered = "0.post%%d" %% pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
rendered += "+g%%s" %% pieces["short"]
return rendered
def render_pep440_post_branch(pieces):
"""TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] .
The ".dev0" means not master branch.
Exceptions:
1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += ".post%%d" %% pieces["distance"]
if pieces["branch"] != "master":
rendered += ".dev0"
rendered += plus_or_dot(pieces)
rendered += "g%%s" %% pieces["short"]
if pieces["dirty"]:
rendered += ".dirty"
else:
# exception #1
rendered = "0.post%%d" %% pieces["distance"]
if pieces["branch"] != "master":
rendered += ".dev0"
rendered += "+g%%s" %% pieces["short"]
if pieces["dirty"]:
rendered += ".dirty"
return rendered
def render_pep440_old(pieces):
"""TAG[.postDISTANCE[.dev0]] .
The ".dev0" means dirty.
Exceptions:
1: no tags. 0.postDISTANCE[.dev0]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += ".post%%d" %% pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
else:
# exception #1
rendered = "0.post%%d" %% pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
return rendered
def render_git_describe(pieces):
"""TAG[-DISTANCE-gHEX][-dirty].
Like 'git describe --tags --dirty --always'.
Exceptions:
1: no tags. HEX[-dirty] (note: no 'g' prefix)
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"]:
rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"])
else:
# exception #1
rendered = pieces["short"]
if pieces["dirty"]:
rendered += "-dirty"
return rendered
def render_git_describe_long(pieces):
"""TAG-DISTANCE-gHEX[-dirty].
Like 'git describe --tags --dirty --always -long'.
The distance/hash is unconditional.
Exceptions:
1: no tags. HEX[-dirty] (note: no 'g' prefix)
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"])
else:
# exception #1
rendered = pieces["short"]
if pieces["dirty"]:
rendered += "-dirty"
return rendered
def render(pieces, style):
"""Render the given version pieces into the requested style."""
if pieces["error"]:
return {"version": "unknown",
"full-revisionid": pieces.get("long"),
"dirty": None,
"error": pieces["error"],
"date": None}
if not style or style == "default":
style = "pep440" # the default
if style == "pep440":
rendered = render_pep440(pieces)
elif style == "pep440-branch":
rendered = render_pep440_branch(pieces)
elif style == "pep440-pre":
rendered = render_pep440_pre(pieces)
elif style == "pep440-post":
rendered = render_pep440_post(pieces)
elif style == "pep440-post-branch":
rendered = render_pep440_post_branch(pieces)
elif style == "pep440-old":
rendered = render_pep440_old(pieces)
elif style == "git-describe":
rendered = render_git_describe(pieces)
elif style == "git-describe-long":
rendered = render_git_describe_long(pieces)
else:
raise ValueError("unknown style '%%s'" %% style)
return {"version": rendered, "full-revisionid": pieces["long"],
"dirty": pieces["dirty"], "error": None,
"date": pieces.get("date")}
def get_versions():
"""Get version information or return default if unable to do so."""
# I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
# __file__, we can work backwards from there to the root. Some
# py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
# case we can only use expanded keywords.
cfg = get_config()
verbose = cfg.verbose
try:
return git_versions_from_keywords(get_keywords(), cfg.tag_prefix,
verbose)
except NotThisMethod:
pass
try:
root = os.path.realpath(__file__)
# versionfile_source is the relative path from the top of the source
# tree (where the .git directory might live) to this file. Invert
# this to find the root from __file__.
for _ in cfg.versionfile_source.split('/'):
root = os.path.dirname(root)
except NameError:
return {"version": "0+unknown", "full-revisionid": None,
"dirty": None,
"error": "unable to find root of source tree",
"date": None}
try:
pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
return render(pieces, cfg.style)
except NotThisMethod:
pass
try:
if cfg.parentdir_prefix:
return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
except NotThisMethod:
pass
return {"version": "0+unknown", "full-revisionid": None,
"dirty": None,
"error": "unable to compute version", "date": None}
'''
@register_vcs_handler("git", "get_keywords")
def git_get_keywords(versionfile_abs):
"""Extract version information from the given file."""
# the code embedded in _version.py can just fetch the value of these
# keywords. When used from setup.py, we don't want to import _version.py,
# so we do it with a regexp instead. This function is not used from
# _version.py.
keywords = {}
try:
with open(versionfile_abs, "r") as fobj:
for line in fobj:
if line.strip().startswith("git_refnames ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["refnames"] = mo.group(1)
if line.strip().startswith("git_full ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["full"] = mo.group(1)
if line.strip().startswith("git_date ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["date"] = mo.group(1)
except EnvironmentError:
pass
return keywords
@register_vcs_handler("git", "keywords")
def git_versions_from_keywords(keywords, tag_prefix, verbose):
"""Get version information from git keywords."""
if "refnames" not in keywords:
raise NotThisMethod("Short version file found")
date = keywords.get("date")
if date is not None:
# Use only the last line. Previous lines may contain GPG signature
# information.
date = date.splitlines()[-1]
# git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant
# datestamp. However we prefer "%ci" (which expands to an "ISO-8601
# -like" string, which we must then edit to make compliant), because
# it's been around since git-1.5.3, and it's too difficult to
# discover which version we're using, or to work around using an
# older one.
date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
refnames = keywords["refnames"].strip()
if refnames.startswith("$Format"):
if verbose:
print("keywords are unexpanded, not using")
raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
refs = {r.strip() for r in refnames.strip("()").split(",")}
# starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
# just "foo-1.0". If we see a "tag: " prefix, prefer those.
TAG = "tag: "
tags = {r[len(TAG):] for r in refs if r.startswith(TAG)}
if not tags:
# Either we're using git < 1.8.3, or there really are no tags. We use
# a heuristic: assume all version tags have a digit. The old git %d
# expansion behaves like git log --decorate=short and strips out the
# refs/heads/ and refs/tags/ prefixes that would let us distinguish
# between branches and tags. By ignoring refnames without digits, we
# filter out many common branch names like "release" and
# "stabilization", as well as "HEAD" and "master".
tags = {r for r in refs if re.search(r'\d', r)}
if verbose:
print("discarding '%s', no digits" % ",".join(refs - tags))
if verbose:
print("likely tags: %s" % ",".join(sorted(tags)))
for ref in sorted(tags):
# sorting will prefer e.g. "2.0" over "2.0rc1"
if ref.startswith(tag_prefix):
r = ref[len(tag_prefix):]
# Filter out refs that exactly match prefix or that don't start
# with a number once the prefix is stripped (mostly a concern
# when prefix is '')
if not re.match(r'\d', r):
continue
if verbose:
print("picking %s" % r)
return {"version": r,
"full-revisionid": keywords["full"].strip(),
"dirty": False, "error": None,
"date": date}
# no suitable tags, so version is "0+unknown", but full hex is still there
if verbose:
print("no suitable tags, using unknown + full revision id")
return {"version": "0+unknown",
"full-revisionid": keywords["full"].strip(),
"dirty": False, "error": "no suitable tags", "date": None}
@register_vcs_handler("git", "pieces_from_vcs")
def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command):
"""Get version from 'git describe' in the root of the source tree.
This only gets called if the git-archive 'subst' keywords were *not*
expanded, and _version.py hasn't already been rewritten with a short
version string, meaning we're inside a checked out source tree.
"""
GITS = ["git"]
if sys.platform == "win32":
GITS = ["git.cmd", "git.exe"]
_, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root,
hide_stderr=True)
if rc != 0:
if verbose:
print("Directory %s not under git control" % root)
raise NotThisMethod("'git rev-parse --git-dir' returned error")
# if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
# if there isn't one, this yields HEX[-dirty] (no NUM)
describe_out, rc = runner(GITS, ["describe", "--tags", "--dirty",
"--always", "--long",
"--match", "%s*" % tag_prefix],
cwd=root)
# --long was added in git-1.5.5
if describe_out is None:
raise NotThisMethod("'git describe' failed")
describe_out = describe_out.strip()
full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root)
if full_out is None:
raise NotThisMethod("'git rev-parse' failed")
full_out = full_out.strip()
pieces = {}
pieces["long"] = full_out
pieces["short"] = full_out[:7] # maybe improved later
pieces["error"] = None
branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"],
cwd=root)
# --abbrev-ref was added in git-1.6.3
if rc != 0 or branch_name is None:
raise NotThisMethod("'git rev-parse --abbrev-ref' returned error")
branch_name = branch_name.strip()
if branch_name == "HEAD":
# If we aren't exactly on a branch, pick a branch which represents
# the current commit. If all else fails, we are on a branchless
# commit.
branches, rc = runner(GITS, ["branch", "--contains"], cwd=root)
# --contains was added in git-1.5.4
if rc != 0 or branches is None:
raise NotThisMethod("'git branch --contains' returned error")
branches = branches.split("\n")
# Remove the first line if we're running detached
if "(" in branches[0]:
branches.pop(0)
# Strip off the leading "* " from the list of branches.
branches = [branch[2:] for branch in branches]
if "master" in branches:
branch_name = "master"
elif not branches:
branch_name = None
else:
# Pick the first branch that is returned. Good or bad.
branch_name = branches[0]
pieces["branch"] = branch_name
# parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
# TAG might have hyphens.
git_describe = describe_out
# look for -dirty suffix
dirty = git_describe.endswith("-dirty")
pieces["dirty"] = dirty
if dirty:
git_describe = git_describe[:git_describe.rindex("-dirty")]
# now we have TAG-NUM-gHEX or HEX
if "-" in git_describe:
# TAG-NUM-gHEX
mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
if not mo:
# unparseable. Maybe git-describe is misbehaving?
pieces["error"] = ("unable to parse git-describe output: '%s'"
% describe_out)
return pieces
# tag
full_tag = mo.group(1)
if not full_tag.startswith(tag_prefix):
if verbose:
fmt = "tag '%s' doesn't start with prefix '%s'"
print(fmt % (full_tag, tag_prefix))
pieces["error"] = ("tag '%s' doesn't start with prefix '%s'"
% (full_tag, tag_prefix))
return pieces
pieces["closest-tag"] = full_tag[len(tag_prefix):]
# distance: number of commits since tag
pieces["distance"] = int(mo.group(2))
# commit: short hex revision ID
pieces["short"] = mo.group(3)
else:
# HEX: no tags
pieces["closest-tag"] = None
count_out, rc = runner(GITS, ["rev-list", "HEAD", "--count"], cwd=root)
pieces["distance"] = int(count_out) # total number of commits
# commit date: see ISO-8601 comment in git_versions_from_keywords()
date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip()
# Use only the last line. Previous lines may contain GPG signature
# information.
date = date.splitlines()[-1]
pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
return pieces
def do_vcs_install(manifest_in, versionfile_source, ipy):
"""Git-specific installation logic for Versioneer.
For Git, this means creating/changing .gitattributes to mark _version.py
for export-subst keyword substitution.
"""
GITS = ["git"]
if sys.platform == "win32":
GITS = ["git.cmd", "git.exe"]
files = [manifest_in, versionfile_source]
if ipy:
files.append(ipy)
try:
my_path = __file__
if my_path.endswith(".pyc") or my_path.endswith(".pyo"):
my_path = os.path.splitext(my_path)[0] + ".py"
versioneer_file = os.path.relpath(my_path)
except NameError:
versioneer_file = "versioneer.py"
files.append(versioneer_file)
present = False
try:
with open(".gitattributes", "r") as fobj:
for line in fobj:
if line.strip().startswith(versionfile_source):
if "export-subst" in line.strip().split()[1:]:
present = True
break
except EnvironmentError:
pass
if not present:
with open(".gitattributes", "a+") as fobj:
fobj.write(f"{versionfile_source} export-subst\n")
files.append(".gitattributes")
run_command(GITS, ["add", "--"] + files)
def versions_from_parentdir(parentdir_prefix, root, verbose):
"""Try to determine the version from the parent directory name.
Source tarballs conventionally unpack into a directory that includes both
the project name and a version string. We will also support searching up
two directory levels for an appropriately named parent directory
"""
rootdirs = []
for _ in range(3):
dirname = os.path.basename(root)
if dirname.startswith(parentdir_prefix):
return {"version": dirname[len(parentdir_prefix):],
"full-revisionid": None,
"dirty": False, "error": None, "date": None}
rootdirs.append(root)
root = os.path.dirname(root) # up a level
if verbose:
print("Tried directories %s but none started with prefix %s" %
(str(rootdirs), parentdir_prefix))
raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
SHORT_VERSION_PY = """
# This file was generated by 'versioneer.py' (0.20) from
# revision-control system data, or from the parent directory name of an
# unpacked source archive. Distribution tarballs contain a pre-generated copy
# of this file.
import json
version_json = '''
%s
''' # END VERSION_JSON
def get_versions():
return json.loads(version_json)
"""
def versions_from_file(filename):
"""Try to determine the version from _version.py if present."""
try:
with open(filename) as f:
contents = f.read()
except EnvironmentError:
raise NotThisMethod("unable to read _version.py")
mo = re.search(r"version_json = '''\n(.*)''' # END VERSION_JSON",
contents, re.M | re.S)
if not mo:
mo = re.search(r"version_json = '''\r\n(.*)''' # END VERSION_JSON",
contents, re.M | re.S)
if not mo:
raise NotThisMethod("no version_json in _version.py")
return json.loads(mo.group(1))
def write_to_version_file(filename, versions):
"""Write the given version number to the given _version.py file."""
os.unlink(filename)
contents = json.dumps(versions, sort_keys=True,
indent=1, separators=(",", ": "))
with open(filename, "w") as f:
f.write(SHORT_VERSION_PY % contents)
print("set %s to '%s'" % (filename, versions["version"]))
def plus_or_dot(pieces):
"""Return a + if we don't already have one, else return a ."""
if "+" in pieces.get("closest-tag", ""):
return "."
return "+"
def render_pep440(pieces):
"""Build up version string, with post-release "local version identifier".
Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
Exceptions:
1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += plus_or_dot(pieces)
rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
else:
# exception #1
rendered = "0+untagged.%d.g%s" % (pieces["distance"],
pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
return rendered
def render_pep440_branch(pieces):
"""TAG[[.dev0]+DISTANCE.gHEX[.dirty]] .
The ".dev0" means not master branch. Note that .dev0 sorts backwards
(a feature branch will appear "older" than the master branch).
Exceptions:
1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
if pieces["branch"] != "master":
rendered += ".dev0"
rendered += plus_or_dot(pieces)
rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
else:
# exception #1
rendered = "0"
if pieces["branch"] != "master":
rendered += ".dev0"
rendered += "+untagged.%d.g%s" % (pieces["distance"],
pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
return rendered
def render_pep440_pre(pieces):
"""TAG[.post0.devDISTANCE] -- No -dirty.
Exceptions:
1: no tags. 0.post0.devDISTANCE
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"]:
rendered += ".post0.dev%d" % pieces["distance"]
else:
# exception #1
rendered = "0.post0.dev%d" % pieces["distance"]
return rendered
def render_pep440_post(pieces):
"""TAG[.postDISTANCE[.dev0]+gHEX] .
The ".dev0" means dirty. Note that .dev0 sorts backwards
(a dirty tree will appear "older" than the corresponding clean one),
but you shouldn't be releasing software with -dirty anyways.
Exceptions:
1: no tags. 0.postDISTANCE[.dev0]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += ".post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
rendered += plus_or_dot(pieces)
rendered += "g%s" % pieces["short"]
else:
# exception #1
rendered = "0.post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
rendered += "+g%s" % pieces["short"]
return rendered
def render_pep440_post_branch(pieces):
"""TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] .
The ".dev0" means not master branch.
Exceptions:
1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += ".post%d" % pieces["distance"]
if pieces["branch"] != "master":
rendered += ".dev0"
rendered += plus_or_dot(pieces)
rendered += "g%s" % pieces["short"]
if pieces["dirty"]:
rendered += ".dirty"
else:
# exception #1
rendered = "0.post%d" % pieces["distance"]
if pieces["branch"] != "master":
rendered += ".dev0"
rendered += "+g%s" % pieces["short"]
if pieces["dirty"]:
rendered += ".dirty"
return rendered
def render_pep440_old(pieces):
"""TAG[.postDISTANCE[.dev0]] .
The ".dev0" means dirty.
Exceptions:
1: no tags. 0.postDISTANCE[.dev0]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += ".post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
else:
# exception #1
rendered = "0.post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
return rendered
def render_git_describe(pieces):
"""TAG[-DISTANCE-gHEX][-dirty].
Like 'git describe --tags --dirty --always'.
Exceptions:
1: no tags. HEX[-dirty] (note: no 'g' prefix)
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"]:
rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
else:
# exception #1
rendered = pieces["short"]
if pieces["dirty"]:
rendered += "-dirty"
return rendered
def render_git_describe_long(pieces):
"""TAG-DISTANCE-gHEX[-dirty].
Like 'git describe --tags --dirty --always -long'.
The distance/hash is unconditional.
Exceptions:
1: no tags. HEX[-dirty] (note: no 'g' prefix)
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
else:
# exception #1
rendered = pieces["short"]
if pieces["dirty"]:
rendered += "-dirty"
return rendered
def render(pieces, style):
"""Render the given version pieces into the requested style."""
if pieces["error"]:
return {"version": "unknown",
"full-revisionid": pieces.get("long"),
"dirty": None,
"error": pieces["error"],
"date": None}
if not style or style == "default":
style = "pep440" # the default
if style == "pep440":
rendered = render_pep440(pieces)
elif style == "pep440-branch":
rendered = render_pep440_branch(pieces)
elif style == "pep440-pre":
rendered = render_pep440_pre(pieces)
elif style == "pep440-post":
rendered = render_pep440_post(pieces)
elif style == "pep440-post-branch":
rendered = render_pep440_post_branch(pieces)
elif style == "pep440-old":
rendered = render_pep440_old(pieces)
elif style == "git-describe":
rendered = render_git_describe(pieces)
elif style == "git-describe-long":
rendered = render_git_describe_long(pieces)
else:
raise ValueError("unknown style '%s'" % style)
return {"version": rendered, "full-revisionid": pieces["long"],
"dirty": pieces["dirty"], "error": None,
"date": pieces.get("date")}
class VersioneerBadRootError(Exception):
"""The project root directory is unknown or missing key files."""
def get_versions(verbose=False):
"""Get the project version from whatever source is available.
Returns dict with two keys: 'version' and 'full'.
"""
if "versioneer" in sys.modules:
# see the discussion in cmdclass.py:get_cmdclass()
del sys.modules["versioneer"]
root = get_root()
cfg = get_config_from_root(root)
assert cfg.VCS is not None, "please set [versioneer]VCS= in setup.cfg"
handlers = HANDLERS.get(cfg.VCS)
assert handlers, "unrecognized VCS '%s'" % cfg.VCS
verbose = verbose or cfg.verbose
assert cfg.versionfile_source is not None, \
"please set versioneer.versionfile_source"
assert cfg.tag_prefix is not None, "please set versioneer.tag_prefix"
versionfile_abs = os.path.join(root, cfg.versionfile_source)
# extract version from first of: _version.py, VCS command (e.g. 'git
# describe'), parentdir. This is meant to work for developers using a
# source checkout, for users of a tarball created by 'setup.py sdist',
# and for users of a tarball/zipball created by 'git archive' or github's
# download-from-tag feature or the equivalent in other VCSes.
get_keywords_f = handlers.get("get_keywords")
from_keywords_f = handlers.get("keywords")
if get_keywords_f and from_keywords_f:
try:
keywords = get_keywords_f(versionfile_abs)
ver = from_keywords_f(keywords, cfg.tag_prefix, verbose)
if verbose:
print("got version from expanded keyword %s" % ver)
return ver
except NotThisMethod:
pass
try:
ver = versions_from_file(versionfile_abs)
if verbose:
print("got version from file %s %s" % (versionfile_abs, ver))
return ver
except NotThisMethod:
pass
from_vcs_f = handlers.get("pieces_from_vcs")
if from_vcs_f:
try:
pieces = from_vcs_f(cfg.tag_prefix, root, verbose)
ver = render(pieces, cfg.style)
if verbose:
print("got version from VCS %s" % ver)
return ver
except NotThisMethod:
pass
try:
if cfg.parentdir_prefix:
ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
if verbose:
print("got version from parentdir %s" % ver)
return ver
except NotThisMethod:
pass
if verbose:
print("unable to compute version")
return {"version": "0+unknown", "full-revisionid": None,
"dirty": None, "error": "unable to compute version",
"date": None}
def get_version():
"""Get the short version string for this project."""
return get_versions()["version"]
def get_cmdclass(cmdclass=None):
"""Get the custom setuptools/distutils subclasses used by Versioneer.
If the package uses a different cmdclass (e.g. one from numpy), it
should be provide as an argument.
"""
if "versioneer" in sys.modules:
del sys.modules["versioneer"]
# this fixes the "python setup.py develop" case (also 'install' and
# 'easy_install .'), in which subdependencies of the main project are
# built (using setup.py bdist_egg) in the same python process. Assume
# a main project A and a dependency B, which use different versions
# of Versioneer. A's setup.py imports A's Versioneer, leaving it in
# sys.modules by the time B's setup.py is executed, causing B to run
# with the wrong versioneer. Setuptools wraps the sub-dep builds in a
# sandbox that restores sys.modules to it's pre-build state, so the
# parent is protected against the child's "import versioneer". By
# removing ourselves from sys.modules here, before the child build
# happens, we protect the child from the parent's versioneer too.
# Also see https://github.com/python-versioneer/python-versioneer/issues/52
cmds = {} if cmdclass is None else cmdclass.copy()
# we add "version" to both distutils and setuptools
from distutils.core import Command
class cmd_version(Command):
description = "report generated version string"
user_options = []
boolean_options = []
def initialize_options(self):
pass
def finalize_options(self):
pass
def run(self):
vers = get_versions(verbose=True)
print("Version: %s" % vers["version"])
print(" full-revisionid: %s" % vers.get("full-revisionid"))
print(" dirty: %s" % vers.get("dirty"))
print(" date: %s" % vers.get("date"))
if vers["error"]:
print(" error: %s" % vers["error"])
cmds["version"] = cmd_version
# we override "build_py" in both distutils and setuptools
#
# most invocation pathways end up running build_py:
# distutils/build -> build_py
# distutils/install -> distutils/build ->..
# setuptools/bdist_wheel -> distutils/install ->..
# setuptools/bdist_egg -> distutils/install_lib -> build_py
# setuptools/install -> bdist_egg ->..
# setuptools/develop -> ?
# pip install:
# copies source tree to a tempdir before running egg_info/etc
# if .git isn't copied too, 'git describe' will fail
# then does setup.py bdist_wheel, or sometimes setup.py install
# setup.py egg_info -> ?
# we override different "build_py" commands for both environments
if 'build_py' in cmds:
_build_py = cmds['build_py']
elif "setuptools" in sys.modules:
from setuptools.command.build_py import build_py as _build_py
else:
from distutils.command.build_py import build_py as _build_py
class cmd_build_py(_build_py):
def run(self):
root = get_root()
cfg = get_config_from_root(root)
versions = get_versions()
_build_py.run(self)
# now locate _version.py in the new build/ directory and replace
# it with an updated value
if cfg.versionfile_build:
target_versionfile = os.path.join(self.build_lib,
cfg.versionfile_build)
print("UPDATING %s" % target_versionfile)
write_to_version_file(target_versionfile, versions)
cmds["build_py"] = cmd_build_py
if 'build_ext' in cmds:
_build_ext = cmds['build_ext']
elif "setuptools" in sys.modules:
from setuptools.command.build_ext import build_ext as _build_ext
else:
from distutils.command.build_ext import build_ext as _build_ext
class cmd_build_ext(_build_ext):
def run(self):
root = get_root()
cfg = get_config_from_root(root)
versions = get_versions()
_build_ext.run(self)
if self.inplace:
# build_ext --inplace will only build extensions in
# build/lib<..> dir with no _version.py to write to.
# As in place builds will already have a _version.py
# in the module dir, we do not need to write one.
return
# now locate _version.py in the new build/ directory and replace
# it with an updated value
target_versionfile = os.path.join(self.build_lib,
cfg.versionfile_build)
print("UPDATING %s" % target_versionfile)
write_to_version_file(target_versionfile, versions)
cmds["build_ext"] = cmd_build_ext
if "cx_Freeze" in sys.modules: # cx_freeze enabled?
from cx_Freeze.dist import build_exe as _build_exe
# nczeczulin reports that py2exe won't like the pep440-style string
# as FILEVERSION, but it can be used for PRODUCTVERSION, e.g.
# setup(console=[{
# "version": versioneer.get_version().split("+", 1)[0], # FILEVERSION
# "product_version": versioneer.get_version(),
# ...
class cmd_build_exe(_build_exe):
def run(self):
root = get_root()
cfg = get_config_from_root(root)
versions = get_versions()
target_versionfile = cfg.versionfile_source
print("UPDATING %s" % target_versionfile)
write_to_version_file(target_versionfile, versions)
_build_exe.run(self)
os.unlink(target_versionfile)
with open(cfg.versionfile_source, "w") as f:
LONG = LONG_VERSION_PY[cfg.VCS]
f.write(LONG %
{"DOLLAR": "$",
"STYLE": cfg.style,
"TAG_PREFIX": cfg.tag_prefix,
"PARENTDIR_PREFIX": cfg.parentdir_prefix,
"VERSIONFILE_SOURCE": cfg.versionfile_source,
})
cmds["build_exe"] = cmd_build_exe
del cmds["build_py"]
if 'py2exe' in sys.modules: # py2exe enabled?
from py2exe.distutils_buildexe import py2exe as _py2exe
class cmd_py2exe(_py2exe):
def run(self):
root = get_root()
cfg = get_config_from_root(root)
versions = get_versions()
target_versionfile = cfg.versionfile_source
print("UPDATING %s" % target_versionfile)
write_to_version_file(target_versionfile, versions)
_py2exe.run(self)
os.unlink(target_versionfile)
with open(cfg.versionfile_source, "w") as f:
LONG = LONG_VERSION_PY[cfg.VCS]
f.write(LONG %
{"DOLLAR": "$",
"STYLE": cfg.style,
"TAG_PREFIX": cfg.tag_prefix,
"PARENTDIR_PREFIX": cfg.parentdir_prefix,
"VERSIONFILE_SOURCE": cfg.versionfile_source,
})
cmds["py2exe"] = cmd_py2exe
# we override different "sdist" commands for both environments
if 'sdist' in cmds:
_sdist = cmds['sdist']
elif "setuptools" in sys.modules:
from setuptools.command.sdist import sdist as _sdist
else:
from distutils.command.sdist import sdist as _sdist
class cmd_sdist(_sdist):
def run(self):
versions = get_versions()
# pylint:disable=attribute-defined-outside-init # noqa
self._versioneer_generated_versions = versions
# unless we update this, the command will keep using the old
# version
self.distribution.metadata.version = versions["version"]
return _sdist.run(self)
def make_release_tree(self, base_dir, files):
root = get_root()
cfg = get_config_from_root(root)
_sdist.make_release_tree(self, base_dir, files)
# now locate _version.py in the new base_dir directory
# (remembering that it may be a hardlink) and replace it with an
# updated value
target_versionfile = os.path.join(base_dir, cfg.versionfile_source)
print("UPDATING %s" % target_versionfile)
write_to_version_file(target_versionfile,
self._versioneer_generated_versions)
cmds["sdist"] = cmd_sdist
return cmds
CONFIG_ERROR = """
setup.cfg is missing the necessary Versioneer configuration. You need
a section like:
[versioneer]
VCS = git
style = pep440
versionfile_source = src/myproject/_version.py
versionfile_build = myproject/_version.py
tag_prefix =
parentdir_prefix = myproject-
You will also need to edit your setup.py to use the results:
import versioneer
setup(version=versioneer.get_version(),
cmdclass=versioneer.get_cmdclass(), ...)
Please read the docstring in ./versioneer.py for configuration instructions,
edit setup.cfg, and re-run the installer or 'python versioneer.py setup'.
"""
SAMPLE_CONFIG = """
# See the docstring in versioneer.py for instructions. Note that you must
# re-run 'versioneer.py setup' after changing this section, and commit the
# resulting files.
[versioneer]
#VCS = git
#style = pep440
#versionfile_source =
#versionfile_build =
#tag_prefix =
#parentdir_prefix =
"""
OLD_SNIPPET = """
from ._version import get_versions
__version__ = get_versions()['version']
del get_versions
"""
INIT_PY_SNIPPET = """
from . import {0}
__version__ = {0}.get_versions()['version']
"""
def do_setup():
"""Do main VCS-independent setup function for installing Versioneer."""
root = get_root()
try:
cfg = get_config_from_root(root)
except (EnvironmentError, configparser.NoSectionError,
configparser.NoOptionError) as e:
if isinstance(e, (EnvironmentError, configparser.NoSectionError)):
print("Adding sample versioneer config to setup.cfg",
file=sys.stderr)
with open(os.path.join(root, "setup.cfg"), "a") as f:
f.write(SAMPLE_CONFIG)
print(CONFIG_ERROR, file=sys.stderr)
return 1
print(" creating %s" % cfg.versionfile_source)
with open(cfg.versionfile_source, "w") as f:
LONG = LONG_VERSION_PY[cfg.VCS]
f.write(LONG % {"DOLLAR": "$",
"STYLE": cfg.style,
"TAG_PREFIX": cfg.tag_prefix,
"PARENTDIR_PREFIX": cfg.parentdir_prefix,
"VERSIONFILE_SOURCE": cfg.versionfile_source,
})
ipy = os.path.join(os.path.dirname(cfg.versionfile_source),
"__init__.py")
if os.path.exists(ipy):
try:
with open(ipy, "r") as f:
old = f.read()
except EnvironmentError:
old = ""
module = os.path.splitext(os.path.basename(cfg.versionfile_source))[0]
snippet = INIT_PY_SNIPPET.format(module)
if OLD_SNIPPET in old:
print(" replacing boilerplate in %s" % ipy)
with open(ipy, "w") as f:
f.write(old.replace(OLD_SNIPPET, snippet))
elif snippet not in old:
print(" appending to %s" % ipy)
with open(ipy, "a") as f:
f.write(snippet)
else:
print(" %s unmodified" % ipy)
else:
print(" %s doesn't exist, ok" % ipy)
ipy = None
# Make sure both the top-level "versioneer.py" and versionfile_source
# (PKG/_version.py, used by runtime code) are in MANIFEST.in, so
# they'll be copied into source distributions. Pip won't be able to
# install the package without this.
manifest_in = os.path.join(root, "MANIFEST.in")
simple_includes = set()
try:
with open(manifest_in, "r") as f:
for line in f:
if line.startswith("include "):
for include in line.split()[1:]:
simple_includes.add(include)
except EnvironmentError:
pass
# That doesn't cover everything MANIFEST.in can do
# (http://docs.python.org/2/distutils/sourcedist.html#commands), so
# it might give some false negatives. Appending redundant 'include'
# lines is safe, though.
if "versioneer.py" not in simple_includes:
print(" appending 'versioneer.py' to MANIFEST.in")
with open(manifest_in, "a") as f:
f.write("include versioneer.py\n")
else:
print(" 'versioneer.py' already in MANIFEST.in")
if cfg.versionfile_source not in simple_includes:
print(" appending versionfile_source ('%s') to MANIFEST.in" %
cfg.versionfile_source)
with open(manifest_in, "a") as f:
f.write("include %s\n" % cfg.versionfile_source)
else:
print(" versionfile_source already in MANIFEST.in")
# Make VCS-specific changes. For git, this means creating/changing
# .gitattributes to mark _version.py for export-subst keyword
# substitution.
do_vcs_install(manifest_in, cfg.versionfile_source, ipy)
return 0
def scan_setup_py():
"""Validate the contents of setup.py against Versioneer's expectations."""
found = set()
setters = False
errors = 0
with open("setup.py", "r") as f:
for line in f.readlines():
if "import versioneer" in line:
found.add("import")
if "versioneer.get_cmdclass()" in line:
found.add("cmdclass")
if "versioneer.get_version()" in line:
found.add("get_version")
if "versioneer.VCS" in line:
setters = True
if "versioneer.versionfile_source" in line:
setters = True
if len(found) != 3:
print("")
print("Your setup.py appears to be missing some important items")
print("(but I might be wrong). Please make sure it has something")
print("roughly like the following:")
print("")
print(" import versioneer")
print(" setup( version=versioneer.get_version(),")
print(" cmdclass=versioneer.get_cmdclass(), ...)")
print("")
errors += 1
if setters:
print("You should remove lines like 'versioneer.VCS = ' and")
print("'versioneer.versionfile_source = ' . This configuration")
print("now lives in setup.cfg, and should be removed from setup.py")
print("")
errors += 1
return errors
if __name__ == "__main__":
cmd = sys.argv[1]
if cmd == "setup":
errors = do_setup()
errors += scan_setup_py()
if errors:
sys.exit(1)
| cuda-python-main | versioneer.py |
# This file helps to compute a version number in source trees obtained from
# git-archive tarball (such as those provided by githubs download-from-tag
# feature). Distribution tarballs (built by setup.py sdist) and build
# directories (produced by setup.py build) will contain a much shorter file
# that just contains the computed version number.
# This file is released into the public domain. Generated by
# versioneer-0.20 (https://github.com/python-versioneer/python-versioneer)
"""Git implementation of _version.py."""
import errno
import os
import re
import subprocess
import sys
def get_keywords():
"""Get the keywords needed to look up the version information."""
# these strings will be replaced by git during git-archive.
# setup.py/versioneer.py will grep for the variable names, so they must
# each be defined on a line of their own. _version.py will just call
# get_keywords().
git_refnames = " (HEAD -> main, tag: v12.2.0, gh-pages)"
git_full = "2ae98f9338f9c13e777f6fc647637d8b87086a49"
git_date = "2023-06-27 16:04:48 -0700"
keywords = {"refnames": git_refnames, "full": git_full, "date": git_date}
return keywords
class VersioneerConfig: # pylint: disable=too-few-public-methods
"""Container for Versioneer configuration parameters."""
def get_config():
"""Create, populate and return the VersioneerConfig() object."""
# these strings are filled in when 'setup.py versioneer' creates
# _version.py
cfg = VersioneerConfig()
cfg.VCS = "git"
cfg.style = "pep440"
cfg.tag_prefix = "v"
cfg.parentdir_prefix = "cuda-python-"
cfg.versionfile_source = "cuda/_version.py"
cfg.verbose = False
return cfg
class NotThisMethod(Exception):
"""Exception raised if a method is not valid for the current scenario."""
LONG_VERSION_PY = {}
HANDLERS = {}
def register_vcs_handler(vcs, method): # decorator
"""Create decorator to mark a method as the handler of a VCS."""
def decorate(f):
"""Store f in HANDLERS[vcs][method]."""
if vcs not in HANDLERS:
HANDLERS[vcs] = {}
HANDLERS[vcs][method] = f
return f
return decorate
# pylint:disable=too-many-arguments,consider-using-with # noqa
def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False,
env=None):
"""Call the given command(s)."""
assert isinstance(commands, list)
process = None
for command in commands:
try:
dispcmd = str([command] + args)
# remember shell=False, so use git.cmd on windows, not just git
process = subprocess.Popen([command] + args, cwd=cwd, env=env,
stdout=subprocess.PIPE,
stderr=(subprocess.PIPE if hide_stderr
else None))
break
except EnvironmentError:
e = sys.exc_info()[1]
if e.errno == errno.ENOENT:
continue
if verbose:
print("unable to run %s" % dispcmd)
print(e)
return None, None
else:
if verbose:
print("unable to find command, tried %s" % (commands,))
return None, None
stdout = process.communicate()[0].strip().decode()
if process.returncode != 0:
if verbose:
print("unable to run %s (error)" % dispcmd)
print("stdout was %s" % stdout)
return None, process.returncode
return stdout, process.returncode
def versions_from_parentdir(parentdir_prefix, root, verbose):
"""Try to determine the version from the parent directory name.
Source tarballs conventionally unpack into a directory that includes both
the project name and a version string. We will also support searching up
two directory levels for an appropriately named parent directory
"""
rootdirs = []
for _ in range(3):
dirname = os.path.basename(root)
if dirname.startswith(parentdir_prefix):
return {"version": dirname[len(parentdir_prefix):],
"full-revisionid": None,
"dirty": False, "error": None, "date": None}
rootdirs.append(root)
root = os.path.dirname(root) # up a level
if verbose:
print("Tried directories %s but none started with prefix %s" %
(str(rootdirs), parentdir_prefix))
raise NotThisMethod("rootdir doesn't start with parentdir_prefix")
@register_vcs_handler("git", "get_keywords")
def git_get_keywords(versionfile_abs):
"""Extract version information from the given file."""
# the code embedded in _version.py can just fetch the value of these
# keywords. When used from setup.py, we don't want to import _version.py,
# so we do it with a regexp instead. This function is not used from
# _version.py.
keywords = {}
try:
with open(versionfile_abs, "r") as fobj:
for line in fobj:
if line.strip().startswith("git_refnames ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["refnames"] = mo.group(1)
if line.strip().startswith("git_full ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["full"] = mo.group(1)
if line.strip().startswith("git_date ="):
mo = re.search(r'=\s*"(.*)"', line)
if mo:
keywords["date"] = mo.group(1)
except EnvironmentError:
pass
return keywords
@register_vcs_handler("git", "keywords")
def git_versions_from_keywords(keywords, tag_prefix, verbose):
"""Get version information from git keywords."""
if "refnames" not in keywords:
raise NotThisMethod("Short version file found")
date = keywords.get("date")
if date is not None:
# Use only the last line. Previous lines may contain GPG signature
# information.
date = date.splitlines()[-1]
# git-2.2.0 added "%cI", which expands to an ISO-8601 -compliant
# datestamp. However we prefer "%ci" (which expands to an "ISO-8601
# -like" string, which we must then edit to make compliant), because
# it's been around since git-1.5.3, and it's too difficult to
# discover which version we're using, or to work around using an
# older one.
date = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
refnames = keywords["refnames"].strip()
if refnames.startswith("$Format"):
if verbose:
print("keywords are unexpanded, not using")
raise NotThisMethod("unexpanded keywords, not a git-archive tarball")
refs = {r.strip() for r in refnames.strip("()").split(",")}
# starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of
# just "foo-1.0". If we see a "tag: " prefix, prefer those.
TAG = "tag: "
tags = {r[len(TAG):] for r in refs if r.startswith(TAG)}
if not tags:
# Either we're using git < 1.8.3, or there really are no tags. We use
# a heuristic: assume all version tags have a digit. The old git %d
# expansion behaves like git log --decorate=short and strips out the
# refs/heads/ and refs/tags/ prefixes that would let us distinguish
# between branches and tags. By ignoring refnames without digits, we
# filter out many common branch names like "release" and
# "stabilization", as well as "HEAD" and "master".
tags = {r for r in refs if re.search(r'\d', r)}
if verbose:
print("discarding '%s', no digits" % ",".join(refs - tags))
if verbose:
print("likely tags: %s" % ",".join(sorted(tags)))
for ref in sorted(tags):
# sorting will prefer e.g. "2.0" over "2.0rc1"
if ref.startswith(tag_prefix):
r = ref[len(tag_prefix):]
# Filter out refs that exactly match prefix or that don't start
# with a number once the prefix is stripped (mostly a concern
# when prefix is '')
if not re.match(r'\d', r):
continue
if verbose:
print("picking %s" % r)
return {"version": r,
"full-revisionid": keywords["full"].strip(),
"dirty": False, "error": None,
"date": date}
# no suitable tags, so version is "0+unknown", but full hex is still there
if verbose:
print("no suitable tags, using unknown + full revision id")
return {"version": "0+unknown",
"full-revisionid": keywords["full"].strip(),
"dirty": False, "error": "no suitable tags", "date": None}
@register_vcs_handler("git", "pieces_from_vcs")
def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command):
"""Get version from 'git describe' in the root of the source tree.
This only gets called if the git-archive 'subst' keywords were *not*
expanded, and _version.py hasn't already been rewritten with a short
version string, meaning we're inside a checked out source tree.
"""
GITS = ["git"]
if sys.platform == "win32":
GITS = ["git.cmd", "git.exe"]
_, rc = runner(GITS, ["rev-parse", "--git-dir"], cwd=root,
hide_stderr=True)
if rc != 0:
if verbose:
print("Directory %s not under git control" % root)
raise NotThisMethod("'git rev-parse --git-dir' returned error")
# if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]
# if there isn't one, this yields HEX[-dirty] (no NUM)
describe_out, rc = runner(GITS, ["describe", "--tags", "--dirty",
"--always", "--long",
"--match", "%s*" % tag_prefix],
cwd=root)
# --long was added in git-1.5.5
if describe_out is None:
raise NotThisMethod("'git describe' failed")
describe_out = describe_out.strip()
full_out, rc = runner(GITS, ["rev-parse", "HEAD"], cwd=root)
if full_out is None:
raise NotThisMethod("'git rev-parse' failed")
full_out = full_out.strip()
pieces = {}
pieces["long"] = full_out
pieces["short"] = full_out[:7] # maybe improved later
pieces["error"] = None
branch_name, rc = runner(GITS, ["rev-parse", "--abbrev-ref", "HEAD"],
cwd=root)
# --abbrev-ref was added in git-1.6.3
if rc != 0 or branch_name is None:
raise NotThisMethod("'git rev-parse --abbrev-ref' returned error")
branch_name = branch_name.strip()
if branch_name == "HEAD":
# If we aren't exactly on a branch, pick a branch which represents
# the current commit. If all else fails, we are on a branchless
# commit.
branches, rc = runner(GITS, ["branch", "--contains"], cwd=root)
# --contains was added in git-1.5.4
if rc != 0 or branches is None:
raise NotThisMethod("'git branch --contains' returned error")
branches = branches.split("\n")
# Remove the first line if we're running detached
if "(" in branches[0]:
branches.pop(0)
# Strip off the leading "* " from the list of branches.
branches = [branch[2:] for branch in branches]
if "master" in branches:
branch_name = "master"
elif not branches:
branch_name = None
else:
# Pick the first branch that is returned. Good or bad.
branch_name = branches[0]
pieces["branch"] = branch_name
# parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]
# TAG might have hyphens.
git_describe = describe_out
# look for -dirty suffix
dirty = git_describe.endswith("-dirty")
pieces["dirty"] = dirty
if dirty:
git_describe = git_describe[:git_describe.rindex("-dirty")]
# now we have TAG-NUM-gHEX or HEX
if "-" in git_describe:
# TAG-NUM-gHEX
mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe)
if not mo:
# unparseable. Maybe git-describe is misbehaving?
pieces["error"] = ("unable to parse git-describe output: '%s'"
% describe_out)
return pieces
# tag
full_tag = mo.group(1)
if not full_tag.startswith(tag_prefix):
if verbose:
fmt = "tag '%s' doesn't start with prefix '%s'"
print(fmt % (full_tag, tag_prefix))
pieces["error"] = ("tag '%s' doesn't start with prefix '%s'"
% (full_tag, tag_prefix))
return pieces
pieces["closest-tag"] = full_tag[len(tag_prefix):]
# distance: number of commits since tag
pieces["distance"] = int(mo.group(2))
# commit: short hex revision ID
pieces["short"] = mo.group(3)
else:
# HEX: no tags
pieces["closest-tag"] = None
count_out, rc = runner(GITS, ["rev-list", "HEAD", "--count"], cwd=root)
pieces["distance"] = int(count_out) # total number of commits
# commit date: see ISO-8601 comment in git_versions_from_keywords()
date = runner(GITS, ["show", "-s", "--format=%ci", "HEAD"], cwd=root)[0].strip()
# Use only the last line. Previous lines may contain GPG signature
# information.
date = date.splitlines()[-1]
pieces["date"] = date.strip().replace(" ", "T", 1).replace(" ", "", 1)
return pieces
def plus_or_dot(pieces):
"""Return a + if we don't already have one, else return a ."""
if "+" in pieces.get("closest-tag", ""):
return "."
return "+"
def render_pep440(pieces):
"""Build up version string, with post-release "local version identifier".
Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you
get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty
Exceptions:
1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += plus_or_dot(pieces)
rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
else:
# exception #1
rendered = "0+untagged.%d.g%s" % (pieces["distance"],
pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
return rendered
def render_pep440_branch(pieces):
"""TAG[[.dev0]+DISTANCE.gHEX[.dirty]] .
The ".dev0" means not master branch. Note that .dev0 sorts backwards
(a feature branch will appear "older" than the master branch).
Exceptions:
1: no tags. 0[.dev0]+untagged.DISTANCE.gHEX[.dirty]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
if pieces["branch"] != "master":
rendered += ".dev0"
rendered += plus_or_dot(pieces)
rendered += "%d.g%s" % (pieces["distance"], pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
else:
# exception #1
rendered = "0"
if pieces["branch"] != "master":
rendered += ".dev0"
rendered += "+untagged.%d.g%s" % (pieces["distance"],
pieces["short"])
if pieces["dirty"]:
rendered += ".dirty"
return rendered
def render_pep440_pre(pieces):
"""TAG[.post0.devDISTANCE] -- No -dirty.
Exceptions:
1: no tags. 0.post0.devDISTANCE
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"]:
rendered += ".post0.dev%d" % pieces["distance"]
else:
# exception #1
rendered = "0.post0.dev%d" % pieces["distance"]
return rendered
def render_pep440_post(pieces):
"""TAG[.postDISTANCE[.dev0]+gHEX] .
The ".dev0" means dirty. Note that .dev0 sorts backwards
(a dirty tree will appear "older" than the corresponding clean one),
but you shouldn't be releasing software with -dirty anyways.
Exceptions:
1: no tags. 0.postDISTANCE[.dev0]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += ".post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
rendered += plus_or_dot(pieces)
rendered += "g%s" % pieces["short"]
else:
# exception #1
rendered = "0.post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
rendered += "+g%s" % pieces["short"]
return rendered
def render_pep440_post_branch(pieces):
"""TAG[.postDISTANCE[.dev0]+gHEX[.dirty]] .
The ".dev0" means not master branch.
Exceptions:
1: no tags. 0.postDISTANCE[.dev0]+gHEX[.dirty]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += ".post%d" % pieces["distance"]
if pieces["branch"] != "master":
rendered += ".dev0"
rendered += plus_or_dot(pieces)
rendered += "g%s" % pieces["short"]
if pieces["dirty"]:
rendered += ".dirty"
else:
# exception #1
rendered = "0.post%d" % pieces["distance"]
if pieces["branch"] != "master":
rendered += ".dev0"
rendered += "+g%s" % pieces["short"]
if pieces["dirty"]:
rendered += ".dirty"
return rendered
def render_pep440_old(pieces):
"""TAG[.postDISTANCE[.dev0]] .
The ".dev0" means dirty.
Exceptions:
1: no tags. 0.postDISTANCE[.dev0]
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"] or pieces["dirty"]:
rendered += ".post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
else:
# exception #1
rendered = "0.post%d" % pieces["distance"]
if pieces["dirty"]:
rendered += ".dev0"
return rendered
def render_git_describe(pieces):
"""TAG[-DISTANCE-gHEX][-dirty].
Like 'git describe --tags --dirty --always'.
Exceptions:
1: no tags. HEX[-dirty] (note: no 'g' prefix)
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
if pieces["distance"]:
rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
else:
# exception #1
rendered = pieces["short"]
if pieces["dirty"]:
rendered += "-dirty"
return rendered
def render_git_describe_long(pieces):
"""TAG-DISTANCE-gHEX[-dirty].
Like 'git describe --tags --dirty --always -long'.
The distance/hash is unconditional.
Exceptions:
1: no tags. HEX[-dirty] (note: no 'g' prefix)
"""
if pieces["closest-tag"]:
rendered = pieces["closest-tag"]
rendered += "-%d-g%s" % (pieces["distance"], pieces["short"])
else:
# exception #1
rendered = pieces["short"]
if pieces["dirty"]:
rendered += "-dirty"
return rendered
def render(pieces, style):
"""Render the given version pieces into the requested style."""
if pieces["error"]:
return {"version": "unknown",
"full-revisionid": pieces.get("long"),
"dirty": None,
"error": pieces["error"],
"date": None}
if not style or style == "default":
style = "pep440" # the default
if style == "pep440":
rendered = render_pep440(pieces)
elif style == "pep440-branch":
rendered = render_pep440_branch(pieces)
elif style == "pep440-pre":
rendered = render_pep440_pre(pieces)
elif style == "pep440-post":
rendered = render_pep440_post(pieces)
elif style == "pep440-post-branch":
rendered = render_pep440_post_branch(pieces)
elif style == "pep440-old":
rendered = render_pep440_old(pieces)
elif style == "git-describe":
rendered = render_git_describe(pieces)
elif style == "git-describe-long":
rendered = render_git_describe_long(pieces)
else:
raise ValueError("unknown style '%s'" % style)
return {"version": rendered, "full-revisionid": pieces["long"],
"dirty": pieces["dirty"], "error": None,
"date": pieces.get("date")}
def get_versions():
"""Get version information or return default if unable to do so."""
# I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have
# __file__, we can work backwards from there to the root. Some
# py2exe/bbfreeze/non-CPython implementations don't do __file__, in which
# case we can only use expanded keywords.
cfg = get_config()
verbose = cfg.verbose
try:
return git_versions_from_keywords(get_keywords(), cfg.tag_prefix,
verbose)
except NotThisMethod:
pass
try:
root = os.path.realpath(__file__)
# versionfile_source is the relative path from the top of the source
# tree (where the .git directory might live) to this file. Invert
# this to find the root from __file__.
for _ in cfg.versionfile_source.split('/'):
root = os.path.dirname(root)
except NameError:
return {"version": "0+unknown", "full-revisionid": None,
"dirty": None,
"error": "unable to find root of source tree",
"date": None}
try:
pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose)
return render(pieces, cfg.style)
except NotThisMethod:
pass
try:
if cfg.parentdir_prefix:
return versions_from_parentdir(cfg.parentdir_prefix, root, verbose)
except NotThisMethod:
pass
return {"version": "0+unknown", "full-revisionid": None,
"dirty": None,
"error": "unable to compute version", "date": None}
| cuda-python-main | cuda/_version.py |
from . import _version
__version__ = _version.get_versions()['version']
| cuda-python-main | cuda/__init__.py |
cuda-python-main | cuda/_cuda/__init__.py |
|
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import pytest
from cuda import nvrtc
def ASSERT_DRV(err):
if isinstance(err, nvrtc.nvrtcResult):
if err != nvrtc.nvrtcResult.NVRTC_SUCCESS:
raise RuntimeError('Nvrtc Error: {}'.format(err))
else:
raise RuntimeError('Unknown error type: {}'.format(err))
def nvrtcVersionLessThan(major, minor):
err, major_version, minor_version = nvrtc.nvrtcVersion()
ASSERT_DRV(err)
return major_version < major or (major == major_version and minor_version < minor)
@pytest.mark.skipif(nvrtcVersionLessThan(11, 3), reason='When nvrtcGetSupportedArchs was introduced')
def test_nvrtcGetSupportedArchs():
err, supportedArchs = nvrtc.nvrtcGetSupportedArchs()
ASSERT_DRV(err)
assert len(supportedArchs) != 0
| cuda-python-main | cuda/tests/test_nvrtc.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import pytest
import cuda.cuda as cuda
import cuda.cudart as cudart
import numpy as np
def supportsMemoryPool():
err, isSupported = cudart.cudaDeviceGetAttribute(cudart.cudaDeviceAttr.cudaDevAttrMemoryPoolsSupported, 0)
return err == cudart.cudaError_t.cudaSuccess and isSupported
def test_interop_stream():
err_dr, = cuda.cuInit(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, device = cuda.cuDeviceGet(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, ctx = cuda.cuCtxCreate(0, device)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
# DRV to RT
err_dr, stream = cuda.cuStreamCreate(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_rt, = cudart.cudaStreamDestroy(stream)
assert(err_rt == cudart.cudaError_t.cudaSuccess)
# RT to DRV
err_rt, stream = cudart.cudaStreamCreate()
assert(err_rt == cudart.cudaError_t.cudaSuccess)
err_dr, = cuda.cuStreamDestroy(stream)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, = cuda.cuCtxDestroy(ctx)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
def test_interop_event():
err_dr, = cuda.cuInit(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, device = cuda.cuDeviceGet(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, ctx = cuda.cuCtxCreate(0, device)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
# DRV to RT
err_dr, event = cuda.cuEventCreate(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_rt, = cudart.cudaEventDestroy(event)
assert(err_rt == cudart.cudaError_t.cudaSuccess)
# RT to DRV
err_rt, event = cudart.cudaEventCreate()
assert(err_rt == cudart.cudaError_t.cudaSuccess)
err_dr, = cuda.cuEventDestroy(event)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, = cuda.cuCtxDestroy(ctx)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
def test_interop_graph():
err_dr, = cuda.cuInit(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, device = cuda.cuDeviceGet(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, ctx = cuda.cuCtxCreate(0, device)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
# DRV to RT
err_dr, graph = cuda.cuGraphCreate(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_rt, = cudart.cudaGraphDestroy(graph)
assert(err_rt == cudart.cudaError_t.cudaSuccess)
# RT to DRV
err_rt, graph = cudart.cudaGraphCreate(0)
assert(err_rt == cudart.cudaError_t.cudaSuccess)
err_dr, = cuda.cuGraphDestroy(graph)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, = cuda.cuCtxDestroy(ctx)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
def test_interop_graphNode():
err_dr, = cuda.cuInit(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, device = cuda.cuDeviceGet(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, ctx = cuda.cuCtxCreate(0, device)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, graph = cuda.cuGraphCreate(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
# DRV to RT
err_dr, node = cuda.cuGraphAddEmptyNode(graph, [], 0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_rt, = cudart.cudaGraphDestroyNode(node)
assert(err_rt == cudart.cudaError_t.cudaSuccess)
# RT to DRV
err_rt, node = cudart.cudaGraphAddEmptyNode(graph, [], 0)
assert(err_rt == cudart.cudaError_t.cudaSuccess)
err_dr, = cuda.cuGraphDestroyNode(node)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_rt, = cudart.cudaGraphDestroy(graph)
assert(err_rt == cudart.cudaError_t.cudaSuccess)
err_dr, = cuda.cuCtxDestroy(ctx)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
def test_interop_userObject():
err_dr, = cuda.cuInit(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, device = cuda.cuDeviceGet(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, ctx = cuda.cuCtxCreate(0, device)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
# cudaUserObject_t
# TODO
err_dr, = cuda.cuCtxDestroy(ctx)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
def test_interop_function():
err_dr, = cuda.cuInit(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, device = cuda.cuDeviceGet(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, ctx = cuda.cuCtxCreate(0, device)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
# cudaFunction_t
# TODO
err_dr, = cuda.cuCtxDestroy(ctx)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
@pytest.mark.skipif(not supportsMemoryPool(), reason='Requires mempool operations')
def test_interop_memPool():
err_dr, = cuda.cuInit(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, device = cuda.cuDeviceGet(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, ctx = cuda.cuCtxCreate(0, device)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
# DRV to RT
err_dr, pool = cuda.cuDeviceGetDefaultMemPool(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_rt, = cudart.cudaDeviceSetMemPool(0, pool)
assert(err_rt == cudart.cudaError_t.cudaSuccess)
# RT to DRV
err_rt, pool = cudart.cudaDeviceGetDefaultMemPool(0)
assert(err_rt == cudart.cudaError_t.cudaSuccess)
err_dr, = cuda.cuDeviceSetMemPool(0, pool)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, = cuda.cuCtxDestroy(ctx)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
def test_interop_graphExec():
err_dr, = cuda.cuInit(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, device = cuda.cuDeviceGet(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, ctx = cuda.cuCtxCreate(0, device)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, graph = cuda.cuGraphCreate(0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_dr, node = cuda.cuGraphAddEmptyNode(graph, [], 0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
# DRV to RT
err_dr, graphExec = cuda.cuGraphInstantiate(graph, 0)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_rt, = cudart.cudaGraphExecDestroy(graphExec)
assert(err_rt == cudart.cudaError_t.cudaSuccess)
# RT to DRV
err_rt, graphExec = cudart.cudaGraphInstantiate(graph, 0)
assert(err_rt == cudart.cudaError_t.cudaSuccess)
err_dr, = cuda.cuGraphExecDestroy(graphExec)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
err_rt, = cudart.cudaGraphDestroy(graph)
assert(err_rt == cudart.cudaError_t.cudaSuccess)
err_dr, = cuda.cuCtxDestroy(ctx)
assert(err_dr == cuda.CUresult.CUDA_SUCCESS)
| cuda-python-main | cuda/tests/test_interoperability.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import pytest
from cuda import cuda, cudart, nvrtc
import numpy as np
import ctypes
def ASSERT_DRV(err):
if isinstance(err, cuda.CUresult):
if err != cuda.CUresult.CUDA_SUCCESS:
raise RuntimeError('Cuda Error: {}'.format(err))
elif isinstance(err, cudart.cudaError_t):
if err != cudart.cudaError_t.cudaSuccess:
raise RuntimeError('Cudart Error: {}'.format(err))
elif isinstance(err, nvrtc.nvrtcResult):
if err != nvrtc.nvrtcResult.NVRTC_SUCCESS:
raise RuntimeError('Nvrtc Error: {}'.format(err))
else:
raise RuntimeError('Unknown error type: {}'.format(err))
def common_nvrtc(allKernelStrings, dev):
err, major = cuda.cuDeviceGetAttribute(cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, dev)
ASSERT_DRV(err)
err, minor = cuda.cuDeviceGetAttribute(cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, dev)
ASSERT_DRV(err)
err, _, nvrtc_minor = nvrtc.nvrtcVersion()
ASSERT_DRV(err)
use_cubin = (nvrtc_minor >= 1)
prefix = 'sm' if use_cubin else 'compute'
arch_arg = bytes(f'--gpu-architecture={prefix}_{major}{minor}', 'ascii')
err, prog = nvrtc.nvrtcCreateProgram(str.encode(allKernelStrings), b'allKernelStrings.cu', 0, [], [])
ASSERT_DRV(err)
opts = [b'--fmad=false', arch_arg]
err, = nvrtc.nvrtcCompileProgram(prog, len(opts), opts)
err_log, logSize = nvrtc.nvrtcGetProgramLogSize(prog)
ASSERT_DRV(err_log)
log = b' ' * logSize
err_log, = nvrtc.nvrtcGetProgramLog(prog, log)
ASSERT_DRV(err_log)
result = log.decode()
if len(result) > 1:
print(result)
ASSERT_DRV(err)
if use_cubin:
err, dataSize = nvrtc.nvrtcGetCUBINSize(prog)
ASSERT_DRV(err)
data = b' ' * dataSize
err, = nvrtc.nvrtcGetCUBIN(prog, data)
ASSERT_DRV(err)
else:
err, dataSize = nvrtc.nvrtcGetPTXSize(prog)
ASSERT_DRV(err)
data = b' ' * dataSize
err, = nvrtc.nvrtcGetPTX(prog, data)
ASSERT_DRV(err)
err, module = cuda.cuModuleLoadData(np.char.array(data))
ASSERT_DRV(err)
return module
def test_kernelParams_empty():
err, = cuda.cuInit(0)
ASSERT_DRV(err)
err, cuDevice = cuda.cuDeviceGet(0)
ASSERT_DRV(err)
err, context = cuda.cuCtxCreate(0, cuDevice)
ASSERT_DRV(err)
kernelString = '''\
static __device__ bool isDone;
extern "C" __global__
void empty_kernel()
{
isDone = true;
if (isDone) return;
}
'''
module = common_nvrtc(kernelString, cuDevice)
# cudaStructs kernel
err, kernel = cuda.cuModuleGetFunction(module, b'empty_kernel')
ASSERT_DRV(err)
err, stream = cuda.cuStreamCreate(0)
ASSERT_DRV(err)
err, = cuda.cuLaunchKernel(kernel,
1, 1, 1, # grid dim
1, 1, 1, # block dim
0, stream, # shared mem and stream
((), ()), 0) # arguments
ASSERT_DRV(err)
err, = cuda.cuLaunchKernel(kernel,
1, 1, 1, # grid dim
1, 1, 1, # block dim
0, stream, # shared mem and stream
None, 0) # arguments
ASSERT_DRV(err)
# Retrieve global and validate
isDone_host = ctypes.c_bool()
err, isDonePtr_device, isDonePtr_device_size = cuda.cuModuleGetGlobal(module, b'isDone')
ASSERT_DRV(err)
assert(isDonePtr_device_size == ctypes.sizeof(ctypes.c_bool))
err, = cuda.cuMemcpyDtoHAsync(isDone_host, isDonePtr_device, ctypes.sizeof(ctypes.c_bool), stream)
ASSERT_DRV(err)
err, = cuda.cuStreamSynchronize(stream)
ASSERT_DRV(err)
assert(isDone_host.value == True)
err, = cuda.cuStreamDestroy(stream)
ASSERT_DRV(err)
err, = cuda.cuModuleUnload(module)
ASSERT_DRV(err)
err, = cuda.cuCtxDestroy(context)
ASSERT_DRV(err)
def kernelParams_basic(use_ctypes_as_values):
err, = cuda.cuInit(0)
ASSERT_DRV(err)
err, cuDevice = cuda.cuDeviceGet(0)
ASSERT_DRV(err)
err, context = cuda.cuCtxCreate(0, cuDevice)
ASSERT_DRV(err)
if use_ctypes_as_values:
assertValues_host = (ctypes.c_bool(True),
ctypes.c_char(b'Z'), ctypes.c_wchar('Ā'),
ctypes.c_byte(-127), ctypes.c_ubyte(255),
ctypes.c_short(1), ctypes.c_ushort(1),
ctypes.c_int(2), ctypes.c_uint(2),
ctypes.c_long(3), ctypes.c_ulong(3),
ctypes.c_longlong(4), ctypes.c_ulonglong(4),
ctypes.c_size_t(5),
ctypes.c_float(float(123.456)), ctypes.c_float(float(123.456)),
ctypes.c_void_p(0xdeadbeef))
else:
assertValues_host = (True,
b'Z', 'Ā',
-127, 255,
90, 72,
85, 82,
66, 65,
86, 90,
33,
float(123.456), float(123.456),
0xdeadbeef)
assertTypes_host = (ctypes.c_bool,
ctypes.c_char, ctypes.c_wchar,
ctypes.c_byte, ctypes.c_ubyte,
ctypes.c_short, ctypes.c_ushort,
ctypes.c_int, ctypes.c_uint,
ctypes.c_long, ctypes.c_ulong,
ctypes.c_longlong, ctypes.c_ulonglong,
ctypes.c_size_t,
ctypes.c_float, ctypes.c_double,
ctypes.c_void_p)
basicKernelString = '''\
extern "C" __global__
void basic(bool b,
char c, wchar_t wc,
signed char byte, unsigned char ubyte,
short s, unsigned short us,
int i, unsigned int ui,
long l, unsigned long ul,
long long ll, unsigned long long ull,
size_t size,
float f, double d,
void *p,
bool *pb,
char *pc, wchar_t *pwc,
signed char *pbyte, unsigned char *pubyte,
short *ps, unsigned short *pus,
int *pi, unsigned int *pui,
long *pl, unsigned long *pul,
long long *pll, unsigned long long *pull,
size_t *psize,
float *pf, double *pd)
{
assert(b == {});
assert(c == {});
assert(wc == {});
assert(byte == {});
assert(ubyte == {});
assert(s == {});
assert(us == {});
assert(i == {});
assert(ui == {});
assert(l == {});
assert(ul == {});
assert(ll == {});
assert(ull == {});
assert(size == {});
assert(f == {});
assert(d == {});
assert(p == (void*){});
*pb = b;
*pc = c;
*pwc = wc;
*pbyte = byte;
*pubyte = ubyte;
*ps = s;
*pus = us;
*pi = i;
*pui = ui;
*pl = l;
*pul = ul;
*pll = ll;
*pull = ull;
*psize = size;
*pf = f;
*pd = d;
}
'''
idx = 0
while '{}' in basicKernelString:
val = assertValues_host[idx].value if use_ctypes_as_values else assertValues_host[idx]
if assertTypes_host[idx] == ctypes.c_float:
basicKernelString = basicKernelString.replace('{}', str(float(val)) + 'f', 1)
elif assertTypes_host[idx] == ctypes.c_double:
basicKernelString = basicKernelString.replace('{}', str(float(val)), 1)
elif assertTypes_host[idx] == ctypes.c_char:
basicKernelString = basicKernelString.replace('{}', str(val)[1:], 1)
elif assertTypes_host[idx] == ctypes.c_wchar:
basicKernelString = basicKernelString.replace('{}', str(ord(val)), 1)
else:
basicKernelString = basicKernelString.replace('{}', str(int(val)), 1)
idx += 1
module = common_nvrtc(basicKernelString, cuDevice)
err, kernel = cuda.cuModuleGetFunction(module, b'basic')
ASSERT_DRV(err)
err, stream = cuda.cuStreamCreate(0)
ASSERT_DRV(err)
# Prepare kernel
err, pb = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_bool))
ASSERT_DRV(err)
err, pc = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_char))
ASSERT_DRV(err)
err, pwc = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_wchar))
ASSERT_DRV(err)
err, pbyte = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_byte))
ASSERT_DRV(err)
err, pubyte = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_ubyte))
ASSERT_DRV(err)
err, ps = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_short))
ASSERT_DRV(err)
err, pus = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_ushort))
ASSERT_DRV(err)
err, pi = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_int))
ASSERT_DRV(err)
err, pui = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_uint))
ASSERT_DRV(err)
err, pl = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_long))
ASSERT_DRV(err)
err, pul = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_ulong))
ASSERT_DRV(err)
err, pll = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_longlong))
ASSERT_DRV(err)
err, pull = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_ulonglong))
ASSERT_DRV(err)
err, psize = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_size_t))
ASSERT_DRV(err)
err, pf = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_float))
ASSERT_DRV(err)
err, pd = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_double))
ASSERT_DRV(err)
assertValues_device = (pb,
pc, pwc,
pbyte, pubyte,
ps, pus,
pi, pui,
pl, pul,
pll, pull,
psize,
pf, pd)
assertTypes_device = (None,
None, None,
None, None,
None, None,
None, None,
None, None,
None, None,
None,
None, None)
basicKernelValues = assertValues_host + assertValues_device
basicKernelTypes = assertTypes_host + assertTypes_device
err, = cuda.cuLaunchKernel(kernel,
1, 1, 1, # grid dim
1, 1, 1, # block dim
0, stream, # shared mem and stream
(basicKernelValues, basicKernelTypes), 0) # arguments
ASSERT_DRV(err)
# Retrieve each dptr
host_params = tuple([valueType() for valueType in assertTypes_host[:-1]])
for i in range(len(host_params)):
err, = cuda.cuMemcpyDtoHAsync(host_params[i], assertValues_device[i], ctypes.sizeof(assertTypes_host[i]), stream)
ASSERT_DRV(err)
# Validate retrieved values
err, = cuda.cuStreamSynchronize(stream)
ASSERT_DRV(err)
for i in range(len(host_params)):
val = basicKernelValues[i].value if use_ctypes_as_values else basicKernelValues[i]
if basicKernelTypes[i] == ctypes.c_float:
if use_ctypes_as_values:
assert(val == host_params[i].value)
else:
assert(val == (int(host_params[i].value * 1000) / 1000))
else:
assert(val == host_params[i].value)
err, = cuda.cuMemFree(pb)
ASSERT_DRV(err)
err, = cuda.cuMemFree(pc)
ASSERT_DRV(err)
err, = cuda.cuMemFree(pwc)
ASSERT_DRV(err)
err, = cuda.cuMemFree(pbyte)
ASSERT_DRV(err)
err, = cuda.cuMemFree(pubyte)
ASSERT_DRV(err)
err, = cuda.cuMemFree(ps)
ASSERT_DRV(err)
err, = cuda.cuMemFree(pus)
ASSERT_DRV(err)
err, = cuda.cuMemFree(pi)
ASSERT_DRV(err)
err, = cuda.cuMemFree(pui)
ASSERT_DRV(err)
err, = cuda.cuMemFree(pl)
ASSERT_DRV(err)
err, = cuda.cuMemFree(pul)
ASSERT_DRV(err)
err, = cuda.cuMemFree(pll)
ASSERT_DRV(err)
err, = cuda.cuMemFree(pull)
ASSERT_DRV(err)
err, = cuda.cuMemFree(psize)
ASSERT_DRV(err)
err, = cuda.cuMemFree(pf)
ASSERT_DRV(err)
err, = cuda.cuMemFree(pd)
ASSERT_DRV(err)
err, = cuda.cuStreamDestroy(stream)
ASSERT_DRV(err)
err, = cuda.cuModuleUnload(module)
ASSERT_DRV(err)
err, = cuda.cuCtxDestroy(context)
ASSERT_DRV(err)
def test_kernelParams_basic():
# Kernel is given basic Python primative values as value input
kernelParams_basic(use_ctypes_as_values = False)
def test_kernelParams_basic_ctypes():
# Kernel is given basic c_type instances as primative value input
kernelParams_basic(use_ctypes_as_values = True)
def test_kernelParams_types_cuda():
err, = cuda.cuInit(0)
ASSERT_DRV(err)
err, cuDevice = cuda.cuDeviceGet(0)
ASSERT_DRV(err)
err, context = cuda.cuCtxCreate(0, cuDevice)
ASSERT_DRV(err)
err, uvaSupported = cuda.cuDeviceGetAttribute(cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING, cuDevice)
ASSERT_DRV(err)
err, perr = cudart.cudaMalloc(ctypes.sizeof(ctypes.c_int))
ASSERT_DRV(err)
err, pSurface_host = cudart.cudaHostAlloc(cudart.sizeof(cudart.cudaSurfaceObject_t), cudart.cudaHostAllocMapped)
ASSERT_DRV(err)
err, pDim3_host = cudart.cudaHostAlloc(cudart.sizeof(cudart.dim3), cudart.cudaHostAllocMapped)
ASSERT_DRV(err)
# Get device pointer if UVM is not enabled
if uvaSupported:
kernelValues = (cudart.cudaError_t.cudaErrorUnknown, perr, # enums
cudart.cudaSurfaceObject_t(248), cudart.cudaSurfaceObject_t(_ptr=pSurface_host), # typedef of primative
cudart.dim3(), cudart.dim3(_ptr=pDim3_host)) # struct
else:
err, pSurface_device = cudart.cudaHostGetDevicePointer(pSurface_host, 0)
ASSERT_DRV(err)
err, pDim3_device = cudart.cudaHostGetDevicePointer(pDim3_host, 0)
ASSERT_DRV(err)
kernelValues = (cudart.cudaError_t.cudaErrorUnknown, perr, # enums
cudart.cudaSurfaceObject_t(248), cudart.cudaSurfaceObject_t(_ptr=pSurface_device), # typedef of primative
cudart.dim3(), cudart.dim3(_ptr=pDim3_device)) # struct
kernelTypes = (None, ctypes.c_void_p,
None, ctypes.c_void_p,
None, ctypes.c_void_p)
kernelValues[4].x = 1
kernelValues[4].y = 2
kernelValues[4].z = 3
kernelString = '''\
extern "C" __global__
void structsCuda(cudaError_t err, cudaError_t *perr,
cudaSurfaceObject_t surface, cudaSurfaceObject_t *pSurface,
dim3 dim, dim3* pdim)
{
*perr = err;
*pSurface = surface;
pdim->x = dim.x;
pdim->y = dim.y;
pdim->z = dim.z;
}
'''
module = common_nvrtc(kernelString, cuDevice)
# cudaStructs kernel
err, kernel = cuda.cuModuleGetFunction(module, b'structsCuda')
ASSERT_DRV(err)
err, stream = cuda.cuStreamCreate(0)
ASSERT_DRV(err)
err, = cuda.cuLaunchKernel(kernel,
1, 1, 1, # grid dim
1, 1, 1, # block dim
0, stream, # shared mem and stream
(kernelValues, kernelTypes), 0) # arguments
ASSERT_DRV(err)
# Retrieve each dptr
host_err = ctypes.c_int()
err, = cudart.cudaMemcpyAsync(ctypes.addressof(host_err), perr, ctypes.sizeof(ctypes.c_int()), cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost, stream)
ASSERT_DRV(err)
# Validate kernel values
err, = cuda.cuStreamSynchronize(stream)
ASSERT_DRV(err)
cuda_err = cudart.cudaError_t(host_err.value)
if uvaSupported:
assert(kernelValues[0] == cuda_err)
assert(int(kernelValues[2]) == int(kernelValues[3]))
assert(kernelValues[4].x == kernelValues[5].x)
assert(kernelValues[4].y == kernelValues[5].y)
assert(kernelValues[4].z == kernelValues[5].z)
else:
surface_host = cudart.cudaSurfaceObject_t(_ptr=pSurface_host)
dim3_host = cudart.dim3(_ptr=pDim3_host)
assert(kernelValues[0] == cuda_err)
assert(int(kernelValues[2]) == int(surface_host))
assert(kernelValues[4].x == dim3_host.x)
assert(kernelValues[4].y == dim3_host.y)
assert(kernelValues[4].z == dim3_host.z)
err, = cudart.cudaFree(perr)
ASSERT_DRV(err)
err, = cudart.cudaFreeHost(pSurface_host)
ASSERT_DRV(err)
err, = cudart.cudaFreeHost(pDim3_host)
ASSERT_DRV(err)
err, = cuda.cuStreamDestroy(stream)
ASSERT_DRV(err)
err, = cuda.cuModuleUnload(module)
ASSERT_DRV(err)
err, = cuda.cuCtxDestroy(context)
ASSERT_DRV(err)
def test_kernelParams_struct_custom():
err, = cuda.cuInit(0)
ASSERT_DRV(err)
err, cuDevice = cuda.cuDeviceGet(0)
ASSERT_DRV(err)
err, context = cuda.cuCtxCreate(0, cuDevice)
ASSERT_DRV(err)
err, uvaSupported = cuda.cuDeviceGetAttribute(cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING, cuDevice)
ASSERT_DRV(err)
kernelString = '''\
struct testStruct {
int value;
};
extern "C" __global__
void structCustom(struct testStruct src, struct testStruct *dst)
{
dst->value = src.value;
}
'''
module = common_nvrtc(kernelString, cuDevice)
err, kernel = cuda.cuModuleGetFunction(module, b'structCustom')
ASSERT_DRV(err)
err, stream = cuda.cuStreamCreate(0)
ASSERT_DRV(err)
# structCustom kernel
class testStruct(ctypes.Structure):
_fields_ = [('value',ctypes.c_int)]
err, pStruct_host = cudart.cudaHostAlloc(ctypes.sizeof(testStruct), cudart.cudaHostAllocMapped)
ASSERT_DRV(err)
# Get device pointer if UVM is not enabled
if uvaSupported:
kernelValues = (testStruct(5), pStruct_host)
else:
err, pStruct_device = cudart.cudaHostGetDevicePointer(pStruct_host, 0)
ASSERT_DRV(err)
kernelValues = (testStruct(5), pStruct_device)
kernelTypes = (None, ctypes.c_void_p)
err, = cuda.cuLaunchKernel(kernel,
1, 1, 1, # grid dim
1, 1, 1, # block dim
0, stream, # shared mem and stream
(kernelValues, kernelTypes), 0) # arguments
ASSERT_DRV(err)
# Validate kernel values
err, = cuda.cuStreamSynchronize(stream)
ASSERT_DRV(err)
struct_shared = testStruct.from_address(pStruct_host)
assert(kernelValues[0].value == struct_shared.value)
err, = cudart.cudaFreeHost(pStruct_host)
ASSERT_DRV(err)
err, = cuda.cuStreamDestroy(stream)
ASSERT_DRV(err)
err, = cuda.cuModuleUnload(module)
ASSERT_DRV(err)
err, = cuda.cuCtxDestroy(context)
ASSERT_DRV(err)
def kernelParams_buffer_protocol_ctypes_common(pass_by_address):
err, = cuda.cuInit(0)
ASSERT_DRV(err)
err, cuDevice = cuda.cuDeviceGet(0)
ASSERT_DRV(err)
err, context = cuda.cuCtxCreate(0, cuDevice)
ASSERT_DRV(err)
err, uvaSupported = cuda.cuDeviceGetAttribute(cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING, cuDevice)
ASSERT_DRV(err)
kernelString = '''\
struct testStruct {
int value;
};
extern "C" __global__
void testkernel(int i, int *pi,
float f, float *pf,
struct testStruct s, struct testStruct *ps)
{
*pi = i;
*pf = f;
ps->value = s.value;
}
'''
module = common_nvrtc(kernelString, cuDevice)
err, kernel = cuda.cuModuleGetFunction(module, b'testkernel')
ASSERT_DRV(err)
err, stream = cuda.cuStreamCreate(0)
ASSERT_DRV(err)
# testkernel kernel
class testStruct(ctypes.Structure):
_fields_ = [('value',ctypes.c_int)]
err, pInt_host = cudart.cudaHostAlloc(ctypes.sizeof(ctypes.c_int), cudart.cudaHostAllocMapped)
ASSERT_DRV(err)
err, pFloat_host = cudart.cudaHostAlloc(ctypes.sizeof(ctypes.c_float), cudart.cudaHostAllocMapped)
ASSERT_DRV(err)
err, pStruct_host = cudart.cudaHostAlloc(ctypes.sizeof(testStruct), cudart.cudaHostAllocMapped)
ASSERT_DRV(err)
# Get device pointer if UVM is not enabled
if uvaSupported:
kernelValues = (ctypes.c_int(1), ctypes.c_void_p(pInt_host),
ctypes.c_float(float(123.456)), ctypes.c_void_p(pFloat_host),
testStruct(5), ctypes.c_void_p(pStruct_host))
else:
err, pInt_device = cudart.cudaHostGetDevicePointer(pInt_host, 0)
ASSERT_DRV(err)
err, pFloat_device = cudart.cudaHostGetDevicePointer(pFloat_host, 0)
ASSERT_DRV(err)
err, pStruct_device = cudart.cudaHostGetDevicePointer(pStruct_host, 0)
ASSERT_DRV(err)
kernelValues = (ctypes.c_int(1), ctypes.c_void_p(pInt_device),
ctypes.c_float(float(123.456)), ctypes.c_void_p(pFloat_device),
testStruct(5), ctypes.c_void_p(pStruct_device))
packagedParams = (ctypes.c_void_p*len(kernelValues))()
for idx in range(len(packagedParams)):
packagedParams[idx] = ctypes.addressof(kernelValues[idx])
err, = cuda.cuLaunchKernel(kernel,
1, 1, 1, # grid dim
1, 1, 1, # block dim
0, stream, # shared mem and stream
ctypes.addressof(packagedParams) if pass_by_address else packagedParams, 0) # arguments
ASSERT_DRV(err)
# Validate kernel values
err, = cuda.cuStreamSynchronize(stream)
ASSERT_DRV(err)
assert(kernelValues[0].value == ctypes.c_int.from_address(pInt_host).value)
assert(kernelValues[2].value == ctypes.c_float.from_address(pFloat_host).value)
assert(kernelValues[4].value == testStruct.from_address(pStruct_host).value)
err, = cudart.cudaFreeHost(pStruct_host)
ASSERT_DRV(err)
err, = cuda.cuStreamDestroy(stream)
ASSERT_DRV(err)
err, = cuda.cuModuleUnload(module)
ASSERT_DRV(err)
err, = cuda.cuCtxDestroy(context)
ASSERT_DRV(err)
def test_kernelParams_buffer_protocol_ctypes():
kernelParams_buffer_protocol_ctypes_common(pass_by_address=True)
kernelParams_buffer_protocol_ctypes_common(pass_by_address=False)
def test_kernelParams_buffer_protocol_numpy():
err, = cuda.cuInit(0)
ASSERT_DRV(err)
err, cuDevice = cuda.cuDeviceGet(0)
ASSERT_DRV(err)
err, context = cuda.cuCtxCreate(0, cuDevice)
ASSERT_DRV(err)
err, uvaSupported = cuda.cuDeviceGetAttribute(cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING, cuDevice)
ASSERT_DRV(err)
kernelString = '''\
struct testStruct {
int value;
};
extern "C" __global__
void testkernel(int i, int *pi,
float f, float *pf,
struct testStruct s, struct testStruct *ps)
{
*pi = i;
*pf = f;
ps->value = s.value;
}
'''
module = common_nvrtc(kernelString, cuDevice)
err, kernel = cuda.cuModuleGetFunction(module, b'testkernel')
ASSERT_DRV(err)
err, stream = cuda.cuStreamCreate(0)
ASSERT_DRV(err)
# testkernel kernel
testStruct = np.dtype([('value', np.int32)])
err, pInt_host = cudart.cudaHostAlloc(np.dtype(np.int32).itemsize, cudart.cudaHostAllocMapped)
ASSERT_DRV(err)
err, pFloat_host = cudart.cudaHostAlloc(np.dtype(np.float32).itemsize, cudart.cudaHostAllocMapped)
ASSERT_DRV(err)
err, pStruct_host = cudart.cudaHostAlloc(testStruct.itemsize, cudart.cudaHostAllocMapped)
ASSERT_DRV(err)
# Get device pointer if UVM is not enabled
if uvaSupported:
kernelValues = (np.array(1, dtype=np.uint32), np.array([pInt_host], dtype=np.uint64),
np.array(float(123.456), dtype=np.float32), np.array([pFloat_host], dtype=np.uint64),
np.array([5], testStruct), np.array([pStruct_host], dtype=np.uint64))
else:
err, pInt_device = cudart.cudaHostGetDevicePointer(pInt_host, 0)
ASSERT_DRV(err)
err, pFloat_device = cudart.cudaHostGetDevicePointer(pFloat_host, 0)
ASSERT_DRV(err)
err, pStruct_device = cudart.cudaHostGetDevicePointer(pStruct_host, 0)
ASSERT_DRV(err)
kernelValues = (np.array(1, dtype=np.int32), np.array([pInt_device], dtype=np.uint64),
np.array(float(123.456), dtype=np.float32), np.array([pFloat_device], dtype=np.uint64),
np.array([5], testStruct), np.array([pStruct_device], dtype=np.uint64))
packagedParams = np.array([arg.ctypes.data for arg in kernelValues], dtype=np.uint64)
err, = cuda.cuLaunchKernel(kernel,
1, 1, 1, # grid dim
1, 1, 1, # block dim
0, stream, # shared mem and stream
packagedParams, 0) # arguments
ASSERT_DRV(err)
# Validate kernel values
err, = cuda.cuStreamSynchronize(stream)
ASSERT_DRV(err)
class numpy_address_wrapper():
def __init__(self, address, typestr):
self.__array_interface__ = {'data': (address, False),
'typestr': typestr,
'shape': (1,)}
assert(kernelValues[0] == np.array(numpy_address_wrapper(pInt_host, '<i4')))
assert(kernelValues[2] == np.array(numpy_address_wrapper(pFloat_host, '<f4')))
assert(kernelValues[4]['value'] == np.array(numpy_address_wrapper(pStruct_host, '<i4'), dtype=testStruct)['value'])
err, = cudart.cudaFreeHost(pStruct_host)
ASSERT_DRV(err)
err, = cuda.cuStreamDestroy(stream)
ASSERT_DRV(err)
err, = cuda.cuModuleUnload(module)
ASSERT_DRV(err)
err, = cuda.cuCtxDestroy(context)
ASSERT_DRV(err)
| cuda-python-main | cuda/tests/test_kernelParams.py |
cuda-python-main | cuda/tests/__init__.py |
|
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import functools
import importlib
import sys
def py_func(func):
"""
Wraps func in a plain Python function.
"""
@functools.wraps(func)
def wrapped(*args, **kwargs):
return func(*args, **kwargs)
return wrapped
cython_test_modules = ["cuda.tests.test_ccuda",
"cuda.tests.test_ccudart",
"cuda.tests.test_interoperability_cython"]
for mod in cython_test_modules:
try:
# For each callable in `mod` with name `test_*`,
# wrap the callable in a plain Python function
# and set the result as an attribute of this module.
mod = importlib.import_module(mod)
for name in dir(mod):
item = getattr(mod, name)
if callable(item) and name.startswith("test_"):
item = py_func(item)
setattr(sys.modules[__name__], name, item)
except ImportError:
raise
| cuda-python-main | cuda/tests/test_cython.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
from _pytest.mark.structures import store_mark
import pytest
import cuda.cudart as cudart
import cuda.cuda as cuda
import numpy as np
import math
def isSuccess(err):
return err == cudart.cudaError_t.cudaSuccess
def assertSuccess(err):
assert(isSuccess(err))
def driverVersionLessThan(target):
err, version = cudart.cudaDriverGetVersion()
assertSuccess(err)
return version < target
def supportsMemoryPool():
err, isSupported = cudart.cudaDeviceGetAttribute(cudart.cudaDeviceAttr.cudaDevAttrMemoryPoolsSupported, 0)
return isSuccess(err) and isSupported
def supportsSparseTexturesDeviceFilter():
err, isSupported = cudart.cudaDeviceGetAttribute(cudart.cudaDeviceAttr.cudaDevAttrSparseCudaArraySupported, 0)
return isSuccess(err) and isSupported
def test_cudart_memcpy():
# Allocate dev memory
size = 1024 * np.uint8().itemsize
err, dptr = cudart.cudaMalloc(size)
assertSuccess(err)
# Set h1 and h2 memory to be different
h1 = np.full(size, 1).astype(np.uint8)
h2 = np.full(size, 2).astype(np.uint8)
assert(np.array_equal(h1, h2) is False)
# h1 to D
err, = cudart.cudaMemcpy(dptr, h1, size, cudart.cudaMemcpyKind.cudaMemcpyHostToDevice)
assertSuccess(err)
# D to h2
err, = cudart.cudaMemcpy(h2, dptr, size, cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost)
assertSuccess(err)
# Validate h1 == h2
assert(np.array_equal(h1, h2))
# Cleanup
err, = cudart.cudaFree(dptr)
assertSuccess(err)
def test_cudart_hostRegister():
# Use hostRegister API to check for correct enum return values
page_size = 80
addr_host = np.full(page_size * 3, 1).astype(np.uint8)
addr = addr_host.ctypes.data
size_0 = ((16 * page_size) / 8)
addr_0 = addr + int(((0 * page_size) / 8))
size_1 = ((16 * page_size) / 8)
addr_1 = addr + int(((8 * page_size) / 8))
err, = cudart.cudaHostRegister(addr_0, size_0, 3)
assertSuccess(err)
err, = cudart.cudaHostRegister(addr_1, size_1, 3)
assert(err == cudart.cudaError_t.cudaErrorHostMemoryAlreadyRegistered)
err, = cudart.cudaHostUnregister(addr_1)
assert(err == cudart.cudaError_t.cudaErrorInvalidValue)
err, = cudart.cudaHostUnregister(addr_0)
assertSuccess(err)
def test_cudart_class_reference():
offset = 1
width = 4
height = 5
depth = 6
flags = 0
numMipLevels = 1
extent = cudart.cudaExtent()
formatDesc = cudart.cudaChannelFormatDesc()
externalMemoryMipmappedArrayDesc = cudart.cudaExternalMemoryMipmappedArrayDesc()
# Get/set class attributes
extent.width = width
extent.height = height
extent.depth = depth
formatDesc.x = 8
formatDesc.y = 0
formatDesc.z = 0
formatDesc.w = 0
formatDesc.f = cudart.cudaChannelFormatKind.cudaChannelFormatKindSigned
externalMemoryMipmappedArrayDesc.offset = offset
externalMemoryMipmappedArrayDesc.formatDesc = formatDesc
externalMemoryMipmappedArrayDesc.extent = extent
externalMemoryMipmappedArrayDesc.flags = flags
externalMemoryMipmappedArrayDesc.numLevels = numMipLevels
# Can manipulate child structure values directly
externalMemoryMipmappedArrayDesc.extent.width = width+1
externalMemoryMipmappedArrayDesc.extent.height = height+1
externalMemoryMipmappedArrayDesc.extent.depth = depth+1
assert(externalMemoryMipmappedArrayDesc.extent.width == width+1)
assert(externalMemoryMipmappedArrayDesc.extent.height == height+1)
assert(externalMemoryMipmappedArrayDesc.extent.depth == depth+1)
externalMemoryMipmappedArrayDesc.formatDesc.x = 20
externalMemoryMipmappedArrayDesc.formatDesc.y = 21
externalMemoryMipmappedArrayDesc.formatDesc.z = 22
externalMemoryMipmappedArrayDesc.formatDesc.w = 23
externalMemoryMipmappedArrayDesc.formatDesc.f = cudart.cudaChannelFormatKind.cudaChannelFormatKindFloat
assert(externalMemoryMipmappedArrayDesc.formatDesc.x == 20)
assert(externalMemoryMipmappedArrayDesc.formatDesc.y == 21)
assert(externalMemoryMipmappedArrayDesc.formatDesc.z == 22)
assert(externalMemoryMipmappedArrayDesc.formatDesc.w == 23)
assert(externalMemoryMipmappedArrayDesc.formatDesc.f == cudart.cudaChannelFormatKind.cudaChannelFormatKindFloat)
# Can copy classes over
externalMemoryMipmappedArrayDesc.extent = extent
assert(externalMemoryMipmappedArrayDesc.extent.width == width)
assert(externalMemoryMipmappedArrayDesc.extent.height == height)
assert(externalMemoryMipmappedArrayDesc.extent.depth == depth)
externalMemoryMipmappedArrayDesc.formatDesc = formatDesc
assert(externalMemoryMipmappedArrayDesc.formatDesc.x == 8)
assert(externalMemoryMipmappedArrayDesc.formatDesc.y == 0)
assert(externalMemoryMipmappedArrayDesc.formatDesc.z == 0)
assert(externalMemoryMipmappedArrayDesc.formatDesc.w == 0)
assert(externalMemoryMipmappedArrayDesc.formatDesc.f == cudart.cudaChannelFormatKind.cudaChannelFormatKindSigned)
@pytest.mark.skipif(not supportsSparseTexturesDeviceFilter(), reason='Sparse Texture Device Filter')
def test_cudart_class_inline():
extent = cudart.cudaExtent()
extent.width = 1000
extent.height = 500
extent.depth = 0
desc = cudart.cudaChannelFormatDesc()
desc.x = 32
desc.y = 32
desc.z = 32
desc.w = 32
desc.f = cudart.cudaChannelFormatKind.cudaChannelFormatKindFloat
numChannels = 4
numBytesPerChannel = desc.x/8
numBytesPerTexel = numChannels * numBytesPerChannel
flags = cudart.cudaArraySparse
maxDim = max(extent.width, extent.height)
numLevels = int(float(1.0) + math.log(maxDim, 2))
err, mipmap = cudart.cudaMallocMipmappedArray(desc, extent, numLevels, flags)
assertSuccess(err)
err, sparseProp = cudart.cudaMipmappedArrayGetSparseProperties(mipmap)
assertSuccess(err)
# tileExtent
# TODO: Will these values always be this same? Maybe need a more stable test?
# TODO: Are these values even correct? Need to research the function some more.. Maybe need an easier API test
assert(sparseProp.tileExtent.width == 64)
assert(sparseProp.tileExtent.height == 64)
assert(sparseProp.tileExtent.depth == 1)
sparsePropNew = cudart.cudaArraySparseProperties()
sparsePropNew.tileExtent.width = 15
sparsePropNew.tileExtent.height = 16
sparsePropNew.tileExtent.depth = 17
# Check that we can copy inner structs
sparseProp.tileExtent = sparsePropNew.tileExtent
assert(sparseProp.tileExtent.width == 15)
assert(sparseProp.tileExtent.height == 16)
assert(sparseProp.tileExtent.depth == 17)
assert(sparseProp.miptailFirstLevel == 3)
assert(sparseProp.miptailSize == 196608)
assert(sparseProp.flags == 0)
err, = cudart.cudaFreeMipmappedArray(mipmap)
assertSuccess(err)
# TODO
example = cudart.cudaExternalSemaphoreSignalNodeParams()
example.extSemArray = [cudart.cudaExternalSemaphore_t(0), cudart.cudaExternalSemaphore_t(123), cudart.cudaExternalSemaphore_t(999)]
a1 = cudart.cudaExternalSemaphoreSignalParams()
a1.params.fence.value = 7
a1.params.nvSciSync.fence = 999
a1.params.keyedMutex.key = 9
a1.flags = 1
a2 = cudart.cudaExternalSemaphoreSignalParams()
a2.params.fence.value = 7
a2.params.nvSciSync.fence = 999
a2.params.keyedMutex.key = 9
a2.flags = 2
a3 = cudart.cudaExternalSemaphoreSignalParams()
a3.params.fence.value = 7
a3.params.nvSciSync.fence = 999
a3.params.keyedMutex.key = 9
a3.flags = 3
example.paramsArray = [a1]
# Note: Setting is a pass by value. Changing the object does not reflect internal value
a3.params.fence.value = 4
a3.params.nvSciSync.fence = 4
a3.params.keyedMutex.key = 4
a3.flags = 4
example.numExtSems = 3
def test_cudart_graphs():
err, graph = cudart.cudaGraphCreate(0)
assertSuccess(err)
err, pGraphNode0 = cudart.cudaGraphAddEmptyNode(graph, None, 0)
assertSuccess(err)
err, pGraphNode1 = cudart.cudaGraphAddEmptyNode(graph, [pGraphNode0], 1)
assertSuccess(err)
err, pGraphNode2 = cudart.cudaGraphAddEmptyNode(graph, [pGraphNode0, pGraphNode1], 2)
assertSuccess(err)
err, nodes, numNodes = cudart.cudaGraphGetNodes(graph)
err, nodes, numNodes = cudart.cudaGraphGetNodes(graph, numNodes)
stream_legacy = cudart.cudaStream_t(cudart.cudaStreamLegacy)
stream_per_thread = cudart.cudaStream_t(cudart.cudaStreamPerThread)
err, stream_with_flags = cudart.cudaStreamCreateWithFlags(cudart.cudaStreamNonBlocking)
assertSuccess(err)
def test_cudart_list_access():
err, prop = cudart.cudaGetDeviceProperties(0)
prop.name = prop.name + b' '*(256-len(prop.name))
def test_cudart_class_setters():
dim = cudart.dim3()
dim.x = 1
dim.y = 2
dim.z = 3
assert dim.x == 1
assert dim.y == 2
assert dim.z == 3
def test_cudart_both_type():
err, mode = cudart.cudaThreadExchangeStreamCaptureMode(cudart.cudaStreamCaptureMode.cudaStreamCaptureModeGlobal)
assertSuccess(err)
err, mode = cudart.cudaThreadExchangeStreamCaptureMode(cudart.cudaStreamCaptureMode.cudaStreamCaptureModeRelaxed)
assertSuccess(err)
assert(mode == cudart.cudaStreamCaptureMode.cudaStreamCaptureModeGlobal)
err, mode = cudart.cudaThreadExchangeStreamCaptureMode(cudart.cudaStreamCaptureMode.cudaStreamCaptureModeThreadLocal)
assertSuccess(err)
assert(mode == cudart.cudaStreamCaptureMode.cudaStreamCaptureModeRelaxed)
err, mode = cudart.cudaThreadExchangeStreamCaptureMode(cudart.cudaStreamCaptureMode.cudaStreamCaptureModeGlobal)
assertSuccess(err)
assert(mode == cudart.cudaStreamCaptureMode.cudaStreamCaptureModeThreadLocal)
def test_cudart_cudaGetDeviceProperties():
err, prop = cudart.cudaGetDeviceProperties(0)
assertSuccess(err)
attrs = ['accessPolicyMaxWindowSize', 'asyncEngineCount', 'canMapHostMemory', 'canUseHostPointerForRegisteredMem', 'clockRate', 'computeMode', 'computePreemptionSupported', 'concurrentKernels', 'concurrentManagedAccess', 'cooperativeLaunch', 'cooperativeMultiDeviceLaunch', 'deviceOverlap', 'directManagedMemAccessFromHost', 'getPtr', 'globalL1CacheSupported', 'hostNativeAtomicSupported', 'integrated', 'isMultiGpuBoard', 'kernelExecTimeoutEnabled', 'l2CacheSize', 'localL1CacheSupported', 'luid', 'luidDeviceNodeMask', 'major', 'managedMemory', 'maxBlocksPerMultiProcessor', 'maxGridSize', 'maxSurface1D', 'maxSurface1DLayered', 'maxSurface2D', 'maxSurface2DLayered', 'maxSurface3D', 'maxSurfaceCubemap', 'maxSurfaceCubemapLayered', 'maxTexture1D', 'maxTexture1DLayered', 'maxTexture1DLinear', 'maxTexture1DMipmap', 'maxTexture2D', 'maxTexture2DGather', 'maxTexture2DLayered', 'maxTexture2DLinear', 'maxTexture2DMipmap', 'maxTexture3D', 'maxTexture3DAlt', 'maxTextureCubemap', 'maxTextureCubemapLayered', 'maxThreadsDim', 'maxThreadsPerBlock', 'maxThreadsPerMultiProcessor', 'memPitch', 'memoryBusWidth', 'memoryClockRate', 'minor', 'multiGpuBoardGroupID', 'multiProcessorCount', 'name', 'pageableMemoryAccess', 'pageableMemoryAccessUsesHostPageTables', 'pciBusID', 'pciDeviceID', 'pciDomainID', 'persistingL2CacheMaxSize', 'regsPerBlock', 'regsPerMultiprocessor', 'reservedSharedMemPerBlock', 'sharedMemPerBlock', 'sharedMemPerBlockOptin', 'sharedMemPerMultiprocessor', 'singleToDoublePrecisionPerfRatio', 'streamPrioritiesSupported', 'surfaceAlignment', 'tccDriver', 'textureAlignment', 'texturePitchAlignment', 'totalConstMem', 'totalGlobalMem', 'unifiedAddressing', 'uuid', 'warpSize']
for attr in attrs:
assert hasattr(prop, attr)
assert len(prop.name.decode("utf-8")) != 0
assert len(prop.uuid.bytes.hex()) != 0
example = cudart.cudaExternalSemaphoreSignalNodeParams()
example.extSemArray = [cudart.cudaExternalSemaphore_t(0), cudart.cudaExternalSemaphore_t(123), cudart.cudaExternalSemaphore_t(999)]
a1 = cudart.cudaExternalSemaphoreSignalParams()
a1.params.fence.value = 7
a1.params.nvSciSync.fence = 999
a1.params.keyedMutex.key = 9
a1.flags = 1
a2 = cudart.cudaExternalSemaphoreSignalParams()
a2.params.fence.value = 7
a2.params.nvSciSync.fence = 999
a2.params.keyedMutex.key = 9
a2.flags = 2
a3 = cudart.cudaExternalSemaphoreSignalParams()
a3.params.fence.value = 7
a3.params.nvSciSync.fence = 999
a3.params.keyedMutex.key = 9
a3.flags = 3
example.paramsArray = [a1]
# Note: Setting is a pass by value. Changing the object does not reflect internal value
a3.params.fence.value = 4
a3.params.nvSciSync.fence = 4
a3.params.keyedMutex.key = 4
a3.flags = 4
example.numExtSems = 3
@pytest.mark.skipif(driverVersionLessThan(11030) or not supportsMemoryPool(), reason='When new attributes were introduced')
def test_cudart_MemPool_attr():
poolProps = cudart.cudaMemPoolProps()
poolProps.allocType = cudart.cudaMemAllocationType.cudaMemAllocationTypePinned
poolProps.location.id = 0
poolProps.location.type = cudart.cudaMemLocationType.cudaMemLocationTypeDevice
attr_list = [None] * 8
err, pool = cudart.cudaMemPoolCreate(poolProps)
assertSuccess(err)
for idx, attr in enumerate([cudart.cudaMemPoolAttr.cudaMemPoolReuseFollowEventDependencies,
cudart.cudaMemPoolAttr.cudaMemPoolReuseAllowOpportunistic,
cudart.cudaMemPoolAttr.cudaMemPoolReuseAllowInternalDependencies,
cudart.cudaMemPoolAttr.cudaMemPoolAttrReleaseThreshold,
cudart.cudaMemPoolAttr.cudaMemPoolAttrReservedMemCurrent,
cudart.cudaMemPoolAttr.cudaMemPoolAttrReservedMemHigh,
cudart.cudaMemPoolAttr.cudaMemPoolAttrUsedMemCurrent,
cudart.cudaMemPoolAttr.cudaMemPoolAttrUsedMemHigh]):
err, attr_tmp = cudart.cudaMemPoolGetAttribute(pool, attr)
assertSuccess(err)
attr_list[idx] = attr_tmp
for idxA, attr in enumerate([cudart.cudaMemPoolAttr.cudaMemPoolReuseFollowEventDependencies,
cudart.cudaMemPoolAttr.cudaMemPoolReuseAllowOpportunistic,
cudart.cudaMemPoolAttr.cudaMemPoolReuseAllowInternalDependencies]):
err, = cudart.cudaMemPoolSetAttribute(pool, attr, 0)
assertSuccess(err)
for idx, attr in enumerate([cudart.cudaMemPoolAttr.cudaMemPoolAttrReleaseThreshold]):
err, = cudart.cudaMemPoolSetAttribute(pool, attr, cuda.cuuint64_t(9))
assertSuccess(err)
for idx, attr in enumerate([cudart.cudaMemPoolAttr.cudaMemPoolReuseFollowEventDependencies,
cudart.cudaMemPoolAttr.cudaMemPoolReuseAllowOpportunistic,
cudart.cudaMemPoolAttr.cudaMemPoolReuseAllowInternalDependencies,
cudart.cudaMemPoolAttr.cudaMemPoolAttrReleaseThreshold]):
err, attr_tmp = cudart.cudaMemPoolGetAttribute(pool, attr)
assertSuccess(err)
attr_list[idx] = attr_tmp
assert(attr_list[0] == 0)
assert(attr_list[1] == 0)
assert(attr_list[2] == 0)
assert(int(attr_list[3]) == 9)
err, = cudart.cudaMemPoolDestroy(pool)
assertSuccess(err)
def test_cudart_make_api():
err, channelDesc = cudart.cudaCreateChannelDesc(32,0,0,0,cudart.cudaChannelFormatKind.cudaChannelFormatKindFloat)
assertSuccess(err)
assert(channelDesc.x == 32)
assert(channelDesc.y == 0)
assert(channelDesc.z == 0)
assert(channelDesc.w == 0)
assert(channelDesc.f == cudart.cudaChannelFormatKind.cudaChannelFormatKindFloat)
# make_cudaPitchedPtr
cudaPitchedPtr = cudart.make_cudaPitchedPtr(1,2,3,4)
assert(cudaPitchedPtr.ptr == 1)
assert(cudaPitchedPtr.pitch == 2)
assert(cudaPitchedPtr.xsize == 3)
assert(cudaPitchedPtr.ysize == 4)
# make_cudaPos
cudaPos = cudart.make_cudaPos(1,2,3)
assert(cudaPos.x == 1)
assert(cudaPos.y == 2)
assert(cudaPos.z == 3)
# make_cudaExtent
cudaExtent = cudart.make_cudaExtent(1,2,3)
assert(cudaExtent.width == 1)
assert(cudaExtent.height == 2)
assert(cudaExtent.depth == 3)
def test_cudart_cudaStreamGetCaptureInfo():
# create stream
err, stream = cudart.cudaStreamCreate()
assertSuccess(err)
# validate that stream is not capturing
err, status, *info = cudart.cudaStreamGetCaptureInfo(stream)
assertSuccess(err)
assert(status == cudart.cudaStreamCaptureStatus.cudaStreamCaptureStatusNone)
# start capture
err, = cudart.cudaStreamBeginCapture(
stream, cudart.cudaStreamCaptureMode.cudaStreamCaptureModeGlobal
)
assertSuccess(err)
# validate that stream is capturing now
err, status, *info = cudart.cudaStreamGetCaptureInfo(stream)
assertSuccess(err)
assert(status == cudart.cudaStreamCaptureStatus.cudaStreamCaptureStatusActive)
# clean up
err, pgraph = cudart.cudaStreamEndCapture(stream)
assertSuccess(err)
def test_cudart_cudaArrayGetInfo():
# create channel descriptor
x, y, z, w = 8, 0, 0, 0
f = cudart.cudaChannelFormatKind.cudaChannelFormatKindUnsigned
err, desc = cudart.cudaCreateChannelDesc(
x, y, z, w, f
)
assertSuccess(err)
# allocate device array
width = 10
height = 0
inFlags = 0
err, arr = cudart.cudaMallocArray(desc, width, height, inFlags)
assertSuccess(err)
# get device array info
err, desc, extent, outFlags = cudart.cudaArrayGetInfo(arr)
assertSuccess(err)
# validate descriptor, extent, flags
assert(desc.x == x)
assert(desc.y == y)
assert(desc.z == z)
assert(desc.w == w)
assert(desc.f == f)
assert(extent.width == width)
assert(extent.height == height)
assert(inFlags == outFlags)
# clean up
err, = cudart.cudaFreeArray(arr)
assertSuccess(err)
def test_cudart_cudaMemcpy2DToArray():
# create host arrays
size = int(1024 * np.uint8().itemsize)
h1 = np.full(size, 1).astype(np.uint8)
h2 = np.full(size, 2).astype(np.uint8)
assert(np.array_equal(h1, h2) is False)
# create channel descriptor
err, desc = cudart.cudaCreateChannelDesc(
8, 0, 0, 0, cudart.cudaChannelFormatKind.cudaChannelFormatKindUnsigned
)
assertSuccess(err)
# allocate device array
err, arr = cudart.cudaMallocArray(desc, size, 0, 0)
assertSuccess(err)
# h1 to arr
err, = cudart.cudaMemcpy2DToArray(
arr, 0, 0, h1, size, size, 1,
cudart.cudaMemcpyKind.cudaMemcpyHostToDevice
)
assertSuccess(err)
# arr to h2
err, = cudart.cudaMemcpy2DFromArray(
h2, size, arr, 0, 0, size, 1,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost
)
assertSuccess(err)
# validate h1 == h2
assert(np.array_equal(h1, h2))
# clean up
err, = cudart.cudaFreeArray(arr)
assertSuccess(err)
def test_cudart_cudaMemcpy2DToArray_DtoD():
# allocate device memory
size = 1024 * np.uint8().itemsize
err, d1 = cudart.cudaMalloc(size)
assertSuccess(err)
err, d2 = cudart.cudaMalloc(size)
assertSuccess(err)
# create host arrays
h1 = np.full(size, 1).astype(np.uint8)
h2 = np.full(size, 2).astype(np.uint8)
assert(np.array_equal(h1, h2) is False)
# create channel descriptor
err, desc = cudart.cudaCreateChannelDesc(
8, 0, 0, 0, cudart.cudaChannelFormatKind.cudaChannelFormatKindUnsigned
)
assertSuccess(err)
# allocate device array
err, arr = cudart.cudaMallocArray(desc, size, 0, 0)
assertSuccess(err)
# h1 to d1
err, = cudart.cudaMemcpy(d1, h1, size, cudart.cudaMemcpyKind.cudaMemcpyHostToDevice)
assertSuccess(err)
# d1 to arr
err, = cudart.cudaMemcpy2DToArray(
arr, 0, 0, d1, size, size, 1,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToDevice
)
assertSuccess(err)
# arr to d2
err, = cudart.cudaMemcpy2DFromArray(
d2, size, arr, 0, 0, size, 1,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToDevice
)
assertSuccess(err)
# d2 to h2
err, = cudart.cudaMemcpy(h2, d2, size, cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost)
assertSuccess(err)
# validate h1 == h2
assert(np.array_equal(h1, h2))
# clean up
err, = cudart.cudaFreeArray(arr)
assertSuccess(err)
err, = cudart.cudaFree(d2)
assertSuccess(err)
err, = cudart.cudaFree(d1)
assertSuccess(err)
def test_cudart_cudaMemcpy2DArrayToArray():
# create host arrays
size = 1024 * np.uint8().itemsize
h1 = np.full(size, 1).astype(np.uint8)
h2 = np.full(size, 2).astype(np.uint8)
assert(np.array_equal(h1, h2) is False)
# create channel descriptor
err, desc = cudart.cudaCreateChannelDesc(
8, 0, 0, 0, cudart.cudaChannelFormatKind.cudaChannelFormatKindUnsigned
)
assertSuccess(err)
# allocate device arrays
err, a1 = cudart.cudaMallocArray(desc, size, 0, 0)
assertSuccess(err)
err, a2 = cudart.cudaMallocArray(desc, size, 0, 0)
assertSuccess(err)
# h1 to a1
err, = cudart.cudaMemcpy2DToArray(
a1, 0, 0, h1, size, size, 1,
cudart.cudaMemcpyKind.cudaMemcpyHostToDevice
)
assertSuccess(err)
# a1 to a2
err, = cudart.cudaMemcpy2DArrayToArray(
a2, 0, 0, a1, 0, 0, size, 1,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToDevice
)
assertSuccess(err)
# a2 to h2
err, = cudart.cudaMemcpy2DFromArray(
h2, size, a2, 0, 0, size, 1,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost
)
assertSuccess(err)
# validate h1 == h2
assert(np.array_equal(h1, h2))
# clean up
err, = cudart.cudaFreeArray(a2)
assertSuccess(err)
err, = cudart.cudaFreeArray(a1)
assertSuccess(err)
def test_cudart_cudaMemcpyArrayToArray():
# create host arrays
size = 1024 * np.uint8().itemsize
h1 = np.full(size, 1).astype(np.uint8)
h2 = np.full(size, 2).astype(np.uint8)
assert(np.array_equal(h1, h2) is False)
# create channel descriptor
err, desc = cudart.cudaCreateChannelDesc(
8, 0, 0, 0, cudart.cudaChannelFormatKind.cudaChannelFormatKindUnsigned
)
assertSuccess(err)
# allocate device arrays
err, a1 = cudart.cudaMallocArray(desc, size, 0, 0)
assertSuccess(err)
err, a2 = cudart.cudaMallocArray(desc, size, 0, 0)
assertSuccess(err)
# h1 to a1
err, = cudart.cudaMemcpy2DToArray(
a1, 0, 0, h1, size, size, 1,
cudart.cudaMemcpyKind.cudaMemcpyHostToDevice
)
assertSuccess(err)
# a1 to a2
err, = cudart.cudaMemcpyArrayToArray(
a2, 0, 0, a1, 0, 0, size,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToDevice
)
assertSuccess(err)
# a2 to h2
err, = cudart.cudaMemcpy2DFromArray(
h2, size, a2, 0, 0, size, 1,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost
)
assertSuccess(err)
# validate h1 == h2
assert(np.array_equal(h1, h2))
# clean up
err, = cudart.cudaFreeArray(a2)
assertSuccess(err)
err, = cudart.cudaFreeArray(a1)
assertSuccess(err)
def test_cudart_cudaGetChannelDesc():
# create channel descriptor
x, y, z, w = 8, 0, 0, 0
f = cudart.cudaChannelFormatKind.cudaChannelFormatKindUnsigned
err, desc = cudart.cudaCreateChannelDesc(
x, y, z, w, f
)
assertSuccess(err)
# allocate device array
width = 10
height = 0
flags = 0
err, arr = cudart.cudaMallocArray(desc, width, height, flags)
assertSuccess(err)
# get channel descriptor from array
err, desc = cudart.cudaGetChannelDesc(arr)
assertSuccess(err)
# validate array channel descriptor
assert(desc.x == x)
assert(desc.y == y)
assert(desc.z == z)
assert(desc.w == w)
assert(desc.f == f)
# clean up
err, = cudart.cudaFreeArray(arr)
assertSuccess(err)
def test_cudart_cudaGetTextureObjectTextureDesc():
# create channel descriptor
err, channelDesc = cudart.cudaCreateChannelDesc(
8, 0, 0, 0, cudart.cudaChannelFormatKind.cudaChannelFormatKindUnsigned
)
assertSuccess(err)
# allocate device arrays
err, arr = cudart.cudaMallocArray(channelDesc, 1024, 0, 0)
assertSuccess(err)
# create descriptors for texture object
resDesc = cudart.cudaResourceDesc()
resDesc.res.array.array = arr
inTexDesc = cudart.cudaTextureDesc()
# create texture object
err, texObject = cudart.cudaCreateTextureObject(resDesc, inTexDesc, None)
assertSuccess(err)
# get texture descriptor
err, outTexDesc = cudart.cudaGetTextureObjectTextureDesc(texObject)
assertSuccess(err)
# validate texture descriptor
for attr in dir(outTexDesc):
if attr in ["borderColor", "getPtr"]:
continue
if not attr.startswith("_"):
assert(getattr(outTexDesc, attr) == getattr(inTexDesc, attr))
# clean up
err, = cudart.cudaDestroyTextureObject(texObject)
assertSuccess(err)
def test_cudart_cudaMemset3D():
# create host arrays
size = 1024 * np.uint8().itemsize
h1 = np.full(size, 1).astype(np.uint8)
h2 = np.full(size, 2).astype(np.uint8)
assert(np.array_equal(h1, h2) is False)
# allocate device memory
devExtent = cudart.make_cudaExtent(32, 32, 1)
err, devPitchedPtr = cudart.cudaMalloc3D(devExtent)
assertSuccess(err)
# set memory
memExtent = cudart.make_cudaExtent(devPitchedPtr.pitch, devPitchedPtr.ysize, 1)
err, = cudart.cudaMemset3D(devPitchedPtr, 1, memExtent)
assertSuccess(err)
# D to h2
err, = cudart.cudaMemcpy(
h2, devPitchedPtr.ptr, size, cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost
)
# validate h1 == h2
assert(np.array_equal(h1, h2))
# clean up
err, = cudart.cudaFree(devPitchedPtr.ptr)
assertSuccess(err)
def test_cudart_cudaMemset3D_2D():
# create host arrays
size = 512 * np.uint8().itemsize
h1 = np.full(size, 1).astype(np.uint8)
h2 = np.full(size, 2).astype(np.uint8)
assert(np.array_equal(h1, h2) is False)
# allocate device memory
devExtent = cudart.make_cudaExtent(1024, 1, 1)
err, devPitchedPtr = cudart.cudaMalloc3D(devExtent)
assertSuccess(err)
# set memory
memExtent = cudart.make_cudaExtent(size, devPitchedPtr.ysize, 1)
err, = cudart.cudaMemset3D(devPitchedPtr, 1, memExtent)
assertSuccess(err)
# D to h2
err, = cudart.cudaMemcpy(
h2, devPitchedPtr.ptr, size, cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost
)
# validate h1 == h2
assert(np.array_equal(h1, h2))
# clean up
err, = cudart.cudaFree(devPitchedPtr.ptr)
assertSuccess(err)
def test_cudart_cudaMemcpyToArray():
# create host arrays
size = 1024 * np.uint8().itemsize
h1 = np.full(size, 1).astype(np.uint8)
h2 = np.full(size, 2).astype(np.uint8)
assert(np.array_equal(h1, h2) is False)
# create channel descriptor
err, desc = cudart.cudaCreateChannelDesc(
8, 0, 0, 0, cudart.cudaChannelFormatKind.cudaChannelFormatKindUnsigned
)
assertSuccess(err)
# allocate device array
err, arr = cudart.cudaMallocArray(desc, size, 0, 0)
assertSuccess(err)
# h1 to arr
err, = cudart.cudaMemcpyToArray(
arr, 0, 0, h1, size, cudart.cudaMemcpyKind.cudaMemcpyHostToDevice
)
assertSuccess(err)
# arr to h2
err, = cudart.cudaMemcpyFromArray(
h2, arr, 0, 0, size,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost
)
assertSuccess(err)
# validate h1 == h2
assert(np.array_equal(h1, h2))
# clean up
err, = cudart.cudaFreeArray(arr)
assertSuccess(err)
def test_cudart_cudaMemcpyToArray_DtoD():
# allocate device memory
size = int(1024 * np.uint8().itemsize)
err, d1 = cudart.cudaMalloc(size)
assertSuccess(err)
err, d2 = cudart.cudaMalloc(size)
assertSuccess(err)
# create host arrays
h1 = np.full(size, 1).astype(np.uint8)
h2 = np.full(size, 2).astype(np.uint8)
assert(np.array_equal(h1, h2) is False)
# create channel descriptor
err, desc = cudart.cudaCreateChannelDesc(
8, 0, 0, 0, cudart.cudaChannelFormatKind.cudaChannelFormatKindUnsigned
)
assertSuccess(err)
# allocate device array
err, arr = cudart.cudaMallocArray(desc, size, 0, 0)
assertSuccess(err)
# h1 to d1
err, = cudart.cudaMemcpy(d1, h1, size, cudart.cudaMemcpyKind.cudaMemcpyHostToDevice)
assertSuccess(err)
# d1 to arr
err, = cudart.cudaMemcpyToArray(
arr, 0, 0, d1, size, cudart.cudaMemcpyKind.cudaMemcpyDeviceToDevice
)
assertSuccess(err)
# arr to d2
err, = cudart.cudaMemcpyFromArray(
d2, arr, 0, 0, size,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToDevice
)
assertSuccess(err)
# d2 to h2
err, = cudart.cudaMemcpy(h2, d2, size, cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost)
assertSuccess(err)
# validate h1 == h2
assert(np.array_equal(h1, h2))
# clean up
err, = cudart.cudaFreeArray(arr)
assertSuccess(err)
err, = cudart.cudaFree(d2)
assertSuccess(err)
err, = cudart.cudaFree(d1)
assertSuccess(err)
def test_cudart_cudaMemcpy3DAsync():
# create host arrays
size = int(1024 * np.uint8().itemsize)
h1 = np.full(size, 1).astype(np.uint8)
h2 = np.full(size, 2).astype(np.uint8)
assert(np.array_equal(h1, h2) is False)
# create channel descriptor
err, desc = cudart.cudaCreateChannelDesc(
8, 0, 0, 0, cudart.cudaChannelFormatKind.cudaChannelFormatKindUnsigned
)
assertSuccess(err)
# allocate device array
err, arr = cudart.cudaMallocArray(desc, size, 0, 0)
assertSuccess(err)
# create stream
err, stream = cudart.cudaStreamCreate()
assertSuccess(err)
# create memcpy params
params = cudart.cudaMemcpy3DParms()
params.srcPtr = cudart.make_cudaPitchedPtr(h1, size, 1, 1)
params.dstArray = arr
params.extent = cudart.make_cudaExtent(size, 1, 1)
params.kind = cudart.cudaMemcpyKind.cudaMemcpyHostToDevice
# h1 to arr
err, = cudart.cudaMemcpy3DAsync(params, stream)
assertSuccess(err)
# await results
err, = cudart.cudaStreamSynchronize(stream)
assertSuccess(err)
# arr to h2
err, = cudart.cudaMemcpy2DFromArray(
h2, size, arr, 0, 0, size, 1,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost
)
assertSuccess(err)
# validate h1 == h2
assert(np.array_equal(h1, h2))
# clean up
err, = cudart.cudaFreeArray(arr)
assertSuccess(err)
def test_cudart_cudaGraphAddMemcpyNode1D():
# allocate device memory
size = 1024 * np.uint8().itemsize
err, dptr = cudart.cudaMalloc(size)
assertSuccess(err)
# create host arrays
h1 = np.full(size, 1).astype(np.uint8)
h2 = np.full(size, 2).astype(np.uint8)
assert(np.array_equal(h1, h2) is False)
# build graph
err, graph = cudart.cudaGraphCreate(0)
assertSuccess(err)
# add nodes
err, hToDNode = cudart.cudaGraphAddMemcpyNode1D(
graph, [], 0, dptr, h1, size, cudart.cudaMemcpyKind.cudaMemcpyHostToDevice
)
assertSuccess(err)
err, dToHNode = cudart.cudaGraphAddMemcpyNode1D(
graph, [hToDNode], 1, h2, dptr, size,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost
)
assertSuccess(err)
# create stream
err, stream = cudart.cudaStreamCreate()
assertSuccess(err)
# execute graph
err, execGraph = cudart.cudaGraphInstantiate(graph, 0)
assertSuccess(err)
err, = cudart.cudaGraphLaunch(execGraph, stream)
# await results
err, = cudart.cudaStreamSynchronize(stream)
assertSuccess(err)
# validate h1 == h2
assert(np.array_equal(h1, h2))
# clean up
err, = cudart.cudaFree(dptr)
assertSuccess(err)
def test_cudart_cudaGraphAddMemsetNode():
# allocate device memory
size = 1024 * np.uint8().itemsize
err, dptr = cudart.cudaMalloc(size)
assertSuccess(err)
# create host arrays
h1 = np.full(size, 1).astype(np.uint8)
h2 = np.full(size, 2).astype(np.uint8)
assert(np.array_equal(h1, h2) is False)
# build graph
err, graph = cudart.cudaGraphCreate(0)
assertSuccess(err)
# set memset params
params = cudart.cudaMemsetParams()
params.dst = dptr
params.pitch = size
params.value = 1
params.elementSize = 1
params.width = size
params.height = 1
# add nodes
err, setNode = cudart.cudaGraphAddMemsetNode(
graph, [], 0, params
)
assertSuccess(err)
err, cpyNode = cudart.cudaGraphAddMemcpyNode1D(
graph, [setNode], 1, h2, dptr, size,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost
)
assertSuccess(err)
# create stream
err, stream = cudart.cudaStreamCreate()
assertSuccess(err)
# execute graph
err, execGraph = cudart.cudaGraphInstantiate(graph, 0)
assertSuccess(err)
err, = cudart.cudaGraphLaunch(execGraph, stream)
assertSuccess(err)
# await results
err, = cudart.cudaStreamSynchronize(stream)
assertSuccess(err)
# validate h1 == h2
assert(np.array_equal(h1, h2))
# clean up
err, = cudart.cudaFree(dptr)
assertSuccess(err)
def test_cudart_cudaMemcpy3DPeer():
# allocate device memory
size = int(1024 * np.uint8().itemsize)
err, dptr = cudart.cudaMalloc(size)
assertSuccess(err)
# create host arrays
h1 = np.full(size, 1).astype(np.uint8)
h2 = np.full(size, 2).astype(np.uint8)
assert(np.array_equal(h1, h2) is False)
# create channel descriptor
err, desc = cudart.cudaCreateChannelDesc(
8, 0, 0, 0, cudart.cudaChannelFormatKind.cudaChannelFormatKindUnsigned
)
assertSuccess(err)
# allocate device array
err, arr = cudart.cudaMallocArray(desc, size, 0, 0)
assertSuccess(err)
# create memcpy params
params = cudart.cudaMemcpy3DPeerParms()
params.srcPtr = cudart.make_cudaPitchedPtr(dptr, size, 1, 1)
params.dstArray = arr
params.extent = cudart.make_cudaExtent(size, 1, 1)
# h1 to D
err, = cudart.cudaMemcpy(dptr, h1, size, cudart.cudaMemcpyKind.cudaMemcpyHostToDevice)
assertSuccess(err)
# D to arr
err, = cudart.cudaMemcpy3DPeer(params)
assertSuccess(err)
# arr to h2
err, = cudart.cudaMemcpy2DFromArray(
h2, size, arr, 0, 0, size, 1,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost
)
assertSuccess(err)
# validate h1 == h2
assert(np.array_equal(h1, h2))
# clean up
err, = cudart.cudaFreeArray(arr)
assertSuccess(err)
err, = cudart.cudaFree(dptr)
assertSuccess(err)
def test_cudart_cudaMemcpy3DPeerAsync():
# allocate device memory
size = 1024 * np.uint8().itemsize
err, dptr = cudart.cudaMalloc(size)
assertSuccess(err)
# create host arrays
h1 = np.full(size, 1).astype(np.uint8)
h2 = np.full(size, 2).astype(np.uint8)
assert(np.array_equal(h1, h2) is False)
# create channel descriptor
err, desc = cudart.cudaCreateChannelDesc(
8, 0, 0, 0, cudart.cudaChannelFormatKind.cudaChannelFormatKindUnsigned
)
assertSuccess(err)
# allocate device array
err, arr = cudart.cudaMallocArray(desc, size, 0, 0)
assertSuccess(err)
# create stream
err, stream = cudart.cudaStreamCreate()
assertSuccess(err)
# create memcpy params
params = cudart.cudaMemcpy3DPeerParms()
params.srcPtr = cudart.make_cudaPitchedPtr(dptr, size, 1, 1)
params.dstArray = arr
params.extent = cudart.make_cudaExtent(size, 1, 1)
# h1 to D
err, = cudart.cudaMemcpy(dptr, h1, size, cudart.cudaMemcpyKind.cudaMemcpyHostToDevice)
assertSuccess(err)
# D to arr
err, = cudart.cudaMemcpy3DPeerAsync(params, stream)
assertSuccess(err)
# await results
err, = cudart.cudaStreamSynchronize(stream)
assertSuccess(err)
# arr to h2
err, = cudart.cudaMemcpy2DFromArray(
h2, size, arr, 0, 0, size, 1,
cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost
)
assertSuccess(err)
# validate h1 == h2
assert(np.array_equal(h1, h2))
# clean up
err, = cudart.cudaFreeArray(arr)
assertSuccess(err)
err, = cudart.cudaFree(dptr)
assertSuccess(err)
def test_profiler():
err, = cudart.cudaProfilerStart()
assertSuccess(err)
err, = cudart.cudaProfilerStop()
assertSuccess(err)
def test_cudart_eglFrame():
frame = cudart.cudaEglFrame()
# [<cudaArray_t 0x0>, <cudaArray_t 0x0>, <cudaArray_t 0x0>]
assert(int(frame.frame.pArray[0]) == 0)
assert(int(frame.frame.pArray[1]) == 0)
assert(int(frame.frame.pArray[2]) == 0)
frame.frame.pArray = [1,2,3]
# [<cudaArray_t 0x1>, <cudaArray_t 0x2>, <cudaArray_t 0x3>]
assert(int(frame.frame.pArray[0]) == 1)
assert(int(frame.frame.pArray[1]) == 2)
assert(int(frame.frame.pArray[2]) == 3)
frame.frame.pArray = [1,2,cudart.cudaArray_t(4)]
# [<cudaArray_t 0x1>, <cudaArray_t 0x2>, <cudaArray_t 0x4>]
assert(int(frame.frame.pArray[0]) == 1)
assert(int(frame.frame.pArray[1]) == 2)
assert(int(frame.frame.pArray[2]) == 4)
# frame.frame.pPitch
# [ptr : 0x1
# pitch : 2
# xsize : 4
# ysize : 0, ptr : 0x0
# pitch : 0
# xsize : 0
# ysize : 0, ptr : 0x0
# pitch : 0
# xsize : 0
# ysize : 0]
assert(int(frame.frame.pPitch[0].ptr) == 1)
assert(int(frame.frame.pPitch[0].pitch) == 2)
assert(int(frame.frame.pPitch[0].xsize) == 4)
assert(int(frame.frame.pPitch[0].ysize) == 0)
assert(int(frame.frame.pPitch[1].ptr) == 0)
assert(int(frame.frame.pPitch[1].pitch) == 0)
assert(int(frame.frame.pPitch[1].xsize) == 0)
assert(int(frame.frame.pPitch[1].ysize) == 0)
assert(int(frame.frame.pPitch[2].ptr) == 0)
assert(int(frame.frame.pPitch[2].pitch) == 0)
assert(int(frame.frame.pPitch[2].xsize) == 0)
assert(int(frame.frame.pPitch[2].ysize) == 0)
frame.frame.pPitch = [cudart.cudaPitchedPtr(), cudart.cudaPitchedPtr(), cudart.cudaPitchedPtr()]
# [ptr : 0x0
# pitch : 0
# xsize : 0
# ysize : 0, ptr : 0x0
# pitch : 0
# xsize : 0
# ysize : 0, ptr : 0x0
# pitch : 0
# xsize : 0
# ysize : 0]
assert(int(frame.frame.pPitch[0].ptr) == 0)
assert(int(frame.frame.pPitch[0].pitch) == 0)
assert(int(frame.frame.pPitch[0].xsize) == 0)
assert(int(frame.frame.pPitch[0].ysize) == 0)
assert(int(frame.frame.pPitch[1].ptr) == 0)
assert(int(frame.frame.pPitch[1].pitch) == 0)
assert(int(frame.frame.pPitch[1].xsize) == 0)
assert(int(frame.frame.pPitch[1].ysize) == 0)
assert(int(frame.frame.pPitch[2].ptr) == 0)
assert(int(frame.frame.pPitch[2].pitch) == 0)
assert(int(frame.frame.pPitch[2].xsize) == 0)
assert(int(frame.frame.pPitch[2].ysize) == 0)
x = frame.frame.pPitch[0]
x.pitch = 123
frame.frame.pPitch = [x,x,x]
# [ptr : 0x0
# pitch : 123
# xsize : 0
# ysize : 0, ptr : 0x0
# pitch : 123
# xsize : 0
# ysize : 0, ptr : 0x0
# pitch : 123
# xsize : 0
# ysize : 0]
assert(int(frame.frame.pPitch[0].ptr) == 0)
assert(int(frame.frame.pPitch[0].pitch) == 123)
assert(int(frame.frame.pPitch[0].xsize) == 0)
assert(int(frame.frame.pPitch[0].ysize) == 0)
assert(int(frame.frame.pPitch[1].ptr) == 0)
assert(int(frame.frame.pPitch[1].pitch) == 123)
assert(int(frame.frame.pPitch[1].xsize) == 0)
assert(int(frame.frame.pPitch[1].ysize) == 0)
assert(int(frame.frame.pPitch[2].ptr) == 0)
assert(int(frame.frame.pPitch[2].pitch) == 123)
assert(int(frame.frame.pPitch[2].xsize) == 0)
assert(int(frame.frame.pPitch[2].ysize) == 0)
x.pitch = 1234
# [ptr : 0x0
# pitch : 123
# xsize : 0
# ysize : 0, ptr : 0x0
# pitch : 123
# xsize : 0
# ysize : 0, ptr : 0x0
# pitch : 123
# xsize : 0
# ysize : 0]
assert(int(frame.frame.pPitch[0].ptr) == 0)
assert(int(frame.frame.pPitch[0].pitch) == 123)
assert(int(frame.frame.pPitch[0].xsize) == 0)
assert(int(frame.frame.pPitch[0].ysize) == 0)
assert(int(frame.frame.pPitch[1].ptr) == 0)
assert(int(frame.frame.pPitch[1].pitch) == 123)
assert(int(frame.frame.pPitch[1].xsize) == 0)
assert(int(frame.frame.pPitch[1].ysize) == 0)
assert(int(frame.frame.pPitch[2].ptr) == 0)
assert(int(frame.frame.pPitch[2].pitch) == 123)
assert(int(frame.frame.pPitch[2].xsize) == 0)
assert(int(frame.frame.pPitch[2].ysize) == 0)
| cuda-python-main | cuda/tests/test_cudart.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import platform
import pytest
import cuda.cuda as cuda
import cuda.cudart as cudart
import numpy as np
import textwrap
import shutil
from sysconfig import get_paths
def driverVersionLessThan(target):
err, = cuda.cuInit(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, version = cuda.cuDriverGetVersion()
assert(err == cuda.CUresult.CUDA_SUCCESS)
return version < target
def supportsMemoryPool():
err, isSupported = cudart.cudaDeviceGetAttribute(cudart.cudaDeviceAttr.cudaDevAttrMemoryPoolsSupported, 0)
return err == cudart.cudaError_t.cudaSuccess and isSupported
def supportsManagedMemory():
err, isSupported = cudart.cudaDeviceGetAttribute(cudart.cudaDeviceAttr.cudaDevAttrManagedMemory, 0)
return err == cudart.cudaError_t.cudaSuccess and isSupported
def supportsCudaAPI(name):
return name in dir(cuda)
def callableBinary(name):
return shutil.which(name) != None
def test_cuda_memcpy():
# Init CUDA
err, = cuda.cuInit(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
# Get device
err, device = cuda.cuDeviceGet(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
# Construct context
err, ctx = cuda.cuCtxCreate(0, device)
assert(err == cuda.CUresult.CUDA_SUCCESS)
# Allocate dev memory
size = int(1024 * np.uint8().itemsize)
err, dptr = cuda.cuMemAlloc(size)
assert(err == cuda.CUresult.CUDA_SUCCESS)
# Set h1 and h2 memory to be different
h1 = np.full(size, 1).astype(np.uint8)
h2 = np.full(size, 2).astype(np.uint8)
assert(np.array_equal(h1, h2) is False)
# h1 to D
err, = cuda.cuMemcpyHtoD(dptr, h1, size)
assert(err == cuda.CUresult.CUDA_SUCCESS)
# D to h2
err, = cuda.cuMemcpyDtoH(h2, dptr, size)
assert(err == cuda.CUresult.CUDA_SUCCESS)
# Validate h1 == h2
assert(np.array_equal(h1, h2))
# Cleanup
err, = cuda.cuMemFree(dptr)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuCtxDestroy(ctx)
assert(err == cuda.CUresult.CUDA_SUCCESS)
def test_cuda_array():
err, = cuda.cuInit(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, device = cuda.cuDeviceGet(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
# No context created
desc = cuda.CUDA_ARRAY_DESCRIPTOR()
err, arr = cuda.cuArrayCreate(desc)
assert(err == cuda.CUresult.CUDA_ERROR_INVALID_CONTEXT or err == cuda.CUresult.CUDA_ERROR_INVALID_VALUE)
err, ctx = cuda.cuCtxCreate(0, device)
assert(err == cuda.CUresult.CUDA_SUCCESS)
# Desciption not filled
err, arr = cuda.cuArrayCreate(desc)
assert(err == cuda.CUresult.CUDA_ERROR_INVALID_VALUE)
# Pass
desc.Format = cuda.CUarray_format.CU_AD_FORMAT_SIGNED_INT8
desc.NumChannels = 1
desc.Width = 1
err, arr = cuda.cuArrayCreate(desc)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuArrayDestroy(arr)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuCtxDestroy(ctx)
assert(err == cuda.CUresult.CUDA_SUCCESS)
def test_cuda_repr_primitive():
err, = cuda.cuInit(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, device = cuda.cuDeviceGet(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
assert(str(device) == '<CUdevice 0>')
assert(int(device) == 0)
err, ctx = cuda.cuCtxCreate(0, device)
assert(err == cuda.CUresult.CUDA_SUCCESS)
assert(str(ctx).startswith('<CUcontext 0x'))
assert(int(ctx) > 0)
assert(hex(ctx) == hex(int(ctx)))
# CUdeviceptr
err, dptr = cuda.cuMemAlloc(1024 * np.uint8().itemsize)
assert(err == cuda.CUresult.CUDA_SUCCESS)
assert(str(dptr).startswith('<CUdeviceptr '))
assert(int(dptr) > 0)
err, = cuda.cuMemFree(dptr)
size = 7
dptr = cuda.CUdeviceptr(size)
assert(str(dptr) == '<CUdeviceptr {}>'.format(size))
assert(int(dptr) == size)
size = 4294967295
dptr = cuda.CUdeviceptr(size)
assert(str(dptr) == '<CUdeviceptr {}>'.format(size))
assert(int(dptr) == size)
size = 18446744073709551615
dptr = cuda.CUdeviceptr(size)
assert(str(dptr) == '<CUdeviceptr {}>'.format(size))
assert(int(dptr) == size)
# cuuint32_t
size = 7
int32 = cuda.cuuint32_t(size)
assert(str(int32) == '<cuuint32_t {}>'.format(size))
assert(int(int32) == size)
size = 4294967295
int32 = cuda.cuuint32_t(size)
assert(str(int32) == '<cuuint32_t {}>'.format(size))
assert(int(int32) == size)
size = 18446744073709551615
try:
int32 = cuda.cuuint32_t(size)
raise RuntimeError('int32 = cuda.cuuint32_t(18446744073709551615) did not fail')
except OverflowError as err:
pass
# cuuint64_t
size = 7
int64 = cuda.cuuint64_t(size)
assert(str(int64) == '<cuuint64_t {}>'.format(size))
assert(int(int64) == size)
size = 4294967295
int64 = cuda.cuuint64_t(size)
assert(str(int64) == '<cuuint64_t {}>'.format(size))
assert(int(int64) == size)
size = 18446744073709551615
int64 = cuda.cuuint64_t(size)
assert(str(int64) == '<cuuint64_t {}>'.format(size))
assert(int(int64) == size)
err, = cuda.cuCtxDestroy(ctx)
assert(err == cuda.CUresult.CUDA_SUCCESS)
def test_cuda_repr_pointer():
err, = cuda.cuInit(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, device = cuda.cuDeviceGet(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
# Test 1: Classes representing pointers
err, ctx = cuda.cuCtxCreate(0, device)
assert(err == cuda.CUresult.CUDA_SUCCESS)
assert(str(ctx).startswith('<CUcontext 0x'))
assert(int(ctx) > 0)
assert(hex(ctx) == hex(int(ctx)))
randomCtxPointer = 12345
randomCtx = cuda.CUcontext(randomCtxPointer)
assert(str(randomCtx) == '<CUcontext {}>'.format(hex(randomCtxPointer)))
assert(int(randomCtx) == randomCtxPointer)
assert(hex(randomCtx) == hex(randomCtxPointer))
# Test 2: Function pointers
func = 12345
b2d_cb = cuda.CUoccupancyB2DSize(func)
assert(str(b2d_cb) == '<CUoccupancyB2DSize {}>'.format(hex(func)))
assert(int(b2d_cb) == func)
assert(hex(b2d_cb) == hex(func))
err, = cuda.cuCtxDestroy(ctx)
assert(err == cuda.CUresult.CUDA_SUCCESS)
def test_cuda_uuid_list_access():
err, = cuda.cuInit(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, device = cuda.cuDeviceGet(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, ctx = cuda.cuCtxCreate(0, device)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, uuid = cuda.cuDeviceGetUuid(device)
assert(err == cuda.CUresult.CUDA_SUCCESS)
assert(len(uuid.bytes) <= 16)
jit_option = cuda.CUjit_option
options = {
jit_option.CU_JIT_INFO_LOG_BUFFER: 1,
jit_option.CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES: 2,
jit_option.CU_JIT_ERROR_LOG_BUFFER: 3,
jit_option.CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES: 4,
jit_option.CU_JIT_LOG_VERBOSE: 5,
}
err, = cuda.cuCtxDestroy(ctx)
assert(err == cuda.CUresult.CUDA_SUCCESS)
def test_cuda_cuModuleLoadDataEx():
err, = cuda.cuInit(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, dev = cuda.cuDeviceGet(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, ctx = cuda.cuCtxCreate(0, dev)
assert(err == cuda.CUresult.CUDA_SUCCESS)
option_keys = [
cuda.CUjit_option.CU_JIT_INFO_LOG_BUFFER,
cuda.CUjit_option.CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES,
cuda.CUjit_option.CU_JIT_ERROR_LOG_BUFFER,
cuda.CUjit_option.CU_JIT_ERROR_LOG_BUFFER_SIZE_BYTES,
cuda.CUjit_option.CU_JIT_LOG_VERBOSE
]
err, mod = cuda.cuModuleLoadDataEx(0, 0, option_keys, [])
err, = cuda.cuCtxDestroy(ctx)
assert(err == cuda.CUresult.CUDA_SUCCESS)
def test_cuda_repr():
actual = cuda.CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS()
assert isinstance(actual, cuda.CUDA_EXTERNAL_SEMAPHORE_SIGNAL_PARAMS)
actual_repr = actual.__repr__()
expected_repr = textwrap.dedent("""
params :
fence :
value : 0
nvSciSync :
fence : 0x0
reserved : 0
keyedMutex :
key : 0
reserved : [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
flags : 0
reserved : [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
""")
assert actual_repr.split() == expected_repr.split()
actual_repr = cuda.CUDA_KERNEL_NODE_PARAMS_st().__repr__()
expected_repr = textwrap.dedent("""
func : <CUfunction 0x0>
gridDimX : 0
gridDimY : 0
gridDimZ : 0
blockDimX : 0
blockDimY : 0
blockDimZ : 0
sharedMemBytes : 0
kernelParams : 0
extra : 0
""")
assert actual_repr.split() == expected_repr.split()
def test_cuda_struct_list_of_enums():
desc = cuda.CUDA_TEXTURE_DESC_st()
desc.addressMode = [cuda.CUaddress_mode.CU_TR_ADDRESS_MODE_WRAP,
cuda.CUaddress_mode.CU_TR_ADDRESS_MODE_CLAMP,
cuda.CUaddress_mode.CU_TR_ADDRESS_MODE_MIRROR]
# # Too many args
# desc.addressMode = [cuda.CUaddress_mode.CU_TR_ADDRESS_MODE_WRAP,
# cuda.CUaddress_mode.CU_TR_ADDRESS_MODE_CLAMP,
# cuda.CUaddress_mode.CU_TR_ADDRESS_MODE_MIRROR,
# cuda.CUaddress_mode.CU_TR_ADDRESS_MODE_BORDER]
# # Too little args
# desc.addressMode = [cuda.CUaddress_mode.CU_TR_ADDRESS_MODE_WRAP,
# cuda.CUaddress_mode.CU_TR_ADDRESS_MODE_CLAMP]
def test_cuda_CUstreamBatchMemOpParams():
params = cuda.CUstreamBatchMemOpParams()
params.operation = cuda.CUstreamBatchMemOpType.CU_STREAM_MEM_OP_WAIT_VALUE_32
params.waitValue.operation = cuda.CUstreamBatchMemOpType.CU_STREAM_MEM_OP_WAIT_VALUE_32
params.writeValue.operation = cuda.CUstreamBatchMemOpType.CU_STREAM_MEM_OP_WAIT_VALUE_32
params.flushRemoteWrites.operation = cuda.CUstreamBatchMemOpType.CU_STREAM_MEM_OP_WAIT_VALUE_32
params.waitValue.value64 = 666
assert(int(params.waitValue.value64) == 666)
@pytest.mark.skipif(driverVersionLessThan(11030) or not supportsMemoryPool(), reason='When new attributes were introduced')
def test_cuda_memPool_attr():
err, = cuda.cuInit(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, device = cuda.cuDeviceGet(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, ctx = cuda.cuCtxCreate(0, device)
assert(err == cuda.CUresult.CUDA_SUCCESS)
poolProps = cuda.CUmemPoolProps()
poolProps.allocType = cuda.CUmemAllocationType.CU_MEM_ALLOCATION_TYPE_PINNED
poolProps.location.id = 0
poolProps.location.type = cuda.CUmemLocationType.CU_MEM_LOCATION_TYPE_DEVICE
attr_list = [None] * 8
err, pool = cuda.cuMemPoolCreate(poolProps)
assert(err == cuda.CUresult.CUDA_SUCCESS)
for idx, attr in enumerate([cuda.CUmemPool_attribute.CU_MEMPOOL_ATTR_REUSE_FOLLOW_EVENT_DEPENDENCIES,
cuda.CUmemPool_attribute.CU_MEMPOOL_ATTR_REUSE_ALLOW_OPPORTUNISTIC,
cuda.CUmemPool_attribute.CU_MEMPOOL_ATTR_REUSE_ALLOW_INTERNAL_DEPENDENCIES,
cuda.CUmemPool_attribute.CU_MEMPOOL_ATTR_RELEASE_THRESHOLD,
cuda.CUmemPool_attribute.CU_MEMPOOL_ATTR_RESERVED_MEM_CURRENT,
cuda.CUmemPool_attribute.CU_MEMPOOL_ATTR_RESERVED_MEM_HIGH,
cuda.CUmemPool_attribute.CU_MEMPOOL_ATTR_USED_MEM_CURRENT,
cuda.CUmemPool_attribute.CU_MEMPOOL_ATTR_USED_MEM_HIGH]):
err, attr_tmp = cuda.cuMemPoolGetAttribute(pool, attr)
assert(err == cuda.CUresult.CUDA_SUCCESS)
attr_list[idx] = attr_tmp
for idxA, attr in enumerate([cuda.CUmemPool_attribute.CU_MEMPOOL_ATTR_REUSE_FOLLOW_EVENT_DEPENDENCIES,
cuda.CUmemPool_attribute.CU_MEMPOOL_ATTR_REUSE_ALLOW_OPPORTUNISTIC,
cuda.CUmemPool_attribute.CU_MEMPOOL_ATTR_REUSE_ALLOW_INTERNAL_DEPENDENCIES]):
err, = cuda.cuMemPoolSetAttribute(pool, attr, 0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
for idx, attr in enumerate([cuda.CUmemPool_attribute.CU_MEMPOOL_ATTR_RELEASE_THRESHOLD]):
err, = cuda.cuMemPoolSetAttribute(pool, attr, cuda.cuuint64_t(9))
assert(err == cuda.CUresult.CUDA_SUCCESS)
for idx, attr in enumerate([cuda.CUmemPool_attribute.CU_MEMPOOL_ATTR_REUSE_FOLLOW_EVENT_DEPENDENCIES,
cuda.CUmemPool_attribute.CU_MEMPOOL_ATTR_REUSE_ALLOW_OPPORTUNISTIC,
cuda.CUmemPool_attribute.CU_MEMPOOL_ATTR_REUSE_ALLOW_INTERNAL_DEPENDENCIES,
cuda.CUmemPool_attribute.CU_MEMPOOL_ATTR_RELEASE_THRESHOLD]):
err, attr_tmp = cuda.cuMemPoolGetAttribute(pool, attr)
assert(err == cuda.CUresult.CUDA_SUCCESS)
attr_list[idx] = attr_tmp
assert(attr_list[0] == 0)
assert(attr_list[1] == 0)
assert(attr_list[2] == 0)
assert(int(attr_list[3]) == 9)
err, = cuda.cuMemPoolDestroy(pool)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuCtxDestroy(ctx)
assert(err == cuda.CUresult.CUDA_SUCCESS)
@pytest.mark.skipif(driverVersionLessThan(11030) or not supportsManagedMemory(), reason='When new attributes were introduced')
def test_cuda_pointer_attr():
err, = cuda.cuInit(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, device = cuda.cuDeviceGet(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, ctx = cuda.cuCtxCreate(0, device)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, ptr = cuda.cuMemAllocManaged(0x1000, cuda.CUmemAttach_flags.CU_MEM_ATTACH_GLOBAL.value)
assert(err == cuda.CUresult.CUDA_SUCCESS)
# Individual version
attr_type_list = [cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_CONTEXT,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_MEMORY_TYPE,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_DEVICE_POINTER,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_HOST_POINTER,
# cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_P2P_TOKENS, # TODO: Can I somehow test this?
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_SYNC_MEMOPS,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_BUFFER_ID,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_IS_MANAGED,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_DEVICE_ORDINAL,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_IS_LEGACY_CUDA_IPC_CAPABLE,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_RANGE_START_ADDR,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_RANGE_SIZE,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_MAPPED,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_ALLOWED_HANDLE_TYPES,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_IS_GPU_DIRECT_RDMA_CAPABLE,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_ACCESS_FLAGS,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_MEMPOOL_HANDLE]
attr_value_list = [None] * len(attr_type_list)
for idx, attr in enumerate(attr_type_list):
err, attr_tmp = cuda.cuPointerGetAttribute(attr, ptr)
assert(err == cuda.CUresult.CUDA_SUCCESS)
attr_value_list[idx] = attr_tmp
# List version
err, attr_value_list_v2 = cuda.cuPointerGetAttributes(len(attr_type_list), attr_type_list, ptr)
assert(err == cuda.CUresult.CUDA_SUCCESS)
for attr1, attr2 in zip(attr_value_list, attr_value_list_v2):
assert(str(attr1) == str(attr2))
# Test setting values
for val in (True, False):
err, = cuda.cuPointerSetAttribute(val, cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_SYNC_MEMOPS, ptr)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, attr_tmp = cuda.cuPointerGetAttribute(cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_SYNC_MEMOPS, ptr)
assert(err == cuda.CUresult.CUDA_SUCCESS)
assert(attr_tmp == val)
err, = cuda.cuMemFree(ptr)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuCtxDestroy(ctx)
assert(err == cuda.CUresult.CUDA_SUCCESS)
@pytest.mark.skipif(not supportsManagedMemory(), reason='When new attributes were introduced')
def test_cuda_mem_range_attr():
err, = cuda.cuInit(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, device = cuda.cuDeviceGet(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, ctx = cuda.cuCtxCreate(0, device)
assert(err == cuda.CUresult.CUDA_SUCCESS)
size = int(0x1000)
err, ptr = cuda.cuMemAllocManaged(size, cuda.CUmemAttach_flags.CU_MEM_ATTACH_GLOBAL.value)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuMemAdvise(ptr, size, cuda.CUmem_advise.CU_MEM_ADVISE_SET_READ_MOSTLY, device)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuMemAdvise(ptr, size, cuda.CUmem_advise.CU_MEM_ADVISE_SET_PREFERRED_LOCATION, cuda.CU_DEVICE_CPU)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuMemAdvise(ptr, size, cuda.CUmem_advise.CU_MEM_ADVISE_SET_ACCESSED_BY, cuda.CU_DEVICE_CPU)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, concurrentSupported = cuda.cuDeviceGetAttribute(cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_CONCURRENT_MANAGED_ACCESS, device)
assert(err == cuda.CUresult.CUDA_SUCCESS)
if concurrentSupported:
err, = cuda.cuMemAdvise(ptr, size, cuda.CUmem_advise.CU_MEM_ADVISE_SET_ACCESSED_BY, device)
assert(err == cuda.CUresult.CUDA_SUCCESS)
expected_values_list = ([1, -1, [0, -1, -2], -2],)
else:
expected_values_list = ([1, -1, [-1, -2, -2], -2], [0, -2, [-2, -2, -2], -2])
# Individual version
attr_type_list = [cuda.CUmem_range_attribute.CU_MEM_RANGE_ATTRIBUTE_READ_MOSTLY,
cuda.CUmem_range_attribute.CU_MEM_RANGE_ATTRIBUTE_PREFERRED_LOCATION,
cuda.CUmem_range_attribute.CU_MEM_RANGE_ATTRIBUTE_ACCESSED_BY,
cuda.CUmem_range_attribute.CU_MEM_RANGE_ATTRIBUTE_LAST_PREFETCH_LOCATION]
attr_type_size_list = [4, 4, 12, 4]
attr_value_list = [None] * len(attr_type_list)
for idx in range(len(attr_type_list)):
err, attr_tmp = cuda.cuMemRangeGetAttribute(attr_type_size_list[idx], attr_type_list[idx], ptr, size)
assert(err == cuda.CUresult.CUDA_SUCCESS)
attr_value_list[idx] = attr_tmp
matched = False
for expected_values in expected_values_list:
if expected_values == attr_value_list:
matched = True
break
if not matched:
raise RuntimeError(f'attr_value_list {attr_value_list} did not match any {expected_values_list}')
# List version
err, attr_value_list_v2 = cuda.cuMemRangeGetAttributes(attr_type_size_list, attr_type_list, len(attr_type_list), ptr, size)
assert(err == cuda.CUresult.CUDA_SUCCESS)
for attr1, attr2 in zip(attr_value_list, attr_value_list_v2):
assert(str(attr1) == str(attr2))
err, = cuda.cuMemFree(ptr)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuCtxDestroy(ctx)
assert(err == cuda.CUresult.CUDA_SUCCESS)
@pytest.mark.skipif(driverVersionLessThan(11040) or not supportsMemoryPool(), reason='Mempool for graphs not supported')
def test_cuda_graphMem_attr():
err, = cuda.cuInit(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, device = cuda.cuDeviceGet(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, ctx = cuda.cuCtxCreate(0, device)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, stream = cuda.cuStreamCreate(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, graph = cuda.cuGraphCreate(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
allocSize = 1
params = cuda.CUDA_MEM_ALLOC_NODE_PARAMS()
params.poolProps.location.type = cuda.CUmemLocationType.CU_MEM_LOCATION_TYPE_DEVICE
params.poolProps.location.id = device
params.poolProps.allocType = cuda.CUmemAllocationType.CU_MEM_ALLOCATION_TYPE_PINNED
params.bytesize = allocSize
err, allocNode = cuda.cuGraphAddMemAllocNode(graph, None, 0, params)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, freeNode = cuda.cuGraphAddMemFreeNode(graph, [allocNode], 1, params.dptr)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, graphExec = cuda.cuGraphInstantiate(graph, 0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuGraphLaunch(graphExec, stream)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, used = cuda.cuDeviceGetGraphMemAttribute(device, cuda.CUgraphMem_attribute.CU_GRAPH_MEM_ATTR_USED_MEM_CURRENT)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, usedHigh = cuda.cuDeviceGetGraphMemAttribute(device, cuda.CUgraphMem_attribute.CU_GRAPH_MEM_ATTR_USED_MEM_HIGH)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, reserved = cuda.cuDeviceGetGraphMemAttribute(device, cuda.CUgraphMem_attribute.CU_GRAPH_MEM_ATTR_RESERVED_MEM_CURRENT)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, reservedHigh = cuda.cuDeviceGetGraphMemAttribute(device, cuda.CUgraphMem_attribute.CU_GRAPH_MEM_ATTR_RESERVED_MEM_HIGH)
assert(err == cuda.CUresult.CUDA_SUCCESS)
assert int(used) >= allocSize
assert int(usedHigh) == int(used)
assert int(reserved) == int(usedHigh)
assert int(reservedHigh) == int(reserved)
err, = cuda.cuGraphDestroy(graph)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuStreamDestroy(stream)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuCtxDestroy(ctx)
assert(err == cuda.CUresult.CUDA_SUCCESS)
@pytest.mark.skipif(driverVersionLessThan(12010)
or not supportsCudaAPI('cuCoredumpSetAttributeGlobal')
or not supportsCudaAPI('cuCoredumpGetAttributeGlobal'), reason='Coredump API not present')
def test_cuda_coredump_attr():
err, = cuda.cuInit(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, device = cuda.cuDeviceGet(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, ctx = cuda.cuCtxCreate(0, device)
assert(err == cuda.CUresult.CUDA_SUCCESS)
attr_list = [None] * 6
err, = cuda.cuCoredumpSetAttributeGlobal(cuda.CUcoredumpSettings.CU_COREDUMP_TRIGGER_HOST, False)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuCoredumpSetAttributeGlobal(cuda.CUcoredumpSettings.CU_COREDUMP_FILE, b'corefile')
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuCoredumpSetAttributeGlobal(cuda.CUcoredumpSettings.CU_COREDUMP_PIPE, b'corepipe')
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuCoredumpSetAttributeGlobal(cuda.CUcoredumpSettings.CU_COREDUMP_LIGHTWEIGHT, True)
assert(err == cuda.CUresult.CUDA_SUCCESS)
for idx, attr in enumerate([cuda.CUcoredumpSettings.CU_COREDUMP_TRIGGER_HOST,
cuda.CUcoredumpSettings.CU_COREDUMP_FILE,
cuda.CUcoredumpSettings.CU_COREDUMP_PIPE,
cuda.CUcoredumpSettings.CU_COREDUMP_LIGHTWEIGHT,
]):
err, attr_tmp = cuda.cuCoredumpGetAttributeGlobal(attr)
assert(err == cuda.CUresult.CUDA_SUCCESS)
attr_list[idx] = attr_tmp
assert(attr_list[0] == False)
assert(attr_list[1] == b'corefile')
assert(attr_list[2] == b'corepipe')
assert(attr_list[3] == True)
err, = cuda.cuCtxDestroy(ctx)
assert(err == cuda.CUresult.CUDA_SUCCESS)
def test_get_error_name_and_string():
err, = cuda.cuInit(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, device = cuda.cuDeviceGet(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, ctx = cuda.cuCtxCreate(0, device)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, device = cuda.cuDeviceGet(0)
_, s = cuda.cuGetErrorString(err)
assert s == b"no error"
_, s = cuda.cuGetErrorName(err)
assert s == b"CUDA_SUCCESS"
err, device = cuda.cuDeviceGet(-1)
_, s = cuda.cuGetErrorString(err)
assert s == b"invalid device ordinal"
_, s = cuda.cuGetErrorName(err)
assert s == b"CUDA_ERROR_INVALID_DEVICE"
err, = cuda.cuCtxDestroy(ctx)
assert(err == cuda.CUresult.CUDA_SUCCESS)
@pytest.mark.skipif(not callableBinary('nvidia-smi'), reason='Binary existance needed')
def test_device_get_name():
import subprocess
err, = cuda.cuInit(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, device = cuda.cuDeviceGet(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, ctx = cuda.cuCtxCreate(0, device)
assert(err == cuda.CUresult.CUDA_SUCCESS)
p = subprocess.run(
["nvidia-smi", "--query-gpu=name", "--format=csv,noheader"],
stdout=subprocess.PIPE, stderr=subprocess.PIPE
)
delimiter = b'\r\n' if platform.system() == "Windows" else b'\n'
expect = p.stdout.split(delimiter)
size = 64
_, got = cuda.cuDeviceGetName(size, device)
# Returned value is bytes, and we expect it to be of requested size
# assert len(got) == size
got = got.split(b'\x00')[0]
assert got in expect
err, = cuda.cuCtxDestroy(ctx)
assert(err == cuda.CUresult.CUDA_SUCCESS)
# TODO: cuStreamGetCaptureInfo_v2
@pytest.mark.skipif(driverVersionLessThan(11030), reason='Driver too old for cuStreamGetCaptureInfo_v2')
def test_stream_capture():
pass
def test_c_func_callback():
err, = cuda.cuInit(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, device = cuda.cuDeviceGet(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, ctx = cuda.cuCtxCreate(0, device)
assert(err == cuda.CUresult.CUDA_SUCCESS)
# TODO
err, = cuda.cuCtxDestroy(ctx)
assert(err == cuda.CUresult.CUDA_SUCCESS)
def test_profiler():
err, = cuda.cuInit(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, device = cuda.cuDeviceGet(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, ctx = cuda.cuCtxCreate(0, device)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuProfilerStart()
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuProfilerStop()
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuCtxDestroy(ctx)
assert(err == cuda.CUresult.CUDA_SUCCESS)
def test_eglFrame():
val = cuda.CUeglFrame()
# [<CUarray 0x0>, <CUarray 0x0>, <CUarray 0x0>]
assert(int(val.frame.pArray[0]) == 0)
assert(int(val.frame.pArray[1]) == 0)
assert(int(val.frame.pArray[2]) == 0)
val.frame.pArray = [1,2,3]
# [<CUarray 0x1>, <CUarray 0x2>, <CUarray 0x3>]
assert(int(val.frame.pArray[0]) == 1)
assert(int(val.frame.pArray[1]) == 2)
assert(int(val.frame.pArray[2]) == 3)
val.frame.pArray = [cuda.CUarray(4),2,3]
# [<CUarray 0x4>, <CUarray 0x2>, <CUarray 0x3>]
assert(int(val.frame.pArray[0]) == 4)
assert(int(val.frame.pArray[1]) == 2)
assert(int(val.frame.pArray[2]) == 3)
val.frame.pPitch = [4, 2, 3]
# [4, 2, 3]
assert(int(val.frame.pPitch[0]) == 4)
assert(int(val.frame.pPitch[1]) == 2)
assert(int(val.frame.pPitch[2]) == 3)
val.frame.pPitch = [1,2,3]
assert(int(val.frame.pPitch[0]) == 1)
assert(int(val.frame.pPitch[1]) == 2)
assert(int(val.frame.pPitch[2]) == 3)
def test_char_range():
val = cuda.CUipcMemHandle_st()
for x in range(-128, 0):
val.reserved = [x] * 64
assert(val.reserved[0] == 256 + x)
for x in range(0, 256):
val.reserved = [x] * 64
assert(val.reserved[0] == x)
def test_anon_assign():
val1 = cuda.CUexecAffinityParam_st()
val2 = cuda.CUexecAffinityParam_st()
assert(val1.param.smCount.val == 0)
val1.param.smCount.val = 5
assert(val1.param.smCount.val == 5)
val2.param.smCount.val = 11
assert(val2.param.smCount.val == 11)
val1.param = val2.param
assert(val1.param.smCount.val == 11)
def test_union_assign():
val = cuda.CUlaunchAttributeValue()
val.clusterDim.x, val.clusterDim.y, val.clusterDim.z = 9,9,9
attr = cuda.CUlaunchAttribute()
attr.value = val
assert(val.clusterDim.x == 9)
assert(val.clusterDim.y == 9)
assert(val.clusterDim.z == 9)
def test_invalid_repr_attribute():
val = cuda.CUlaunchAttributeValue()
string = str(val)
@pytest.mark.skipif(driverVersionLessThan(12020)
or not supportsCudaAPI('cuGraphAddNode')
or not supportsCudaAPI('cuGraphNodeSetParams')
or not supportsCudaAPI('cuGraphExecNodeSetParams'), reason='Polymorphic graph APIs required')
def test_graph_poly():
err, = cuda.cuInit(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, device = cuda.cuDeviceGet(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, ctx = cuda.cuCtxCreate(0, device)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, stream = cuda.cuStreamCreate(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
# cuGraphAddNode
# Create 2 buffers
size = int(1024 * np.uint8().itemsize)
buffers = []
for _ in range(2):
err, dptr = cuda.cuMemAlloc(size)
assert(err == cuda.CUresult.CUDA_SUCCESS)
buffers += [(np.full(size, 2).astype(np.uint8), dptr)]
# Update dev buffers
for host, device in buffers:
err, = cuda.cuMemcpyHtoD(device, host, size)
assert(err == cuda.CUresult.CUDA_SUCCESS)
# Create graph
nodes = []
err, graph = cuda.cuGraphCreate(0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
# Memset
host, device = buffers[0]
memsetParams = cuda.CUgraphNodeParams()
memsetParams.type = cuda.CUgraphNodeType.CU_GRAPH_NODE_TYPE_MEMSET
memsetParams.memset.elementSize = np.uint8().itemsize
memsetParams.memset.width = size
memsetParams.memset.height = 1
memsetParams.memset.dst = device
memsetParams.memset.value = 1
err, node = cuda.cuGraphAddNode(graph, None, 0, memsetParams)
assert(err == cuda.CUresult.CUDA_SUCCESS)
nodes += [node]
# Memcpy
host, device = buffers[1]
memcpyParams = cuda.CUgraphNodeParams()
memcpyParams.type = cuda.CUgraphNodeType.CU_GRAPH_NODE_TYPE_MEMCPY
memcpyParams.memcpy.copyParams.srcMemoryType = cuda.CUmemorytype.CU_MEMORYTYPE_DEVICE
memcpyParams.memcpy.copyParams.srcDevice = device
memcpyParams.memcpy.copyParams.dstMemoryType = cuda.CUmemorytype.CU_MEMORYTYPE_HOST
memcpyParams.memcpy.copyParams.dstHost = host
memcpyParams.memcpy.copyParams.WidthInBytes = size
memcpyParams.memcpy.copyParams.Height = 1
memcpyParams.memcpy.copyParams.Depth = 1
err, node = cuda.cuGraphAddNode(graph, None, 0, memcpyParams)
assert(err == cuda.CUresult.CUDA_SUCCESS)
nodes += [node]
# Instantiate, execute, validate
err, graphExec = cuda.cuGraphInstantiate(graph, 0)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuGraphLaunch(graphExec, stream)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuStreamSynchronize(stream)
assert(err == cuda.CUresult.CUDA_SUCCESS)
# Validate
for host, device in buffers:
err, = cuda.cuMemcpyDtoH(host, device, size)
assert(err == cuda.CUresult.CUDA_SUCCESS)
assert(np.array_equal(buffers[0][0], np.full(size, 1).astype(np.uint8)))
assert(np.array_equal(buffers[1][0], np.full(size, 2).astype(np.uint8)))
# cuGraphNodeSetParams
host, device = buffers[1]
err, memcpyParamsCopy = cuda.cuGraphMemcpyNodeGetParams(nodes[1])
assert(err == cuda.CUresult.CUDA_SUCCESS)
assert(int(memcpyParamsCopy.srcDevice) == int(device))
host, device = buffers[0]
memcpyParams.memcpy.copyParams.srcDevice = device
err, = cuda.cuGraphNodeSetParams(nodes[1], memcpyParams)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, memcpyParamsCopy = cuda.cuGraphMemcpyNodeGetParams(nodes[1])
assert(err == cuda.CUresult.CUDA_SUCCESS)
assert(int(memcpyParamsCopy.srcDevice) == int(device))
# cuGraphExecNodeSetParams
memsetParams.memset.value = 11
err, = cuda.cuGraphExecNodeSetParams(graphExec, nodes[0], memsetParams)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuGraphLaunch(graphExec, stream)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuStreamSynchronize(stream)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuMemcpyDtoH(buffers[0][0], buffers[0][1], size)
assert(err == cuda.CUresult.CUDA_SUCCESS)
assert(np.array_equal(buffers[0][0], np.full(size, 11).astype(np.uint8)))
# Cleanup
err, = cuda.cuMemFree(buffers[0][1])
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuMemFree(buffers[1][1])
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuGraphExecDestroy(graphExec)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuGraphDestroy(graph)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuStreamDestroy(stream)
assert(err == cuda.CUresult.CUDA_SUCCESS)
err, = cuda.cuCtxDestroy(ctx)
assert(err == cuda.CUresult.CUDA_SUCCESS)
| cuda-python-main | cuda/tests/test_cuda.py |
cuda-python-main | cuda/_lib/__init__.py |
|
cuda-python-main | cuda/_lib/ccudart/__init__.py |
|
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import pytest
from cuda import cuda
import ctypes
import random
from .perf_test_utils import ASSERT_DRV, init_cuda
random.seed(0)
idx = 0
def query_attribute(attribute, ptrs):
global idx
ptr = ptrs[idx]
idx = (idx + 1 ) % len(ptrs)
cuda.cuPointerGetAttribute(attribute, ptr)
def query_attributes(attributes, ptrs):
global idx
ptr = ptrs[idx]
idx = (idx + 1 ) % len(ptrs)
cuda.cuPointerGetAttributes(len(attributes), attributes, ptr)
@pytest.mark.benchmark(group="pointer-attributes")
# Measure cuPointerGetAttribute in the same way as C benchmarks
def test_pointer_get_attribute(benchmark, init_cuda):
_ = init_cuda
ptrs = []
for _ in range(500):
err, ptr = cuda.cuMemAlloc(1 << 18)
ASSERT_DRV(err)
ptrs.append(ptr)
random.shuffle(ptrs)
benchmark(query_attribute, cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_MEMORY_TYPE, ptrs)
for p in ptrs:
err, = cuda.cuMemFree(p)
ASSERT_DRV(err)
@pytest.mark.benchmark(group="pointer-attributes")
# Measure cuPointerGetAttributes with all attributes
def test_pointer_get_attributes_all(benchmark, init_cuda):
_ = init_cuda
ptrs = []
for _ in range(500):
err, ptr = cuda.cuMemAlloc(1 << 18)
ASSERT_DRV(err)
ptrs.append(ptr)
random.shuffle(ptrs)
attributes = [cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_CONTEXT,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_MEMORY_TYPE,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_HOST_POINTER,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_P2P_TOKENS,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_SYNC_MEMOPS,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_BUFFER_ID,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_IS_MANAGED,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_DEVICE_ORDINAL,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_IS_LEGACY_CUDA_IPC_CAPABLE,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_RANGE_START_ADDR,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_RANGE_SIZE,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_MAPPED,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_ALLOWED_HANDLE_TYPES,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_IS_GPU_DIRECT_RDMA_CAPABLE,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_ACCESS_FLAGS,
cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_MEMPOOL_HANDLE]
benchmark(query_attributes, attributes, ptrs)
for p in ptrs:
err, = cuda.cuMemFree(p)
ASSERT_DRV(err)
@pytest.mark.benchmark(group="pointer-attributes")
# Measure cuPointerGetAttributes with a single attribute
def test_pointer_get_attributes_single(benchmark, init_cuda):
_ = init_cuda
ptrs = []
for _ in range(500):
err, ptr = cuda.cuMemAlloc(1 << 18)
ASSERT_DRV(err)
ptrs.append(ptr)
random.shuffle(ptrs)
attributes = [cuda.CUpointer_attribute.CU_POINTER_ATTRIBUTE_MEMORY_TYPE,]
benchmark(query_attributes, attributes, ptrs)
for p in ptrs:
err, = cuda.cuMemFree(p)
ASSERT_DRV(err)
| cuda-python-main | cuda/benchmarks/test_pointer_attributes.py |
cuda-python-main | cuda/benchmarks/__init__.py |
|
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
kernel_string = '''\
#define ITEM_PARAM(x, T) T x
#define REP1(x, T) , ITEM_PARAM(x, T)
#define REP2(x, T) REP1(x##0, T) REP1(x##1, T)
#define REP4(x, T) REP2(x##0, T) REP2(x##1, T)
#define REP8(x, T) REP4(x##0, T) REP4(x##1, T)
#define REP16(x, T) REP8(x##0, T) REP8(x##1, T)
#define REP32(x, T) REP16(x##0, T) REP16(x##1, T)
#define REP64(x, T) REP32(x##0, T) REP32(x##1, T)
#define REP128(x, T) REP64(x##0, T) REP64(x##1, T)
#define REP256(x, T) REP128(x##0, T) REP128(x##1, T)
template<size_t maxBytes>
struct KernelFunctionParam
{
unsigned char p[maxBytes];
};
extern "C" __global__ void small_kernel(float *f)
{
*f = 0.0f;
}
extern "C" __global__ void empty_kernel()
{
return;
}
extern "C" __global__
void small_kernel_512_args(
ITEM_PARAM(F, int*)
REP1(A, int*)
REP2(A, int*)
REP4(A, int*)
REP8(A, int*)
REP16(A, int*)
REP32(A, int*)
REP64(A, int*)
REP128(A, int*)
REP256(A, int*))
{
*F = 0;
}
extern "C" __global__
void small_kernel_512_bools(
ITEM_PARAM(F, bool)
REP1(A, bool)
REP2(A, bool)
REP4(A, bool)
REP8(A, bool)
REP16(A, bool)
REP32(A, bool)
REP64(A, bool)
REP128(A, bool)
REP256(A, bool))
{
return;
}
extern "C" __global__
void small_kernel_512_ints(
ITEM_PARAM(F, int)
REP1(A, int)
REP2(A, int)
REP4(A, int)
REP8(A, int)
REP16(A, int)
REP32(A, int)
REP64(A, int)
REP128(A, int)
REP256(A, int))
{
return;
}
extern "C" __global__
void small_kernel_512_doubles(
ITEM_PARAM(F, double)
REP1(A, double)
REP2(A, double)
REP4(A, double)
REP8(A, double)
REP16(A, double)
REP32(A, double)
REP64(A, double)
REP128(A, double)
REP256(A, double))
{
return;
}
extern "C" __global__
void small_kernel_512_chars(
ITEM_PARAM(F, char)
REP1(A, char)
REP2(A, char)
REP4(A, char)
REP8(A, char)
REP16(A, char)
REP32(A, char)
REP64(A, char)
REP128(A, char)
REP256(A, char))
{
return;
}
extern "C" __global__
void small_kernel_512_longlongs(
ITEM_PARAM(F, long long)
REP1(A, long long)
REP2(A, long long)
REP4(A, long long)
REP8(A, long long)
REP16(A, long long)
REP32(A, long long)
REP64(A, long long)
REP128(A, long long)
REP256(A, long long))
{
return;
}
extern "C" __global__
void small_kernel_256_args(
ITEM_PARAM(F, int*)
REP1(A, int*)
REP2(A, int*)
REP4(A, int*)
REP8(A, int*)
REP16(A, int*)
REP32(A, int*)
REP64(A, int*)
REP128(A, int*))
{
*F = 0;
}
extern "C" __global__
void small_kernel_16_args(
ITEM_PARAM(F, int*)
REP1(A, int*)
REP2(A, int*)
REP4(A, int*)
REP8(A, int*))
{
*F = 0;
}
extern "C" __global__ void small_kernel_2048B(KernelFunctionParam<2048> param)
{
// Do not touch param to prevent compiler from copying
// the whole structure from const bank to lmem.
}
'''
| cuda-python-main | cuda/benchmarks/kernels.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import pytest
import ctypes
# Always skip since cupy is not CTK 12.x yet
skip_tests = True
if not skip_tests:
try:
import cupy
skip_tests = False
except ImportError:
skip_tests = True
from .kernels import kernel_string
def launch(kernel, args=()):
kernel((1,), (1,), args)
# Measure launch latency with no parmaeters
@pytest.mark.skipif(skip_tests, reason="cupy is not installed")
@pytest.mark.benchmark(group="cupy")
def test_launch_latency_empty_kernel(benchmark):
module = cupy.RawModule(code=kernel_string)
kernel = module.get_function('empty_kernel')
stream = cupy.cuda.stream.Stream(non_blocking=True)
with stream:
benchmark(launch, kernel)
stream.synchronize()
# Measure launch latency with a single parameter
@pytest.mark.skipif(skip_tests, reason="cupy is not installed")
@pytest.mark.benchmark(group="cupy")
def test_launch_latency_small_kernel(benchmark):
module = cupy.RawModule(code=kernel_string)
kernel = module.get_function('small_kernel')
cupy.cuda.set_allocator()
arg = cupy.cuda.alloc(ctypes.sizeof(ctypes.c_float))
stream = cupy.cuda.stream.Stream(non_blocking=True)
with stream:
benchmark(launch, kernel, (arg,))
stream.synchronize()
# Measure launch latency with many parameters using builtin parameter packing
@pytest.mark.skipif(skip_tests, reason="cupy is not installed")
@pytest.mark.benchmark(group="cupy")
def test_launch_latency_small_kernel_512_args(benchmark):
module = cupy.RawModule(code=kernel_string)
kernel = module.get_function('small_kernel_512_args')
cupy.cuda.set_allocator()
args = []
for _ in range(512):
args.append(cupy.cuda.alloc(ctypes.sizeof(ctypes.c_int)))
args = tuple(args)
stream = cupy.cuda.stream.Stream(non_blocking=True)
with stream:
benchmark(launch, kernel, args)
stream.synchronize()
# Measure launch latency with many parameters using builtin parameter packing
@pytest.mark.skipif(skip_tests, reason="cupy is not installed")
@pytest.mark.benchmark(group="cupy")
def test_launch_latency_small_kernel_512_bools(benchmark):
module = cupy.RawModule(code=kernel_string)
kernel = module.get_function('small_kernel_512_bools')
cupy.cuda.set_allocator()
args = [True] * 512
args = tuple(args)
stream = cupy.cuda.stream.Stream(non_blocking=True)
with stream:
benchmark(launch, kernel, args)
stream.synchronize()
# Measure launch latency with many parameters using builtin parameter packing
@pytest.mark.skipif(skip_tests, reason="cupy is not installed")
@pytest.mark.benchmark(group="cupy")
def test_launch_latency_small_kernel_512_doubles(benchmark):
module = cupy.RawModule(code=kernel_string)
kernel = module.get_function('small_kernel_512_doubles')
cupy.cuda.set_allocator()
args = [1.2345] * 512
args = tuple(args)
stream = cupy.cuda.stream.Stream(non_blocking=True)
with stream:
benchmark(launch, kernel, args)
stream.synchronize()
# Measure launch latency with many parameters using builtin parameter packing
@pytest.mark.skipif(skip_tests, reason="cupy is not installed")
@pytest.mark.benchmark(group="cupy")
def test_launch_latency_small_kernel_512_ints(benchmark):
module = cupy.RawModule(code=kernel_string)
kernel = module.get_function('small_kernel_512_ints')
cupy.cuda.set_allocator()
args = [123] * 512
args = tuple(args)
stream = cupy.cuda.stream.Stream(non_blocking=True)
with stream:
benchmark(launch, kernel, args)
stream.synchronize()
# Measure launch latency with many parameters using builtin parameter packing
@pytest.mark.skipif(skip_tests, reason="cupy is not installed")
@pytest.mark.benchmark(group="cupy")
def test_launch_latency_small_kernel_512_bytes(benchmark):
module = cupy.RawModule(code=kernel_string)
kernel = module.get_function('small_kernel_512_chars')
cupy.cuda.set_allocator()
args = [127] * 512
args = tuple(args)
stream = cupy.cuda.stream.Stream(non_blocking=True)
with stream:
benchmark(launch, kernel, args)
stream.synchronize()
# Measure launch latency with many parameters using builtin parameter packing
@pytest.mark.skipif(skip_tests, reason="cupy is not installed")
@pytest.mark.benchmark(group="cupy")
def test_launch_latency_small_kernel_512_longlongs(benchmark):
module = cupy.RawModule(code=kernel_string)
kernel = module.get_function('small_kernel_512_longlongs')
cupy.cuda.set_allocator()
args = [9223372036854775806] * 512
args = tuple(args)
stream = cupy.cuda.stream.Stream(non_blocking=True)
with stream:
benchmark(launch, kernel, args)
stream.synchronize()
# Measure launch latency with many parameters using builtin parameter packing
@pytest.mark.skipif(skip_tests, reason="cupy is not installed")
@pytest.mark.benchmark(group="cupy")
def test_launch_latency_small_kernel_256_args(benchmark):
module = cupy.RawModule(code=kernel_string)
kernel = module.get_function('small_kernel_256_args')
cupy.cuda.set_allocator()
args = []
for _ in range(256):
args.append(cupy.cuda.alloc(ctypes.sizeof(ctypes.c_int)))
args = tuple(args)
stream = cupy.cuda.stream.Stream(non_blocking=True)
with stream:
benchmark(launch, kernel, args)
stream.synchronize()
# Measure launch latency with many parameters using builtin parameter packing
@pytest.mark.skipif(skip_tests, reason="cupy is not installed")
@pytest.mark.benchmark(group="cupy")
def test_launch_latency_small_kernel_16_args(benchmark):
module = cupy.RawModule(code=kernel_string)
kernel = module.get_function('small_kernel_16_args')
cupy.cuda.set_allocator()
args = []
for _ in range(16):
args.append(cupy.cuda.alloc(ctypes.sizeof(ctypes.c_int)))
args = tuple(args)
stream = cupy.cuda.stream.Stream(non_blocking=True)
with stream:
benchmark(launch, kernel, args)
stream.synchronize()
| cuda-python-main | cuda/benchmarks/test_cupy.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import pytest
import numpy as np
try:
from numba import cuda
skip_tests = False
except ImportError:
skip_tests = True
def launch_empty(kernel, stream):
kernel[1,1, stream]()
def launch(kernel, stream, arg):
kernel[1,1, stream](arg)
# Measure launch latency with no parmaeters
@pytest.mark.skipif(skip_tests, reason="Numba is not installed")
@pytest.mark.benchmark(group="numba", min_rounds=1000)
def test_launch_latency_empty_kernel(benchmark):
stream = cuda.stream()
@cuda.jit
def empty_kernel():
return
benchmark(launch_empty, empty_kernel, stream)
cuda.synchronize()
# Measure launch latency with a single parameter
@pytest.mark.skipif(skip_tests, reason="Numba is not installed")
@pytest.mark.benchmark(group="numba", min_rounds=1000)
def test_launch_latency_small_kernel(benchmark):
stream = cuda.stream()
arg = cuda.device_array(1, dtype=np.float32, stream=stream)
@cuda.jit
def small_kernel(array):
array[0] = 0.0
benchmark(launch, small_kernel, stream, arg)
cuda.synchronize()
| cuda-python-main | cuda/benchmarks/test_numba.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import pytest
from cuda import cuda
import ctypes
from .perf_test_utils import ASSERT_DRV, init_cuda, load_module
from .kernels import kernel_string
def launch(kernel, stream, args=(), arg_types=()):
cuda.cuLaunchKernel(kernel,
1, 1, 1, # grid dim
1, 1, 1, # block dim
0, stream, # shared mem and stream
(args, arg_types), 0) # arguments
def launch_packed(kernel, stream, params):
cuda.cuLaunchKernel(kernel,
1, 1, 1, # grid dim
1, 1, 1, # block dim
0, stream, # shared mem and stream
params, 0) # arguments
# Measure launch latency with no parmaeters
@pytest.mark.benchmark(group="launch-latency")
def test_launch_latency_empty_kernel(benchmark, init_cuda, load_module):
device, ctx, stream = init_cuda
module = load_module(kernel_string, device)
err, func = cuda.cuModuleGetFunction(module, b'empty_kernel')
ASSERT_DRV(err)
benchmark(launch, func, stream)
cuda.cuCtxSynchronize()
# Measure launch latency with a single parameter
@pytest.mark.benchmark(group="launch-latency")
def test_launch_latency_small_kernel(benchmark, init_cuda, load_module):
device, ctx, stream = init_cuda
module = load_module(kernel_string, device)
err, func = cuda.cuModuleGetFunction(module, b'small_kernel')
ASSERT_DRV(err)
err, f = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_float))
ASSERT_DRV(err)
benchmark(launch, func, stream, args=(f,), arg_types=(None,))
cuda.cuCtxSynchronize()
err, = cuda.cuMemFree(f)
ASSERT_DRV(err)
# Measure launch latency with many parameters using builtin parameter packing
@pytest.mark.benchmark(group="launch-latency")
def test_launch_latency_small_kernel_512_args(benchmark, init_cuda, load_module):
device, ctx, stream = init_cuda
module = load_module(kernel_string, device)
err, func = cuda.cuModuleGetFunction(module, b'small_kernel_512_args')
ASSERT_DRV(err)
args = []
arg_types = [None] * 512
for _ in arg_types:
err, p = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_int))
ASSERT_DRV(err)
args.append(p)
args = tuple(args)
arg_types = tuple(arg_types)
benchmark(launch, func, stream, args=args, arg_types=arg_types)
cuda.cuCtxSynchronize()
for p in args:
err, = cuda.cuMemFree(p)
ASSERT_DRV(err)
@pytest.mark.benchmark(group="launch-latency")
def test_launch_latency_small_kernel_512_bools(benchmark, init_cuda, load_module):
device, ctx, stream = init_cuda
module = load_module(kernel_string, device)
err, func = cuda.cuModuleGetFunction(module, b'small_kernel_512_bools')
ASSERT_DRV(err)
args = [True] * 512
arg_types = [ctypes.c_bool] * 512
args = tuple(args)
arg_types = tuple(arg_types)
benchmark(launch, func, stream, args=args, arg_types=arg_types)
cuda.cuCtxSynchronize()
@pytest.mark.benchmark(group="launch-latency")
def test_launch_latency_small_kernel_512_doubles(benchmark, init_cuda, load_module):
device, ctx, stream = init_cuda
module = load_module(kernel_string, device)
err, func = cuda.cuModuleGetFunction(module, b'small_kernel_512_doubles')
ASSERT_DRV(err)
args = [1.2345] * 512
arg_types = [ctypes.c_double] * 512
args = tuple(args)
arg_types = tuple(arg_types)
benchmark(launch, func, stream, args=args, arg_types=arg_types)
cuda.cuCtxSynchronize()
@pytest.mark.benchmark(group="launch-latency")
def test_launch_latency_small_kernel_512_ints(benchmark, init_cuda, load_module):
device, ctx, stream = init_cuda
module = load_module(kernel_string, device)
err, func = cuda.cuModuleGetFunction(module, b'small_kernel_512_ints')
ASSERT_DRV(err)
args = [123] * 512
arg_types = [ctypes.c_int] * 512
args = tuple(args)
arg_types = tuple(arg_types)
benchmark(launch, func, stream, args=args, arg_types=arg_types)
cuda.cuCtxSynchronize()
@pytest.mark.benchmark(group="launch-latency")
def test_launch_latency_small_kernel_512_bytes(benchmark, init_cuda, load_module):
device, ctx, stream = init_cuda
module = load_module(kernel_string, device)
err, func = cuda.cuModuleGetFunction(module, b'small_kernel_512_chars')
ASSERT_DRV(err)
args = [127] * 512
arg_types = [ctypes.c_byte] * 512
args = tuple(args)
arg_types = tuple(arg_types)
benchmark(launch, func, stream, args=args, arg_types=arg_types)
cuda.cuCtxSynchronize()
@pytest.mark.benchmark(group="launch-latency")
def test_launch_latency_small_kernel_512_longlongs(benchmark, init_cuda, load_module):
device, ctx, stream = init_cuda
module = load_module(kernel_string, device)
err, func = cuda.cuModuleGetFunction(module, b'small_kernel_512_longlongs')
ASSERT_DRV(err)
args = [9223372036854775806] * 512
arg_types = [ctypes.c_longlong] * 512
args = tuple(args)
arg_types = tuple(arg_types)
benchmark(launch, func, stream, args=args, arg_types=arg_types)
cuda.cuCtxSynchronize()
# Measure launch latency with many parameters using builtin parameter packing
@pytest.mark.benchmark(group="launch-latency")
def test_launch_latency_small_kernel_256_args(benchmark, init_cuda, load_module):
device, ctx, stream = init_cuda
module = load_module(kernel_string, device)
err, func = cuda.cuModuleGetFunction(module, b'small_kernel_256_args')
ASSERT_DRV(err)
args = []
arg_types = [None] * 256
for _ in arg_types:
err, p = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_int))
ASSERT_DRV(err)
args.append(p)
args = tuple(args)
arg_types = tuple(arg_types)
benchmark(launch, func, stream, args=args, arg_types=arg_types)
cuda.cuCtxSynchronize()
for p in args:
err, = cuda.cuMemFree(p)
ASSERT_DRV(err)
# Measure launch latency with many parameters using builtin parameter packing
@pytest.mark.benchmark(group="launch-latency")
def test_launch_latency_small_kernel_16_args(benchmark, init_cuda, load_module):
device, ctx, stream = init_cuda
module = load_module(kernel_string, device)
err, func = cuda.cuModuleGetFunction(module, b'small_kernel_16_args')
ASSERT_DRV(err)
args = []
arg_types = [None] * 16
for _ in arg_types:
err, p = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_int))
ASSERT_DRV(err)
args.append(p)
args = tuple(args)
arg_types = tuple(arg_types)
benchmark(launch, func, stream, args=args, arg_types=arg_types)
cuda.cuCtxSynchronize()
for p in args:
err, = cuda.cuMemFree(p)
ASSERT_DRV(err)
# Measure launch latency with many parameters, excluding parameter packing
@pytest.mark.benchmark(group="launch-latency")
def test_launch_latency_small_kernel_512_args_ctypes(benchmark, init_cuda, load_module):
device, ctx, stream = init_cuda
module = load_module(kernel_string, device)
err, func = cuda.cuModuleGetFunction(module, b'small_kernel_512_args')
ASSERT_DRV(err)
vals = []
val_ps = []
for i in range(512):
err, p = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_int))
ASSERT_DRV(err)
vals.append(p)
val_ps.append(ctypes.c_void_p(int(vals[i])))
packagedParams = (ctypes.c_void_p * 512)()
for i in range(512):
packagedParams[i] = ctypes.addressof(val_ps[i])
benchmark(launch_packed, func, stream, packagedParams)
cuda.cuCtxSynchronize()
for p in vals:
err, = cuda.cuMemFree(p)
ASSERT_DRV(err)
def pack_and_launch(kernel, stream, params):
packed_params = (ctypes.c_void_p * len(params))()
ptrs = [0] * len(params)
for i in range(len(params)):
ptrs[i] = ctypes.c_void_p(int(params[i]))
packed_params[i] = ctypes.addressof(ptrs[i])
cuda.cuLaunchKernel(kernel,
1, 1, 1,
1, 1, 1,
0, stream,
packed_params, 0)
# Measure launch latency plus parameter packing using ctypes
@pytest.mark.benchmark(group="launch-latency")
def test_launch_latency_small_kernel_512_args_ctypes_with_packing(benchmark, init_cuda, load_module):
device, ctx, stream = init_cuda
module = load_module(kernel_string, device)
err, func = cuda.cuModuleGetFunction(module, b'small_kernel_512_args')
ASSERT_DRV(err)
vals = []
for i in range(512):
err, p = cuda.cuMemAlloc(ctypes.sizeof(ctypes.c_int))
ASSERT_DRV(err)
vals.append(p)
benchmark(pack_and_launch, func, stream, vals)
cuda.cuCtxSynchronize()
for p in vals:
err, = cuda.cuMemFree(p)
ASSERT_DRV(err)
# Measure launch latency with a single large struct parameter
@pytest.mark.benchmark(group="launch-latency")
def test_launch_latency_small_kernel_2048B(benchmark, init_cuda, load_module):
device, ctx, stream = init_cuda
module = load_module(kernel_string, device)
err, func = cuda.cuModuleGetFunction(module, b'small_kernel_2048B')
ASSERT_DRV(err)
class struct_2048B(ctypes.Structure):
_fields_ = [('values',ctypes.c_uint8 * 2048)]
benchmark(launch, func, stream, args=(struct_2048B(),), arg_types=(None,))
cuda.cuCtxSynchronize()
| cuda-python-main | cuda/benchmarks/test_launch_latency.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import pytest
from cuda import cuda, cudart, nvrtc
import numpy as np
def ASSERT_DRV(err):
if isinstance(err, cuda.CUresult):
if err != cuda.CUresult.CUDA_SUCCESS:
raise RuntimeError('Cuda Error: {}'.format(err))
elif isinstance(err, cudart.cudaError_t):
if err != cudart.cudaError_t.cudaSuccess:
raise RuntimeError('Cudart Error: {}'.format(err))
elif isinstance(err, nvrtc.nvrtcResult):
if err != nvrtc.nvrtcResult.NVRTC_SUCCESS:
raise RuntimeError('Nvrtc Error: {}'.format(err))
else:
raise RuntimeError('Unknown error type: {}'.format(err))
@pytest.fixture
def init_cuda():
# Initialize
err, = cuda.cuInit(0)
ASSERT_DRV(err)
err, device = cuda.cuDeviceGet(0)
ASSERT_DRV(err)
err, ctx = cuda.cuCtxCreate(0, device)
ASSERT_DRV(err)
# create stream
err, stream = cuda.cuStreamCreate(cuda.CUstream_flags.CU_STREAM_NON_BLOCKING.value)
ASSERT_DRV(err)
yield device, ctx, stream
err, = cuda.cuStreamDestroy(stream)
ASSERT_DRV(err)
err, = cuda.cuCtxDestroy(ctx)
ASSERT_DRV(err)
@pytest.fixture
def load_module():
module = None
def _load_module(kernel_string, device):
nonlocal module
# Get module
err, major = cuda.cuDeviceGetAttribute(cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, device)
ASSERT_DRV(err)
err, minor = cuda.cuDeviceGetAttribute(cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, device)
ASSERT_DRV(err)
err, prog = nvrtc.nvrtcCreateProgram(str.encode(kernel_string), b'kernelString.cu', 0, [], [])
ASSERT_DRV(err)
opts = [b'--fmad=false', bytes('--gpu-architecture=sm_' + str(major) + str(minor), 'ascii')]
err, = nvrtc.nvrtcCompileProgram(prog, 2, opts)
err_log, logSize = nvrtc.nvrtcGetProgramLogSize(prog)
ASSERT_DRV(err_log)
log = b' ' * logSize
err_log, = nvrtc.nvrtcGetProgramLog(prog, log)
ASSERT_DRV(err_log)
result = log.decode()
if len(result) > 1:
print(result)
ASSERT_DRV(err)
err, cubinSize = nvrtc.nvrtcGetCUBINSize(prog)
ASSERT_DRV(err)
cubin = b' ' * cubinSize
err, = nvrtc.nvrtcGetCUBIN(prog, cubin)
ASSERT_DRV(err)
cubin = np.char.array(cubin)
err, module = cuda.cuModuleLoadData(cubin)
ASSERT_DRV(err)
return module
yield _load_module
err, = cuda.cuModuleUnload(module)
ASSERT_DRV(err)
| cuda-python-main | cuda/benchmarks/perf_test_utils.py |
cuda-python-main | examples/__init__.py |
|
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import ctypes
import math
import numpy as np
import sys
from cuda import cuda, cudart
from examples.common import common
from examples.common.helper_cuda import checkCudaErrors, findCudaDevice
from random import random
conjugateGradientMultiBlockCG = '''\
#line __LINE__
#include <cooperative_groups.h>
#include <cooperative_groups/reduce.h>
namespace cg = cooperative_groups;
__device__ void gpuSpMV(int *I, int *J, float *val, int nnz, int num_rows,
float alpha, float *inputVecX, float *outputVecY,
cg::thread_block &cta, const cg::grid_group &grid) {
for (int i = grid.thread_rank(); i < num_rows; i += grid.size()) {
int row_elem = I[i];
int next_row_elem = I[i + 1];
int num_elems_this_row = next_row_elem - row_elem;
float output = 0.0;
for (int j = 0; j < num_elems_this_row; j++) {
// I or J or val arrays - can be put in shared memory
// as the access is random and reused in next calls of gpuSpMV function.
output += alpha * val[row_elem + j] * inputVecX[J[row_elem + j]];
}
outputVecY[i] = output;
}
}
__device__ void gpuSaxpy(float *x, float *y, float a, int size,
const cg::grid_group &grid) {
for (int i = grid.thread_rank(); i < size; i += grid.size()) {
y[i] = a * x[i] + y[i];
}
}
__device__ void gpuDotProduct(float *vecA, float *vecB, double *result,
int size, const cg::thread_block &cta,
const cg::grid_group &grid) {
extern __shared__ double tmp[];
double temp_sum = 0.0;
for (int i = grid.thread_rank(); i < size; i += grid.size()) {
temp_sum += static_cast<double>(vecA[i] * vecB[i]);
}
cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);
temp_sum = cg::reduce(tile32, temp_sum, cg::plus<double>());
if (tile32.thread_rank() == 0) {
tmp[tile32.meta_group_rank()] = temp_sum;
}
cg::sync(cta);
if (tile32.meta_group_rank() == 0) {
temp_sum = tile32.thread_rank() < tile32.meta_group_size() ? tmp[tile32.thread_rank()] : 0.0;
temp_sum = cg::reduce(tile32, temp_sum, cg::plus<double>());
if (tile32.thread_rank() == 0) {
atomicAdd(result, temp_sum);
}
}
}
__device__ void gpuCopyVector(float *srcA, float *destB, int size,
const cg::grid_group &grid) {
for (int i = grid.thread_rank(); i < size; i += grid.size()) {
destB[i] = srcA[i];
}
}
__device__ void gpuScaleVectorAndSaxpy(const float *x, float *y, float a, float scale, int size,
const cg::grid_group &grid) {
for (int i = grid.thread_rank(); i < size; i += grid.size()) {
y[i] = a * x[i] + scale * y[i];
}
}
extern "C" __global__ void gpuConjugateGradient(int *I, int *J, float *val,
float *x, float *Ax, float *p,
float *r, double *dot_result,
int nnz, int N, float tol) {
cg::thread_block cta = cg::this_thread_block();
cg::grid_group grid = cg::this_grid();
int max_iter = 10000;
float alpha = 1.0;
float alpham1 = -1.0;
float r0 = 0.0, r1, b, a, na;
gpuSpMV(I, J, val, nnz, N, alpha, x, Ax, cta, grid);
cg::sync(grid);
gpuSaxpy(Ax, r, alpham1, N, grid);
cg::sync(grid);
gpuDotProduct(r, r, dot_result, N, cta, grid);
cg::sync(grid);
r1 = *dot_result;
int k = 1;
while (r1 > tol * tol && k <= max_iter) {
if (k > 1) {
b = r1 / r0;
gpuScaleVectorAndSaxpy(r, p, alpha, b, N, grid);
} else {
gpuCopyVector(r, p, N, grid);
}
cg::sync(grid);
gpuSpMV(I, J, val, nnz, N, alpha, p, Ax, cta, grid);
if (threadIdx.x == 0 && blockIdx.x == 0) *dot_result = 0.0;
cg::sync(grid);
gpuDotProduct(p, Ax, dot_result, N, cta, grid);
cg::sync(grid);
a = r1 / *dot_result;
gpuSaxpy(p, x, a, N, grid);
na = -a;
gpuSaxpy(Ax, r, na, N, grid);
r0 = r1;
cg::sync(grid);
if (threadIdx.x == 0 && blockIdx.x == 0) *dot_result = 0.0;
cg::sync(grid);
gpuDotProduct(r, r, dot_result, N, cta, grid);
cg::sync(grid);
r1 = *dot_result;
k++;
}
}
'''
def genTridiag(I, J, val, N, nz):
I[0] = 0
J[0] = 0
J[1]= 0
val[0] = float(random()) + 10.0
val[1] = float(random())
for i in range(1, N):
if i > 1:
I[i] = I[i - 1] + 3
else:
I[1] = 2
start = (i - 1) * 3 + 2
J[start] = i - 1
J[start + 1] = i
if i < N - 1:
J[start + 2] = i + 1
val[start] = val[start - 1]
val[start + 1] = float(random()) + 10.0
if i < N - 1:
val[start + 2] = float(random())
I[N] = nz
THREADS_PER_BLOCK = 512
sSDKname = "conjugateGradientMultiBlockCG";
def main():
tol = 1e-5
print("Starting [%s]...\n" % sSDKname);
# WAIVE: Due to bug in NVRTC
return
# This will pick the best possible CUDA capable device
devID = findCudaDevice()
deviceProp = checkCudaErrors(cudart.cudaGetDeviceProperties(devID))
if not deviceProp.managedMemory:
# This sample requires being run on a device that supports Unified Memory
print("Unified Memory not supported on this device")
return
# This sample requires being run on a device that supports Cooperative Kernel
# Launch
if not deviceProp.cooperativeLaunch:
print("\nSelected GPU (%d) does not support Cooperative Kernel Launch, Waiving the run" %
(devID))
return
# Statistics about the GPU device
print("> GPU device has %d Multi-Processors, SM %d.%d compute capabilities\n" %
(deviceProp.multiProcessorCount, deviceProp.major, deviceProp.minor))
# Get kernel
kernelHelper = common.KernelHelper(conjugateGradientMultiBlockCG, devID)
_gpuConjugateGradient = kernelHelper.getFunction(b'gpuConjugateGradient')
# Generate a random tridiagonal symmetric matrix in CSR format
N = 1048576
nz = (N - 2) * 3 + 4
I = checkCudaErrors(cudart.cudaMallocManaged(np.dtype(np.int32).itemsize * (N+1), cudart.cudaMemAttachGlobal))
J = checkCudaErrors(cudart.cudaMallocManaged(np.dtype(np.int32).itemsize * nz, cudart.cudaMemAttachGlobal))
val = checkCudaErrors(cudart.cudaMallocManaged(np.dtype(np.float32).itemsize * nz, cudart.cudaMemAttachGlobal))
I_local = (ctypes.c_int * (N + 1)).from_address(I)
J_local = (ctypes.c_int * nz).from_address(J)
val_local = (ctypes.c_float * nz).from_address(val)
genTridiag(I_local, J_local, val_local, N, nz)
x = checkCudaErrors(cudart.cudaMallocManaged(np.dtype(np.float32).itemsize * N, cudart.cudaMemAttachGlobal))
rhs = checkCudaErrors(cudart.cudaMallocManaged(np.dtype(np.float32).itemsize * N, cudart.cudaMemAttachGlobal))
dot_result = checkCudaErrors(cudart.cudaMallocManaged(np.dtype(np.float64).itemsize, cudart.cudaMemAttachGlobal))
x_local = (ctypes.c_float * N).from_address(x)
rhs_local = (ctypes.c_float * N).from_address(rhs)
dot_result_local = (ctypes.c_double).from_address(dot_result)
dot_result_local = 0
# temp memory for CG
r = checkCudaErrors(cudart.cudaMallocManaged(np.dtype(np.float32).itemsize * N, cudart.cudaMemAttachGlobal))
p = checkCudaErrors(cudart.cudaMallocManaged(np.dtype(np.float32).itemsize * N, cudart.cudaMemAttachGlobal))
Ax = checkCudaErrors(cudart.cudaMallocManaged(np.dtype(np.float32).itemsize * N, cudart.cudaMemAttachGlobal))
r_local = (ctypes.c_float * N).from_address(r)
p_local = (ctypes.c_float * N).from_address(p)
Ax_local = (ctypes.c_float * N).from_address(Ax)
checkCudaErrors(cudart.cudaDeviceSynchronize())
start = checkCudaErrors(cudart.cudaEventCreate())
stop = checkCudaErrors(cudart.cudaEventCreate())
for i in range(N):
r_local[i] = rhs_local[i] = 1.0
x_local[i] = 0.0
kernelArgs_value = (I, J, val, x,
Ax, p, r, dot_result,
nz, N, tol)
kernelArgs_types = (ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p,
ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p,
ctypes.c_int, ctypes.c_int, ctypes.c_float)
kernelArgs = (kernelArgs_value, kernelArgs_types)
sMemSize = np.dtype(np.float64).itemsize * ((THREADS_PER_BLOCK/32) + 1)
numThreads = THREADS_PER_BLOCK
numBlocksPerSm = checkCudaErrors(cuda.cuOccupancyMaxActiveBlocksPerMultiprocessor(
_gpuConjugateGradient, numThreads, sMemSize))
numSms = deviceProp.multiProcessorCount
dimGrid = cudart.dim3()
dimGrid.x = numSms * numBlocksPerSm
dimGrid.y = 1
dimGrid.z = 1
dimBlock = cudart.dim3()
dimBlock.x = THREADS_PER_BLOCK
dimBlock.y = 1
dimBlock.z = 1
checkCudaErrors(cudart.cudaEventRecord(start, 0))
checkCudaErrors(cuda.cuLaunchCooperativeKernel(_gpuConjugateGradient,
dimGrid.x, dimGrid.y, dimGrid.z,
dimBlock.x, dimBlock.y, dimBlock.z,
0, 0,
kernelArgs))
checkCudaErrors(cudart.cudaEventRecord(stop, 0))
checkCudaErrors(cudart.cudaDeviceSynchronize())
time = checkCudaErrors(cudart.cudaEventElapsedTime(start, stop));
print("GPU Final, residual = %e, kernel execution time = %f ms" %
(math.sqrt(dot_result_local), time))
err = 0.0
for i in range(N):
rsum = 0.0
for j in range(I_local[i], I_local[i+1]):
rsum += val_local[j] * x_local[J_local[j]]
diff = math.fabs(rsum - rhs_local[i])
if diff > err:
err = diff
checkCudaErrors(cudart.cudaFree(I))
checkCudaErrors(cudart.cudaFree(J))
checkCudaErrors(cudart.cudaFree(val))
checkCudaErrors(cudart.cudaFree(x))
checkCudaErrors(cudart.cudaFree(rhs))
checkCudaErrors(cudart.cudaFree(r))
checkCudaErrors(cudart.cudaFree(p))
checkCudaErrors(cudart.cudaFree(Ax))
checkCudaErrors(cudart.cudaFree(dot_result))
checkCudaErrors(cudart.cudaEventDestroy(start))
checkCudaErrors(cudart.cudaEventDestroy(stop))
print("Test Summary: Error amount = %f" % err)
print("&&&& conjugateGradientMultiBlockCG %s\n" %
("PASSED" if math.sqrt(dot_result_local) < tol else "FAILED"))
if math.sqrt(dot_result_local) >= tol:
sys.exit(-1)
| cuda-python-main | examples/4_CUDA_Libraries/conjugateGradientMultiBlockCG_test.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import ctypes
import math
import numpy as np
import random as rnd
import sys
from cuda import cuda, cudart
from examples.common import common
from examples.common.helper_cuda import checkCudaErrors, findCudaDevice
from examples.common.helper_string import checkCmdLineFlag
streamOrderedAllocation = '''\
/* Add two vectors on the GPU */
extern "C"
__global__ void vectorAddGPU(const float *a, const float *b, float *c, int N)
{
int idx = blockIdx.x*blockDim.x + threadIdx.x;
if (idx < N) {
c[idx] = a[idx] + b[idx];
}
}
'''
MAX_ITER = 20
def basicStreamOrderedAllocation(dev, nelem, a, b, c):
num_bytes = nelem*np.dtype(np.float32).itemsize
print("Starting basicStreamOrderedAllocation()")
checkCudaErrors(cudart.cudaSetDevice(dev))
stream = checkCudaErrors(cudart.cudaStreamCreateWithFlags(cudart.cudaStreamNonBlocking))
d_a = checkCudaErrors(cudart.cudaMallocAsync(num_bytes, stream))
d_b = checkCudaErrors(cudart.cudaMallocAsync(num_bytes, stream))
d_c = checkCudaErrors(cudart.cudaMallocAsync(num_bytes, stream))
checkCudaErrors(cudart.cudaMemcpyAsync(d_a, a, num_bytes, cudart.cudaMemcpyKind.cudaMemcpyHostToDevice, stream))
checkCudaErrors(cudart.cudaMemcpyAsync(d_b, b, num_bytes, cudart.cudaMemcpyKind.cudaMemcpyHostToDevice, stream))
block = cudart.dim3()
block.x = 256
block.y = 1
block.z = 1
grid = cudart.dim3()
grid.x = math.ceil(nelem/float(block.x))
grid.y = 1
grid.z = 1
kernelArgs = ((d_a, d_b, d_c, nelem),
(ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_int))
checkCudaErrors(cuda.cuLaunchKernel(_vectorAddGPU,
grid.x, grid.y, grid.z, # grid dim
block.x, block.y, block.z, # block dim
0, stream, # shared mem and stream
kernelArgs, 0)) # arguments
checkCudaErrors(cudart.cudaFreeAsync(d_a, stream))
checkCudaErrors(cudart.cudaFreeAsync(d_b, stream))
checkCudaErrors(cudart.cudaMemcpyAsync(c, d_c, num_bytes, cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost, stream))
checkCudaErrors(cudart.cudaFreeAsync(d_c, stream))
checkCudaErrors(cudart.cudaStreamSynchronize(stream))
# Compare the results
print("> Checking the results from vectorAddGPU() ...");
errorNorm = 0.0
refNorm = 0.0
for n in range(nelem):
ref = a[n] + b[n]
diff = c[n] - ref
errorNorm += diff*diff
refNorm += ref*ref
errorNorm = math.sqrt(errorNorm)
refNorm = math.sqrt(refNorm)
if errorNorm/refNorm < 1.e-6:
print("basicStreamOrderedAllocation PASSED")
checkCudaErrors(cudart.cudaStreamDestroy(stream))
return errorNorm/refNorm < 1.e-6
# streamOrderedAllocationPostSync(): demonstrates If the application wants the memory to persist in the pool beyond
# synchronization, then it sets the release threshold on the pool. This way, when the application reaches the "steady state",
# it is no longer allocating/freeing memory from the OS.
def streamOrderedAllocationPostSync(dev, nelem, a, b, c) :
num_bytes = nelem*np.dtype(np.float32).itemsize
print("Starting streamOrderedAllocationPostSync()")
checkCudaErrors(cudart.cudaSetDevice(dev))
stream = checkCudaErrors(cudart.cudaStreamCreateWithFlags(cudart.cudaStreamNonBlocking))
start = checkCudaErrors(cudart.cudaEventCreate())
end = checkCudaErrors(cudart.cudaEventCreate())
memPool = checkCudaErrors(cudart.cudaDeviceGetDefaultMemPool(dev))
thresholdVal = cuda.cuuint64_t(ctypes.c_uint64(-1).value)
# Set high release threshold on the default pool so that cudaFreeAsync will not actually release memory to the system.
# By default, the release threshold for a memory pool is set to zero. This implies that the CUDA driver is
# allowed to release a memory chunk back to the system as long as it does not contain any active suballocations.
checkCudaErrors(cudart.cudaMemPoolSetAttribute(memPool, cudart.cudaMemPoolAttr.cudaMemPoolAttrReleaseThreshold, thresholdVal));
# Record teh start event
checkCudaErrors(cudart.cudaEventRecord(start, stream))
for i in range(MAX_ITER):
d_a = checkCudaErrors(cudart.cudaMallocAsync(num_bytes, stream))
d_b = checkCudaErrors(cudart.cudaMallocAsync(num_bytes, stream))
d_c = checkCudaErrors(cudart.cudaMallocAsync(num_bytes, stream))
checkCudaErrors(cudart.cudaMemcpyAsync(d_a, a, num_bytes, cudart.cudaMemcpyKind.cudaMemcpyHostToDevice, stream))
checkCudaErrors(cudart.cudaMemcpyAsync(d_b, b, num_bytes, cudart.cudaMemcpyKind.cudaMemcpyHostToDevice, stream))
block = cudart.dim3()
block.x = 256
block.y = 1
block.z = 1
grid = cudart.dim3()
grid.x = math.ceil(nelem/float(block.x))
grid.y = 1
grid.z = 1
kernelArgs = ((d_a, d_b, d_c, nelem),
(ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_int))
checkCudaErrors(cuda.cuLaunchKernel(_vectorAddGPU,
grid.x, grid.y, grid.z, # grid dim
block.x, block.y, block.z, # block dim
0, stream, # shared mem and stream
kernelArgs, 0)) # arguments
checkCudaErrors(cudart.cudaFreeAsync(d_a, stream))
checkCudaErrors(cudart.cudaFreeAsync(d_b, stream))
checkCudaErrors(cudart.cudaMemcpyAsync(c, d_c, num_bytes, cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost, stream))
checkCudaErrors(cudart.cudaFreeAsync(d_c, stream))
checkCudaErrors(cudart.cudaStreamSynchronize(stream))
checkCudaErrors(cudart.cudaEventRecord(end, stream))
# Wait for the end event to complete
checkCudaErrors(cudart.cudaEventSynchronize(end))
msecTotal = checkCudaErrors(cudart.cudaEventElapsedTime(start, end))
print("Total elapsed time = {} ms over {} iterations".format(msecTotal, MAX_ITER))
# Compare the results
print("> Checking the results from vectorAddGPU() ...")
errorNorm = 0.0
refNorm = 0.0
for n in range(nelem):
ref = a[n] + b[n]
diff = c[n] - ref
errorNorm += diff*diff
refNorm += ref*ref
errorNorm = math.sqrt(errorNorm)
refNorm = math.sqrt(refNorm)
if errorNorm/refNorm < 1.e-6:
print("streamOrderedAllocationPostSync PASSED")
checkCudaErrors(cudart.cudaStreamDestroy(stream))
return errorNorm/refNorm < 1.e-6
def main():
if checkCmdLineFlag("help"):
print("Usage: streamOrderedAllocation [OPTION]\n");
print("Options:");
print(" device=[device #] Specify the device to be used");
return
dev = findCudaDevice()
version = checkCudaErrors(cudart.cudaDriverGetVersion())
if version < 11030:
isMemPoolSupported = False
else:
isMemPoolSupported = checkCudaErrors(cudart.cudaDeviceGetAttribute(cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_MEMORY_POOLS_SUPPORTED, dev))
if not isMemPoolSupported:
print("Waiving execution as device does not support Memory Pools")
return
global _vectorAddGPU
kernelHelper = common.KernelHelper(streamOrderedAllocation, dev)
_vectorAddGPU = kernelHelper.getFunction(b'vectorAddGPU')
# Allocate CPU memory
nelem = 1048576
num_bytes = nelem*np.dtype(np.float32).itemsize
a = np.zeros(nelem, dtype='float32')
b = np.zeros(nelem, dtype='float32')
c = np.zeros(nelem, dtype='float32')
# Initialize the vectors
for i in range(nelem):
a[i] = rnd.random()
b[i] = rnd.random()
ret1 = basicStreamOrderedAllocation(dev, nelem, a, b, c)
ret2 = streamOrderedAllocationPostSync(dev, nelem, a, b, c)
if not ret1 or not ret2:
sys.exit(-1)
if __name__=="__main__":
main()
| cuda-python-main | examples/2_Concepts_and_Techniques/streamOrderedAllocation_test.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import ctypes
import numpy as np
import random as rnd
from cuda import cuda, cudart
from examples.common import common
from examples.common.helper_cuda import checkCudaErrors, findCudaDevice
THREADS_PER_BLOCK = 512
GRAPH_LAUNCH_ITERATIONS = 3
simpleCudaGraphs = '''\
#include <cooperative_groups.h>
#include <cuda_runtime.h>
namespace cg = cooperative_groups;
#define THREADS_PER_BLOCK 512
#define GRAPH_LAUNCH_ITERATIONS 3
extern "C"
__global__ void reduce(float *inputVec, double *outputVec, size_t inputSize,
size_t outputSize) {
__shared__ double tmp[THREADS_PER_BLOCK];
cg::thread_block cta = cg::this_thread_block();
size_t globaltid = blockIdx.x * blockDim.x + threadIdx.x;
double temp_sum = 0.0;
for (int i = globaltid; i < inputSize; i += gridDim.x * blockDim.x) {
temp_sum += (double)inputVec[i];
}
tmp[cta.thread_rank()] = temp_sum;
cg::sync(cta);
cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);
double beta = temp_sum;
double temp;
for (int i = tile32.size() / 2; i > 0; i >>= 1) {
if (tile32.thread_rank() < i) {
temp = tmp[cta.thread_rank() + i];
beta += temp;
tmp[cta.thread_rank()] = beta;
}
cg::sync(tile32);
}
cg::sync(cta);
if (cta.thread_rank() == 0 && blockIdx.x < outputSize) {
beta = 0.0;
for (int i = 0; i < cta.size(); i += tile32.size()) {
beta += tmp[i];
}
outputVec[blockIdx.x] = beta;
}
}
extern "C"
__global__ void reduceFinal(double *inputVec, double *result,
size_t inputSize) {
__shared__ double tmp[THREADS_PER_BLOCK];
cg::thread_block cta = cg::this_thread_block();
size_t globaltid = blockIdx.x * blockDim.x + threadIdx.x;
double temp_sum = 0.0;
for (int i = globaltid; i < inputSize; i += gridDim.x * blockDim.x) {
temp_sum += (double)inputVec[i];
}
tmp[cta.thread_rank()] = temp_sum;
cg::sync(cta);
cg::thread_block_tile<32> tile32 = cg::tiled_partition<32>(cta);
// do reduction in shared mem
if ((blockDim.x >= 512) && (cta.thread_rank() < 256)) {
tmp[cta.thread_rank()] = temp_sum = temp_sum + tmp[cta.thread_rank() + 256];
}
cg::sync(cta);
if ((blockDim.x >= 256) && (cta.thread_rank() < 128)) {
tmp[cta.thread_rank()] = temp_sum = temp_sum + tmp[cta.thread_rank() + 128];
}
cg::sync(cta);
if ((blockDim.x >= 128) && (cta.thread_rank() < 64)) {
tmp[cta.thread_rank()] = temp_sum = temp_sum + tmp[cta.thread_rank() + 64];
}
cg::sync(cta);
if (cta.thread_rank() < 32) {
// Fetch final intermediate sum from 2nd warp
if (blockDim.x >= 64) temp_sum += tmp[cta.thread_rank() + 32];
// Reduce final warp using shuffle
for (int offset = tile32.size() / 2; offset > 0; offset /= 2) {
temp_sum += tile32.shfl_down(temp_sum, offset);
}
}
// write result for this block to global mem
if (cta.thread_rank() == 0) result[0] = temp_sum;
}
'''
def init_input(a, size):
ctypes.c_float.from_address(a)
a_list = ctypes.pointer(ctypes.c_float.from_address(a))
for i in range(0, size):
a_list[i] = rnd.random()
def cudaGraphsManual(inputVec_h, inputVec_d, outputVec_d, result_d, inputSize, numOfBlocks):
result_h = ctypes.c_double(0.0)
nodeDependencies = []
streamForGraph = checkCudaErrors(cudart.cudaStreamCreate())
kernelNodeParams = cuda.CUDA_KERNEL_NODE_PARAMS()
memcpyParams = cudart.cudaMemcpy3DParms()
memsetParams = cudart.cudaMemsetParams()
memcpyParams.srcArray = None
memcpyParams.srcPos = cudart.make_cudaPos(0, 0, 0)
memcpyParams.srcPtr = cudart.make_cudaPitchedPtr(inputVec_h, np.dtype(np.float32).itemsize * inputSize, inputSize, 1)
memcpyParams.dstArray = None
memcpyParams.dstPos = cudart.make_cudaPos(0, 0, 0)
memcpyParams.dstPtr = cudart.make_cudaPitchedPtr(inputVec_d, np.dtype(np.float32).itemsize * inputSize, inputSize, 1)
memcpyParams.extent = cudart.make_cudaExtent(np.dtype(np.float32).itemsize * inputSize, 1, 1)
memcpyParams.kind = cudart.cudaMemcpyKind.cudaMemcpyHostToDevice
memsetParams.dst = outputVec_d
memsetParams.value = 0
memsetParams.pitch = 0
memsetParams.elementSize = np.dtype(np.float32).itemsize # elementSize can be max 4 bytes
memsetParams.width = numOfBlocks * 2
memsetParams.height = 1
graph = checkCudaErrors(cudart.cudaGraphCreate(0))
memcpyNode = checkCudaErrors(cudart.cudaGraphAddMemcpyNode(graph, None, 0, memcpyParams))
memsetNode = checkCudaErrors(cudart.cudaGraphAddMemsetNode(graph, None, 0, memsetParams))
nodeDependencies.append(memsetNode)
nodeDependencies.append(memcpyNode)
kernelArgs = ((inputVec_d, outputVec_d, inputSize, numOfBlocks),
(ctypes.c_void_p, ctypes.c_void_p, ctypes.c_size_t, ctypes.c_uint))
kernelNodeParams.func = _reduce
kernelNodeParams.gridDimX = numOfBlocks
kernelNodeParams.gridDimY = kernelNodeParams.gridDimZ = 1
kernelNodeParams.blockDimX = THREADS_PER_BLOCK
kernelNodeParams.blockDimY = kernelNodeParams.blockDimZ = 1
kernelNodeParams.sharedMemBytes = 0
kernelNodeParams.kernelParams = kernelArgs
# kernelNodeParams.extra = None
kernelNode = checkCudaErrors(cuda.cuGraphAddKernelNode(graph, nodeDependencies, len(nodeDependencies), kernelNodeParams))
nodeDependencies.clear()
nodeDependencies.append(kernelNode)
memsetParams = cudart.cudaMemsetParams()
memsetParams.dst = result_d
memsetParams.value = 0
memsetParams.elementSize = np.dtype(np.float32).itemsize
memsetParams.width = 2
memsetParams.height = 1
memsetNode = checkCudaErrors(cudart.cudaGraphAddMemsetNode(graph, None, 0, memsetParams))
nodeDependencies.append(memsetNode)
kernelNodeParams = cuda.CUDA_KERNEL_NODE_PARAMS()
kernelNodeParams.func = _reduceFinal
kernelNodeParams.gridDimX = kernelNodeParams.gridDimY = kernelNodeParams.gridDimZ = 1
kernelNodeParams.blockDimX = THREADS_PER_BLOCK
kernelNodeParams.blockDimY = kernelNodeParams.blockDimZ = 1
kernelNodeParams.sharedMemBytes = 0
kernelArgs2 = ((outputVec_d, result_d, numOfBlocks),
(ctypes.c_void_p, ctypes.c_void_p, ctypes.c_uint))
kernelNodeParams.kernelParams = kernelArgs2
# kernelNodeParams.extra = None
kernelNode = checkCudaErrors(cuda.cuGraphAddKernelNode(graph, nodeDependencies, len(nodeDependencies), kernelNodeParams))
nodeDependencies.clear()
nodeDependencies.append(kernelNode)
memcpyParams = cudart.cudaMemcpy3DParms()
memcpyParams.srcArray = None
memcpyParams.srcPos = cudart.make_cudaPos(0, 0, 0)
memcpyParams.srcPtr = cudart.make_cudaPitchedPtr(result_d, np.dtype(np.float64).itemsize, 1, 1)
memcpyParams.dstArray = None
memcpyParams.dstPos = cudart.make_cudaPos(0, 0, 0)
memcpyParams.dstPtr = cudart.make_cudaPitchedPtr(result_h, np.dtype(np.float64).itemsize, 1, 1)
memcpyParams.extent = cudart.make_cudaExtent(np.dtype(np.float64).itemsize, 1, 1)
memcpyParams.kind = cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost
memcpyNode = checkCudaErrors(cudart.cudaGraphAddMemcpyNode(graph, nodeDependencies, len(nodeDependencies), memcpyParams))
nodeDependencies.clear()
nodeDependencies.append(memcpyNode)
# WIP: Host nodes
nodes, numNodes = checkCudaErrors(cudart.cudaGraphGetNodes(graph))
print("\nNum of nodes in the graph created manually = {}".format(numNodes))
graphExec = checkCudaErrors(cudart.cudaGraphInstantiate(graph, 0))
clonedGraph = checkCudaErrors(cudart.cudaGraphClone(graph))
clonedGraphExec = checkCudaErrors(cudart.cudaGraphInstantiate(clonedGraph, 0))
for i in range(GRAPH_LAUNCH_ITERATIONS):
checkCudaErrors(cudart.cudaGraphLaunch(graphExec, streamForGraph))
checkCudaErrors(cudart.cudaStreamSynchronize(streamForGraph))
print("Cloned Graph Output..")
for i in range(GRAPH_LAUNCH_ITERATIONS):
checkCudaErrors(cudart.cudaGraphLaunch(clonedGraphExec, streamForGraph))
checkCudaErrors(cudart.cudaStreamSynchronize(streamForGraph))
checkCudaErrors(cudart.cudaGraphExecDestroy(graphExec))
checkCudaErrors(cudart.cudaGraphExecDestroy(clonedGraphExec))
checkCudaErrors(cudart.cudaGraphDestroy(graph))
checkCudaErrors(cudart.cudaGraphDestroy(clonedGraph))
checkCudaErrors(cudart.cudaStreamDestroy(streamForGraph))
def cudaGraphsUsingStreamCapture(inputVec_h, inputVec_d, outputVec_d, result_d, inputSize, numOfBlocks):
result_h = ctypes.c_double(0.0)
stream1 = checkCudaErrors(cudart.cudaStreamCreate())
stream2 = checkCudaErrors(cudart.cudaStreamCreate())
stream3 = checkCudaErrors(cudart.cudaStreamCreate())
streamForGraph = checkCudaErrors(cudart.cudaStreamCreate())
forkStreamEvent = checkCudaErrors(cudart.cudaEventCreate())
memsetEvent1 = checkCudaErrors(cudart.cudaEventCreate())
memsetEvent2 = checkCudaErrors(cudart.cudaEventCreate())
checkCudaErrors(cudart.cudaStreamBeginCapture(stream1, cudart.cudaStreamCaptureMode.cudaStreamCaptureModeGlobal))
checkCudaErrors(cudart.cudaEventRecord(forkStreamEvent, stream1))
checkCudaErrors(cudart.cudaStreamWaitEvent(stream2, forkStreamEvent, 0))
checkCudaErrors(cudart.cudaStreamWaitEvent(stream3, forkStreamEvent, 0))
checkCudaErrors(cudart.cudaMemcpyAsync(inputVec_d, inputVec_h,
np.dtype(np.float32).itemsize * inputSize, cudart.cudaMemcpyKind.cudaMemcpyDefault,
stream1))
checkCudaErrors(cudart.cudaMemsetAsync(outputVec_d, 0, np.dtype(np.float64).itemsize * numOfBlocks, stream2))
checkCudaErrors(cudart.cudaEventRecord(memsetEvent1, stream2))
checkCudaErrors(cudart.cudaMemsetAsync(result_d, 0, np.dtype(np.float64).itemsize, stream3))
checkCudaErrors(cudart.cudaEventRecord(memsetEvent2, stream3))
checkCudaErrors(cudart.cudaStreamWaitEvent(stream1, memsetEvent1, 0))
kernelArgs = ((inputVec_d, outputVec_d, inputSize, numOfBlocks),
(ctypes.c_void_p, ctypes.c_void_p, ctypes.c_size_t, ctypes.c_uint))
checkCudaErrors(cuda.cuLaunchKernel(_reduce,
numOfBlocks, 1, 1,
THREADS_PER_BLOCK, 1, 1,
0, stream1,
kernelArgs, 0))
checkCudaErrors(cudart.cudaStreamWaitEvent(stream1, memsetEvent2, 0))
kernelArgs2 = ((outputVec_d, result_d, numOfBlocks),
(ctypes.c_void_p, ctypes.c_void_p, ctypes.c_uint))
checkCudaErrors(cuda.cuLaunchKernel(_reduceFinal,
1, 1, 1,
THREADS_PER_BLOCK, 1, 1,
0, stream1,
kernelArgs2, 0))
checkCudaErrors(cudart.cudaMemcpyAsync(result_h, result_d, np.dtype(np.float64).itemsize,
cudart.cudaMemcpyKind.cudaMemcpyDefault, stream1))
# WIP: Host nodes
graph = checkCudaErrors(cudart.cudaStreamEndCapture(stream1))
nodes, numNodes = checkCudaErrors(cudart.cudaGraphGetNodes(graph))
print("\nNum of nodes in the graph created using stream capture API = {}".format(numNodes))
graphExec = checkCudaErrors(cudart.cudaGraphInstantiate(graph, 0))
clonedGraph = checkCudaErrors(cudart.cudaGraphClone(graph))
clonedGraphExec = checkCudaErrors(cudart.cudaGraphInstantiate(clonedGraph, 0))
for i in range(GRAPH_LAUNCH_ITERATIONS):
checkCudaErrors(cudart.cudaGraphLaunch(graphExec, streamForGraph))
checkCudaErrors(cudart.cudaStreamSynchronize(streamForGraph))
print("Cloned Graph Output..")
for i in range(GRAPH_LAUNCH_ITERATIONS):
checkCudaErrors(cudart.cudaGraphLaunch(clonedGraphExec, streamForGraph))
checkCudaErrors(cudart.cudaStreamSynchronize(streamForGraph))
checkCudaErrors(cudart.cudaGraphExecDestroy(graphExec))
checkCudaErrors(cudart.cudaGraphExecDestroy(clonedGraphExec))
checkCudaErrors(cudart.cudaGraphDestroy(graph))
checkCudaErrors(cudart.cudaGraphDestroy(clonedGraph))
checkCudaErrors(cudart.cudaStreamDestroy(stream1))
checkCudaErrors(cudart.cudaStreamDestroy(stream2))
checkCudaErrors(cudart.cudaStreamDestroy(streamForGraph))
def main():
size = 1 << 24 # number of elements to reduce
maxBlocks = 512
# This will pick the best possible CUDA capable device
devID = findCudaDevice()
global _reduce
global _reduceFinal
kernelHelper = common.KernelHelper(simpleCudaGraphs, devID)
_reduce = kernelHelper.getFunction(b'reduce')
_reduceFinal = kernelHelper.getFunction(b'reduceFinal')
print("{} elements".format(size))
print("threads per block = {}".format(THREADS_PER_BLOCK))
print("Graph Launch iterations = {}".format(GRAPH_LAUNCH_ITERATIONS))
inputVec_h = checkCudaErrors(cudart.cudaMallocHost(size * np.dtype(np.float32).itemsize))
inputVec_d = checkCudaErrors(cudart.cudaMalloc(size * np.dtype(np.float32).itemsize))
outputVec_d = checkCudaErrors(cudart.cudaMalloc(maxBlocks * np.dtype(np.float64).itemsize))
result_d = checkCudaErrors(cudart.cudaMalloc(np.dtype(np.float64).itemsize))
init_input(inputVec_h, size)
cudaGraphsManual(inputVec_h, inputVec_d, outputVec_d, result_d, size, maxBlocks)
cudaGraphsUsingStreamCapture(inputVec_h, inputVec_d, outputVec_d, result_d, size, maxBlocks)
checkCudaErrors(cudart.cudaFree(inputVec_d))
checkCudaErrors(cudart.cudaFree(outputVec_d))
checkCudaErrors(cudart.cudaFree(result_d))
checkCudaErrors(cudart.cudaFreeHost(inputVec_h))
if __name__ == "__main__":
main()
| cuda-python-main | examples/3_CUDA_Features/simpleCudaGraphs_test.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import ctypes
import math
import numpy as np
import sys
import pytest
from cuda import cuda, cudart
from enum import Enum
from examples.common import common
from examples.common.helper_cuda import checkCudaErrors, findCudaDevice
from examples.common.helper_string import checkCmdLineFlag, getCmdLineArgumentInt
blockSize = 16
class kernels(Enum):
AsyncCopyMultiStageLargeChunk = 0
AsyncCopyLargeChunk = 1
AsyncCopyLargeChunkAWBarrier = 2
AsyncCopyMultiStageSharedState = 3
AsyncCopyMultiStage = 4
AsyncCopySingleStage = 5
Naive = 6
NaiveLargeChunk = 7
kernelNames = ["AsyncCopyMultiStageLargeChunk", "AsyncCopyLargeChunk",
"AsyncCopyLargeChunkAWBarrier", "AsyncCopyMultiStageSharedState",
"AsyncCopyMultiStage", "AsyncCopySingleStage", "Naive", "NaiveLargeChunk"]
globalToShmemAsyncCopy = '''\
#line __LINE__
#if __CUDA_ARCH__ >= 700
#include <cuda/barrier>
#endif
#include <cooperative_groups.h>
#include <cooperative_groups/reduce.h>
#include <cuda/pipeline>
namespace cg = cooperative_groups;
#define BLOCK_SIZE 16
#define BLOCK_SIZE_X 16
// Multi Stage memcpy_async pipeline with large chunk copy
extern "C"
__global__ void MatrixMulAsyncCopyMultiStageLargeChunk(float* __restrict__ C,
const float* __restrict__ A,
const float* __restrict__ B, int wA,
int wB) {
// Requires BLOCK_SIZE % 4 == 0
// Multi-stage pipeline version
constexpr size_t maxPipelineStages = 4;
// Declaration of the shared memory array As used to
// store the sub-matrix of A for each stage
__shared__ alignas(alignof(float4)) float As[maxPipelineStages][BLOCK_SIZE][BLOCK_SIZE];
// Declaration of the shared memory array Bs used to
// store the sub-matrix of B for each stage
__shared__ alignas(alignof(float4)) float Bs[maxPipelineStages][BLOCK_SIZE][BLOCK_SIZE];
float Csub = 0.0;
// Index of the first sub-matrix of A processed by the block
const int aBegin = wA * (BLOCK_SIZE) * blockIdx.y;
// Index of the last sub-matrix of A processed by the block
const int aEnd = aBegin + wA - 1;
// Step size used to iterate through the sub-matrices of A
int aStep = BLOCK_SIZE;
// Index of the first sub-matrix of B processed by the block
const int bBegin = BLOCK_SIZE * blockIdx.x;
// Step size used to iterate through the sub-matrices of B
int bStep = BLOCK_SIZE * wB;
const int t4x = threadIdx.x * 4;
const auto shape4 = cuda::aligned_size_t<alignof(float4)>(sizeof(float4));
cuda::pipeline<cuda::thread_scope_thread> pipe = cuda::make_pipeline();
// Loop over all the sub-matrices of A and B
// required to compute the block sub-matrix
for (int a = aBegin, b = bBegin, i = 0, aStage = aBegin, bStage = bBegin, iStage = 0; a <= aEnd; a += aStep, b += bStep, ++i ) {
// Load the matrices from device memory to shared memory; each thread loads
// one element of each matrix
for ( ; aStage <= a + aStep * maxPipelineStages ; aStage += aStep, bStage += bStep, ++iStage )
{
pipe.producer_acquire();
if ( aStage <= aEnd && t4x < BLOCK_SIZE )
{
// Rotating buffer
const int j = iStage % maxPipelineStages;
cuda::memcpy_async(&As[j][threadIdx.y][t4x], &A[aStage + wA * threadIdx.y + t4x], shape4, pipe);
cuda::memcpy_async(&Bs[j][threadIdx.y][t4x], &B[aStage + wA * threadIdx.y + t4x], shape4, pipe);
}
pipe.producer_commit();
}
pipe.consumer_wait();
// Synchronize to make sure the matrices are loaded
__syncthreads();
// Rotating buffer
const int j = i % maxPipelineStages;
// Multiply the two matrices together;
// each thread computes one element
// of the block sub-matrix
#pragma unroll
for (int k = 0; k < BLOCK_SIZE; ++k) {
Csub += As[j][threadIdx.y][k] * Bs[j][k][threadIdx.x];
}
pipe.consumer_release();
// Don't have to synchronize because maxPipelineStages is greater than one
// therefore next iteration is loading to a different buffer.
}
// Write the block sub-matrix to device memory;
// each thread writes four element
int c = wB * BLOCK_SIZE * blockIdx.y + BLOCK_SIZE * blockIdx.x;
C[c + wB * threadIdx.y + threadIdx.x] = Csub;
}
// Single Stage memcpy_async pipeline with Large copy chunk (float4)
extern "C"
__global__ void MatrixMulAsyncCopyLargeChunk(float* __restrict__ C,
const float* __restrict__ A,
const float* __restrict__ B, int wA,
int wB) {
// Requires BLOCK_SIZE % 4 == 0
// Declaration of the shared memory array As used to
// store the sub-matrix of A
__shared__ alignas(alignof(float4)) float As[BLOCK_SIZE][BLOCK_SIZE];
// Declaration of the shared memory array Bs used to
// store the sub-matrix of B
__shared__ alignas(alignof(float4)) float Bs[BLOCK_SIZE][BLOCK_SIZE];
// Index of the first sub-matrix of A processed by the block
int aBegin = wA * BLOCK_SIZE * blockIdx.y;
// Index of the last sub-matrix of A processed by the block
int aEnd = aBegin + wA - 1;
// Step size used to iterate through the sub-matrices of A
int aStep = BLOCK_SIZE;
// Index of the first sub-matrix of B processed by the block
int bBegin = BLOCK_SIZE * blockIdx.x;
// Step size used to iterate through the sub-matrices of B
int bStep = BLOCK_SIZE * wB;
// Single-stage pipeline version
float Csub = 0.0;
const int t4x = threadIdx.x * 4;
const auto shape4 = cuda::aligned_size_t<alignof(float4)>(sizeof(float4));
cuda::pipeline<cuda::thread_scope_thread> pipe = cuda::make_pipeline();
// Loop over all the sub-matrices of A and B
// required to compute the block sub-matrix
for (int a = aBegin, b = bBegin; a <= aEnd; a += aStep, b += bStep) {
// Load the matrices from device memory to shared memory;
// a subset of threads loads a contiguous chunk of elements.
// Previously, per-thread:
// As[ty][tx] = A[a + wA * ty + tx];
// Bs[ty][tx] = B[b + wB * ty + tx];
// Now, one fourth of the threads load four elements of each matrix
if ( t4x < BLOCK_SIZE ) {
pipe.producer_acquire();
cuda::memcpy_async(&As[threadIdx.y][t4x], &A[a + wA * threadIdx.y + t4x], shape4, pipe);
cuda::memcpy_async(&Bs[threadIdx.y][t4x], &B[a + wA * threadIdx.y + t4x], shape4, pipe);
pipe.producer_commit();
pipe.consumer_wait();
}
// Synchronize to make sure the matrices are loaded
__syncthreads();
// Multiply the two matrices together;
// each thread computes one element
// of the block sub-matrix
#pragma unroll
for (int k = 0; k < BLOCK_SIZE; ++k) {
Csub += As[threadIdx.y][k] * Bs[k][threadIdx.x];
}
pipe.consumer_release();
// Synchronize to make sure that the preceding
// computation is done before overwriting the
// shared memory sub-matrix buffers As and Bs in the next iteration.
__syncthreads();
}
// Write the block sub-matrix to device memory;
// each thread writes four element
int c = wB * BLOCK_SIZE * blockIdx.y + BLOCK_SIZE * blockIdx.x;
C[c + wB * threadIdx.y + threadIdx.x] = Csub;
}
// Single Stage memcpy_async pipeline with Large copy chunk (float4) using arrive-wait barrier
extern "C"
__global__ void MatrixMulAsyncCopyLargeChunkAWBarrier(float* __restrict__ C,
const float* __restrict__ A,
const float* __restrict__ B, int wA,
int wB) {
#if __CUDA_ARCH__ >= 700
#pragma diag_suppress static_var_with_dynamic_init
// Requires BLOCK_SIZE % 4 == 0
__shared__ cuda::barrier<cuda::thread_scope_block> bar;
// Declaration of the shared memory array As used to
// store the sub-matrix of A
__shared__ alignas(alignof(float4)) float As[BLOCK_SIZE][BLOCK_SIZE];
// Declaration of the shared memory array Bs used to
// store the sub-matrix of B
__shared__ alignas(alignof(float4)) float Bs[BLOCK_SIZE][BLOCK_SIZE];
if (threadIdx.x == 0) {
init(&bar, blockDim.x*blockDim.y);
}
__syncthreads();
// Index of the first sub-matrix of A processed by the block
int aBegin = wA * BLOCK_SIZE * blockIdx.y;
// Index of the last sub-matrix of A processed by the block
int aEnd = aBegin + wA - 1;
// Step size used to iterate through the sub-matrices of A
int aStep = BLOCK_SIZE;
// Index of the first sub-matrix of B processed by the block
int bBegin = BLOCK_SIZE * blockIdx.x;
// Step size used to iterate through the sub-matrices of B
int bStep = BLOCK_SIZE * wB;
float Csub = 0.0;
const int t4x = threadIdx.x * 4;
// Loop over all the sub-matrices of A and B
// required to compute the block sub-matrix
for (int a = aBegin, b = bBegin; a <= aEnd; a += aStep, b += bStep) {
// Load the matrices from device memory to shared memory;
// a subset of threads loads a contiguous chunk of elements.
// Now, one fourth of the threads load four elements of each matrix
if ( t4x < BLOCK_SIZE ) {
float4 * const A4s = reinterpret_cast<float4*>(& As[threadIdx.y][t4x]);
float4 * const B4s = reinterpret_cast<float4*>(& Bs[threadIdx.y][t4x]);
const float4 * const A4 = reinterpret_cast<const float4*>(& A[a + wA * threadIdx.y + t4x]);
const float4 * const B4 = reinterpret_cast<const float4*>(& B[a + wA * threadIdx.y + t4x]);
cuda::memcpy_async(A4s, A4, sizeof(float4), bar);
cuda::memcpy_async(B4s, B4, sizeof(float4), bar);
}
// Synchronize to make sure the matrices are loaded
bar.arrive_and_wait();
// Multiply the two matrices together;
// each thread computes one element
// of the block sub-matrix
#pragma unroll
for (int k = 0; k < BLOCK_SIZE; ++k) {
Csub += As[threadIdx.y][k] * Bs[k][threadIdx.x];
}
// Synchronize to make sure that the preceding
// computation is done before overwriting the
// shared memory sub-matrix buffers As and Bs in the next iteration.
bar.arrive_and_wait();
}
// Write the block sub-matrix to device memory;
// each thread writes four element
int c = wB * BLOCK_SIZE * blockIdx.y + BLOCK_SIZE * blockIdx.x;
C[c + wB * threadIdx.y + threadIdx.x] = Csub;
#endif
}
// Single Stage memcpy_async pipeline with float copy
extern "C"
__global__ void MatrixMulAsyncCopySingleStage(float *C, const float *A,
const float *B, int wA,
int wB) {
// Declaration of the shared memory array As used to
// store the sub-matrix of A
__shared__ float As[BLOCK_SIZE][BLOCK_SIZE];
// Declaration of the shared memory array Bs used to
// store the sub-matrix of B
__shared__ float Bs[BLOCK_SIZE][BLOCK_SIZE];
// Index of the first sub-matrix of A processed by the block
int aBegin = wA * BLOCK_SIZE * blockIdx.y;
// Index of the last sub-matrix of A processed by the block
int aEnd = aBegin + wA - 1;
// Step size used to iterate through the sub-matrices of A
int aStep = BLOCK_SIZE;
// Index of the first sub-matrix of B processed by the block
int bBegin = BLOCK_SIZE * blockIdx.x;
// Step size used to iterate through the sub-matrices of B
int bStep = BLOCK_SIZE * wB;
// Single-stage pipeline version
float Csub = 0.0;
cuda::pipeline<cuda::thread_scope_thread> pipe = cuda::make_pipeline();
const auto shape1 = cuda::aligned_size_t<alignof(float)>(sizeof(float));
// Loop over all the sub-matrices of A and B
// required to compute the block sub-matrix
for (int a = aBegin, b = bBegin; a <= aEnd; a += aStep, b += bStep) {
// Load the matrices from device memory to shared memory; each thread loads
// one element of each matrix
{
pipe.producer_acquire();
cuda::memcpy_async(&As[threadIdx.y][threadIdx.x], &A[a + wA * threadIdx.y + threadIdx.x], shape1, pipe);
cuda::memcpy_async(&Bs[threadIdx.y][threadIdx.x], &B[b + wB * threadIdx.y + threadIdx.x], shape1, pipe);
pipe.producer_commit();
}
pipe.consumer_wait();
// Synchronize to make sure the matrices are loaded
__syncthreads();
// Multiply the two matrices together;
// each thread computes one element
// of the block sub-matrix
#pragma unroll
for (int k = 0; k < BLOCK_SIZE; ++k) {
Csub += As[threadIdx.y][k] * Bs[k][threadIdx.x];
}
// Synchronize to make sure that the preceding
// computation is done before overwriting the
// shared memory sub-matrix buffers As and Bs in the next iteration.
__syncthreads();
}
// Write the block sub-matrix to device memory;
// each thread writes four element
int c = wB * BLOCK_SIZE * blockIdx.y + BLOCK_SIZE * blockIdx.x;
C[c + wB * threadIdx.y + threadIdx.x] = Csub;
}
// Multi Stage memcpy_async thread_scope_thread pipeline with single-element async-copy
extern "C"
__global__ void MatrixMulAsyncCopyMultiStage(float* __restrict__ C,
const float* __restrict__ A,
const float* __restrict__ B, int wA,
int wB) {
// Multi-stage pipeline version
constexpr size_t maxPipelineStages = 4;
// Declaration of the shared memory array As used to
// store the sub-matrix of A for each stage
__shared__ float As[maxPipelineStages][BLOCK_SIZE][BLOCK_SIZE];
// Declaration of the shared memory array Bs used to
// store the sub-matrix of B for each stage
__shared__ float Bs[maxPipelineStages][BLOCK_SIZE][BLOCK_SIZE];
float Csub = 0.0;
// Index of the first sub-matrix of A processed by the block
const int aBegin = wA * BLOCK_SIZE * blockIdx.y;
// Index of the last sub-matrix of A processed by the block
const int aEnd = aBegin + wA - 1;
// Step size used to iterate through the sub-matrices of A
int aStep = BLOCK_SIZE;
// Index of the first sub-matrix of B processed by the block
const int bBegin = BLOCK_SIZE * blockIdx.x;
// Step size used to iterate through the sub-matrices of B
int bStep = BLOCK_SIZE * wB;
cuda::pipeline<cuda::thread_scope_thread> pipe = cuda::make_pipeline();
const auto shape1 = cuda::aligned_size_t<alignof(float)>(sizeof(float));
// Loop over all the sub-matrices of A and B
// required to compute the block sub-matrix
for (int a = aBegin, b = bBegin, i = 0, aStage = aBegin, bStage = bBegin, iStage = 0; a <= aEnd; a += aStep, b += bStep, ++i ) {
// Load the matrices from device memory to shared memory; each thread loads
// one element of each matrix
for ( ; aStage <= a + aStep * maxPipelineStages ; aStage += aStep, bStage += bStep, ++iStage )
{
if ( aStage <= aEnd )
{
// Rotating buffer
const int j = iStage % maxPipelineStages;
pipe.producer_acquire();
cuda::memcpy_async(&As[j][threadIdx.y][threadIdx.x], &A[aStage + wA * threadIdx.y + threadIdx.x], shape1, pipe);
cuda::memcpy_async(&Bs[j][threadIdx.y][threadIdx.x], &B[bStage + wB * threadIdx.y + threadIdx.x], shape1, pipe);
pipe.producer_commit();
}
}
pipe.consumer_wait();
// Synchronize to make sure the matrices are loaded
__syncthreads();
const int j = i % maxPipelineStages;
// Multiply the two matrices together;
// each thread computes one element
// of the block sub-matrix
for (int k = 0; k < BLOCK_SIZE; ++k) {
Csub += As[j][threadIdx.y][k] * Bs[j][k][threadIdx.x];
}
pipe.consumer_release();
// Don't have to synchronize because maxPipelineStages is greater than one
// therefore next iteration is loading to a different buffer.
}
// Write the block sub-matrix to device memory;
// each thread writes four element
int c = wB * BLOCK_SIZE * blockIdx.y + BLOCK_SIZE * blockIdx.x;
C[c + wB * threadIdx.y + threadIdx.x] = Csub;
}
// Multi Stage shared state memcpy_async pipeline thread_scope_block
// with parititioned producer & consumer, here we've 1 warp as producer
// group which issues memcpy_async operations and rest all warps are part of
// consumer group which perform gemm computation on the loaded matrices by producer.
extern "C"
__global__ void MatrixMulAsyncCopyMultiStageSharedState(float* __restrict__ C,
const float* __restrict__ A,
const float* __restrict__ B, int wA,
int wB) {
// Multi-stage pipeline version
constexpr size_t maxPipelineStages = 4;
// Declaration of the shared memory array As used to
// store the sub-matrix of A for each stage
__shared__ float As[maxPipelineStages][BLOCK_SIZE_X][BLOCK_SIZE_X];
// Declaration of the shared memory array Bs used to
// store the sub-matrix of B for each stage
__shared__ float Bs[maxPipelineStages][BLOCK_SIZE_X][BLOCK_SIZE_X];
float Csub = 0.0;
// Index of the first sub-matrix of A processed by the block
const int aBegin = wA * BLOCK_SIZE_X * blockIdx.y;
// Index of the last sub-matrix of A processed by the block
const int aEnd = aBegin + wA - 1;
// Step size used to iterate through the sub-matrices of A
constexpr int aStep = BLOCK_SIZE_X;
// Index of the first sub-matrix of B processed by the block
const int bBegin = BLOCK_SIZE_X * blockIdx.x;
// Step size used to iterate through the sub-matrices of B
int bStep = BLOCK_SIZE_X * wB;
auto cta = cg::this_thread_block();
const auto shape1 = cuda::aligned_size_t<alignof(float)>(sizeof(float));
__shared__ cuda::pipeline_shared_state<cuda::thread_scope_block, maxPipelineStages> shared_state;
constexpr int consumer_row_count = BLOCK_SIZE_X;
const auto thread_role = (cta.thread_index().y < consumer_row_count)
? cuda::pipeline_role::consumer
: cuda::pipeline_role::producer;
auto pipe = cuda::make_pipeline(cta, &shared_state, thread_role);
// Loop over all the sub-matrices of A and B
// required to compute the block sub-matrix
for (int a = aBegin, b = bBegin, i = 0, aStage = aBegin, bStage = bBegin, iStage = 0;
a <= aEnd; a += aStep, b += bStep, ++i) {
if (threadIdx.y >= consumer_row_count) {
// this is a whole producer warp because threadIdx.y >= 16 where 16 == consumer_row_count,
// which loads the matrices from device memory to shared memory;
for (; aStage <= a + aStep * maxPipelineStages; aStage += aStep, bStage += bStep, ++iStage) {
if (aStage <= aEnd) {
// Rotating buffer
const int j = iStage % maxPipelineStages;
const int strideRows = (blockDim.y - consumer_row_count);
pipe.producer_acquire();
for (int rowId = threadIdx.y - consumer_row_count; rowId < BLOCK_SIZE_X; rowId += strideRows) {
cuda::memcpy_async(&As[j][rowId][threadIdx.x],
&A[aStage + wA * rowId + threadIdx.x], shape1, pipe);
cuda::memcpy_async(&Bs[j][rowId][threadIdx.x],
&B[bStage + wB * rowId + threadIdx.x], shape1, pipe);
}
pipe.producer_commit();
}
}
}
else {
// this is a whole set of consumer group because threadIdx.y < consumer_row_count where consumer_row_count == 16,
// which computes gemm operation on matrices loaded in shared memory by producer warp.
const int j = i % maxPipelineStages;
// Synchronize consumer group to make sure the matrices are loaded by producer group.
pipe.consumer_wait();
// Multiply the two matrices together;
// each thread computes one element
// of the block sub-matrix
#pragma unroll
for (int k = 0; k < BLOCK_SIZE_X; ++k) {
Csub += As[j][threadIdx.y][k] * Bs[j][k][threadIdx.x];
}
pipe.consumer_release();
}
}
// Write the block sub-matrix to device memory;
// each thread writes four element
if (threadIdx.y < consumer_row_count)
{
const int c = wB * BLOCK_SIZE_X * blockIdx.y + BLOCK_SIZE_X * blockIdx.x;
C[c + wB * threadIdx.y + threadIdx.x] = Csub;
}
}
/**
* Matrix multiplication (CUDA Kernel) on the device: C = A * B
* wA is A's width and wB is B's width
*/
extern "C"
__global__ void MatrixMulNaive(float *C, float *A,
float *B, int wA,
int wB) {
// Declaration of the shared memory array As used to
// store the sub-matrix of A
__shared__ float As[BLOCK_SIZE][BLOCK_SIZE];
// Declaration of the shared memory array Bs used to
// store the sub-matrix of B
__shared__ float Bs[BLOCK_SIZE][BLOCK_SIZE];
// Index of the first sub-matrix of A processed by the block
int aBegin = wA * BLOCK_SIZE * blockIdx.y;
// Index of the last sub-matrix of A processed by the block
int aEnd = aBegin + wA - 1;
// Step size used to iterate through the sub-matrices of A
int aStep = BLOCK_SIZE;
// Index of the first sub-matrix of B processed by the block
int bBegin = BLOCK_SIZE * blockIdx.x;
// Step size used to iterate through the sub-matrices of B
int bStep = BLOCK_SIZE * wB;
// Csub is used to store the element of the block sub-matrix
// that is computed by the thread
float Csub = 0;
// Loop over all the sub-matrices of A and B
// required to compute the block sub-matrix
for (int a = aBegin, b = bBegin;
a <= aEnd;
a += aStep, b += bStep) {
// Load the matrices from device memory
// to shared memory; each thread loads
// one element of each matrix
As[threadIdx.y][threadIdx.x] = A[a + wA * threadIdx.y + threadIdx.x];
Bs[threadIdx.y][threadIdx.x] = B[b + wB * threadIdx.y + threadIdx.x];
// Synchronize to make sure the matrices are loaded
__syncthreads();
// Multiply the two matrices together;
// each thread computes one element
// of the block sub-matrix
#pragma unroll
for (int k = 0; k < BLOCK_SIZE; ++k) {
Csub += As[threadIdx.y][k] * Bs[k][threadIdx.x];
}
// Synchronize to make sure that the preceding
// computation is done before loading two new
// sub-matrices of A and B in the next iteration
__syncthreads();
}
// Write the block sub-matrix to device memory;
// each thread writes one element
int c = wB * BLOCK_SIZE * blockIdx.y + BLOCK_SIZE * blockIdx.x;
C[c + wB * threadIdx.y + threadIdx.x] = Csub;
}
extern "C"
__global__ void MatrixMulNaiveLargeChunk(float *C, float *A,
float *B, int wA,
int wB) {
// Declaration of the shared memory array As used to
// store the sub-matrix of A
__shared__ alignas(alignof(float4)) float As[BLOCK_SIZE][BLOCK_SIZE];
// Declaration of the shared memory array Bs used to
// store the sub-matrix of B
__shared__ alignas(alignof(float4)) float Bs[BLOCK_SIZE][BLOCK_SIZE];
int t4x = threadIdx.x * 4 ;
// Index of the first sub-matrix of A processed by the block
int aBegin = wA * BLOCK_SIZE * blockIdx.y;
// Index of the last sub-matrix of A processed by the block
int aEnd = aBegin + wA - 1;
// Step size used to iterate through the sub-matrices of A
int aStep = BLOCK_SIZE;
// Index of the first sub-matrix of B processed by the block
int bBegin = BLOCK_SIZE * blockIdx.x;
// Step size used to iterate through the sub-matrices of B
int bStep = BLOCK_SIZE * wB;
// Csub is used to store the element of the block sub-matrix
// that is computed by the thread
float Csub = 0;
// Loop over all the sub-matrices of A and B
// required to compute the block sub-matrix
for (int a = aBegin, b = bBegin;
a <= aEnd;
a += aStep, b += bStep) {
// Load the matrices from device memory
// to shared memory;
// One fourth of the threads load four elements of each matrix
if ( t4x < BLOCK_SIZE ) {
float4 * const A4s = reinterpret_cast<float4*>(& As[threadIdx.y][t4x]);
float4 * const B4s = reinterpret_cast<float4*>(& Bs[threadIdx.y][t4x]);
const float4 * const A4 = reinterpret_cast<float4*>(& A[a + wA * threadIdx.y + t4x]);
const float4 * const B4 = reinterpret_cast<float4*>(& B[a + wA * threadIdx.y + t4x]);
*A4s = *A4 ;
*B4s = *B4 ;
}
// Synchronize to make sure the matrices are loaded
__syncthreads();
// Multiply the two matrices together;
// each thread computes one element
// of the block sub-matrix
#pragma unroll
for (int k = 0; k < BLOCK_SIZE; ++k) {
Csub += As[threadIdx.y][k] * Bs[k][threadIdx.x];
}
// Synchronize to make sure that the preceding
// computation is done before loading two new
// sub-matrices of A and B in the next iteration
__syncthreads();
}
// Write the block sub-matrix to device memory;
// each thread writes one element
int c = wB * BLOCK_SIZE * blockIdx.y + BLOCK_SIZE * blockIdx.x;
C[c + wB * threadIdx.y + threadIdx.x] = Csub;
}
'''
def ConstantInit(data, size, val):
p_data = (ctypes.c_float * size).from_address(data)
for i in range(size):
p_data[i] = val
#
# Run matrix multiplication using CUDA
#
def MatrixMultiply(dimsA, dimsB, kernel_number):
# Allocate host memory for matricies A and B
size_A = dimsA.x * dimsA.y
mem_size_A = np.dtype(np.float32).itemsize * size_A
h_A = checkCudaErrors(cudart.cudaMallocHost(mem_size_A))
size_B = dimsB.x * dimsB.y
mem_size_B = np.dtype(np.float32).itemsize * size_B
h_B = checkCudaErrors(cudart.cudaMallocHost(mem_size_B))
# Initialize host memory
valB = 2.10
ConstantInit(h_A, size_A, 1.0)
ConstantInit(h_B, size_B, valB)
# Allocate Device Memory
# Allocate host matrix C
dimsC = cudart.dim3()
dimsC.x = dimsB.x
dimsC.y = dimsA.y
dimsC.z = 1
mem_size_C = dimsC.x * dimsC.y * np.dtype(np.float32).itemsize
h_C = checkCudaErrors(cudart.cudaMallocHost(mem_size_C))
if h_C == 0:
print("Failed to allocate host matri C!")
exit(-1)
d_A = checkCudaErrors(cudart.cudaMalloc(mem_size_A))
d_B = checkCudaErrors(cudart.cudaMalloc(mem_size_B))
d_C = checkCudaErrors(cudart.cudaMalloc(mem_size_C))
# Allocate CUDA events that we'll use for timing
start = checkCudaErrors(cudart.cudaEventCreate())
stop = checkCudaErrors(cudart.cudaEventCreate())
stream = checkCudaErrors(cudart.cudaStreamCreateWithFlags(cudart.cudaStreamNonBlocking))
# Copy host memory to device
checkCudaErrors(cudart.cudaMemcpyAsync(d_A, h_A, mem_size_A, cudart.cudaMemcpyKind.cudaMemcpyHostToDevice, stream))
checkCudaErrors(cudart.cudaMemcpyAsync(d_B, h_B, mem_size_B, cudart.cudaMemcpyKind.cudaMemcpyHostToDevice, stream))
checkCudaErrors(cudart.cudaMemsetAsync(d_C, 0, mem_size_C, stream))
# Setup execution parameters
threads = cudart.dim3()
threads.x = threads.y = blockSize
threads.z = 1
grid = cudart.dim3()
grid.x = dimsB.x / threads.x
grid.y = dimsA.y / threads.y
grid.z = 1
# Here the block size is 16x18, where first 16 rows are consumer thread group
# and last 2 rows (1 warp) is producer thread group
threadsSharedStateKernel = cudart.dim3()
threadsSharedStateKernel.x = blockSize
threadsSharedStateKernel.y = blockSize + 2
threadsSharedStateKernel.z = 1
gridSharedStateKernel = cudart.dim3()
gridSharedStateKernel.x = dimsB.x / threadsSharedStateKernel.x
gridSharedStateKernel.y = dimsA.y / threadsSharedStateKernel.x
print("Running kernel = {} - {}".format(kernel_number, kernelNames[kernel_number.value]))
# Create and start timer
print("Computing result using CUDA Kernel...")
# Performs warmup operation using matrixMul CUDA kernel
kernelArguments = ((d_C, d_A, d_B, dimsA.x, dimsB.x),
(ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_int, ctypes.c_int))
if kernel_number == kernels.AsyncCopyMultiStageLargeChunk:
checkCudaErrors(cuda.cuLaunchKernel(_MatrixMulAsyncCopyMultiStageLargeChunk,
grid.x, grid.y, grid.z, # grid dim
threads.x, threads.y, threads.z, # block dim
0, # shared mem
stream, # stream
kernelArguments, 0)) # arguments
elif kernel_number == kernels.AsyncCopyLargeChunk:
checkCudaErrors(cuda.cuLaunchKernel(_MatrixMulAsyncCopyLargeChunk,
grid.x, grid.y, grid.z, # grid dim
threads.x, threads.y, threads.z, # block dim
0, # shared mem
stream, # stream
kernelArguments, 0)) # arguments
elif kernel_number == kernels.AsyncCopyLargeChunkAWBarrier:
checkCudaErrors(cuda.cuLaunchKernel(_MatrixMulAsyncCopyLargeChunkAWBarrier,
grid.x, grid.y, grid.z, # grid dim
threads.x, threads.y, threads.z, # block dim
0, # shared mem
stream, # stream
kernelArguments, 0)) # arguments
elif kernel_number == kernels.AsyncCopyMultiStageSharedState:
checkCudaErrors(cuda.cuLaunchKernel(_MatrixMulAsyncCopyMultiStageSharedState,
gridSharedStateKernel.x, gridSharedStateKernel.y, gridSharedStateKernel.z, # grid dim
threadsSharedStateKernel.x, threadsSharedStateKernel.y, threadsSharedStateKernel.z, # block dim
0, # shared mem
stream, # stream
kernelArguments, 0)) # arguments
elif kernel_number == kernels.AsyncCopyMultiStage:
checkCudaErrors(cuda.cuLaunchKernel(_MatrixMulAsyncCopyMultiStage,
grid.x, grid.y, grid.z, # grid dim
threads.x, threads.y, threads.z, # block dim
0, # shared mem
stream, # stream
kernelArguments, 0)) # arguments
elif kernel_number == kernels.AsyncCopySingleStage:
checkCudaErrors(cuda.cuLaunchKernel(_MatrixMulAsyncCopySingleStage,
grid.x, grid.y, grid.z, # grid dim
threads.x, threads.y, threads.z, # block dim
0, # shared mem
stream, # stream
kernelArguments, 0)) # arguments
elif kernel_number == kernels.Naive:
checkCudaErrors(cuda.cuLaunchKernel(_MatrixMulNaive,
grid.x, grid.y, grid.z, # grid dim
threads.x, threads.y, threads.z, # block dim
0, # shared mem
stream, # stream
kernelArguments, 0)) # arguments
elif kernel_number == kernels.NaiveLargeChunk:
checkCudaErrors(cuda.cuLaunchKernel(_MatrixMulNaiveLargeChunk,
grid.x, grid.y, grid.z, # grid dim
threads.x, threads.y, threads.z, # block dim
0, # shared mem
stream, # stream
kernelArguments, 0)) # arguments
print('done')
checkCudaErrors(cudart.cudaStreamSynchronize(stream))
# Execute the kernel
nIter = 100
# Record the start event
checkCudaErrors(cudart.cudaEventRecord(start, stream))
if kernel_number == kernels.AsyncCopyMultiStageLargeChunk:
checkCudaErrors(cuda.cuLaunchKernel(_MatrixMulAsyncCopyMultiStageLargeChunk,
grid.x, grid.y, grid.z, # grid dim
threads.x, threads.y, threads.z, # block dim
0, # shared mem
stream, # stream
kernelArguments, 0)) # arguments
elif kernel_number == kernels.AsyncCopyLargeChunk:
checkCudaErrors(cuda.cuLaunchKernel(_MatrixMulAsyncCopyLargeChunk,
grid.x, grid.y, grid.z, # grid dim
threads.x, threads.y, threads.z, # block dim
0, # shared mem
stream, # stream
kernelArguments, 0)) # arguments
elif kernel_number == kernels.AsyncCopyLargeChunkAWBarrier:
checkCudaErrors(cuda.cuLaunchKernel(_MatrixMulAsyncCopyLargeChunkAWBarrier,
grid.x, grid.y, grid.z, # grid dim
threads.x, threads.y, threads.z, # block dim
0, # shared mem
stream, # stream
kernelArguments, 0)) # arguments
elif kernel_number == kernels.AsyncCopyMultiStageSharedState:
checkCudaErrors(cuda.cuLaunchKernel(_MatrixMulAsyncCopyMultiStageSharedState,
gridSharedStateKernel.x, gridSharedStateKernel.y, gridSharedStateKernel.z, # grid dim
threadsSharedStateKernel.x, threadsSharedStateKernel.y, threadsSharedStateKernel.z, # block dim
0, # shared mem
stream, # stream
kernelArguments, 0)) # arguments
elif kernel_number == kernels.AsyncCopyMultiStage:
checkCudaErrors(cuda.cuLaunchKernel(_MatrixMulAsyncCopyMultiStage,
grid.x, grid.y, grid.z, # grid dim
threads.x, threads.y, threads.z, # block dim
0, # shared mem
stream, # stream
kernelArguments, 0)) # arguments
elif kernel_number == kernels.AsyncCopySingleStage:
checkCudaErrors(cuda.cuLaunchKernel(_MatrixMulAsyncCopySingleStage,
grid.x, grid.y, grid.z, # grid dim
threads.x, threads.y, threads.z, # block dim
0, # shared mem
stream, # stream
kernelArguments, 0)) # arguments
elif kernel_number == kernels.Naive:
checkCudaErrors(cuda.cuLaunchKernel(_MatrixMulNaive,
grid.x, grid.y, grid.z, # grid dim
threads.x, threads.y, threads.z, # block dim
0, # shared mem
stream, # stream
kernelArguments, 0)) # arguments
elif kernel_number == kernels.NaiveLargeChunk:
checkCudaErrors(cuda.cuLaunchKernel(_MatrixMulNaiveLargeChunk,
grid.x, grid.y, grid.z, # grid dim
threads.x, threads.y, threads.z, # block dim
0, # shared mem
stream, # stream
kernelArguments, 0)) # arguments
# Record the stop event
checkCudaErrors(cudart.cudaEventRecord(stop, stream))
# Wait for the stop event to complete
checkCudaErrors(cudart.cudaEventSynchronize(stop))
msecTotal = checkCudaErrors(cudart.cudaEventElapsedTime(start, stop))
# Compute and print the performance
msecPerMatrixMul = msecTotal / nIter
flopsPerMatrixMul = 2.0 * dimsA.x * dimsA.y * dimsB.x
gigaFlops = (flopsPerMatrixMul * 1.0e-9) / (msecPerMatrixMul / 1000.0)
print("Performance= {:.2f} GFlop/s, Time= {:.2f} msec, Size= {:.0f} Ops, WorkgroupSize= {} threads/block".format(
gigaFlops,
msecPerMatrixMul,
flopsPerMatrixMul,
threads.x * threads.y))
# Copy result from device to host
checkCudaErrors(cudart.cudaMemcpyAsync(h_C, d_C, mem_size_C, cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost, stream))
checkCudaErrors(cudart.cudaStreamSynchronize(stream))
print("Checking computed result for correctness: ")
correct = True
# test relative error by the formula
# |<x, y>_cpu - <x,y>_gpu|/<|x|, |y|> < eps
eps = 1.e-6
h_C_local = (ctypes.c_float * (dimsC.x * dimsC.y)).from_address(h_C)
for i in range(dimsC.x * dimsC.y):
abs_err = math.fabs(h_C_local[i] - (dimsA.x * valB))
dot_length = dimsA.x
abs_val = math.fabs(h_C_local[i])
rel_err = abs_err / abs_val / dot_length
if rel_err > eps:
print("Error! Matrix[{:.5f}]={:.8f} ref={:.8f} err term is > {}".format(i, h_C_local[i], dimsA.x * valB, rel_err))
correct = False
print("Result = PASS" if correct else "Result = FAIL")
# Clean up memory
checkCudaErrors(cudart.cudaFreeHost(h_A))
checkCudaErrors(cudart.cudaFreeHost(h_B))
checkCudaErrors(cudart.cudaFreeHost(h_C))
checkCudaErrors(cudart.cudaFree(d_A))
checkCudaErrors(cudart.cudaFree(d_B))
checkCudaErrors(cudart.cudaFree(d_C))
checkCudaErrors(cudart.cudaEventDestroy(start))
checkCudaErrors(cudart.cudaEventDestroy(stop))
print("\nNOTE: The CUDA Samples are not meant for performance "\
"measurements. Results may vary when GPU Boost is enabled.");
if correct:
return 0
return -1
def checkKernelCompiles():
kernel_headers = '''\
#line __LINE__
#if __CUDA_ARCH__ >= 700
#include <cuda/barrier>
#endif
#include <cooperative_groups.h>
#include <cooperative_groups/reduce.h>
#include <cuda/pipeline>
'''
try:
common.KernelHelper(kernel_headers, findCudaDevice())
except:
# Filters out test from automation for two reasons
# 1. Headers are not found
# 2. Incompatible device
return False
return True
@pytest.mark.skipif(not checkKernelCompiles(), reason="Automation filter against incompatible kernel")
def main():
print("[globalToShmemAsyncCopy] - Starting...")
version = checkCudaErrors(cuda.cuDriverGetVersion())
if version < 11010:
print("CUDA Toolkit 11.1 or greater is required")
return
if (checkCmdLineFlag("help") or checkCmdLineFlag("?")):
print("Usage device=n (n >= 0 for deviceID)")
print(" wA=WidthA hA=HeightA (Width x Height of Matrix A)")
print(" wB=WidthB hB=HeightB (Width x Height of Matrix B)")
print(" kernel=kernel_number (0 - AsyncCopyMultiStageLargeChunk; 1 - AsyncCopyLargeChunk)")
print(" (2 - AsyncCopyLargeChunkAWBarrier; 3 - AsyncCopyMultiStageSharedState)")
print(" (4 - AsyncCopyMultiStage; 5 - AsyncCopySingleStage; 6 - Naive without memcpy_async)")
print(" (7 - NaiveLargeChunk without memcpy_async)")
print(" Note: Outer matrix dimensions of A & B matrices must be equal.")
return
# This will pick the best possible CUDA capable device, otherwise
# override the device ID based on input provided at the command line
devID = findCudaDevice()
matrixBlock = 32
dimsA = cudart.dim3()
dimsA.x = dimsA.y = 10 * 4 * matrixBlock
dimsA.z = 1
dimsB = cudart.dim3()
dimsB.x = dimsB.y = 10 * 4 * matrixBlock
dimsB.z = 1
# width of Matrix A
if checkCmdLineFlag("wA="):
dimsA.x = int(getCmdLineArgumentInt("wA="))
# height of Matrix A
if checkCmdLineFlag("hA="):
dimsA.y = int(getCmdLineArgumentInt("hA="))
# width of Matrix B
if checkCmdLineFlag("wB="):
dimsB.x = int(getCmdLineArgumentInt("wB="))
# height of Matrix B
if checkCmdLineFlag("hB="):
dimsB.y = int(getCmdLineArgumentInt("hB="))
if dimsA.x != dimsB.y:
print("Error: outer matrix dimensions must be equal. ({} != {})".format(dimsA.x, dimsB.y))
sys.exit(-1)
selected_kernel = kernels.AsyncCopyMultiStageLargeChunk
# kernel to run - default (AsyncCopyMultiStageLargeChunk == 0)
if checkCmdLineFlag("kernel="):
kernel_number = int(getCmdLineArgumentInt("kernel="))
if kernel_number < 8:
selected_kernel = kernels(kernel_number)
else:
print("Error: kernel number should be between 0 to 7, you have entered %d".format(kernel_number))
sys.exit(-1)
major = checkCudaErrors(cudart.cudaDeviceGetAttribute(cudart.cudaDeviceAttr.cudaDevAttrComputeCapabilityMajor, devID))
if major < 7:
print("globalToShmemAsyncCopy requires SM 7.0 or higher. Exiting...")
return
print("MatrixA({},{}), MatrixB({},{})".format(dimsA.x, dimsA.y,
dimsB.x, dimsB.y))
global _MatrixMulAsyncCopyMultiStageLargeChunk
global _MatrixMulAsyncCopyLargeChunk
global _MatrixMulAsyncCopyLargeChunkAWBarrier
global _MatrixMulAsyncCopyMultiStageSharedState
global _MatrixMulAsyncCopyMultiStage
global _MatrixMulAsyncCopySingleStage
global _MatrixMulNaive
global _MatrixMulNaiveLargeChunk
kernelHelper = common.KernelHelper(globalToShmemAsyncCopy, devID)
_MatrixMulAsyncCopyMultiStageLargeChunk = kernelHelper.getFunction(b'MatrixMulAsyncCopyMultiStageLargeChunk')
_MatrixMulAsyncCopyLargeChunk = kernelHelper.getFunction(b'MatrixMulAsyncCopyLargeChunk')
_MatrixMulAsyncCopyLargeChunkAWBarrier = kernelHelper.getFunction(b'MatrixMulAsyncCopyLargeChunkAWBarrier')
_MatrixMulAsyncCopyMultiStageSharedState = kernelHelper.getFunction(b'MatrixMulAsyncCopyMultiStageSharedState')
_MatrixMulAsyncCopyMultiStage = kernelHelper.getFunction(b'MatrixMulAsyncCopyMultiStage')
_MatrixMulAsyncCopySingleStage = kernelHelper.getFunction(b'MatrixMulAsyncCopySingleStage')
_MatrixMulNaive = kernelHelper.getFunction(b'MatrixMulNaive')
_MatrixMulNaiveLargeChunk = kernelHelper.getFunction(b'MatrixMulNaiveLargeChunk')
matrix_result = MatrixMultiply(dimsA, dimsB, selected_kernel)
if matrix_result != 0:
sys.exit(-1)
if __name__ == "__main__":
main()
| cuda-python-main | examples/3_CUDA_Features/globalToShmemAsyncCopy_test.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import sys
def checkCmdLineFlag(stringRef):
k = 0
for i in sys.argv:
if stringRef == i and k < len(sys.argv) - 1:
return True
k += 1
return False
def getCmdLineArgumentInt(stringRef):
k = 0
for i in sys.argv:
if stringRef == i and k < len(sys.argv) - 1:
return sys.argv[k+1]
k += 1
return 0
| cuda-python-main | examples/common/helper_string.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import ctypes
import numpy as np
import os
from cuda import cuda, cudart, nvrtc
from examples.common.helper_cuda import checkCudaErrors
class KernelHelper:
def __init__(self, code, devID):
prog = checkCudaErrors(nvrtc.nvrtcCreateProgram(str.encode(code), b'sourceCode.cu', 0, [], []))
CUDA_HOME = os.getenv('CUDA_HOME')
if CUDA_HOME == None:
CUDA_HOME = os.getenv('CUDA_PATH')
if CUDA_HOME == None:
raise RuntimeError('Environment variable CUDA_HOME or CUDA_PATH is not set')
include_dirs = os.path.join(CUDA_HOME, 'include')
# Initialize CUDA
checkCudaErrors(cudart.cudaFree(0))
major = checkCudaErrors(cudart.cudaDeviceGetAttribute(cudart.cudaDeviceAttr.cudaDevAttrComputeCapabilityMajor, devID))
minor = checkCudaErrors(cudart.cudaDeviceGetAttribute(cudart.cudaDeviceAttr.cudaDevAttrComputeCapabilityMinor, devID))
_, nvrtc_minor = checkCudaErrors(nvrtc.nvrtcVersion())
use_cubin = (nvrtc_minor >= 1)
prefix = 'sm' if use_cubin else 'compute'
arch_arg = bytes(f'--gpu-architecture={prefix}_{major}{minor}', 'ascii')
try:
opts = [b'--fmad=true', arch_arg, '--include-path={}'.format(include_dirs).encode('UTF-8'),
b'--std=c++11', b'-default-device']
checkCudaErrors(nvrtc.nvrtcCompileProgram(prog, len(opts), opts))
except RuntimeError as err:
logSize = checkCudaErrors(nvrtc.nvrtcGetProgramLogSize(prog))
log = b' ' * logSize
checkCudaErrors(nvrtc.nvrtcGetProgramLog(prog, log))
print(log.decode())
print(err)
exit(-1)
if use_cubin:
dataSize = checkCudaErrors(nvrtc.nvrtcGetCUBINSize(prog))
data = b' ' * dataSize
checkCudaErrors(nvrtc.nvrtcGetCUBIN(prog, data))
else:
dataSize = checkCudaErrors(nvrtc.nvrtcGetPTXSize(prog))
data = b' ' * dataSize
checkCudaErrors(nvrtc.nvrtcGetPTX(prog, data))
self.module = checkCudaErrors(cuda.cuModuleLoadData(np.char.array(data)))
def getFunction(self, name):
return checkCudaErrors(cuda.cuModuleGetFunction(self.module, name))
| cuda-python-main | examples/common/common.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
from cuda import cuda, cudart, nvrtc
from examples.common.helper_string import getCmdLineArgumentInt, checkCmdLineFlag
def _cudaGetErrorEnum(error):
if isinstance(error, cuda.CUresult):
err, name = cuda.cuGetErrorName(error)
return name if err == cuda.CUresult.CUDA_SUCCESS else "<unknown>"
elif isinstance(error, cudart.cudaError_t):
return cudart.cudaGetErrorName(error)[1]
elif isinstance(error, nvrtc.nvrtcResult):
return nvrtc.nvrtcGetErrorString(error)[1]
else:
raise RuntimeError('Unknown error type: {}'.format(error))
def checkCudaErrors(result):
if result[0].value:
raise RuntimeError("CUDA error code={}({})".format(result[0].value, _cudaGetErrorEnum(result[0])))
if len(result) == 1:
return None
elif len(result) == 2:
return result[1]
else:
return result[1:]
def findCudaDevice():
devID = 0
if checkCmdLineFlag("device="):
devID = getCmdLineArgumentInt("device=")
checkCudaErrors(cudart.cudaSetDevice(devID))
return devID
def findCudaDeviceDRV():
devID = 0
if checkCmdLineFlag("device="):
devID = getCmdLineArgumentInt("device=")
checkCudaErrors(cuda.cuInit(0))
cuDevice = checkCudaErrors(cuda.cuDeviceGet(devID))
return cuDevice
| cuda-python-main | examples/common/helper_cuda.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import numpy as np
import time
from cuda import cuda, cudart
from examples.common import common
from examples.common.helper_cuda import checkCudaErrors
isoPropagator = '''\
extern "C"
__global__ void injectSource(float *__restrict__ in, float *__restrict__ src, int it)
{
if (threadIdx.x == 0)
in[0] = src[it];
}
extern "C"
__global__ void createVelocity(float *__restrict__ vel, float vmult, int nz, int nx, int stride)
{
int ix = blockIdx.x * blockDim.x + threadIdx.x;
int iy = blockIdx.y * blockDim.y + threadIdx.y;
int idx_out = iy * nx + ix;
for (int iz = 0; iz < nz ; iz++) {
vel[idx_out] = 3.0f * 3.0f * vmult;
idx_out += stride;
}
}
extern "C"
__global__ void createSource(float *__restrict__ x, float dt, float freq, int nt)
{
int istart = (int) (60.0f/dt); // start max at 30 ms
float pi2 = 2.0f * 3.141592654f;
float agauss = 0.5f * freq;
for ( int i=threadIdx.x; i < nt; ++ i) {
float arg = 1.0e-3 * fabsf(i - istart) * agauss;
x[i] = 1000.0f * expf(-2.0f * arg * arg) * cosf(pi2 * arg);
}
}
extern "C"
__global__ void fwd_3D_orderX2k(float *g_curr_1, float *g_prev_1, float *g_vsq_1,
int nz, int dimx, int stride);
#define radius 4
#define diameter (2*radius+1)
#define BDIMX 32
#define BDIMY 16
inline __device__ void advance(float2 *field, const int num_points) {
#pragma unroll
for (int i = 0; i < num_points; i++)
field[i] = field[i + 1];
}
__global__ void fwd_3D_orderX2k(float *g_curr_1, float *g_prev_1, float *g_vsq_1,
int nz, int nx, int stride) {
stride = stride / 2;
nx = nx / 2;
const float c_coeff[5] = {-3.0f * 2.847222222f,
1.600000f,
-0.200000f,
0.025396825f,
-0.001785f};
float2 *g_prev = (float2 *)g_prev_1;
float2 *g_curr = (float2 *)g_curr_1;
float2 *g_vsq = (float2 *)g_vsq_1;
__shared__ float s_data[BDIMY + 2 * radius][2 * BDIMX + 2 * (radius + (radius % 2))];
int ix = blockIdx.x * blockDim.x + threadIdx.x;
int iy = blockIdx.y * blockDim.y + threadIdx.y;
int offset = -radius * stride;
int idx_out = iy * nx + ix;
int idx_in = idx_out + offset;
float2 local_input[diameter], tmp1, tmp2;
int tx = 2 * threadIdx.x + radius + (radius % 2);
int ty = threadIdx.y + radius;
#pragma unroll
for (int i = 1; i < diameter; i++) {
local_input[i] = g_curr[idx_in];
idx_in += stride;
}
for (int iz = 0; iz < nz ; iz++) {
advance(local_input, diameter - 1);
local_input[diameter - 1] = g_curr[idx_in];
// update the data slice in smem
s_data[ty][tx] = local_input[radius].x;
s_data[ty][tx + 1] = local_input[radius].y;
// halo above/below
if (threadIdx.y < radius) {
tmp1 = (g_curr[idx_out - radius * nx]);
s_data[threadIdx.y][tx] = tmp1.x;
s_data[threadIdx.y][tx + 1] = tmp1.y;
}
if (threadIdx.y >= radius && threadIdx.y < 2 * radius) {
tmp1 = (g_curr[idx_out + (BDIMY - radius) * nx]);
s_data[threadIdx.y + BDIMY][tx] = tmp1.x;
s_data[threadIdx.y + BDIMY][tx + 1] = tmp1.y;
}
// halo left/right
if (threadIdx.x < (radius + 1) / 2) {
tmp1 = (g_curr[idx_out - (radius + 1) / 2]);
s_data[ty][tx - radius - (radius % 2)] = tmp1.x;
s_data[ty][tx - radius - (radius % 2) + 1] = tmp1.y;
tmp2 = (g_curr[idx_out + BDIMX]);
s_data[ty][tx + 2 * BDIMX] = tmp2.x;
s_data[ty][tx + 2 * BDIMX + 1] = tmp2.y;
}
__syncthreads();
// compute the output values
float2 temp, div;
temp.x = 2.f * local_input[radius].x - g_prev[idx_out].x;
temp.y = 2.f * local_input[radius].y - g_prev[idx_out].y;
div.x = c_coeff[0] * local_input[radius].x;
div.y = c_coeff[0] * local_input[radius].y;
#pragma unroll
for (int d = 1; d <= radius; d++) {
div.x += c_coeff[d] * (local_input[radius + d].x + local_input[radius - d].x + s_data[ty - d][tx] +
s_data[ty + d][tx] + s_data[ty][tx - d] + s_data[ty][tx + d]);
div.y += c_coeff[d] * (local_input[radius + d].y + local_input[radius - d].y + s_data[ty - d][tx + 1] +
s_data[ty + d][tx + 1] + s_data[ty][tx - d + 1] + s_data[ty][tx + d + 1]);
}
g_prev[idx_out].x = temp.x + div.x * g_vsq[idx_out].x;
g_prev[idx_out].y = temp.y + div.y * g_vsq[idx_out].y;
__syncthreads();
idx_out += stride;
idx_in += stride;
}
}
'''
display_graph = False
verbose_prints = False
def align_nx(nx, blk, nops):
n_align = (int)((nx - 1)/blk) + 1
n_align *= blk
n_align += 2*nops
n_align = (int)((n_align - 1) / 64) + 1
n_align *= 64
return (int)(n_align)
def align_ny(ny, blk, nops):
n_align = (int)((ny - 1)/blk) + 1
n_align *= blk
n_align += 2*nops
return (int)(n_align)
#
# this class contains the input params
#
class params ():
def __init__(self):
self.BDIMX = 32 # tiles x y for fd operators
self.BDIMY = 16
self.FD_ORDER = 4
self.lead = 64 - self.FD_ORDER
self.nx = align_nx(700, 2*self.BDIMX, self.FD_ORDER)
self.ny = align_ny(600, self.BDIMY, self.FD_ORDER)
self.blkx = (int) ((self.nx - 2*self.FD_ORDER) / (2*self.BDIMX))
self.blky = (int) ((self.ny - 2*self.FD_ORDER) / self.BDIMY)
self.nz = (int)(200)
self.delta = 25.0
self.dt = 0.3 * 1000.0 * self.delta / 4500.0
self.tmax_propag = 1000.0
self.nt = int(self.tmax_propag / self.dt)
self.freqMax = 3.5* 1000.0 / (4.0 * self.delta)
print("dt= ",self.dt, " delta= ", self.delta, " nt= ", self.nt, " freq max= " , self.freqMax)
#
# this class contains all the kernels to be used bu propagator
#
class cudaKernels():
def __init__ (self, cntx):
checkCudaErrors(cuda.cuInit(0))
checkCudaErrors(cuda.cuCtxSetCurrent(cntx))
dev = checkCudaErrors(cuda.cuCtxGetDevice())
self.kernelHelper = common.KernelHelper(isoPropagator, int(dev))
# kernel to create a source fnction with some max frequency
self.creatSource = self.kernelHelper.getFunction(b'createSource')
# create a velocity to try things: just a sphere on the middle 4500 m/s and 2500 m/s all around
self.createVelocity = self.kernelHelper.getFunction(b'createVelocity')
# kernel to propagate the wavefield by 1 step in time
self.fdPropag = self.kernelHelper.getFunction(b'fwd_3D_orderX2k')
# kernel to propagate the wavefield by 1 step in time
self.injectSource = self.kernelHelper.getFunction(b'injectSource')
#
# this class contains: propagator, source creation, velocity creation
# injection of data and domain exchange
#
class propagator:
def __init__(self, params, _dev):
print("init object for device ", _dev)
self.dev = _dev
checkCudaErrors(cuda.cuInit(0))
self.cuDevice = checkCudaErrors(cuda.cuDeviceGet(_dev))
self.context = checkCudaErrors(cuda.cuCtxCreate(0, self.cuDevice))
self.waveOut = 0
self.waveIn = 0
self.streamCenter = checkCudaErrors(cuda.cuStreamCreate(0))
self.streamHalo = checkCudaErrors(cuda.cuStreamCreate(0))
self.params = params
def __del__(self):
checkCudaErrors(cuda.cuCtxSetCurrent(self.context))
checkCudaErrors(cuda.cuStreamDestroy(self.streamHalo))
checkCudaErrors(cuda.cuStreamDestroy(self.streamCenter))
if self.waveIn != 0:
checkCudaErrors(cuda.cuMemFree(self.waveIn))
if self.waveOut != 0:
checkCudaErrors(cuda.cuMemFree(self.waveOut))
checkCudaErrors(cuda.cuCtxDestroy(self.context))
#
# swap waveIn with waveOut
#
def swap(self):
if verbose_prints:
print("swap in out ", int(self.waveIn), " " , int(self.waveOut))
i = int(self.waveIn)
j = int(self.waveOut)
a = i
i = j
j = a
self.waveIn = cuda.CUdeviceptr(i)
self.waveOut = cuda.CUdeviceptr(j)
#
# allocate the device memory
#
def allocate(self):
nel = self.params.nx * self.params.ny * self.params.nz
n = np.array( nel, dtype=np.uint32)
bufferSize = n * np.dtype(np.float32).itemsize
checkCudaErrors(cuda.cuCtxSetCurrent(self.context))
self.velocity = checkCudaErrors(cuda.cuMemAlloc(bufferSize))
checkCudaErrors(cuda.cuMemsetD32(self.velocity, 0, n))
nel += self.params.lead
n = np.array(nel, dtype=np.uint32) ## we need to align at the beginning of the tile
bufferSize = n * np.dtype(np.float32).itemsize
self.waveIn = checkCudaErrors(cuda.cuMemAlloc(bufferSize))
checkCudaErrors(cuda.cuMemsetD32(self.waveIn, 0, n))
self.waveOut = checkCudaErrors(cuda.cuMemAlloc(bufferSize))
checkCudaErrors(cuda.cuMemsetD32(self.waveOut, 0, n))
n = np.array(self.params.nt, dtype=np.uint32)
bufferSize = n * np.dtype(np.float32).itemsize
self.source = checkCudaErrors(cuda.cuMemAlloc(bufferSize))
checkCudaErrors(cuda.cuMemsetD32(self.source, 0, n))
#
# create source data
#
def createSource(self, kernel):
print("creating source on device ", self.dev)
buf = np.array([int(self.source)], dtype=np.uint64)
nt = np.array(self.params.nt, dtype=np.uint32)
dt = np.array(self.params.dt, dtype=np.float32)
freq = np.array(self.params.freqMax, dtype=np.float32)
args = [buf, dt, freq, nt]
args = np.array([arg.ctypes.data for arg in args], dtype=np.uint64)
checkCudaErrors(cuda.cuCtxSetCurrent(self.context))
checkCudaErrors(cuda.cuLaunchKernel(kernel.creatSource,
1, 1, 1, # grid dim
1024, 1, 1, # block dim
0, self.streamHalo, # shared mem and stream
args.ctypes.data, 0)) # arguments
checkCudaErrors(cuda.cuStreamSynchronize(self.streamHalo))
#
# inject source function: ony on the domain 0
#
def injectSource(self, kernel, iter):
checkCudaErrors(cuda.cuCtxSetCurrent(self.context))
if self.dev != 0:
return
wavein = np.array([int(self.waveIn)], dtype=np.uint64)
src = np.array([int(self.source)], dtype=np.uint64)
offset_sourceInject = self.params.lead + (int)(self.params.nz/2) * self.params.nx * self.params.ny + \
(int)(self.params.ny/2) * self.params.nx + (int) (self.params.nx/2)
offset_sourceInject *= np.dtype(np.float32).itemsize
np_it = np.array(iter, dtype=np.uint32)
args = [wavein+offset_sourceInject, src, np_it]
args = np.array([arg.ctypes.data for arg in args], dtype=np.uint64)
checkCudaErrors(cuda.cuLaunchKernel(kernel.injectSource,
1, 1, 1, # grid dim
1, 1, 1, # block dim
0, self.streamHalo, # shared mem and stream
args.ctypes.data, 0)) # arguments
#
# create velocity
#
def createVelocity(self, kernel):
print("running create velocity on device ", self.dev)
offset_velocity = self.params.FD_ORDER * self.params.nx * self.params.ny + \
self.params.FD_ORDER * self.params.nx + self.params.FD_ORDER
offset_velocity *= np.dtype(np.float32).itemsize
vel = np.array([int(self.velocity)], dtype=np.uint64)
dx_dt2 = (self.params.dt * self.params.dt) / (self.params.delta * self.params.delta)
stride = self.params.nx * self.params.ny
np_dx_dt2 = np.array(dx_dt2, dtype=np.float32)
np_nz = np.array((self.params.nz-2*self.params.FD_ORDER), dtype=np.uint32)
np_nx = np.array(self.params.nx, dtype=np.uint32)
np_stride = np.array(stride, dtype=np.uint32)
args = [vel+ offset_velocity, np_dx_dt2, np_nz, np_nx, np_stride]
args = np.array([arg.ctypes.data for arg in args], dtype=np.uint64)
checkCudaErrors(cuda.cuCtxSetCurrent(self.context))
# do halo up
checkCudaErrors(cuda.cuLaunchKernel(kernel.createVelocity,
self.params.blkx, self.params.blky, 1, # grid dim
2*self.params.BDIMX, self.params.BDIMY, 1, # block dim
0, self.streamHalo, # shared mem and stream
args.ctypes.data, 0)) # arguments
checkCudaErrors(cuda.cuStreamSynchronize(self.streamHalo))
#
# execute the center part of propagation
#
def executeCenter(self, kernel):
if verbose_prints:
print("running center on device ", self.dev)
checkCudaErrors(cuda.cuCtxSetCurrent(self.context))
offset_velocity = 2* self.params.FD_ORDER * self.params.nx * self.params.ny + \
self.params.FD_ORDER * self.params.nx + self.params.FD_ORDER
offset_wave = self.params.lead + offset_velocity
offset_wave *= np.dtype(np.float32).itemsize
offset_velocity *= np.dtype(np.float32).itemsize
wavein = np.array([int(self.waveIn)], dtype=np.uint64)
waveout = np.array([int(self.waveOut)], dtype=np.uint64)
vel = np.array([int(self.velocity)], dtype=np.uint64)
stride = self.params.nx * self.params.ny
np_nz = np.array(self.params.nz - 4*self.params.FD_ORDER, dtype=np.uint32)
np_nx = np.array(self.params.nx, dtype=np.uint32)
np_stride = np.array(stride, dtype=np.uint32)
args = [wavein+offset_wave, waveout+offset_wave, vel+offset_velocity, np_nz, np_nx, np_stride]
args = np.array([arg.ctypes.data for arg in args], dtype=np.uint64)
# do center propagation from 2 * fd_order to nz - 2 * fd_order
checkCudaErrors(cuda.cuLaunchKernel(kernel.fdPropag,
self.params.blkx, self.params.blky, 1, # grid dim
self.params.BDIMX, self.params.BDIMY, 1, # block dim
0, self.streamCenter, # shared mem and stream
args.ctypes.data, 0)) # arguments
#
# execute the halo part of propagation
#
def executeHalo(self, kernel):
if verbose_prints:
print("running halos on device ", self.dev)
checkCudaErrors(cuda.cuCtxSetCurrent(self.context))
offset_velocity = self.params.FD_ORDER * self.params.nx * self.params.ny + \
self.params.FD_ORDER * self.params.nx + self.params.FD_ORDER
offset_wave = self.params.lead + offset_velocity
offset_wave *= np.dtype(np.float32).itemsize
offset_velocity *= np.dtype(np.float32).itemsize
wavein = np.array([int(self.waveIn)], dtype=np.uint64)
waveout = np.array([int(self.waveOut)], dtype=np.uint64)
vel = np.array([int(self.velocity)], dtype=np.uint64)
stride = self.params.nx * self.params.ny
np_nz = np.array(self.params.FD_ORDER, dtype=np.uint32)
np_nx = np.array(self.params.nx, dtype=np.uint32)
np_stride = np.array(stride, dtype=np.uint32)
args = [wavein+offset_wave, waveout+offset_wave, vel+offset_velocity, np_nz, np_nx, np_stride]
args = np.array([arg.ctypes.data for arg in args], dtype=np.uint64)
# do halo up
checkCudaErrors(cuda.cuLaunchKernel(kernel.fdPropag,
self.params.blkx, self.params.blky, 1, # grid dim
self.params.BDIMX, self.params.BDIMY, 1, # block dim
0, self.streamHalo, # shared mem and stream
args.ctypes.data, 0)) # arguments
# do halo down
offset_velocity = (self.params.nz - 2*self.params.FD_ORDER) * self.params.nx * self.params.ny + \
self.params.FD_ORDER * self.params.nx + self.params.FD_ORDER
offset_wave = self.params.lead + offset_velocity
offset_wave *= np.dtype(np.float32).itemsize
offset_velocity *= np.dtype(np.float32).itemsize
args = [wavein+offset_wave, waveout+offset_wave, vel+offset_velocity, np_nz, np_nx, np_stride]
args = np.array([arg.ctypes.data for arg in args], dtype=np.uint64)
checkCudaErrors(cuda.cuLaunchKernel(kernel.fdPropag,
self.params.blkx, self.params.blky, 1, # grid dim
self.params.BDIMX, self.params.BDIMY, 1, # block dim
0, self.streamHalo, # shared mem and stream
args.ctypes.data, 0)) # arguments
#
# exchange the halos
#
def exchangeHalo(self, propag):
if verbose_prints:
print("exchange halos on device ", self.dev, "with dev ", propag.dev)
checkCudaErrors(cuda.cuCtxSetCurrent(self.context))
#
# the following variables don't change
#
nstride = self.params.nx * self.params.ny
devS = self.context
devD = propag.context
n_exch = self.params.FD_ORDER * nstride
n_exch *= np.dtype(np.float32).itemsize
if self.dev < propag.dev:
# exchange up
offsetS = self.params.lead + (self.params.nz - 2*self.params.FD_ORDER) * nstride
offsetD = propag.params.lead
offsetS *= np.dtype(np.float32).itemsize
offsetD *= np.dtype(np.float32).itemsize
waveD = cuda.CUdeviceptr(int(propag.waveOut) + offsetD)
waveS = cuda.CUdeviceptr(int(self.waveOut) + offsetS)
checkCudaErrors(cuda.cuMemcpyPeerAsync(waveD, devD, waveS, devS, n_exch, self.streamHalo))
else:
# exchange down
offsetS = self.params.lead + self.params.FD_ORDER * nstride
offsetD = propag.params.lead + (propag.params.nz - propag.params.FD_ORDER) * nstride
offsetS *= np.dtype(np.float32).itemsize
offsetD *= np.dtype(np.float32).itemsize
waveD = cuda.CUdeviceptr(int(propag.waveOut) + offsetD)
waveS = cuda.CUdeviceptr(int(self.waveOut) + offsetS)
checkCudaErrors(cuda.cuMemcpyPeerAsync(waveD, devD, waveS, devS, n_exch, self.streamHalo))
#
# sync stream
#
def syncStream(self, stream):
checkCudaErrors(cuda.cuCtxSetCurrent(self.context))
checkCudaErrors(cuda.cuStreamSynchronize(stream))
def main():
checkCudaErrors(cuda.cuInit(0))
# Number of GPUs
print("Checking for multiple GPUs...")
gpu_n = checkCudaErrors(cuda.cuDeviceGetCount())
print("CUDA-capable device count: {}".format(gpu_n))
if gpu_n < 2:
print("Two or more GPUs with Peer-to-Peer access capability are required")
return
prop = [checkCudaErrors(cudart.cudaGetDeviceProperties(i)) for i in range(gpu_n)]
# Check possibility for peer access
print("\nChecking GPU(s) for support of peer to peer memory access...")
p2pCapableGPUs = [-1, -1]
for i in range(gpu_n):
p2pCapableGPUs[0] = i
for j in range(gpu_n):
if i == j:
continue
i_access_j = checkCudaErrors(cudart.cudaDeviceCanAccessPeer(i, j))
j_access_i = checkCudaErrors(cudart.cudaDeviceCanAccessPeer(j, i))
print("> Peer access from {} (GPU{}) -> {} (GPU{}) : {}\n".format(
prop[i].name, i, prop[j].name, j, "Yes" if i_access_j else "No"))
print("> Peer access from {} (GPU{}) -> {} (GPU{}) : {}\n".format(
prop[j].name, j, prop[i].name, i, "Yes" if i_access_j else "No"))
if i_access_j and j_access_i:
p2pCapableGPUs[1] = j
break
if p2pCapableGPUs[1] != -1:
break
if p2pCapableGPUs[0] == -1 or p2pCapableGPUs[1] == -1:
print("Two or more GPUs with Peer-to-Peer access capability are required.")
print("Peer to Peer access is not available amongst GPUs in the system, waiving test.")
return
# Use first pair of p2p capable GPUs detected
gpuid = [p2pCapableGPUs[0], p2pCapableGPUs[1]]
#
# init device
#
pars = params()
#
# create propagators
#
propags = []
kerns = []
#
# create kernels and propagators that are going to be used on device
#
for i in gpuid:
p = propagator(pars, i)
k = cudaKernels(p.context)
propags.append(p)
kerns.append(k)
# allocate resources in device
for propag, kern in zip(propags, kerns):
propag.allocate()
propag.createSource(kern)
propag.createVelocity(kern)
#
# loop over time iterations
#
start = time.time()
for it in range(pars.nt):
for propag in propags:
propag.syncStream(propag.streamHalo)
for propag, kern in zip(propags, kerns):
propag.injectSource(kern, it)
for propag, kern in zip(propags, kerns):
propag.executeHalo(kern)
for propag in propags:
propag.syncStream(propag.streamHalo)
propags[1].exchangeHalo(propags[0])
propags[0].exchangeHalo(propags[1])
for propag, kern in zip(propags, kerns):
propag.executeCenter(kern)
for propag in propags:
propag.syncStream(propag.streamCenter)
for propag in propags:
propag.swap()
end = time.time()
npoints = (pars.nz - 2 * pars.FD_ORDER) * (pars.blkx * 2 * pars.BDIMX) * (pars.blky * pars.BDIMY)
nops = 1.0e-9 * pars.nt * npoints / (end - start)
print("this code generates " , nops , " GPoints/sec / device ")
#
# get the result out of gpu
#
nz = 2 * (int)(pars.nz - 2 * pars.FD_ORDER)
print(" nz= ", nz, " nx= ", pars.nx)
hOut = np.zeros((nz, pars.nx), dtype='float32')
istart = 0
for propag in propags:
checkCudaErrors(cuda.cuCtxSetCurrent(propag.context))
offset = pars.lead + pars.FD_ORDER * pars.nx * pars.ny + \
(int)(pars.ny/2) * pars.nx
for j in range(pars.nz- 2*pars.FD_ORDER):
ptr = cuda.CUdeviceptr(int(propag.waveOut) + offset*4)
checkCudaErrors(cuda.cuMemcpyDtoH(hOut[istart].ctypes.data, ptr,
pars.nx * np.dtype(np.float32).itemsize))
offset += pars.nx * pars.ny
istart += 1
#
# delete kernels and propagatrs
#
for propag in propags:
del propag
if display_graph:
nrows = nz
ncols = pars.nx
dbz = hOut
dbz = np.reshape(dbz,(nrows, ncols))
##
## those are to plot results
##
import matplotlib.pyplot as plt
import matplotlib.cm as cm
fig, ax = plt.subplots()
title = "test fd kernels up to " + str(pars.tmax_propag) + " ms "
plt.title(title, fontsize=20)
im = ax.imshow(dbz, interpolation='bilinear', cmap=plt.get_cmap('Greys'), aspect='auto',
origin='upper',extent=[1, pars.nx, nz, 1],
vmax=abs(dbz).max(), vmin=-abs(dbz).max())
fig.colorbar(im, ax=ax)
plt.show()
print("Done")
if __name__ == "__main__":
display_graph = True
verbose_prints = True
main()
| cuda-python-main | examples/extra/isoFDModelling_test.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import ctypes
import numpy as np
from cuda import cuda, nvrtc
def ASSERT_DRV(err):
if isinstance(err, cuda.CUresult):
if err != cuda.CUresult.CUDA_SUCCESS:
raise RuntimeError('Cuda Error: {}'.format(err))
elif isinstance(err, nvrtc.nvrtcResult):
if err != nvrtc.nvrtcResult.NVRTC_SUCCESS:
raise RuntimeError('Nvrtc Error: {}'.format(err))
else:
raise RuntimeError('Unknown error type: {}'.format(err))
saxpy = '''\
extern "C" __global__
void saxpy(float a, float *x, float *y, float *out, size_t n)
{
size_t tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < n) {
out[tid] = a * x[tid] + y[tid];
}
}
'''
def main():
# Init
err, = cuda.cuInit(0)
ASSERT_DRV(err)
# Device
err, cuDevice = cuda.cuDeviceGet(0)
ASSERT_DRV(err)
# Ctx
err, context = cuda.cuCtxCreate(0, cuDevice)
ASSERT_DRV(err)
# Create program
err, prog = nvrtc.nvrtcCreateProgram(str.encode(saxpy), b'saxpy.cu', 0, [], [])
ASSERT_DRV(err)
# Get target architecture
err, major = cuda.cuDeviceGetAttribute(cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, cuDevice)
ASSERT_DRV(err)
err, minor = cuda.cuDeviceGetAttribute(cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, cuDevice)
ASSERT_DRV(err)
err, nvrtc_major, nvrtc_minor = nvrtc.nvrtcVersion()
ASSERT_DRV(err)
use_cubin = (nvrtc_minor >= 1)
prefix = 'sm' if use_cubin else 'compute'
arch_arg = bytes(f'--gpu-architecture={prefix}_{major}{minor}', 'ascii')
# Compile program
opts = [b'--fmad=false', arch_arg]
err, = nvrtc.nvrtcCompileProgram(prog, len(opts), opts)
ASSERT_DRV(err)
# Get log from compilation
err, logSize = nvrtc.nvrtcGetProgramLogSize(prog)
ASSERT_DRV(err)
log = b' ' * logSize
err, = nvrtc.nvrtcGetProgramLog(prog, log)
ASSERT_DRV(err)
print(log.decode())
# Get data from compilation
if use_cubin:
err, dataSize = nvrtc.nvrtcGetCUBINSize(prog)
ASSERT_DRV(err)
data = b' ' * dataSize
err, = nvrtc.nvrtcGetCUBIN(prog, data)
ASSERT_DRV(err)
else:
err, dataSize = nvrtc.nvrtcGetPTXSize(prog)
ASSERT_DRV(err)
data = b' ' * dataSize
err, = nvrtc.nvrtcGetPTX(prog, data)
ASSERT_DRV(err)
# Load data as module data and retrieve function
data = np.char.array(data)
err, module = cuda.cuModuleLoadData(data)
ASSERT_DRV(err)
err, kernel = cuda.cuModuleGetFunction(module, b'saxpy')
ASSERT_DRV(err)
# Test the kernel
NUM_THREADS = 128
NUM_BLOCKS = 32
a = np.array([2.0], dtype=np.float32)
n = np.array(NUM_THREADS * NUM_BLOCKS, dtype=np.uint32)
bufferSize = n * a.itemsize
err, dX = cuda.cuMemAlloc(bufferSize)
ASSERT_DRV(err)
err, dY = cuda.cuMemAlloc(bufferSize)
ASSERT_DRV(err)
err, dOut = cuda.cuMemAlloc(bufferSize)
ASSERT_DRV(err)
hX = np.random.rand(n).astype(dtype=np.float32)
hY = np.random.rand(n).astype(dtype=np.float32)
hOut = np.zeros(n).astype(dtype=np.float32)
err, stream = cuda.cuStreamCreate(0)
ASSERT_DRV(err)
err, = cuda.cuMemcpyHtoDAsync(dX, hX, bufferSize, stream)
ASSERT_DRV(err)
err, = cuda.cuMemcpyHtoDAsync(dY, hY, bufferSize, stream)
ASSERT_DRV(err)
err, = cuda.cuStreamSynchronize(stream)
ASSERT_DRV(err)
# Assert values are different before running kernel
hZ = a * hX + hY
if np.allclose(hOut, hZ):
raise ValueError('Error inside tolerence for host-device vectors')
arg_values = (a, dX, dY, dOut, n)
arg_types = (ctypes.c_float, None, None, None, ctypes.c_size_t)
err, = cuda.cuLaunchKernel(kernel,
NUM_BLOCKS, 1, 1, # grid dim
NUM_THREADS, 1, 1, # block dim
0, stream, # shared mem and stream
(arg_values, arg_types), 0) # arguments
ASSERT_DRV(err)
err, = cuda.cuMemcpyDtoHAsync(hOut, dOut, bufferSize, stream)
ASSERT_DRV(err)
err, = cuda.cuStreamSynchronize(stream)
ASSERT_DRV(err)
# Assert values are same after running kernel
hZ = a * hX + hY
if not np.allclose(hOut, hZ):
raise ValueError('Error outside tolerence for host-device vectors')
err, = cuda.cuStreamDestroy(stream)
ASSERT_DRV(err)
err, = cuda.cuMemFree(dX)
ASSERT_DRV(err)
err, = cuda.cuMemFree(dY)
ASSERT_DRV(err)
err, = cuda.cuMemFree(dOut)
ASSERT_DRV(err)
err, = cuda.cuModuleUnload(module)
ASSERT_DRV(err)
err, = cuda.cuCtxDestroy(context)
ASSERT_DRV(err)
if __name__=="__main__":
main()
| cuda-python-main | examples/extra/jit_program_test.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
"""Numba EMM Plugin using the CUDA Python Driver API.
This example provides an External Memory Management (EMM) Plugin for Numba (see
https://numba.readthedocs.io/en/stable/cuda/external-memory.html) that uses the
NVIDIA CUDA Python Driver API for all on-device allocations and frees. For
other operations interacting with the driver, Numba uses its internal ctypes
wrapper. This serves as an example of interoperability between the NVIDIA CUDA
Python Driver API, and other implementations of driver API wrappers (in this
case Numba's ctypes wrapper), and demonstrates an on-ramp to using the NVIDIA
CUDA Python Driver API wrapper by showing that it can co-exist with other
wrappers - it is not necessary to replace all wrappers in all libraries to
start using the NVIDIA wrapper.
The current version of Numba passes all tests using this plugin (with a small
patch to recognize CUDA 11.3 as a supported version). The Numba test suite can
be run with the plugin by executing:
NUMBA_CUDA_MEMORY_MANAGER=numba_emm_plugin \\
python -m numba.runtests numba.cuda.tests -vf -m
when the directory containing this example is on the PYTHONPATH. When tests are
run, the test summary is expected to be close to:
Ran 1121 tests in 159.572s
OK (skipped=17, expected failures=1)
The number of tests may vary with changes between commits in Numba, but the
main result is that there are no unexpected failures.
This example can also be run standalone with:
python numba_emm_plugin.py
in which case it sets up Numba to use the included EMM plugin, then creates and
destroys a device array. When run standalone, the output may look like:
Free before creating device array: 50781159424
Free after creating device array: 50779062272
Free after freeing device array: 50781159424
The initial value may vary, but the expectation is that 2097152 bytes (2MB)
should be taken up by the device array creation, and the original value should
be restored after freeing it.
"""
from numba import cuda
from numba.cuda import (HostOnlyCUDAMemoryManager, GetIpcHandleMixin,
MemoryPointer, MemoryInfo)
from cuda import cuda as cuda_driver
from ctypes import c_size_t
# Python functions for allocation, deallocation, and memory info via the NVIDIA
# CUDA Python Driver API
def driver_alloc(size):
"""
Allocate `size` bytes of device memory and return a device pointer to the
allocated memory.
"""
err, ptr = cuda_driver.cuMemAlloc(size)
if err != cuda_driver.CUresult.CUDA_SUCCESS:
raise RuntimeError(f'Unexpected error code {err} from cuMemAlloc')
return ptr
def driver_free(ptr):
"""
Free device memory pointed to by `ptr`.
"""
err, = cuda_driver.cuMemFree(ptr)
if err != cuda_driver.CUresult.CUDA_SUCCESS:
raise RuntimeError(f'Unexpected error code {err} from cuMemFree')
def driver_memory_info():
"""
Return the free and total amount of device memory in bytes as a tuple.
"""
err, free, total = cuda_driver.cuMemGetInfo()
if err != cuda_driver.CUresult.CUDA_SUCCESS:
raise RuntimeError(f'Unexpected error code {err} from cuMemGetInfo')
return free, total
# EMM Plugin implementation. For documentation of the methods implemented here,
# see:
#
# https://numba.readthedocs.io/en/stable/cuda/external-memory.html#numba.cuda.BaseCUDAMemoryManager
class DriverEMMPlugin(GetIpcHandleMixin, HostOnlyCUDAMemoryManager):
def memalloc(self, size):
ptr = driver_alloc(size)
ctx = self.context
finalizer = make_finalizer(ptr)
# We wrap the pointer value in a c_size_t because Numba expects ctypes
# objects
wrapped_ptr = c_size_t(int(ptr))
return MemoryPointer(ctx, wrapped_ptr, size, finalizer=finalizer)
def initialize(self):
# No setup required to use the EMM Plugin in a given context
pass
def get_memory_info(self):
free, total = driver_memory_info()
return MemoryInfo(free=free, total=total)
@property
def interface_version(self):
return 1
def make_finalizer(ptr):
def finalizer():
driver_free(ptr)
return finalizer
# If NUMBA_CUDA_MEMORY_MANAGER is set to this module (e.g.
# `NUMBA_CUDA_MEMORY_MANAGER=numba_emm_plugin`), then Numba will look at the
# _numba_memory_manager global to determine what class to use for memory
# management.
_numba_memory_manager = DriverEMMPlugin
def main():
"""
A simple test / demonstration setting the memory manager and
allocating/deleting an array.
"""
cuda.set_memory_manager(DriverEMMPlugin)
ctx = cuda.current_context()
print(f"Free before creating device array: {ctx.get_memory_info().free}")
x = cuda.device_array(1000)
print(f"Free after creating device array: {ctx.get_memory_info().free}")
del x
print(f"Free after freeing device array: {ctx.get_memory_info().free}")
if __name__ == '__main__':
import argparse
formatter = argparse.RawDescriptionHelpFormatter
parser = argparse.ArgumentParser(description=__doc__,
formatter_class=formatter)
parser.parse_args()
main()
| cuda-python-main | examples/extra/numba_emm_plugin.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import ctypes
import math
import numpy as np
import random as rnd
import sys
from cuda import cuda, cudart
from examples.common import common
from examples.common.helper_cuda import checkCudaErrors
from examples.common.helper_string import checkCmdLineFlag
simpleZeroCopy = '''\
extern "C"
__global__ void vectorAddGPU(float *a, float *b, float *c, int N)
{
int idx = blockIdx.x*blockDim.x + threadIdx.x;
if (idx < N)
{
c[idx] = a[idx] + b[idx];
}
}
'''
def main():
idev = 0
bPinGenericMemory = False
if checkCmdLineFlag("help"):
print("Usage: simpleZeroCopy [OPTION]\n")
print("Options:")
print(" device=[device #] Specify the device to be used")
print(" use_generic_memory (optional) use generic page-aligned for system memory")
return
# Get the device selected by the user or default to 0, and then set it.
if checkCmdLineFlag("device="):
deviceCount = cudart.cudaGetDeviceCount()
idev = int(getCmdLineArgumentInt("device="))
if idev >= deviceCount or idev < 0:
print("Device number {} is invalid, will use default CUDA device 0.".format(idev))
idev = 0
if checkCmdLineFlag("use_generic_memory"):
bPinGenericMemory = True
if bPinGenericMemory:
print("> Using Generic System Paged Memory (malloc)");
else:
print("> Using CUDA Host Allocated (cudaHostAlloc)");
checkCudaErrors(cudart.cudaSetDevice(idev))
# Verify the selected device supports mapped memory and set the device flags for mapping host memory.
deviceProp = checkCudaErrors(cudart.cudaGetDeviceProperties(idev))
if not deviceProp.canMapHostMemory:
print("Device {} does not support mapping CPU host memory!".format(idev))
return
checkCudaErrors(cudart.cudaSetDeviceFlags(cudart.cudaDeviceMapHost))
# Allocate mapped CPU memory
nelem = 1048576
num_bytes = nelem*np.dtype(np.float32).itemsize
if bPinGenericMemory:
a = np.empty(nelem, dtype=np.float32)
b = np.empty(nelem, dtype=np.float32)
c = np.empty(nelem, dtype=np.float32)
checkCudaErrors(cudart.cudaHostRegister(a, num_bytes, cudart.cudaHostRegisterMapped))
checkCudaErrors(cudart.cudaHostRegister(b, num_bytes, cudart.cudaHostRegisterMapped))
checkCudaErrors(cudart.cudaHostRegister(c, num_bytes, cudart.cudaHostRegisterMapped))
else:
flags = cudart.cudaHostAllocMapped
a_ptr = checkCudaErrors(cudart.cudaHostAlloc(num_bytes, flags))
b_ptr = checkCudaErrors(cudart.cudaHostAlloc(num_bytes, flags))
c_ptr = checkCudaErrors(cudart.cudaHostAlloc(num_bytes, flags))
a = (ctypes.c_float * nelem).from_address(a_ptr)
b = (ctypes.c_float * nelem).from_address(b_ptr)
c = (ctypes.c_float * nelem).from_address(c_ptr)
# Initialize the vectors
for n in range(nelem):
a[n] = rnd.random()
b[n] = rnd.random()
# Get the device pointers for the pinned CPU memory mapped into the GPU memory space
d_a = checkCudaErrors(cudart.cudaHostGetDevicePointer(a, 0))
d_b = checkCudaErrors(cudart.cudaHostGetDevicePointer(b, 0))
d_c = checkCudaErrors(cudart.cudaHostGetDevicePointer(c, 0))
# Call the GPU kernel using the CPU pointers residing in CPU mapped memory
print("> vectorAddGPU kernel will add vectors using mapped CPU memory...")
block = cudart.dim3()
block.x = 256
block.y = 1
block.z = 1
grid = cudart.dim3()
grid.x = math.ceil(nelem/float(block.x))
grid.y = 1
grid.z = 1
kernelHelper = common.KernelHelper(simpleZeroCopy, idev)
_vectorAddGPU = kernelHelper.getFunction(b'vectorAddGPU')
kernelArgs = ((d_a, d_b, d_c, nelem),(ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_int))
checkCudaErrors(cuda.cuLaunchKernel(_vectorAddGPU,
grid.x, grid.y, grid.z,
block.x, block.y, block.z,
0, cuda.CU_STREAM_LEGACY,
kernelArgs, 0))
checkCudaErrors(cudart.cudaDeviceSynchronize())
print("> Checking the results from vectorAddGPU() ...");
# Compare the results
errorNorm = 0.0
refNorm = 0.0
for n in range(nelem):
ref = a[n] + b[n]
diff = c[n] - ref
errorNorm += diff*diff
refNorm += ref*ref
errorNorm = math.sqrt(errorNorm)
refNorm = math.sqrt(refNorm)
# Memory clean up
print("Releasing CPU memory...")
if bPinGenericMemory:
checkCudaErrors(cudart.cudaHostUnregister(a))
checkCudaErrors(cudart.cudaHostUnregister(b))
checkCudaErrors(cudart.cudaHostUnregister(c))
else:
checkCudaErrors(cudart.cudaFreeHost(a))
checkCudaErrors(cudart.cudaFreeHost(b))
checkCudaErrors(cudart.cudaFreeHost(c))
if errorNorm/refNorm >= 1.0e-7:
print("FAILED")
sys.exit(-1)
print("PASSED")
if __name__=="__main__":
main()
| cuda-python-main | examples/0_Introduction/simpleZeroCopy_test.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import numpy as np
from cuda import cuda
from examples.common import common
from examples.common.helper_cuda import checkCudaErrors, findCudaDevice
clock_nvrtc = '''\
extern "C" __global__ void timedReduction(const float *hinput, float *output, clock_t *timer)
{
// __shared__ float shared[2 * blockDim.x];
extern __shared__ float shared[];
const int tid = threadIdx.x;
const int bid = blockIdx.x;
if (tid == 0) timer[bid] = clock();
// Copy hinput.
shared[tid] = hinput[tid];
shared[tid + blockDim.x] = hinput[tid + blockDim.x];
// Perform reduction to find minimum.
for (int d = blockDim.x; d > 0; d /= 2)
{
__syncthreads();
if (tid < d)
{
float f0 = shared[tid];
float f1 = shared[tid + d];
if (f1 < f0)
{
shared[tid] = f1;
}
}
}
// Write result.
if (tid == 0) output[bid] = shared[0];
__syncthreads();
if (tid == 0) timer[bid+gridDim.x] = clock();
}
'''
NUM_BLOCKS = 64
NUM_THREADS = 256
def main():
print("CUDA Clock sample")
timer = np.empty(NUM_BLOCKS * 2, dtype='int64')
hinput = np.empty(NUM_THREADS * 2, dtype='float32')
for i in range(0, NUM_THREADS * 2):
hinput[i] = i
devID = findCudaDevice()
kernelHelper = common.KernelHelper(clock_nvrtc, devID)
kernel_addr = kernelHelper.getFunction(b'timedReduction')
dinput = checkCudaErrors(cuda.cuMemAlloc(np.dtype(np.float32).itemsize * NUM_THREADS * 2))
doutput = checkCudaErrors(cuda.cuMemAlloc(np.dtype(np.float32).itemsize * NUM_BLOCKS))
dtimer = checkCudaErrors(cuda.cuMemAlloc(np.dtype(np.int64).itemsize * NUM_BLOCKS * 2))
checkCudaErrors(cuda.cuMemcpyHtoD(dinput, hinput, np.dtype(np.float32).itemsize * NUM_THREADS * 2))
arr = ((dinput, doutput, dtimer),
(None, None, None))
checkCudaErrors(cuda.cuLaunchKernel(kernel_addr,
NUM_BLOCKS, 1, 1, # grid dim
NUM_THREADS, 1, 1, # block dim
np.dtype(np.float32).itemsize * 2 *NUM_THREADS, 0, # shared mem, stream
arr, 0)) # arguments
checkCudaErrors(cuda.cuCtxSynchronize())
checkCudaErrors(cuda.cuMemcpyDtoH(timer, dtimer, np.dtype(np.int64).itemsize * NUM_BLOCKS * 2))
checkCudaErrors(cuda.cuMemFree(dinput))
checkCudaErrors(cuda.cuMemFree(doutput))
checkCudaErrors(cuda.cuMemFree(dtimer))
avgElapsedClocks = 0.0
for i in range(0,NUM_BLOCKS):
avgElapsedClocks += timer[i + NUM_BLOCKS] - timer[i]
avgElapsedClocks = avgElapsedClocks/NUM_BLOCKS;
print("Average clocks/block = {}".format(avgElapsedClocks))
if __name__=="__main__":
main()
| cuda-python-main | examples/0_Introduction/clock_nvrtc_test.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import ctypes
import math
import numpy as np
from cuda import cuda
from examples.common import common
from examples.common.helper_cuda import checkCudaErrors, findCudaDeviceDRV
vectorAddDrv = '''\
/* Vector addition: C = A + B.
*
* This sample is a very basic sample that implements element by element
* vector addition. It is the same as the sample illustrating Chapter 3
* of the programming guide with some additions like error checking.
*
*/
// Device code
extern "C" __global__ void VecAdd_kernel(const float *A, const float *B, float *C, int N)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < N)
C[i] = A[i] + B[i];
}
'''
def main():
print("Vector Addition (Driver API)")
N = 50000
devID = 0
size = N * np.dtype(np.float32).itemsize
# Initialize
checkCudaErrors(cuda.cuInit(0));
cuDevice = findCudaDeviceDRV()
# Create context
cuContext = checkCudaErrors(cuda.cuCtxCreate(0, cuDevice))
uvaSupported = checkCudaErrors(cuda.cuDeviceGetAttribute(cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_UNIFIED_ADDRESSING, cuDevice))
if not uvaSupported:
print("Accessing pageable memory directly requires UVA")
return
kernelHelper = common.KernelHelper(vectorAddDrv, int(cuDevice))
_VecAdd_kernel = kernelHelper.getFunction(b'VecAdd_kernel')
# Allocate input vectors h_A and h_B in host memory
h_A = np.random.rand(size).astype(dtype=np.float32)
h_B = np.random.rand(size).astype(dtype=np.float32)
h_C = np.random.rand(size).astype(dtype=np.float32)
# Allocate vectors in device memory
d_A = checkCudaErrors(cuda.cuMemAlloc(size))
d_B = checkCudaErrors(cuda.cuMemAlloc(size))
d_C = checkCudaErrors(cuda.cuMemAlloc(size))
# Copy vectors from host memory to device memory
checkCudaErrors(cuda.cuMemcpyHtoD(d_A, h_A, size))
checkCudaErrors(cuda.cuMemcpyHtoD(d_B, h_B, size))
if True:
# Grid/Block configuration
threadsPerBlock = 256
blocksPerGrid = (N + threadsPerBlock - 1) / threadsPerBlock
kernelArgs = ((d_A, d_B, d_C, N),
(None, None, None, ctypes.c_int))
# Launch the CUDA kernel
checkCudaErrors(cuda.cuLaunchKernel(_VecAdd_kernel,
blocksPerGrid, 1, 1,
threadsPerBlock, 1, 1,
0, 0,
kernelArgs, 0))
else:
pass
# Copy result from device memory to host memory
# h_C contains the result in host memory
checkCudaErrors(cuda.cuMemcpyDtoH(h_C, d_C, size))
for i in range(N):
sum_all = h_A[i] + h_B[i]
if math.fabs(h_C[i] - sum_all) > 1e-7:
break
# Free device memory
checkCudaErrors(cuda.cuMemFree(d_A))
checkCudaErrors(cuda.cuMemFree(d_B))
checkCudaErrors(cuda.cuMemFree(d_C))
checkCudaErrors(cuda.cuCtxDestroy(cuContext))
print("{}".format("Result = PASS" if i+1 == N else "Result = FAIL"))
if i+1 != N:
sys.exit(-1)
if __name__ == "__main__":
main()
| cuda-python-main | examples/0_Introduction/vectorAddDrv_test.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import ctypes
import math
import numpy as np
import sys
import time
from cuda import cuda, cudart
from examples.common import common
from examples.common.helper_cuda import checkCudaErrors, findCudaDevice
simpleCubemapTexture = '''\
extern "C"
__global__ void transformKernel(float *g_odata, int width, cudaTextureObject_t tex)
{
// calculate this thread's data point
unsigned int x = blockIdx.x*blockDim.x + threadIdx.x;
unsigned int y = blockIdx.y*blockDim.y + threadIdx.y;
// 0.5f offset and division are necessary to access the original data points
// in the texture (such that bilinear interpolation will not be activated).
// For details, see also CUDA Programming Guide, Appendix D
float u = ((x+0.5f) / (float) width) * 2.f - 1.f;
float v = ((y+0.5f) / (float) width) * 2.f - 1.f;
float cx, cy, cz;
for (unsigned int face = 0; face < 6; face ++)
{
//Layer 0 is positive X face
if (face == 0)
{
cx = 1;
cy = -v;
cz = -u;
}
//Layer 1 is negative X face
else if (face == 1)
{
cx = -1;
cy = -v;
cz = u;
}
//Layer 2 is positive Y face
else if (face == 2)
{
cx = u;
cy = 1;
cz = v;
}
//Layer 3 is negative Y face
else if (face == 3)
{
cx = u;
cy = -1;
cz = -v;
}
//Layer 4 is positive Z face
else if (face == 4)
{
cx = u;
cy = -v;
cz = 1;
}
//Layer 4 is negative Z face
else if (face == 5)
{
cx = -u;
cy = -v;
cz = -1;
}
// read from texture, do expected transformation and write to global memory
g_odata[face*width*width + y*width + x] = -texCubemap<float>(tex, cx, cy, cz);
}
}
'''
def main():
# Use command-line specified CUDA device, otherwise use device with highest Gflops/s
devID = findCudaDevice()
# Get number of SMs on this GPU
deviceProps = checkCudaErrors(cudart.cudaGetDeviceProperties(devID));
print("CUDA device [{}] has {} Multi-Processors SM {}.{}".format(deviceProps.name,
deviceProps.multiProcessorCount,
deviceProps.major,
deviceProps.minor))
if (deviceProps.major < 2):
print("{} requires SM 2.0 or higher for support of Texture Arrays. Test will exit...".format(sSDKname))
sys.exit()
# Generate input data for layered texture
width = 64
num_faces = 6
num_layers = 1
cubemap_size = width * width * num_faces
size = cubemap_size * num_layers * np.dtype(np.float32).itemsize
h_data = np.zeros(cubemap_size * num_layers, dtype='float32')
for i in range(cubemap_size * num_layers):
h_data[i] = i
# This is the expected transformation of the input data (the expected output)
h_data_ref = np.zeros(cubemap_size * num_layers, dtype='float32')
for layer in range(num_layers):
for i in range(cubemap_size):
h_data_ref[layer*cubemap_size + i] = -h_data[layer*cubemap_size + i] + layer
# Allocate device memory for result
d_data = checkCudaErrors(cudart.cudaMalloc(size))
# Allocate array and copy image data
channelDesc = checkCudaErrors(cudart.cudaCreateChannelDesc(32, 0, 0, 0, cudart.cudaChannelFormatKind.cudaChannelFormatKindFloat))
cu_3darray = checkCudaErrors(cudart.cudaMalloc3DArray(channelDesc, cudart.make_cudaExtent(width, width, num_faces), cudart.cudaArrayCubemap))
myparms = cudart.cudaMemcpy3DParms()
myparms.srcPos = cudart.make_cudaPos(0,0,0)
myparms.dstPos = cudart.make_cudaPos(0,0,0)
myparms.srcPtr = cudart.make_cudaPitchedPtr(h_data, width * np.dtype(np.float32).itemsize, width, width)
myparms.dstArray = cu_3darray
myparms.extent = cudart.make_cudaExtent(width, width, num_faces)
myparms.kind = cudart.cudaMemcpyKind.cudaMemcpyHostToDevice
checkCudaErrors(cudart.cudaMemcpy3D(myparms))
texRes = cudart.cudaResourceDesc()
texRes.resType = cudart.cudaResourceType.cudaResourceTypeArray
texRes.res.array.array = cu_3darray
texDescr = cudart.cudaTextureDesc()
texDescr.normalizedCoords = True
texDescr.filterMode = cudart.cudaTextureFilterMode.cudaFilterModeLinear
texDescr.addressMode[0] = cudart.cudaTextureAddressMode.cudaAddressModeWrap
texDescr.addressMode[1] = cudart.cudaTextureAddressMode.cudaAddressModeWrap
texDescr.addressMode[2] = cudart.cudaTextureAddressMode.cudaAddressModeWrap
texDescr.readMode = cudart.cudaTextureReadMode.cudaReadModeElementType
tex = checkCudaErrors(cudart.cudaCreateTextureObject(texRes, texDescr, None))
dimBlock = cudart.dim3()
dimBlock.x = 8
dimBlock.y = 8
dimBlock.z = 1
dimGrid = cudart.dim3()
dimGrid.x = width / dimBlock.x
dimGrid.y = width / dimBlock.y
dimGrid.z = 1
print("Covering Cubemap data array of {}~3 x {}: Grid size is {} x {}, each block has 8 x 8 threads".format(
width, num_layers, dimGrid.x, dimGrid.y))
kernelHelper = common.KernelHelper(simpleCubemapTexture, devID)
_transformKernel = kernelHelper.getFunction(b'transformKernel')
kernelArgs = ((d_data, width, tex),(ctypes.c_void_p, ctypes.c_int, None))
checkCudaErrors(cuda.cuLaunchKernel(_transformKernel,
dimGrid.x, dimGrid.y, dimGrid.z, # grid dim
dimBlock.x, dimBlock.y, dimBlock.z, # block dim
0, 0, # shared mem and stream
kernelArgs, 0)) # arguments
checkCudaErrors(cudart.cudaDeviceSynchronize())
start = time.time()
# Execute the kernel
checkCudaErrors(cuda.cuLaunchKernel(_transformKernel,
dimGrid.x, dimGrid.y, dimGrid.z, # grid dim
dimBlock.x, dimBlock.y, dimBlock.z, # block dim
0, 0, # shared mem and stream
kernelArgs, 0)) # arguments
checkCudaErrors(cudart.cudaDeviceSynchronize())
stop = time.time()
print("Processing time: {:.3f} msec".format(stop - start))
print("{:.2f} Mtexlookups/sec".format(cubemap_size / ((stop - start + 1) / 1000.0) / 1e6))
# Allocate mem for the result on host side
h_odata = np.zeros(cubemap_size * num_layers, dtype='float32')
# Copy result from device to host
checkCudaErrors(cudart.cudaMemcpy(h_odata, d_data, size, cudart.cudaMemcpyKind.cudaMemcpyDeviceToHost))
print("Comparing kernel output to expected data")
MIN_EPSILON_ERROR = 5.0e-3
for i in range(cubemap_size * num_layers):
d = h_odata[i] - h_data_ref[i]
if math.fabs(d) > MIN_EPSILON_ERROR:
print("Failed")
sys.exit(-1)
print("Passed")
checkCudaErrors(cudart.cudaDestroyTextureObject(tex))
checkCudaErrors(cudart.cudaFree(d_data))
checkCudaErrors(cudart.cudaFreeArray(cu_3darray))
if __name__=="__main__":
main()
| cuda-python-main | examples/0_Introduction/simpleCubemapTexture_test.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import ctypes
import numpy as np
import sys
import os
from cuda import cuda, cudart
from examples.common import common
from examples.common.helper_cuda import checkCudaErrors, findCudaDevice
systemWideAtomics = '''\
#define LOOP_NUM 50
extern "C"
__global__ void atomicKernel(int *atom_arr) {
unsigned int tid = blockDim.x * blockIdx.x + threadIdx.x;
for (int i = 0; i < LOOP_NUM; i++) {
// Atomic addition
atomicAdd_system(&atom_arr[0], 10);
// Atomic exchange
atomicExch_system(&atom_arr[1], tid);
// Atomic maximum
atomicMax_system(&atom_arr[2], tid);
// Atomic minimum
atomicMin_system(&atom_arr[3], tid);
// Atomic increment (modulo 17+1)
atomicInc_system((unsigned int *)&atom_arr[4], 17);
// Atomic decrement
atomicDec_system((unsigned int *)&atom_arr[5], 137);
// Atomic compare-and-swap
atomicCAS_system(&atom_arr[6], tid - 1, tid);
// Bitwise atomic instructions
// Atomic AND
atomicAnd_system(&atom_arr[7], 2 * tid + 7);
// Atomic OR
atomicOr_system(&atom_arr[8], 1 << tid);
// Atomic XOR
atomicXor_system(&atom_arr[9], tid);
}
}
'''
LOOP_NUM = 50
#! Compute reference data set
#! Each element is multiplied with the number of threads / array length
#! @param reference reference data, computed but preallocated
#! @param idata input data as provided to device
#! @param len number of elements in reference / idata
def verify(testData, length):
val = 0
for i in range(length * LOOP_NUM):
val += 10
if val != testData[0]:
print(f"atomicAdd failed val = {val} testData = {testData[0]}")
return False
val = 0
found = False
for i in range(length):
# second element should be a member of [0, len)
if i == testData[1]:
found = True
break
if not found:
print("atomicExch failed")
return False
val = -(1 << 8)
for i in range(length):
# third element should be len-1
val = max(val, i)
if val != testData[2]:
print("atomicMax failed")
return False
val = 1 << 8
for i in range(length):
val = min(val, i)
if val != testData[3]:
print("atomicMin failed")
return False
limit = 17
val = 0
for i in range(length * LOOP_NUM):
val = 0 if val >= limit else val + 1
if val != testData[4]:
print("atomicInc failed")
return False
limit = 137
val = 0
for i in range(length * LOOP_NUM):
val = limit if (val == 0) or (val > limit) else val - 1
if val != testData[5]:
print("atomicDec failed")
return False
found = False
for i in range(length):
# seventh element should be a member of [0, len)
if i == testData[6]:
found = True
break
if not found:
print("atomicCAS failed")
return False
val = 0xff
for i in range(length):
# 8th element should be 1
val &= (2 * i + 7)
if val != testData[7]:
print("atomicAnd failed")
return False
# 9th element should be 0xff
val = -1
if val != testData[8]:
print("atomicOr failed")
return False
val = 0xff
for i in range(length):
# 11th element should be 0xff
val ^= i;
if val != testData[9]:
print("atomicXor failed")
return False
return True
def main():
if os.name == 'nt':
print("Atomics not supported on Windows")
return
# set device
dev_id = findCudaDevice()
device_prop = checkCudaErrors(cudart.cudaGetDeviceProperties(dev_id))
if not device_prop.managedMemory:
# This samples requires being run on a device that supports Unified Memory
print("Unified Memory not supported on this device")
return
if device_prop.computeMode == cudart.cudaComputeMode.cudaComputeModeProhibited:
# This sample requires being run with a default or process exclusive mode
print("This sample requires a device in either default or process exclusive mode")
return
if device_prop.major < 6:
print("Requires a minimum CUDA compute 6.0 capability, waiving testing.")
return
numThreads = 256
numBlocks = 64
numData = 10
if device_prop.pageableMemoryAccess:
print("CAN access pageable memory")
atom_arr_h = (ctypes.c_int * numData)(0)
atom_arr = ctypes.addressof(atom_arr_h)
else:
print("CANNOT access pageable memory")
atom_arr = checkCudaErrors(cudart.cudaMallocManaged(np.dtype(np.int32).itemsize * numData, cudart.cudaMemAttachGlobal))
atom_arr_h = (ctypes.c_int * numData).from_address(atom_arr)
for i in range(numData):
atom_arr_h[i] = 0
# To make the AND and XOR tests generate something other than 0...
atom_arr_h[7] = atom_arr_h[9] = 0xff
kernelHelper = common.KernelHelper(systemWideAtomics, dev_id)
_atomicKernel = kernelHelper.getFunction(b'atomicKernel')
kernelArgs = ((atom_arr,),
(ctypes.c_void_p,))
checkCudaErrors(cuda.cuLaunchKernel(_atomicKernel,
numBlocks, 1, 1, # grid dim
numThreads, 1, 1, # block dim
0, cuda.CU_STREAM_LEGACY, # shared mem and stream
kernelArgs, 0)) # arguments
# NOTE: Python doesn't have an equivalent system atomic operations
# atomicKernel_CPU(atom_arr_h, numBlocks * numThreads)
checkCudaErrors(cudart.cudaDeviceSynchronize())
# Compute & verify reference solution
testResult = verify(atom_arr_h, numThreads * numBlocks)
if device_prop.pageableMemoryAccess:
pass
else:
checkCudaErrors(cudart.cudaFree(atom_arr))
print("systemWideAtomics completed, returned {}".format("OK" if testResult else "ERROR!"))
if not testResult:
sys.exit(-1)
if __name__=="__main__":
main()
| cuda-python-main | examples/0_Introduction/systemWideAtomics_test.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import ctypes
import numpy as np
import sys
from cuda import cuda, cudart
from examples.common import common
from examples.common.helper_cuda import checkCudaErrors
simplep2p = '''\
extern "C"
__global__ void SimpleKernel(float *src, float *dst)
{
// Just a dummy kernel, doing enough for us to verify that everything
// worked
const int idx = blockIdx.x * blockDim.x + threadIdx.x;
dst[idx] = src[idx] * 2.0f;
}
'''
def main():
print("Starting...")
# Number of GPUs
print("Checking for multiple GPUs...")
gpu_n = checkCudaErrors(cudart.cudaGetDeviceCount())
print("CUDA-capable device count: {}".format(gpu_n))
if gpu_n < 2:
print("Two or more GPUs with Peer-to-Peer access capability are required")
return
prop = [checkCudaErrors(cudart.cudaGetDeviceProperties(i)) for i in range(gpu_n)]
# Check possibility for peer access
print("\nChecking GPU(s) for support of peer to peer memory access...")
p2pCapableGPUs = [-1, -1]
for i in range(gpu_n):
p2pCapableGPUs[0] = i
for j in range(gpu_n):
if i == j:
continue
i_access_j = checkCudaErrors(cudart.cudaDeviceCanAccessPeer(i, j))
j_access_i = checkCudaErrors(cudart.cudaDeviceCanAccessPeer(j, i))
print("> Peer access from {} (GPU{}) -> {} (GPU{}) : {}\n".format(
prop[i].name, i, prop[j].name, j, "Yes" if i_access_j else "No"))
print("> Peer access from {} (GPU{}) -> {} (GPU{}) : {}\n".format(
prop[j].name, j, prop[i].name, i, "Yes" if i_access_j else "No"))
if i_access_j and j_access_i:
p2pCapableGPUs[1] = j
break
if p2pCapableGPUs[1] != -1:
break
if p2pCapableGPUs[0] == -1 or p2pCapableGPUs[1] == -1:
print("Two or more GPUs with Peer-to-Peer access capability are required.")
print("Peer to Peer access is not available amongst GPUs in the system, waiving test.")
return
# Use first pair of p2p capable GPUs detected
gpuid = [p2pCapableGPUs[0], p2pCapableGPUs[1]]
# Enable peer access
print("Enabling peer access between GPU{} and GPU{}...".format(gpuid[0], gpuid[1]))
checkCudaErrors(cudart.cudaSetDevice(gpuid[0]))
checkCudaErrors(cudart.cudaDeviceEnablePeerAccess(gpuid[1], 0))
checkCudaErrors(cudart.cudaSetDevice(gpuid[1]))
checkCudaErrors(cudart.cudaDeviceEnablePeerAccess(gpuid[0], 0))
# Allocate buffers
buf_size = 1024 * 1024 * 16 * np.dtype(np.float32).itemsize
print("Allocating buffers ({}MB on GPU{}, GPU{} and CPU Host)...".format(int(buf_size / 1024 / 1024), gpuid[0], gpuid[1]))
checkCudaErrors(cudart.cudaSetDevice(gpuid[0]))
g0 = checkCudaErrors(cudart.cudaMalloc(buf_size))
checkCudaErrors(cudart.cudaSetDevice(gpuid[1]))
g1 = checkCudaErrors(cudart.cudaMalloc(buf_size))
h0 = checkCudaErrors(cudart.cudaMallocHost(buf_size)) # Automatically portable with UVA
# Create CUDA event handles
print("Creating event handles...")
eventflags = cudart.cudaEventBlockingSync
start_event = checkCudaErrors(cudart.cudaEventCreateWithFlags(eventflags))
stop_event = checkCudaErrors(cudart.cudaEventCreateWithFlags(eventflags))
# P2P memcopy() benchmark
checkCudaErrors(cudart.cudaEventRecord(start_event, cudart.cudaStream_t(0)))
for i in range(100):
# With UVA we don't need to specify source and target devices, the
# runtime figures this out by itself from the pointers
# Ping-pong copy between GPUs
if i % 2 == 0:
checkCudaErrors(cudart.cudaMemcpy(g1, g0, buf_size, cudart.cudaMemcpyKind.cudaMemcpyDefault))
else:
checkCudaErrors(cudart.cudaMemcpy(g0, g1, buf_size, cudart.cudaMemcpyKind.cudaMemcpyDefault))
checkCudaErrors(cudart.cudaEventRecord(stop_event, cudart.cudaStream_t(0)))
checkCudaErrors(cudart.cudaEventSynchronize(stop_event))
time_memcpy = checkCudaErrors(cudart.cudaEventElapsedTime(start_event, stop_event))
print("cudaMemcpyPeer / cudaMemcpy between GPU{} and GPU{}: {:.2f}GB/s".format(gpuid[0], gpuid[1],
(1.0 / (time_memcpy / 1000.0)) * ((100.0 * buf_size)) / 1024.0 / 1024.0 / 1024.0))
# Prepare host buffer and copy to GPU 0
print("Preparing host buffer and memcpy to GPU{}...".format(gpuid[0]))
h0_local = (ctypes.c_float * int(buf_size / np.dtype(np.float32).itemsize)).from_address(h0)
for i in range(int(buf_size / np.dtype(np.float32).itemsize)):
h0_local[i] = i % 4096
checkCudaErrors(cudart.cudaSetDevice(gpuid[0]))
checkCudaErrors(cudart.cudaMemcpy(g0, h0, buf_size, cudart.cudaMemcpyKind.cudaMemcpyDefault))
# Kernel launch configuration
threads = cudart.dim3()
threads.x = 512
threads.y = 1
threads.z = 1
blocks = cudart.dim3()
blocks.x = (buf_size / np.dtype(np.float32).itemsize) / threads.x
blocks.y = 1
blocks.z = 1
# Run kernel on GPU 1, reading input from the GPU 0 buffer, writing
# output to the GPU 1 buffer
print("Run kernel on GPU{}, taking source data from GPU{} and writing to GPU{}...".format(
gpuid[1], gpuid[0], gpuid[1]))
checkCudaErrors(cudart.cudaSetDevice(gpuid[1]))
kernelHelper = [None]*2
_simpleKernel = [None]*2
kernelArgs = [None]*2
kernelHelper[1] = common.KernelHelper(simplep2p, gpuid[1])
_simpleKernel[1] = kernelHelper[1].getFunction(b'SimpleKernel')
kernelArgs[1] = ((g0, g1), (ctypes.c_void_p, ctypes.c_void_p))
checkCudaErrors(cuda.cuLaunchKernel(_simpleKernel[1],
blocks.x, blocks.y, blocks.z,
threads.x, threads.y, threads.z,
0, 0,
kernelArgs[1], 0))
checkCudaErrors(cudart.cudaDeviceSynchronize())
# Run kernel on GPU 0, reading input from the GPU 1 buffer, writing
# output to the GPU 0 buffer
print("Run kernel on GPU{}, taking source data from GPU{} and writing to GPU{}...".format(
gpuid[0], gpuid[1], gpuid[0]))
checkCudaErrors(cudart.cudaSetDevice(gpuid[0]))
kernelHelper[0] = common.KernelHelper(simplep2p, gpuid[0])
_simpleKernel[0] = kernelHelper[0].getFunction(b'SimpleKernel')
kernelArgs[0] = ((g1, g0), (ctypes.c_void_p, ctypes.c_void_p))
checkCudaErrors(cuda.cuLaunchKernel(_simpleKernel[0],
blocks.x, blocks.y, blocks.z,
threads.x, threads.y, threads.z,
0, 0,
kernelArgs[0], 0))
checkCudaErrors(cudart.cudaDeviceSynchronize())
# Copy data back to host and verify
print("Copy data back to host from GPU{} and verify results...".format(gpuid[0]))
checkCudaErrors(cudart.cudaMemcpy(h0, g0, buf_size, cudart.cudaMemcpyKind.cudaMemcpyDefault))
error_count = 0
for i in range(int(buf_size / np.dtype(np.float32).itemsize)):
# Re-generate input data and apply 2x '* 2.0f' computation of both
# kernel runs
if h0_local[i] != float(i % 4096) * 2.0 * 2.0:
print("Verification error @ element {}: val = {}, ref = {}\n".format(i, h0_local[i], (float(i%4096)*2.0*2.0)))
error_count += 1
if error_count > 10:
break
# Disable peer access (also unregisters memory for non-UVA cases)
print("Disabling peer access...")
checkCudaErrors(cudart.cudaSetDevice(gpuid[0]))
checkCudaErrors(cudart.cudaDeviceDisablePeerAccess(gpuid[1]))
checkCudaErrors(cudart.cudaSetDevice(gpuid[1]))
checkCudaErrors(cudart.cudaDeviceDisablePeerAccess(gpuid[0]))
# Cleanup and shutdown
print("Shutting down...")
checkCudaErrors(cudart.cudaEventDestroy(start_event))
checkCudaErrors(cudart.cudaEventDestroy(stop_event))
checkCudaErrors(cudart.cudaSetDevice(gpuid[0]))
checkCudaErrors(cudart.cudaFree(g0))
checkCudaErrors(cudart.cudaSetDevice(gpuid[1]))
checkCudaErrors(cudart.cudaFree(g1))
checkCudaErrors(cudart.cudaFreeHost(h0))
for i in range(gpu_n):
checkCudaErrors(cudart.cudaSetDevice(i))
if error_count != 0:
print("Test failed!")
sys.exit(-1)
print("Test passed!")
if __name__=="__main__":
main()
| cuda-python-main | examples/0_Introduction/simpleP2P_test.py |
# Copyright 2021-2023 NVIDIA Corporation. All rights reserved.
#
# Please refer to the NVIDIA end user license agreement (EULA) associated
# with this source code for terms and conditions that govern your use of
# this software. Any use, reproduction, disclosure, or distribution of
# this software and related documentation outside the terms of the EULA
# is strictly prohibited.
import ctypes
import math
import numpy as np
import sys
from cuda import cuda
from examples.common import common
from examples.common.helper_cuda import checkCudaErrors, findCudaDeviceDRV
vectorAddMMAP = '''\
/* Vector addition: C = A + B.
*
* This sample is a very basic sample that implements element by element
* vector addition. It is the same as the sample illustrating Chapter 3
* of the programming guide with some additions like error checking.
*
*/
// Device code
extern "C" __global__ void VecAdd_kernel(const float *A, const float *B, float *C, int N)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i < N)
C[i] = A[i] + B[i];
}
'''
def round_up(x, y):
return int((x - 1)/y + 1) * y
def getBackingDevices(cuDevice):
num_devices = checkCudaErrors(cuda.cuDeviceGetCount())
backingDevices = [cuDevice]
for dev in range(num_devices):
# The mapping device is already in the backingDevices vector
if int(dev) == int(cuDevice):
continue
# Only peer capable devices can map each others memory
capable = checkCudaErrors(cuda.cuDeviceCanAccessPeer(cuDevice, dev))
if not capable:
continue
# The device needs to support virtual address management for the required apis to work
attributeVal = checkCudaErrors(cuda.cuDeviceGetAttribute(
cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_VIRTUAL_ADDRESS_MANAGEMENT_SUPPORTED,
cuDevice))
if attributeVal == 0:
continue
backingDevices.append(cuda.CUdevice(dev))
return backingDevices
def simpleMallocMultiDeviceMmap(size, residentDevices, mappingDevices, align = 0):
min_granularity = 0
# Setup the properties common for all the chunks
# The allocations will be device pinned memory.
# This property structure describes the physical location where the memory will be allocated via cuMemCreate allong with additional properties
# In this case, the allocation will be pinnded device memory local to a given device.
prop = cuda.CUmemAllocationProp()
prop.type = cuda.CUmemAllocationType.CU_MEM_ALLOCATION_TYPE_PINNED
prop.location.type = cuda.CUmemLocationType.CU_MEM_LOCATION_TYPE_DEVICE
# Get the minimum granularity needed for the resident devices
# (the max of the minimum granularity of each participating device)
for device in residentDevices:
prop.location.id = device
status, granularity = cuda.cuMemGetAllocationGranularity(prop, cuda.CUmemAllocationGranularity_flags.CU_MEM_ALLOC_GRANULARITY_MINIMUM)
if status != cuda.CUresult.CUDA_SUCCESS:
return status, None, None
if min_granularity < granularity:
min_granularity = granularity
# Get the minimum granularity needed for the accessing devices
# (the max of the minimum granularity of each participating device)
for device in mappingDevices:
prop.location.id = device
status, granularity = cuda.cuMemGetAllocationGranularity(prop, cuda.CUmemAllocationGranularity_flags.CU_MEM_ALLOC_GRANULARITY_MINIMUM)
if status != cuda.CUresult.CUDA_SUCCESS:
return status, None, None
if min_granularity < granularity:
min_granularity = granularity
# Round up the size such that we can evenly split it into a stripe size tha meets the granularity requirements
# Essentially size = N * residentDevices.size() * min_granularity is the requirement,
# since each piece of the allocation will be stripeSize = N * min_granularity
# and the min_granularity requirement applies to each stripeSize piece of the allocation.
size = round_up(size, len(residentDevices) * min_granularity)
stripeSize = size / len(residentDevices)
# Return the rounded up size to the caller for use in the free
allocationSize = size
# Reserve the required contiguous VA space for the allocations
status, dptr = cuda.cuMemAddressReserve(size, align, cuda.CUdeviceptr(0), 0)
if status != cuda.CUresult.CUDA_SUCCESS:
simpleFreeMultiDeviceMmap(dptr, size)
return status, None, None
# Create and map the backings on each gpu
# note: reusing CUmemAllocationProp prop from earlier with prop.type & prop.location.type already specified.
for idx in range(len(residentDevices)):
# Set the location for this chunk to this device
prop.location.id = residentDevices[idx]
# Create the allocation as a pinned allocation on this device
status, allocationHandle = cuda.cuMemCreate(stripeSize, prop, 0)
if status != cuda.CUresult.CUDA_SUCCESS:
simpleFreeMultiDeviceMmap(dptr, size)
return status, None, None
# Assign the chunk to the appropriate VA range and release the handle.
# After mapping the memory, it can be referenced by virtual address.
# Since we do not need to make any other mappings of this memory or export it,
# we no longer need and can release the allocationHandle.
# The allocation will be kept live until it is unmapped.
status, = cuda.cuMemMap(int(dptr) + (stripeSize * idx), stripeSize, 0, allocationHandle, 0)
# the handle needs to be released even if the mapping failed.
status2, = cuda.cuMemRelease(allocationHandle)
if status != cuda.CUresult.CUDA_SUCCESS:
# cuMemRelease should not have failed here
# as the handle was just allocated successfully
# however return an error if it does.
status = status2
# Cleanup in case of any mapping failures.
if status != cuda.CUresult.CUDA_SUCCESS:
simpleFreeMultiDeviceMmap(dptr, size)
return status, None, None
# Each accessDescriptor will describe the mapping requirement for a single device
accessDescriptors = [cuda.CUmemAccessDesc()] * len(mappingDevices)
# Prepare the access descriptor array indicating where and how the backings should be visible.
for idx in range(len(mappingDevices)):
# Specify which device we are adding mappings for.
accessDescriptors[idx].location.type = cuda.CUmemLocationType.CU_MEM_LOCATION_TYPE_DEVICE
accessDescriptors[idx].location.id = mappingDevices[idx]
# Specify both read and write access.
accessDescriptors[idx].flags = cuda.CUmemAccess_flags.CU_MEM_ACCESS_FLAGS_PROT_READWRITE
# Apply the access descriptors to the whole VA range.
status, = cuda.cuMemSetAccess(dptr, size, accessDescriptors, len(accessDescriptors))
if status != cuda.CUresult.CUDA_SUCCESS:
simpleFreeMultiDeviceMmap(dptr, size)
return status, None, None
return (status, dptr, allocationSize)
def simpleFreeMultiDeviceMmap(dptr, size):
# Unmap the mapped virtual memory region
# Since the handles to the mapped backing stores have already been released
# by cuMemRelease, and these are the only/last mappings referencing them,
# The backing stores will be freed.
# Since the memory has been unmapped after this call, accessing the specified
# va range will result in a fault (unitll it is remapped).
status = cuda.cuMemUnmap(dptr, size);
if status[0] != cuda.CUresult.CUDA_SUCCESS:
return status
# Free the virtual address region. This allows the virtual address region
# to be reused by future cuMemAddressReserve calls. This also allows the
# virtual address region to be used by other allocation made through
# opperating system calls like malloc & mmap.
status = cuda.cuMemAddressFree(dptr, size)
if status[0] != cuda.CUresult.CUDA_SUCCESS:
return status
return status
def main():
print("Vector Addition (Driver API)")
N = 50000
size = N * np.dtype(np.float32).itemsize
# Initialize
checkCudaErrors(cuda.cuInit(0))
cuDevice = findCudaDeviceDRV()
# Check that the selected device supports virtual address management
attributeVal = checkCudaErrors(cuda.cuDeviceGetAttribute(
cuda.CUdevice_attribute.CU_DEVICE_ATTRIBUTE_VIRTUAL_ADDRESS_MANAGEMENT_SUPPORTED,
cuDevice))
print("Device {} VIRTUAL ADDRESS MANAGEMENT SUPPORTED = {}.".format(cuDevice, attributeVal))
if not attributeVal:
print("Device {} doesn't support VIRTUAL ADDRESS MANAGEMENT.".format(cuDevice))
return
# The vector addition happens on cuDevice, so the allocations need to be mapped there.
mappingDevices = [cuDevice]
# Collect devices accessible by the mapping device (cuDevice) into the backingDevices vector.
backingDevices = getBackingDevices(cuDevice)
# Create context
cuContext = checkCudaErrors(cuda.cuCtxCreate(0, cuDevice))
kernelHelper = common.KernelHelper(vectorAddMMAP, int(cuDevice))
_VecAdd_kernel = kernelHelper.getFunction(b'VecAdd_kernel')
# Allocate input vectors h_A and h_B in host memory
h_A = np.random.rand(size).astype(dtype=np.float32)
h_B = np.random.rand(size).astype(dtype=np.float32)
h_C = np.random.rand(size).astype(dtype=np.float32)
# Allocate vectors in device memory
# note that a call to cuCtxEnablePeerAccess is not needed even though
# the backing devices and mapping device are not the same.
# This is because the cuMemSetAccess call explicitly specifies
# the cross device mapping.
# cuMemSetAccess is still subject to the constraints of cuDeviceCanAccessPeer
# for cross device mappings (hence why we checked cuDeviceCanAccessPeer earlier).
d_A, allocationSize = checkCudaErrors(simpleMallocMultiDeviceMmap(size, backingDevices, mappingDevices))
d_B, _ = checkCudaErrors(simpleMallocMultiDeviceMmap(size, backingDevices, mappingDevices))
d_C, _ = checkCudaErrors(simpleMallocMultiDeviceMmap(size, backingDevices, mappingDevices))
# Copy vectors from host memory to device memory
checkCudaErrors(cuda.cuMemcpyHtoD(d_A, h_A, size))
checkCudaErrors(cuda.cuMemcpyHtoD(d_B, h_B, size))
# Grid/Block configuration
threadsPerBlock = 256
blocksPerGrid = (N + threadsPerBlock - 1) / threadsPerBlock
kernelArgs = ((d_A, d_B, d_C, N),
(None, None, None, ctypes.c_int))
# Launch the CUDA kernel
checkCudaErrors(cuda.cuLaunchKernel(_VecAdd_kernel,
blocksPerGrid, 1, 1,
threadsPerBlock, 1, 1,
0, 0,
kernelArgs, 0))
# Copy result from device memory to host memory
# h_C contains the result in host memory
checkCudaErrors(cuda.cuMemcpyDtoH(h_C, d_C, size))
# Verify result
for i in range(N):
sum_all = h_A[i] + h_B[i]
if math.fabs(h_C[i] - sum_all) > 1e-7:
break
checkCudaErrors(simpleFreeMultiDeviceMmap(d_A, allocationSize))
checkCudaErrors(simpleFreeMultiDeviceMmap(d_B, allocationSize))
checkCudaErrors(simpleFreeMultiDeviceMmap(d_C, allocationSize))
checkCudaErrors(cuda.cuCtxDestroy(cuContext))
print("{}".format("Result = PASS" if i+1 == N else "Result = FAIL"))
if i+1 != N:
sys.exit(-1)
if __name__ == "__main__":
main()
| cuda-python-main | examples/0_Introduction/vectorAddMMAP_test.py |
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
# import os
# import sys
# sys.path.insert(0, os.path.abspath('.'))
# -- Project information -----------------------------------------------------
project = 'CUDA Python'
copyright = '2021-2023, NVIDIA'
author = 'NVIDIA'
# The full version, including alpha/beta/rc tags
release = '12.2.0'
# -- General configuration ---------------------------------------------------
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.napoleon',
'myst_nb',
'enum_tools.autoenum'
]
jupyter_execute_notebooks = "force"
numfig=True
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
exclude_patterns = []
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_baseurl = 'docs'
html_theme = 'furo'
# html_theme = 'pydata_sphinx_theme'
html_theme_options = {
"light_logo": "logo-light-mode.png",
"dark_logo": "logo-dark-mode.png",
}
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
| cuda-python-main | docs_src/source/conf.py |
#!/bin/bash
# Apache License, Version 2.0
# Copyright 2019-2020 NVIDIA Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import errno
import logging
import logging.config as logging_config
import os
import shutil
from pathlib import Path
import clara
import pydicom
from clara import Driver, Error, Payload
logger = logging.getLogger(__name__)
def setupLogging(
default_path='logging_config.json',
default_level=logging.INFO,
env_key='LOG_CFG'):
""" Setup logging configuration """
path = default_path
value = os.getenv(env_key, None)
if value:
path = value
if os.path.exists(path):
with open(path, 'rt') as f:
config = json.load(f)
logging_config.dictConfig(config)
else:
logging.basicConfig(level=default_level)
def execute(driver, payload):
input_dir = os.environ.get(
'NVIDIA_CLARA_INPUTPATHS', '/input').split(":")[1]
output_dir = os.environ.get(
'NVIDIA_CLARA_OUTPUTPATHS', '/input').split(":")[1]
logger.info("Files in {}: {}".format(
input_dir, [f.name for f in os.scandir(input_dir) if f.is_file()]))
logger.info('Scanning input directory {}'.format(input_dir))
try:
files = _get_all_files(input_dir)
except Exception as ex:
logger.info('Failed to list files: {}'.format(ex))
invalid_dicom_files_count = 0
for file in files:
try:
pydicom.dcmread(file)
except Exception as ex:
invalid_dicom_files_count += 1
logger.info("Copying DICOM from {} to {}".format(input_dir, output_dir))
try:
for item in os.listdir(input_dir):
src = os.path.join(input_dir, item)
dst = os.path.join(output_dir, item)
if os.path.isdir(src):
shutil.copytree(src, dst, False, None)
else:
shutil.copy2(src, dst)
except Exception as ex:
logger.error('Failed to copy files: {}'.format(ex))
logger.info("Files in {}: {}".format(
output_dir, [f.name for f in os.scandir(output_dir) if f.is_file()]))
logger.info('Scanned {} files with {} non-DICOM part-10 file(s).'.format(len(files),
invalid_dicom_files_count))
if invalid_dicom_files_count > 0:
raise Exception('{} invalid DICOM part-10 file(s) found'.format(invalid_dicom_files_count))
def _get_all_files(input_dir):
files = []
for filename in Path(input_dir).glob('**/*'):
if filename.is_file():
files.append(os.path.abspath(filename))
return files
if __name__ == '__main__':
app_name = 'dicom-test'
try:
setupLogging()
except Exception as ex:
logger.error('Logging did not set up successfully. {}'.format(ex))
pass # Best effort
logger = logging.getLogger(__name__)
logger.info('Program {} started.'.format(app_name))
driver = Driver(execute_handler=execute)
driver.start()
driver.wait_for_completion()
logger.info('Program {} exited.'.format(app_name))
| clara-dicom-adapter-main | test/dicom-test-pipeline/main.py |
import torch
import numpy as np
import argparse
from Networks.FlowNet2 import FlowNet2 # the path is depended on where you create this module
from frame_utils import read_gen # the path is depended on where you create this module
if __name__ == '__main__':
# obtain the necessary args for construct the flownet framework
parser = argparse.ArgumentParser()
parser.add_argument('--fp16', action='store_true', help='Run model in pseudo-fp16 mode (fp16 storage fp32 math).')
parser.add_argument("--rgb_max", type=float, default=255.)
args = parser.parse_args()
# initial a Net
net = FlowNet2(args).cuda()
# load the state_dict
dict = torch.load("/home/hjj/PycharmProjects/flownet2_pytorch/FlowNet2_checkpoint.pth.tar")
net.load_state_dict(dict["state_dict"])
# load the image pair, you can find this operation in dataset.py
pim1 = read_gen("/home/hjj/flownet2-master/data/FlyingChairs_examples/0000007-img0.ppm")
pim2 = read_gen("/home/hjj/flownet2-master/data/FlyingChairs_examples/0000007-img1.ppm")
images = [pim1, pim2]
images = np.array(images).transpose(3, 0, 1, 2)
im = torch.from_numpy(images.astype(np.float32)).unsqueeze(0).cuda()
# process the image pair to obtian the flow
result = net(im).squeeze()
# save flow, I reference the code in scripts/run-flownet.py in flownet2-caffe project
def writeFlow(name, flow):
f = open(name, 'wb')
f.write('PIEH'.encode('utf-8'))
np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f)
flow = flow.astype(np.float32)
flow.tofile(f)
f.flush()
f.close()
data = result.data.cpu().numpy().transpose(1, 2, 0)
writeFlow("/home/hjj/flownet2-master/data/FlyingChairs_examples/0000007-img.flo", data)
| flownet2-pytorch-master | run_a_pair.py |
import torch
import torch.nn as nn
from torch.nn import init
import math
import numpy as np
try:
from networks.resample2d_package.resample2d import Resample2d
from networks.channelnorm_package.channelnorm import ChannelNorm
from networks import FlowNetC
from networks import FlowNetS
from networks import FlowNetSD
from networks import FlowNetFusion
from networks.submodules import *
except:
from .networks.resample2d_package.resample2d import Resample2d
from .networks.channelnorm_package.channelnorm import ChannelNorm
from .networks import FlowNetC
from .networks import FlowNetS
from .networks import FlowNetSD
from .networks import FlowNetFusion
from .networks.submodules import *
'Parameter count = 162,518,834'
class FlowNet2(nn.Module):
def __init__(self, args, batchNorm=False, div_flow = 20.):
super(FlowNet2,self).__init__()
self.batchNorm = batchNorm
self.div_flow = div_flow
self.rgb_max = args.rgb_max
self.args = args
self.channelnorm = ChannelNorm()
# First Block (FlowNetC)
self.flownetc = FlowNetC.FlowNetC(args, batchNorm=self.batchNorm)
self.upsample1 = nn.Upsample(scale_factor=4, mode='bilinear')
if args.fp16:
self.resample1 = nn.Sequential(
tofp32(),
Resample2d(),
tofp16())
else:
self.resample1 = Resample2d()
# Block (FlowNetS1)
self.flownets_1 = FlowNetS.FlowNetS(args, batchNorm=self.batchNorm)
self.upsample2 = nn.Upsample(scale_factor=4, mode='bilinear')
if args.fp16:
self.resample2 = nn.Sequential(
tofp32(),
Resample2d(),
tofp16())
else:
self.resample2 = Resample2d()
# Block (FlowNetS2)
self.flownets_2 = FlowNetS.FlowNetS(args, batchNorm=self.batchNorm)
# Block (FlowNetSD)
self.flownets_d = FlowNetSD.FlowNetSD(args, batchNorm=self.batchNorm)
self.upsample3 = nn.Upsample(scale_factor=4, mode='nearest')
self.upsample4 = nn.Upsample(scale_factor=4, mode='nearest')
if args.fp16:
self.resample3 = nn.Sequential(
tofp32(),
Resample2d(),
tofp16())
else:
self.resample3 = Resample2d()
if args.fp16:
self.resample4 = nn.Sequential(
tofp32(),
Resample2d(),
tofp16())
else:
self.resample4 = Resample2d()
# Block (FLowNetFusion)
self.flownetfusion = FlowNetFusion.FlowNetFusion(args, batchNorm=self.batchNorm)
for m in self.modules():
if isinstance(m, nn.Conv2d):
if m.bias is not None:
init.uniform_(m.bias)
init.xavier_uniform_(m.weight)
if isinstance(m, nn.ConvTranspose2d):
if m.bias is not None:
init.uniform_(m.bias)
init.xavier_uniform_(m.weight)
# init_deconv_bilinear(m.weight)
def init_deconv_bilinear(self, weight):
f_shape = weight.size()
heigh, width = f_shape[-2], f_shape[-1]
f = np.ceil(width/2.0)
c = (2 * f - 1 - f % 2) / (2.0 * f)
bilinear = np.zeros([heigh, width])
for x in range(width):
for y in range(heigh):
value = (1 - abs(x / f - c)) * (1 - abs(y / f - c))
bilinear[x, y] = value
min_dim = min(f_shape[0], f_shape[1])
weight.data.fill_(0.)
for i in range(min_dim):
weight.data[i,i,:,:] = torch.from_numpy(bilinear)
return
def forward(self, inputs):
rgb_mean = inputs.contiguous().view(inputs.size()[:2]+(-1,)).mean(dim=-1).view(inputs.size()[:2] + (1,1,1,))
x = (inputs - rgb_mean) / self.rgb_max
x1 = x[:,:,0,:,:]
x2 = x[:,:,1,:,:]
x = torch.cat((x1,x2), dim = 1)
# flownetc
flownetc_flow2 = self.flownetc(x)[0]
flownetc_flow = self.upsample1(flownetc_flow2*self.div_flow)
# warp img1 to img0; magnitude of diff between img0 and and warped_img1,
resampled_img1 = self.resample1(x[:,3:,:,:], flownetc_flow)
diff_img0 = x[:,:3,:,:] - resampled_img1
norm_diff_img0 = self.channelnorm(diff_img0)
# concat img0, img1, img1->img0, flow, diff-mag ;
concat1 = torch.cat((x, resampled_img1, flownetc_flow/self.div_flow, norm_diff_img0), dim=1)
# flownets1
flownets1_flow2 = self.flownets_1(concat1)[0]
flownets1_flow = self.upsample2(flownets1_flow2*self.div_flow)
# warp img1 to img0 using flownets1; magnitude of diff between img0 and and warped_img1
resampled_img1 = self.resample2(x[:,3:,:,:], flownets1_flow)
diff_img0 = x[:,:3,:,:] - resampled_img1
norm_diff_img0 = self.channelnorm(diff_img0)
# concat img0, img1, img1->img0, flow, diff-mag
concat2 = torch.cat((x, resampled_img1, flownets1_flow/self.div_flow, norm_diff_img0), dim=1)
# flownets2
flownets2_flow2 = self.flownets_2(concat2)[0]
flownets2_flow = self.upsample4(flownets2_flow2 * self.div_flow)
norm_flownets2_flow = self.channelnorm(flownets2_flow)
diff_flownets2_flow = self.resample4(x[:,3:,:,:], flownets2_flow)
# if not diff_flownets2_flow.volatile:
# diff_flownets2_flow.register_hook(save_grad(self.args.grads, 'diff_flownets2_flow'))
diff_flownets2_img1 = self.channelnorm((x[:,:3,:,:]-diff_flownets2_flow))
# if not diff_flownets2_img1.volatile:
# diff_flownets2_img1.register_hook(save_grad(self.args.grads, 'diff_flownets2_img1'))
# flownetsd
flownetsd_flow2 = self.flownets_d(x)[0]
flownetsd_flow = self.upsample3(flownetsd_flow2 / self.div_flow)
norm_flownetsd_flow = self.channelnorm(flownetsd_flow)
diff_flownetsd_flow = self.resample3(x[:,3:,:,:], flownetsd_flow)
# if not diff_flownetsd_flow.volatile:
# diff_flownetsd_flow.register_hook(save_grad(self.args.grads, 'diff_flownetsd_flow'))
diff_flownetsd_img1 = self.channelnorm((x[:,:3,:,:]-diff_flownetsd_flow))
# if not diff_flownetsd_img1.volatile:
# diff_flownetsd_img1.register_hook(save_grad(self.args.grads, 'diff_flownetsd_img1'))
# concat img1 flownetsd, flownets2, norm_flownetsd, norm_flownets2, diff_flownetsd_img1, diff_flownets2_img1
concat3 = torch.cat((x[:,:3,:,:], flownetsd_flow, flownets2_flow, norm_flownetsd_flow, norm_flownets2_flow, diff_flownetsd_img1, diff_flownets2_img1), dim=1)
flownetfusion_flow = self.flownetfusion(concat3)
# if not flownetfusion_flow.volatile:
# flownetfusion_flow.register_hook(save_grad(self.args.grads, 'flownetfusion_flow'))
return flownetfusion_flow
class FlowNet2C(FlowNetC.FlowNetC):
def __init__(self, args, batchNorm=False, div_flow=20):
super(FlowNet2C,self).__init__(args, batchNorm=batchNorm, div_flow=20)
self.rgb_max = args.rgb_max
def forward(self, inputs):
rgb_mean = inputs.contiguous().view(inputs.size()[:2]+(-1,)).mean(dim=-1).view(inputs.size()[:2] + (1,1,1,))
x = (inputs - rgb_mean) / self.rgb_max
x1 = x[:,:,0,:,:]
x2 = x[:,:,1,:,:]
# FlownetC top input stream
out_conv1a = self.conv1(x1)
out_conv2a = self.conv2(out_conv1a)
out_conv3a = self.conv3(out_conv2a)
# FlownetC bottom input stream
out_conv1b = self.conv1(x2)
out_conv2b = self.conv2(out_conv1b)
out_conv3b = self.conv3(out_conv2b)
# Merge streams
out_corr = self.corr(out_conv3a, out_conv3b) # False
out_corr = self.corr_activation(out_corr)
# Redirect top input stream and concatenate
out_conv_redir = self.conv_redir(out_conv3a)
in_conv3_1 = torch.cat((out_conv_redir, out_corr), 1)
# Merged conv layers
out_conv3_1 = self.conv3_1(in_conv3_1)
out_conv4 = self.conv4_1(self.conv4(out_conv3_1))
out_conv5 = self.conv5_1(self.conv5(out_conv4))
out_conv6 = self.conv6_1(self.conv6(out_conv5))
flow6 = self.predict_flow6(out_conv6)
flow6_up = self.upsampled_flow6_to_5(flow6)
out_deconv5 = self.deconv5(out_conv6)
concat5 = torch.cat((out_conv5,out_deconv5,flow6_up),1)
flow5 = self.predict_flow5(concat5)
flow5_up = self.upsampled_flow5_to_4(flow5)
out_deconv4 = self.deconv4(concat5)
concat4 = torch.cat((out_conv4,out_deconv4,flow5_up),1)
flow4 = self.predict_flow4(concat4)
flow4_up = self.upsampled_flow4_to_3(flow4)
out_deconv3 = self.deconv3(concat4)
concat3 = torch.cat((out_conv3_1,out_deconv3,flow4_up),1)
flow3 = self.predict_flow3(concat3)
flow3_up = self.upsampled_flow3_to_2(flow3)
out_deconv2 = self.deconv2(concat3)
concat2 = torch.cat((out_conv2a,out_deconv2,flow3_up),1)
flow2 = self.predict_flow2(concat2)
if self.training:
return flow2,flow3,flow4,flow5,flow6
else:
return self.upsample1(flow2*self.div_flow)
class FlowNet2S(FlowNetS.FlowNetS):
def __init__(self, args, batchNorm=False, div_flow=20):
super(FlowNet2S,self).__init__(args, input_channels = 6, batchNorm=batchNorm)
self.rgb_max = args.rgb_max
self.div_flow = div_flow
def forward(self, inputs):
rgb_mean = inputs.contiguous().view(inputs.size()[:2]+(-1,)).mean(dim=-1).view(inputs.size()[:2] + (1,1,1,))
x = (inputs - rgb_mean) / self.rgb_max
x = torch.cat( (x[:,:,0,:,:], x[:,:,1,:,:]), dim = 1)
out_conv1 = self.conv1(x)
out_conv2 = self.conv2(out_conv1)
out_conv3 = self.conv3_1(self.conv3(out_conv2))
out_conv4 = self.conv4_1(self.conv4(out_conv3))
out_conv5 = self.conv5_1(self.conv5(out_conv4))
out_conv6 = self.conv6_1(self.conv6(out_conv5))
flow6 = self.predict_flow6(out_conv6)
flow6_up = self.upsampled_flow6_to_5(flow6)
out_deconv5 = self.deconv5(out_conv6)
concat5 = torch.cat((out_conv5,out_deconv5,flow6_up),1)
flow5 = self.predict_flow5(concat5)
flow5_up = self.upsampled_flow5_to_4(flow5)
out_deconv4 = self.deconv4(concat5)
concat4 = torch.cat((out_conv4,out_deconv4,flow5_up),1)
flow4 = self.predict_flow4(concat4)
flow4_up = self.upsampled_flow4_to_3(flow4)
out_deconv3 = self.deconv3(concat4)
concat3 = torch.cat((out_conv3,out_deconv3,flow4_up),1)
flow3 = self.predict_flow3(concat3)
flow3_up = self.upsampled_flow3_to_2(flow3)
out_deconv2 = self.deconv2(concat3)
concat2 = torch.cat((out_conv2,out_deconv2,flow3_up),1)
flow2 = self.predict_flow2(concat2)
if self.training:
return flow2,flow3,flow4,flow5,flow6
else:
return self.upsample1(flow2*self.div_flow)
class FlowNet2SD(FlowNetSD.FlowNetSD):
def __init__(self, args, batchNorm=False, div_flow=20):
super(FlowNet2SD,self).__init__(args, batchNorm=batchNorm)
self.rgb_max = args.rgb_max
self.div_flow = div_flow
def forward(self, inputs):
rgb_mean = inputs.contiguous().view(inputs.size()[:2]+(-1,)).mean(dim=-1).view(inputs.size()[:2] + (1,1,1,))
x = (inputs - rgb_mean) / self.rgb_max
x = torch.cat( (x[:,:,0,:,:], x[:,:,1,:,:]), dim = 1)
out_conv0 = self.conv0(x)
out_conv1 = self.conv1_1(self.conv1(out_conv0))
out_conv2 = self.conv2_1(self.conv2(out_conv1))
out_conv3 = self.conv3_1(self.conv3(out_conv2))
out_conv4 = self.conv4_1(self.conv4(out_conv3))
out_conv5 = self.conv5_1(self.conv5(out_conv4))
out_conv6 = self.conv6_1(self.conv6(out_conv5))
flow6 = self.predict_flow6(out_conv6)
flow6_up = self.upsampled_flow6_to_5(flow6)
out_deconv5 = self.deconv5(out_conv6)
concat5 = torch.cat((out_conv5,out_deconv5,flow6_up),1)
out_interconv5 = self.inter_conv5(concat5)
flow5 = self.predict_flow5(out_interconv5)
flow5_up = self.upsampled_flow5_to_4(flow5)
out_deconv4 = self.deconv4(concat5)
concat4 = torch.cat((out_conv4,out_deconv4,flow5_up),1)
out_interconv4 = self.inter_conv4(concat4)
flow4 = self.predict_flow4(out_interconv4)
flow4_up = self.upsampled_flow4_to_3(flow4)
out_deconv3 = self.deconv3(concat4)
concat3 = torch.cat((out_conv3,out_deconv3,flow4_up),1)
out_interconv3 = self.inter_conv3(concat3)
flow3 = self.predict_flow3(out_interconv3)
flow3_up = self.upsampled_flow3_to_2(flow3)
out_deconv2 = self.deconv2(concat3)
concat2 = torch.cat((out_conv2,out_deconv2,flow3_up),1)
out_interconv2 = self.inter_conv2(concat2)
flow2 = self.predict_flow2(out_interconv2)
if self.training:
return flow2,flow3,flow4,flow5,flow6
else:
return self.upsample1(flow2*self.div_flow)
class FlowNet2CS(nn.Module):
def __init__(self, args, batchNorm=False, div_flow = 20.):
super(FlowNet2CS,self).__init__()
self.batchNorm = batchNorm
self.div_flow = div_flow
self.rgb_max = args.rgb_max
self.args = args
self.channelnorm = ChannelNorm()
# First Block (FlowNetC)
self.flownetc = FlowNetC.FlowNetC(args, batchNorm=self.batchNorm)
self.upsample1 = nn.Upsample(scale_factor=4, mode='bilinear')
if args.fp16:
self.resample1 = nn.Sequential(
tofp32(),
Resample2d(),
tofp16())
else:
self.resample1 = Resample2d()
# Block (FlowNetS1)
self.flownets_1 = FlowNetS.FlowNetS(args, batchNorm=self.batchNorm)
self.upsample2 = nn.Upsample(scale_factor=4, mode='bilinear')
for m in self.modules():
if isinstance(m, nn.Conv2d):
if m.bias is not None:
init.uniform(m.bias)
init.xavier_uniform(m.weight)
if isinstance(m, nn.ConvTranspose2d):
if m.bias is not None:
init.uniform(m.bias)
init.xavier_uniform(m.weight)
# init_deconv_bilinear(m.weight)
def forward(self, inputs):
rgb_mean = inputs.contiguous().view(inputs.size()[:2]+(-1,)).mean(dim=-1).view(inputs.size()[:2] + (1,1,1,))
x = (inputs - rgb_mean) / self.rgb_max
x1 = x[:,:,0,:,:]
x2 = x[:,:,1,:,:]
x = torch.cat((x1,x2), dim = 1)
# flownetc
flownetc_flow2 = self.flownetc(x)[0]
flownetc_flow = self.upsample1(flownetc_flow2*self.div_flow)
# warp img1 to img0; magnitude of diff between img0 and and warped_img1,
resampled_img1 = self.resample1(x[:,3:,:,:], flownetc_flow)
diff_img0 = x[:,:3,:,:] - resampled_img1
norm_diff_img0 = self.channelnorm(diff_img0)
# concat img0, img1, img1->img0, flow, diff-mag ;
concat1 = torch.cat((x, resampled_img1, flownetc_flow/self.div_flow, norm_diff_img0), dim=1)
# flownets1
flownets1_flow2 = self.flownets_1(concat1)[0]
flownets1_flow = self.upsample2(flownets1_flow2*self.div_flow)
return flownets1_flow
class FlowNet2CSS(nn.Module):
def __init__(self, args, batchNorm=False, div_flow = 20.):
super(FlowNet2CSS,self).__init__()
self.batchNorm = batchNorm
self.div_flow = div_flow
self.rgb_max = args.rgb_max
self.args = args
self.channelnorm = ChannelNorm()
# First Block (FlowNetC)
self.flownetc = FlowNetC.FlowNetC(args, batchNorm=self.batchNorm)
self.upsample1 = nn.Upsample(scale_factor=4, mode='bilinear')
if args.fp16:
self.resample1 = nn.Sequential(
tofp32(),
Resample2d(),
tofp16())
else:
self.resample1 = Resample2d()
# Block (FlowNetS1)
self.flownets_1 = FlowNetS.FlowNetS(args, batchNorm=self.batchNorm)
self.upsample2 = nn.Upsample(scale_factor=4, mode='bilinear')
if args.fp16:
self.resample2 = nn.Sequential(
tofp32(),
Resample2d(),
tofp16())
else:
self.resample2 = Resample2d()
# Block (FlowNetS2)
self.flownets_2 = FlowNetS.FlowNetS(args, batchNorm=self.batchNorm)
self.upsample3 = nn.Upsample(scale_factor=4, mode='nearest')
for m in self.modules():
if isinstance(m, nn.Conv2d):
if m.bias is not None:
init.uniform(m.bias)
init.xavier_uniform(m.weight)
if isinstance(m, nn.ConvTranspose2d):
if m.bias is not None:
init.uniform(m.bias)
init.xavier_uniform(m.weight)
# init_deconv_bilinear(m.weight)
def forward(self, inputs):
rgb_mean = inputs.contiguous().view(inputs.size()[:2]+(-1,)).mean(dim=-1).view(inputs.size()[:2] + (1,1,1,))
x = (inputs - rgb_mean) / self.rgb_max
x1 = x[:,:,0,:,:]
x2 = x[:,:,1,:,:]
x = torch.cat((x1,x2), dim = 1)
# flownetc
flownetc_flow2 = self.flownetc(x)[0]
flownetc_flow = self.upsample1(flownetc_flow2*self.div_flow)
# warp img1 to img0; magnitude of diff between img0 and and warped_img1,
resampled_img1 = self.resample1(x[:,3:,:,:], flownetc_flow)
diff_img0 = x[:,:3,:,:] - resampled_img1
norm_diff_img0 = self.channelnorm(diff_img0)
# concat img0, img1, img1->img0, flow, diff-mag ;
concat1 = torch.cat((x, resampled_img1, flownetc_flow/self.div_flow, norm_diff_img0), dim=1)
# flownets1
flownets1_flow2 = self.flownets_1(concat1)[0]
flownets1_flow = self.upsample2(flownets1_flow2*self.div_flow)
# warp img1 to img0 using flownets1; magnitude of diff between img0 and and warped_img1
resampled_img1 = self.resample2(x[:,3:,:,:], flownets1_flow)
diff_img0 = x[:,:3,:,:] - resampled_img1
norm_diff_img0 = self.channelnorm(diff_img0)
# concat img0, img1, img1->img0, flow, diff-mag
concat2 = torch.cat((x, resampled_img1, flownets1_flow/self.div_flow, norm_diff_img0), dim=1)
# flownets2
flownets2_flow2 = self.flownets_2(concat2)[0]
flownets2_flow = self.upsample3(flownets2_flow2 * self.div_flow)
return flownets2_flow
| flownet2-pytorch-master | models.py |
#!/usr/bin/env python2.7
import caffe
from caffe.proto import caffe_pb2
import sys, os
import torch
import torch.nn as nn
import argparse, tempfile
import numpy as np
parser = argparse.ArgumentParser()
parser.add_argument('caffe_model', help='input model in hdf5 or caffemodel format')
parser.add_argument('prototxt_template',help='prototxt template')
parser.add_argument('flownet2_pytorch', help='path to flownet2-pytorch')
args = parser.parse_args()
args.rgb_max = 255
args.fp16 = False
args.grads = {}
# load models
sys.path.append(args.flownet2_pytorch)
import models
from utils.param_utils import *
width = 256
height = 256
keys = {'TARGET_WIDTH': width,
'TARGET_HEIGHT': height,
'ADAPTED_WIDTH':width,
'ADAPTED_HEIGHT':height,
'SCALE_WIDTH':1.,
'SCALE_HEIGHT':1.,}
template = '\n'.join(np.loadtxt(args.prototxt_template, dtype=str, delimiter='\n'))
for k in keys:
template = template.replace('$%s$'%(k),str(keys[k]))
prototxt = tempfile.NamedTemporaryFile(mode='w', delete=True)
prototxt.write(template)
prototxt.flush()
net = caffe.Net(prototxt.name, args.caffe_model, caffe.TEST)
weights = {}
biases = {}
for k, v in list(net.params.items()):
weights[k] = np.array(v[0].data).reshape(v[0].data.shape)
biases[k] = np.array(v[1].data).reshape(v[1].data.shape)
print((k, weights[k].shape, biases[k].shape))
if 'FlowNet2/' in args.caffe_model:
model = models.FlowNet2(args)
parse_flownetc(model.flownetc.modules(), weights, biases)
parse_flownets(model.flownets_1.modules(), weights, biases, param_prefix='net2_')
parse_flownets(model.flownets_2.modules(), weights, biases, param_prefix='net3_')
parse_flownetsd(model.flownets_d.modules(), weights, biases, param_prefix='netsd_')
parse_flownetfusion(model.flownetfusion.modules(), weights, biases, param_prefix='fuse_')
state = {'epoch': 0,
'state_dict': model.state_dict(),
'best_EPE': 1e10}
torch.save(state, os.path.join(args.flownet2_pytorch, 'FlowNet2_checkpoint.pth.tar'))
elif 'FlowNet2-C/' in args.caffe_model:
model = models.FlowNet2C(args)
parse_flownetc(model.modules(), weights, biases)
state = {'epoch': 0,
'state_dict': model.state_dict(),
'best_EPE': 1e10}
torch.save(state, os.path.join(args.flownet2_pytorch, 'FlowNet2-C_checkpoint.pth.tar'))
elif 'FlowNet2-CS/' in args.caffe_model:
model = models.FlowNet2CS(args)
parse_flownetc(model.flownetc.modules(), weights, biases)
parse_flownets(model.flownets_1.modules(), weights, biases, param_prefix='net2_')
state = {'epoch': 0,
'state_dict': model.state_dict(),
'best_EPE': 1e10}
torch.save(state, os.path.join(args.flownet2_pytorch, 'FlowNet2-CS_checkpoint.pth.tar'))
elif 'FlowNet2-CSS/' in args.caffe_model:
model = models.FlowNet2CSS(args)
parse_flownetc(model.flownetc.modules(), weights, biases)
parse_flownets(model.flownets_1.modules(), weights, biases, param_prefix='net2_')
parse_flownets(model.flownets_2.modules(), weights, biases, param_prefix='net3_')
state = {'epoch': 0,
'state_dict': model.state_dict(),
'best_EPE': 1e10}
torch.save(state, os.path.join(args.flownet2_pytorch, 'FlowNet2-CSS_checkpoint.pth.tar'))
elif 'FlowNet2-CSS-ft-sd/' in args.caffe_model:
model = models.FlowNet2CSS(args)
parse_flownetc(model.flownetc.modules(), weights, biases)
parse_flownets(model.flownets_1.modules(), weights, biases, param_prefix='net2_')
parse_flownets(model.flownets_2.modules(), weights, biases, param_prefix='net3_')
state = {'epoch': 0,
'state_dict': model.state_dict(),
'best_EPE': 1e10}
torch.save(state, os.path.join(args.flownet2_pytorch, 'FlowNet2-CSS-ft-sd_checkpoint.pth.tar'))
elif 'FlowNet2-S/' in args.caffe_model:
model = models.FlowNet2S(args)
parse_flownetsonly(model.modules(), weights, biases, param_prefix='')
state = {'epoch': 0,
'state_dict': model.state_dict(),
'best_EPE': 1e10}
torch.save(state, os.path.join(args.flownet2_pytorch, 'FlowNet2-S_checkpoint.pth.tar'))
elif 'FlowNet2-SD/' in args.caffe_model:
model = models.FlowNet2SD(args)
parse_flownetsd(model.modules(), weights, biases, param_prefix='')
state = {'epoch': 0,
'state_dict': model.state_dict(),
'best_EPE': 1e10}
torch.save(state, os.path.join(args.flownet2_pytorch, 'FlowNet2-SD_checkpoint.pth.tar'))
else:
print(('model type cound not be determined from input caffe model %s'%(args.caffe_model)))
quit()
print(("done converting ", args.caffe_model)) | flownet2-pytorch-master | convert.py |
import torch
import torch.utils.data as data
import os, math, random
from os.path import *
import numpy as np
from glob import glob
import utils.frame_utils as frame_utils
from scipy.misc import imread, imresize
class StaticRandomCrop(object):
def __init__(self, image_size, crop_size):
self.th, self.tw = crop_size
h, w = image_size
self.h1 = random.randint(0, h - self.th)
self.w1 = random.randint(0, w - self.tw)
def __call__(self, img):
return img[self.h1:(self.h1+self.th), self.w1:(self.w1+self.tw),:]
class StaticCenterCrop(object):
def __init__(self, image_size, crop_size):
self.th, self.tw = crop_size
self.h, self.w = image_size
def __call__(self, img):
return img[(self.h-self.th)//2:(self.h+self.th)//2, (self.w-self.tw)//2:(self.w+self.tw)//2,:]
class MpiSintel(data.Dataset):
def __init__(self, args, is_cropped = False, root = '', dstype = 'clean', replicates = 1):
self.args = args
self.is_cropped = is_cropped
self.crop_size = args.crop_size
self.render_size = args.inference_size
self.replicates = replicates
flow_root = join(root, 'flow')
image_root = join(root, dstype)
file_list = sorted(glob(join(flow_root, '*/*.flo')))
self.flow_list = []
self.image_list = []
for file in file_list:
if 'test' in file:
# print file
continue
fbase = file[len(flow_root)+1:]
fprefix = fbase[:-8]
fnum = int(fbase[-8:-4])
img1 = join(image_root, fprefix + "%04d"%(fnum+0) + '.png')
img2 = join(image_root, fprefix + "%04d"%(fnum+1) + '.png')
if not isfile(img1) or not isfile(img2) or not isfile(file):
continue
self.image_list += [[img1, img2]]
self.flow_list += [file]
self.size = len(self.image_list)
self.frame_size = frame_utils.read_gen(self.image_list[0][0]).shape
if (self.render_size[0] < 0) or (self.render_size[1] < 0) or (self.frame_size[0]%64) or (self.frame_size[1]%64):
self.render_size[0] = ( (self.frame_size[0])//64 ) * 64
self.render_size[1] = ( (self.frame_size[1])//64 ) * 64
args.inference_size = self.render_size
assert (len(self.image_list) == len(self.flow_list))
def __getitem__(self, index):
index = index % self.size
img1 = frame_utils.read_gen(self.image_list[index][0])
img2 = frame_utils.read_gen(self.image_list[index][1])
flow = frame_utils.read_gen(self.flow_list[index])
images = [img1, img2]
image_size = img1.shape[:2]
if self.is_cropped:
cropper = StaticRandomCrop(image_size, self.crop_size)
else:
cropper = StaticCenterCrop(image_size, self.render_size)
images = list(map(cropper, images))
flow = cropper(flow)
images = np.array(images).transpose(3,0,1,2)
flow = flow.transpose(2,0,1)
images = torch.from_numpy(images.astype(np.float32))
flow = torch.from_numpy(flow.astype(np.float32))
return [images], [flow]
def __len__(self):
return self.size * self.replicates
class MpiSintelClean(MpiSintel):
def __init__(self, args, is_cropped = False, root = '', replicates = 1):
super(MpiSintelClean, self).__init__(args, is_cropped = is_cropped, root = root, dstype = 'clean', replicates = replicates)
class MpiSintelFinal(MpiSintel):
def __init__(self, args, is_cropped = False, root = '', replicates = 1):
super(MpiSintelFinal, self).__init__(args, is_cropped = is_cropped, root = root, dstype = 'final', replicates = replicates)
class FlyingChairs(data.Dataset):
def __init__(self, args, is_cropped, root = '/path/to/FlyingChairs_release/data', replicates = 1):
self.args = args
self.is_cropped = is_cropped
self.crop_size = args.crop_size
self.render_size = args.inference_size
self.replicates = replicates
images = sorted( glob( join(root, '*.ppm') ) )
self.flow_list = sorted( glob( join(root, '*.flo') ) )
assert (len(images)//2 == len(self.flow_list))
self.image_list = []
for i in range(len(self.flow_list)):
im1 = images[2*i]
im2 = images[2*i + 1]
self.image_list += [ [ im1, im2 ] ]
assert len(self.image_list) == len(self.flow_list)
self.size = len(self.image_list)
self.frame_size = frame_utils.read_gen(self.image_list[0][0]).shape
if (self.render_size[0] < 0) or (self.render_size[1] < 0) or (self.frame_size[0]%64) or (self.frame_size[1]%64):
self.render_size[0] = ( (self.frame_size[0])//64 ) * 64
self.render_size[1] = ( (self.frame_size[1])//64 ) * 64
args.inference_size = self.render_size
def __getitem__(self, index):
index = index % self.size
img1 = frame_utils.read_gen(self.image_list[index][0])
img2 = frame_utils.read_gen(self.image_list[index][1])
flow = frame_utils.read_gen(self.flow_list[index])
images = [img1, img2]
image_size = img1.shape[:2]
if self.is_cropped:
cropper = StaticRandomCrop(image_size, self.crop_size)
else:
cropper = StaticCenterCrop(image_size, self.render_size)
images = list(map(cropper, images))
flow = cropper(flow)
images = np.array(images).transpose(3,0,1,2)
flow = flow.transpose(2,0,1)
images = torch.from_numpy(images.astype(np.float32))
flow = torch.from_numpy(flow.astype(np.float32))
return [images], [flow]
def __len__(self):
return self.size * self.replicates
class FlyingThings(data.Dataset):
def __init__(self, args, is_cropped, root = '/path/to/flyingthings3d', dstype = 'frames_cleanpass', replicates = 1):
self.args = args
self.is_cropped = is_cropped
self.crop_size = args.crop_size
self.render_size = args.inference_size
self.replicates = replicates
image_dirs = sorted(glob(join(root, dstype, 'TRAIN/*/*')))
image_dirs = sorted([join(f, 'left') for f in image_dirs] + [join(f, 'right') for f in image_dirs])
flow_dirs = sorted(glob(join(root, 'optical_flow_flo_format/TRAIN/*/*')))
flow_dirs = sorted([join(f, 'into_future/left') for f in flow_dirs] + [join(f, 'into_future/right') for f in flow_dirs])
assert (len(image_dirs) == len(flow_dirs))
self.image_list = []
self.flow_list = []
for idir, fdir in zip(image_dirs, flow_dirs):
images = sorted( glob(join(idir, '*.png')) )
flows = sorted( glob(join(fdir, '*.flo')) )
for i in range(len(flows)):
self.image_list += [ [ images[i], images[i+1] ] ]
self.flow_list += [flows[i]]
assert len(self.image_list) == len(self.flow_list)
self.size = len(self.image_list)
self.frame_size = frame_utils.read_gen(self.image_list[0][0]).shape
if (self.render_size[0] < 0) or (self.render_size[1] < 0) or (self.frame_size[0]%64) or (self.frame_size[1]%64):
self.render_size[0] = ( (self.frame_size[0])//64 ) * 64
self.render_size[1] = ( (self.frame_size[1])//64 ) * 64
args.inference_size = self.render_size
def __getitem__(self, index):
index = index % self.size
img1 = frame_utils.read_gen(self.image_list[index][0])
img2 = frame_utils.read_gen(self.image_list[index][1])
flow = frame_utils.read_gen(self.flow_list[index])
images = [img1, img2]
image_size = img1.shape[:2]
if self.is_cropped:
cropper = StaticRandomCrop(image_size, self.crop_size)
else:
cropper = StaticCenterCrop(image_size, self.render_size)
images = list(map(cropper, images))
flow = cropper(flow)
images = np.array(images).transpose(3,0,1,2)
flow = flow.transpose(2,0,1)
images = torch.from_numpy(images.astype(np.float32))
flow = torch.from_numpy(flow.astype(np.float32))
return [images], [flow]
def __len__(self):
return self.size * self.replicates
class FlyingThingsClean(FlyingThings):
def __init__(self, args, is_cropped = False, root = '', replicates = 1):
super(FlyingThingsClean, self).__init__(args, is_cropped = is_cropped, root = root, dstype = 'frames_cleanpass', replicates = replicates)
class FlyingThingsFinal(FlyingThings):
def __init__(self, args, is_cropped = False, root = '', replicates = 1):
super(FlyingThingsFinal, self).__init__(args, is_cropped = is_cropped, root = root, dstype = 'frames_finalpass', replicates = replicates)
class ChairsSDHom(data.Dataset):
def __init__(self, args, is_cropped, root = '/path/to/chairssdhom/data', dstype = 'train', replicates = 1):
self.args = args
self.is_cropped = is_cropped
self.crop_size = args.crop_size
self.render_size = args.inference_size
self.replicates = replicates
image1 = sorted( glob( join(root, dstype, 't0/*.png') ) )
image2 = sorted( glob( join(root, dstype, 't1/*.png') ) )
self.flow_list = sorted( glob( join(root, dstype, 'flow/*.flo') ) )
assert (len(image1) == len(self.flow_list))
self.image_list = []
for i in range(len(self.flow_list)):
im1 = image1[i]
im2 = image2[i]
self.image_list += [ [ im1, im2 ] ]
assert len(self.image_list) == len(self.flow_list)
self.size = len(self.image_list)
self.frame_size = frame_utils.read_gen(self.image_list[0][0]).shape
if (self.render_size[0] < 0) or (self.render_size[1] < 0) or (self.frame_size[0]%64) or (self.frame_size[1]%64):
self.render_size[0] = ( (self.frame_size[0])//64 ) * 64
self.render_size[1] = ( (self.frame_size[1])//64 ) * 64
args.inference_size = self.render_size
def __getitem__(self, index):
index = index % self.size
img1 = frame_utils.read_gen(self.image_list[index][0])
img2 = frame_utils.read_gen(self.image_list[index][1])
flow = frame_utils.read_gen(self.flow_list[index])
flow = flow[::-1,:,:]
images = [img1, img2]
image_size = img1.shape[:2]
if self.is_cropped:
cropper = StaticRandomCrop(image_size, self.crop_size)
else:
cropper = StaticCenterCrop(image_size, self.render_size)
images = list(map(cropper, images))
flow = cropper(flow)
images = np.array(images).transpose(3,0,1,2)
flow = flow.transpose(2,0,1)
images = torch.from_numpy(images.astype(np.float32))
flow = torch.from_numpy(flow.astype(np.float32))
return [images], [flow]
def __len__(self):
return self.size * self.replicates
class ChairsSDHomTrain(ChairsSDHom):
def __init__(self, args, is_cropped = False, root = '', replicates = 1):
super(ChairsSDHomTrain, self).__init__(args, is_cropped = is_cropped, root = root, dstype = 'train', replicates = replicates)
class ChairsSDHomTest(ChairsSDHom):
def __init__(self, args, is_cropped = False, root = '', replicates = 1):
super(ChairsSDHomTest, self).__init__(args, is_cropped = is_cropped, root = root, dstype = 'test', replicates = replicates)
class ImagesFromFolder(data.Dataset):
def __init__(self, args, is_cropped, root = '/path/to/frames/only/folder', iext = 'png', replicates = 1):
self.args = args
self.is_cropped = is_cropped
self.crop_size = args.crop_size
self.render_size = args.inference_size
self.replicates = replicates
images = sorted( glob( join(root, '*.' + iext) ) )
self.image_list = []
for i in range(len(images)-1):
im1 = images[i]
im2 = images[i+1]
self.image_list += [ [ im1, im2 ] ]
self.size = len(self.image_list)
self.frame_size = frame_utils.read_gen(self.image_list[0][0]).shape
if (self.render_size[0] < 0) or (self.render_size[1] < 0) or (self.frame_size[0]%64) or (self.frame_size[1]%64):
self.render_size[0] = ( (self.frame_size[0])//64 ) * 64
self.render_size[1] = ( (self.frame_size[1])//64 ) * 64
args.inference_size = self.render_size
def __getitem__(self, index):
index = index % self.size
img1 = frame_utils.read_gen(self.image_list[index][0])
img2 = frame_utils.read_gen(self.image_list[index][1])
images = [img1, img2]
image_size = img1.shape[:2]
if self.is_cropped:
cropper = StaticRandomCrop(image_size, self.crop_size)
else:
cropper = StaticCenterCrop(image_size, self.render_size)
images = list(map(cropper, images))
images = np.array(images).transpose(3,0,1,2)
images = torch.from_numpy(images.astype(np.float32))
return [images], [torch.zeros(images.size()[0:1] + (2,) + images.size()[-2:])]
def __len__(self):
return self.size * self.replicates
'''
import argparse
import sys, os
import importlib
from scipy.misc import imsave
import numpy as np
import datasets
reload(datasets)
parser = argparse.ArgumentParser()
args = parser.parse_args()
args.inference_size = [1080, 1920]
args.crop_size = [384, 512]
args.effective_batch_size = 1
index = 500
v_dataset = datasets.MpiSintelClean(args, True, root='../MPI-Sintel/flow/training')
a, b = v_dataset[index]
im1 = a[0].numpy()[:,0,:,:].transpose(1,2,0)
im2 = a[0].numpy()[:,1,:,:].transpose(1,2,0)
imsave('./img1.png', im1)
imsave('./img2.png', im2)
flow_utils.writeFlow('./flow.flo', b[0].numpy().transpose(1,2,0))
'''
| flownet2-pytorch-master | datasets.py |
flownet2-pytorch-master | __init__.py |
|
'''
Portions of this code copyright 2017, Clement Pinard
'''
# freda (todo) : adversarial loss
import torch
import torch.nn as nn
import math
def EPE(input_flow, target_flow):
return torch.norm(target_flow-input_flow,p=2,dim=1).mean()
class L1(nn.Module):
def __init__(self):
super(L1, self).__init__()
def forward(self, output, target):
lossvalue = torch.abs(output - target).mean()
return lossvalue
class L2(nn.Module):
def __init__(self):
super(L2, self).__init__()
def forward(self, output, target):
lossvalue = torch.norm(output-target,p=2,dim=1).mean()
return lossvalue
class L1Loss(nn.Module):
def __init__(self, args):
super(L1Loss, self).__init__()
self.args = args
self.loss = L1()
self.loss_labels = ['L1', 'EPE']
def forward(self, output, target):
lossvalue = self.loss(output, target)
epevalue = EPE(output, target)
return [lossvalue, epevalue]
class L2Loss(nn.Module):
def __init__(self, args):
super(L2Loss, self).__init__()
self.args = args
self.loss = L2()
self.loss_labels = ['L2', 'EPE']
def forward(self, output, target):
lossvalue = self.loss(output, target)
epevalue = EPE(output, target)
return [lossvalue, epevalue]
class MultiScale(nn.Module):
def __init__(self, args, startScale = 4, numScales = 5, l_weight= 0.32, norm= 'L1'):
super(MultiScale,self).__init__()
self.startScale = startScale
self.numScales = numScales
self.loss_weights = torch.FloatTensor([(l_weight / 2 ** scale) for scale in range(self.numScales)])
self.args = args
self.l_type = norm
self.div_flow = 0.05
assert(len(self.loss_weights) == self.numScales)
if self.l_type == 'L1':
self.loss = L1()
else:
self.loss = L2()
self.multiScales = [nn.AvgPool2d(self.startScale * (2**scale), self.startScale * (2**scale)) for scale in range(self.numScales)]
self.loss_labels = ['MultiScale-'+self.l_type, 'EPE'],
def forward(self, output, target):
lossvalue = 0
epevalue = 0
if type(output) is tuple:
target = self.div_flow * target
for i, output_ in enumerate(output):
target_ = self.multiScales[i](target)
epevalue += self.loss_weights[i]*EPE(output_, target_)
lossvalue += self.loss_weights[i]*self.loss(output_, target_)
return [lossvalue, epevalue]
else:
epevalue += EPE(output, target)
lossvalue += self.loss(output, target)
return [lossvalue, epevalue]
| flownet2-pytorch-master | losses.py |
#!/usr/bin/env python
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.autograd import Variable
from tensorboardX import SummaryWriter
import argparse, os, sys, subprocess
import setproctitle, colorama
import numpy as np
from tqdm import tqdm
from glob import glob
from os.path import *
import models, losses, datasets
from utils import flow_utils, tools
# fp32 copy of parameters for update
global param_copy
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--start_epoch', type=int, default=1)
parser.add_argument('--total_epochs', type=int, default=10000)
parser.add_argument('--batch_size', '-b', type=int, default=8, help="Batch size")
parser.add_argument('--train_n_batches', type=int, default = -1, help='Number of min-batches per epoch. If < 0, it will be determined by training_dataloader')
parser.add_argument('--crop_size', type=int, nargs='+', default = [256, 256], help="Spatial dimension to crop training samples for training")
parser.add_argument('--gradient_clip', type=float, default=None)
parser.add_argument('--schedule_lr_frequency', type=int, default=0, help='in number of iterations (0 for no schedule)')
parser.add_argument('--schedule_lr_fraction', type=float, default=10)
parser.add_argument("--rgb_max", type=float, default = 255.)
parser.add_argument('--number_workers', '-nw', '--num_workers', type=int, default=8)
parser.add_argument('--number_gpus', '-ng', type=int, default=-1, help='number of GPUs to use')
parser.add_argument('--no_cuda', action='store_true')
parser.add_argument('--seed', type=int, default=1)
parser.add_argument('--name', default='run', type=str, help='a name to append to the save directory')
parser.add_argument('--save', '-s', default='./work', type=str, help='directory for saving')
parser.add_argument('--validation_frequency', type=int, default=5, help='validate every n epochs')
parser.add_argument('--validation_n_batches', type=int, default=-1)
parser.add_argument('--render_validation', action='store_true', help='run inference (save flows to file) and every validation_frequency epoch')
parser.add_argument('--inference', action='store_true')
parser.add_argument('--inference_visualize', action='store_true',
help="visualize the optical flow during inference")
parser.add_argument('--inference_size', type=int, nargs='+', default = [-1,-1], help='spatial size divisible by 64. default (-1,-1) - largest possible valid size would be used')
parser.add_argument('--inference_batch_size', type=int, default=1)
parser.add_argument('--inference_n_batches', type=int, default=-1)
parser.add_argument('--save_flow', action='store_true', help='save predicted flows to file')
parser.add_argument('--resume', default='', type=str, metavar='PATH', help='path to latest checkpoint (default: none)')
parser.add_argument('--log_frequency', '--summ_iter', type=int, default=1, help="Log every n batches")
parser.add_argument('--skip_training', action='store_true')
parser.add_argument('--skip_validation', action='store_true')
parser.add_argument('--fp16', action='store_true', help='Run model in pseudo-fp16 mode (fp16 storage fp32 math).')
parser.add_argument('--fp16_scale', type=float, default=1024., help='Loss scaling, positive power of 2 values can improve fp16 convergence.')
tools.add_arguments_for_module(parser, models, argument_for_class='model', default='FlowNet2')
tools.add_arguments_for_module(parser, losses, argument_for_class='loss', default='L1Loss')
tools.add_arguments_for_module(parser, torch.optim, argument_for_class='optimizer', default='Adam', skip_params=['params'])
tools.add_arguments_for_module(parser, datasets, argument_for_class='training_dataset', default='MpiSintelFinal',
skip_params=['is_cropped'],
parameter_defaults={'root': './MPI-Sintel/flow/training'})
tools.add_arguments_for_module(parser, datasets, argument_for_class='validation_dataset', default='MpiSintelClean',
skip_params=['is_cropped'],
parameter_defaults={'root': './MPI-Sintel/flow/training',
'replicates': 1})
tools.add_arguments_for_module(parser, datasets, argument_for_class='inference_dataset', default='MpiSintelClean',
skip_params=['is_cropped'],
parameter_defaults={'root': './MPI-Sintel/flow/training',
'replicates': 1})
main_dir = os.path.dirname(os.path.realpath(__file__))
os.chdir(main_dir)
# Parse the official arguments
with tools.TimerBlock("Parsing Arguments") as block:
args = parser.parse_args()
if args.number_gpus < 0 : args.number_gpus = torch.cuda.device_count()
# Get argument defaults (hastag #thisisahack)
parser.add_argument('--IGNORE', action='store_true')
defaults = vars(parser.parse_args(['--IGNORE']))
# Print all arguments, color the non-defaults
for argument, value in sorted(vars(args).items()):
reset = colorama.Style.RESET_ALL
color = reset if value == defaults[argument] else colorama.Fore.MAGENTA
block.log('{}{}: {}{}'.format(color, argument, value, reset))
args.model_class = tools.module_to_dict(models)[args.model]
args.optimizer_class = tools.module_to_dict(torch.optim)[args.optimizer]
args.loss_class = tools.module_to_dict(losses)[args.loss]
args.training_dataset_class = tools.module_to_dict(datasets)[args.training_dataset]
args.validation_dataset_class = tools.module_to_dict(datasets)[args.validation_dataset]
args.inference_dataset_class = tools.module_to_dict(datasets)[args.inference_dataset]
args.cuda = not args.no_cuda and torch.cuda.is_available()
args.current_hash = subprocess.check_output(["git", "rev-parse", "HEAD"]).rstrip()
args.log_file = join(args.save, 'args.txt')
# dict to collect activation gradients (for training debug purpose)
args.grads = {}
if args.inference:
args.skip_validation = True
args.skip_training = True
args.total_epochs = 1
args.inference_dir = "{}/inference".format(args.save)
print('Source Code')
print((' Current Git Hash: {}\n'.format(args.current_hash)))
# Change the title for `top` and `pkill` commands
setproctitle.setproctitle(args.save)
# Dynamically load the dataset class with parameters passed in via "--argument_[param]=[value]" arguments
with tools.TimerBlock("Initializing Datasets") as block:
args.effective_batch_size = args.batch_size * args.number_gpus
args.effective_inference_batch_size = args.inference_batch_size * args.number_gpus
args.effective_number_workers = args.number_workers * args.number_gpus
gpuargs = {'num_workers': args.effective_number_workers,
'pin_memory': True,
'drop_last' : True} if args.cuda else {}
inf_gpuargs = gpuargs.copy()
inf_gpuargs['num_workers'] = args.number_workers
if exists(args.training_dataset_root):
train_dataset = args.training_dataset_class(args, True, **tools.kwargs_from_args(args, 'training_dataset'))
block.log('Training Dataset: {}'.format(args.training_dataset))
block.log('Training Input: {}'.format(' '.join([str([d for d in x.size()]) for x in train_dataset[0][0]])))
block.log('Training Targets: {}'.format(' '.join([str([d for d in x.size()]) for x in train_dataset[0][1]])))
train_loader = DataLoader(train_dataset, batch_size=args.effective_batch_size, shuffle=True, **gpuargs)
if exists(args.validation_dataset_root):
validation_dataset = args.validation_dataset_class(args, True, **tools.kwargs_from_args(args, 'validation_dataset'))
block.log('Validation Dataset: {}'.format(args.validation_dataset))
block.log('Validation Input: {}'.format(' '.join([str([d for d in x.size()]) for x in validation_dataset[0][0]])))
block.log('Validation Targets: {}'.format(' '.join([str([d for d in x.size()]) for x in validation_dataset[0][1]])))
validation_loader = DataLoader(validation_dataset, batch_size=args.effective_batch_size, shuffle=False, **gpuargs)
if exists(args.inference_dataset_root):
inference_dataset = args.inference_dataset_class(args, False, **tools.kwargs_from_args(args, 'inference_dataset'))
block.log('Inference Dataset: {}'.format(args.inference_dataset))
block.log('Inference Input: {}'.format(' '.join([str([d for d in x.size()]) for x in inference_dataset[0][0]])))
block.log('Inference Targets: {}'.format(' '.join([str([d for d in x.size()]) for x in inference_dataset[0][1]])))
inference_loader = DataLoader(inference_dataset, batch_size=args.effective_inference_batch_size, shuffle=False, **inf_gpuargs)
# Dynamically load model and loss class with parameters passed in via "--model_[param]=[value]" or "--loss_[param]=[value]" arguments
with tools.TimerBlock("Building {} model".format(args.model)) as block:
class ModelAndLoss(nn.Module):
def __init__(self, args):
super(ModelAndLoss, self).__init__()
kwargs = tools.kwargs_from_args(args, 'model')
self.model = args.model_class(args, **kwargs)
kwargs = tools.kwargs_from_args(args, 'loss')
self.loss = args.loss_class(args, **kwargs)
def forward(self, data, target, inference=False ):
output = self.model(data)
loss_values = self.loss(output, target)
if not inference :
return loss_values
else :
return loss_values, output
model_and_loss = ModelAndLoss(args)
block.log('Effective Batch Size: {}'.format(args.effective_batch_size))
block.log('Number of parameters: {}'.format(sum([p.data.nelement() if p.requires_grad else 0 for p in model_and_loss.parameters()])))
# assing to cuda or wrap with dataparallel, model and loss
if args.cuda and (args.number_gpus > 0) and args.fp16:
block.log('Parallelizing')
model_and_loss = nn.parallel.DataParallel(model_and_loss, device_ids=list(range(args.number_gpus)))
block.log('Initializing CUDA')
model_and_loss = model_and_loss.cuda().half()
torch.cuda.manual_seed(args.seed)
param_copy = [param.clone().type(torch.cuda.FloatTensor).detach() for param in model_and_loss.parameters()]
elif args.cuda and args.number_gpus > 0:
block.log('Initializing CUDA')
model_and_loss = model_and_loss.cuda()
block.log('Parallelizing')
model_and_loss = nn.parallel.DataParallel(model_and_loss, device_ids=list(range(args.number_gpus)))
torch.cuda.manual_seed(args.seed)
else:
block.log('CUDA not being used')
torch.manual_seed(args.seed)
# Load weights if needed, otherwise randomly initialize
if args.resume and os.path.isfile(args.resume):
block.log("Loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
if not args.inference:
args.start_epoch = checkpoint['epoch']
best_err = checkpoint['best_EPE']
model_and_loss.module.model.load_state_dict(checkpoint['state_dict'])
block.log("Loaded checkpoint '{}' (at epoch {})".format(args.resume, checkpoint['epoch']))
elif args.resume and args.inference:
block.log("No checkpoint found at '{}'".format(args.resume))
quit()
else:
block.log("Random initialization")
block.log("Initializing save directory: {}".format(args.save))
if not os.path.exists(args.save):
os.makedirs(args.save)
train_logger = SummaryWriter(log_dir = os.path.join(args.save, 'train'), comment = 'training')
validation_logger = SummaryWriter(log_dir = os.path.join(args.save, 'validation'), comment = 'validation')
# Dynamically load the optimizer with parameters passed in via "--optimizer_[param]=[value]" arguments
with tools.TimerBlock("Initializing {} Optimizer".format(args.optimizer)) as block:
kwargs = tools.kwargs_from_args(args, 'optimizer')
if args.fp16:
optimizer = args.optimizer_class([p for p in param_copy if p.requires_grad], **kwargs)
else:
optimizer = args.optimizer_class([p for p in model_and_loss.parameters() if p.requires_grad], **kwargs)
for param, default in list(kwargs.items()):
block.log("{} = {} ({})".format(param, default, type(default)))
# Log all arguments to file
for argument, value in sorted(vars(args).items()):
block.log2file(args.log_file, '{}: {}'.format(argument, value))
# Reusable function for training and validataion
def train(args, epoch, start_iteration, data_loader, model, optimizer, logger, is_validate=False, offset=0):
statistics = []
total_loss = 0
if is_validate:
model.eval()
title = 'Validating Epoch {}'.format(epoch)
args.validation_n_batches = np.inf if args.validation_n_batches < 0 else args.validation_n_batches
progress = tqdm(tools.IteratorTimer(data_loader), ncols=100, total=np.minimum(len(data_loader), args.validation_n_batches), leave=True, position=offset, desc=title)
else:
model.train()
title = 'Training Epoch {}'.format(epoch)
args.train_n_batches = np.inf if args.train_n_batches < 0 else args.train_n_batches
progress = tqdm(tools.IteratorTimer(data_loader), ncols=120, total=np.minimum(len(data_loader), args.train_n_batches), smoothing=.9, miniters=1, leave=True, position=offset, desc=title)
last_log_time = progress._time()
for batch_idx, (data, target) in enumerate(progress):
data, target = [Variable(d) for d in data], [Variable(t) for t in target]
if args.cuda and args.number_gpus == 1:
data, target = [d.cuda(non_blocking=True) for d in data], [t.cuda(non_blocking=True) for t in target]
optimizer.zero_grad() if not is_validate else None
losses = model(data[0], target[0])
losses = [torch.mean(loss_value) for loss_value in losses]
loss_val = losses[0] # Collect first loss for weight update
total_loss += loss_val.item()
loss_values = [v.item() for v in losses]
# gather loss_labels, direct return leads to recursion limit error as it looks for variables to gather'
loss_labels = list(model.module.loss.loss_labels)
assert not np.isnan(total_loss)
if not is_validate and args.fp16:
loss_val.backward()
if args.gradient_clip:
torch.nn.utils.clip_grad_norm(model.parameters(), args.gradient_clip)
params = list(model.parameters())
for i in range(len(params)):
param_copy[i].grad = params[i].grad.clone().type_as(params[i]).detach()
param_copy[i].grad.mul_(1./args.loss_scale)
optimizer.step()
for i in range(len(params)):
params[i].data.copy_(param_copy[i].data)
elif not is_validate:
loss_val.backward()
if args.gradient_clip:
torch.nn.utils.clip_grad_norm(model.parameters(), args.gradient_clip)
optimizer.step()
# Update hyperparameters if needed
global_iteration = start_iteration + batch_idx
if not is_validate:
tools.update_hyperparameter_schedule(args, epoch, global_iteration, optimizer)
loss_labels.append('lr')
loss_values.append(optimizer.param_groups[0]['lr'])
loss_labels.append('load')
loss_values.append(progress.iterable.last_duration)
# Print out statistics
statistics.append(loss_values)
title = '{} Epoch {}'.format('Validating' if is_validate else 'Training', epoch)
progress.set_description(title + ' ' + tools.format_dictionary_of_losses(loss_labels, statistics[-1]))
if ((((global_iteration + 1) % args.log_frequency) == 0 and not is_validate) or
(is_validate and batch_idx == args.validation_n_batches - 1)):
global_iteration = global_iteration if not is_validate else start_iteration
logger.add_scalar('batch logs per second', len(statistics) / (progress._time() - last_log_time), global_iteration)
last_log_time = progress._time()
all_losses = np.array(statistics)
for i, key in enumerate(loss_labels):
logger.add_scalar('average batch ' + str(key), all_losses[:, i].mean(), global_iteration)
logger.add_histogram(str(key), all_losses[:, i], global_iteration)
# Reset Summary
statistics = []
if ( is_validate and ( batch_idx == args.validation_n_batches) ):
break
if ( (not is_validate) and (batch_idx == (args.train_n_batches)) ):
break
progress.close()
return total_loss / float(batch_idx + 1), (batch_idx + 1)
# Reusable function for inference
def inference(args, epoch, data_loader, model, offset=0):
model.eval()
if args.save_flow or args.render_validation:
flow_folder = "{}/inference/{}.epoch-{}-flow-field".format(args.save,args.name.replace('/', '.'),epoch)
if not os.path.exists(flow_folder):
os.makedirs(flow_folder)
# visualization folder
if args.inference_visualize:
flow_vis_folder = "{}/inference/{}.epoch-{}-flow-vis".format(args.save, args.name.replace('/', '.'), epoch)
if not os.path.exists(flow_vis_folder):
os.makedirs(flow_vis_folder)
args.inference_n_batches = np.inf if args.inference_n_batches < 0 else args.inference_n_batches
progress = tqdm(data_loader, ncols=100, total=np.minimum(len(data_loader), args.inference_n_batches), desc='Inferencing ',
leave=True, position=offset)
statistics = []
total_loss = 0
for batch_idx, (data, target) in enumerate(progress):
if args.cuda:
data, target = [d.cuda(non_blocking=True) for d in data], [t.cuda(non_blocking=True) for t in target]
data, target = [Variable(d) for d in data], [Variable(t) for t in target]
# when ground-truth flows are not available for inference_dataset,
# the targets are set to all zeros. thus, losses are actually L1 or L2 norms of compute optical flows,
# depending on the type of loss norm passed in
with torch.no_grad():
losses, output = model(data[0], target[0], inference=True)
losses = [torch.mean(loss_value) for loss_value in losses]
loss_val = losses[0] # Collect first loss for weight update
total_loss += loss_val.item()
loss_values = [v.item() for v in losses]
# gather loss_labels, direct return leads to recursion limit error as it looks for variables to gather'
loss_labels = list(model.module.loss.loss_labels)
statistics.append(loss_values)
# import IPython; IPython.embed()
if args.save_flow or args.render_validation:
for i in range(args.inference_batch_size):
_pflow = output[i].data.cpu().numpy().transpose(1, 2, 0)
flow_utils.writeFlow( join(flow_folder, '%06d.flo'%(batch_idx * args.inference_batch_size + i)), _pflow)
# You can comment out the plt block in visulize_flow_file() for real-time visualization
if args.inference_visualize:
flow_utils.visulize_flow_file(
join(flow_folder, '%06d.flo' % (batch_idx * args.inference_batch_size + i)),flow_vis_folder)
progress.set_description('Inference Averages for Epoch {}: '.format(epoch) + tools.format_dictionary_of_losses(loss_labels, np.array(statistics).mean(axis=0)))
progress.update(1)
if batch_idx == (args.inference_n_batches - 1):
break
progress.close()
return
# Primary epoch loop
best_err = 1e8
progress = tqdm(list(range(args.start_epoch, args.total_epochs + 1)), miniters=1, ncols=100, desc='Overall Progress', leave=True, position=0)
offset = 1
last_epoch_time = progress._time()
global_iteration = 0
for epoch in progress:
if args.inference or (args.render_validation and ((epoch - 1) % args.validation_frequency) == 0):
stats = inference(args=args, epoch=epoch - 1, data_loader=inference_loader, model=model_and_loss, offset=offset)
offset += 1
if not args.skip_validation and ((epoch - 1) % args.validation_frequency) == 0:
validation_loss, _ = train(args=args, epoch=epoch - 1, start_iteration=global_iteration, data_loader=validation_loader, model=model_and_loss, optimizer=optimizer, logger=validation_logger, is_validate=True, offset=offset)
offset += 1
is_best = False
if validation_loss < best_err:
best_err = validation_loss
is_best = True
checkpoint_progress = tqdm(ncols=100, desc='Saving Checkpoint', position=offset)
tools.save_checkpoint({ 'arch' : args.model,
'epoch': epoch,
'state_dict': model_and_loss.module.model.state_dict(),
'best_EPE': best_err},
is_best, args.save, args.model)
checkpoint_progress.update(1)
checkpoint_progress.close()
offset += 1
if not args.skip_training:
train_loss, iterations = train(args=args, epoch=epoch, start_iteration=global_iteration, data_loader=train_loader, model=model_and_loss, optimizer=optimizer, logger=train_logger, offset=offset)
global_iteration += iterations
offset += 1
# save checkpoint after every validation_frequency number of epochs
if ((epoch - 1) % args.validation_frequency) == 0:
checkpoint_progress = tqdm(ncols=100, desc='Saving Checkpoint', position=offset)
tools.save_checkpoint({ 'arch' : args.model,
'epoch': epoch,
'state_dict': model_and_loss.module.model.state_dict(),
'best_EPE': train_loss},
False, args.save, args.model, filename = 'train-checkpoint.pth.tar')
checkpoint_progress.update(1)
checkpoint_progress.close()
train_logger.add_scalar('seconds per epoch', progress._time() - last_epoch_time, epoch)
last_epoch_time = progress._time()
print("\n")
| flownet2-pytorch-master | main.py |
import numpy as np
import matplotlib.pyplot as plt
import os.path
TAG_CHAR = np.array([202021.25], np.float32)
def readFlow(fn):
""" Read .flo file in Middlebury format"""
# Code adapted from:
# http://stackoverflow.com/questions/28013200/reading-middlebury-flow-files-with-python-bytes-array-numpy
# WARNING: this will work on little-endian architectures (eg Intel x86) only!
# print 'fn = %s'%(fn)
with open(fn, 'rb') as f:
magic = np.fromfile(f, np.float32, count=1)
if 202021.25 != magic:
print('Magic number incorrect. Invalid .flo file')
return None
else:
w = np.fromfile(f, np.int32, count=1)
h = np.fromfile(f, np.int32, count=1)
# print 'Reading %d x %d flo file\n' % (w, h)
data = np.fromfile(f, np.float32, count=2*int(w)*int(h))
# Reshape data into 3D array (columns, rows, bands)
# The reshape here is for visualization, the original code is (w,h,2)
return np.resize(data, (int(h), int(w), 2))
def writeFlow(filename,uv,v=None):
""" Write optical flow to file.
If v is None, uv is assumed to contain both u and v channels,
stacked in depth.
Original code by Deqing Sun, adapted from Daniel Scharstein.
"""
nBands = 2
if v is None:
assert(uv.ndim == 3)
assert(uv.shape[2] == 2)
u = uv[:,:,0]
v = uv[:,:,1]
else:
u = uv
assert(u.shape == v.shape)
height,width = u.shape
f = open(filename,'wb')
# write the header
f.write(TAG_CHAR)
np.array(width).astype(np.int32).tofile(f)
np.array(height).astype(np.int32).tofile(f)
# arrange into matrix form
tmp = np.zeros((height, width*nBands))
tmp[:,np.arange(width)*2] = u
tmp[:,np.arange(width)*2 + 1] = v
tmp.astype(np.float32).tofile(f)
f.close()
# ref: https://github.com/sampepose/flownet2-tf/
# blob/18f87081db44939414fc4a48834f9e0da3e69f4c/src/flowlib.py#L240
def visulize_flow_file(flow_filename, save_dir=None):
flow_data = readFlow(flow_filename)
img = flow2img(flow_data)
# plt.imshow(img)
# plt.show()
if save_dir:
idx = flow_filename.rfind("/") + 1
plt.imsave(os.path.join(save_dir, "%s-vis.png" % flow_filename[idx:-4]), img)
def flow2img(flow_data):
"""
convert optical flow into color image
:param flow_data:
:return: color image
"""
# print(flow_data.shape)
# print(type(flow_data))
u = flow_data[:, :, 0]
v = flow_data[:, :, 1]
UNKNOW_FLOW_THRESHOLD = 1e7
pr1 = abs(u) > UNKNOW_FLOW_THRESHOLD
pr2 = abs(v) > UNKNOW_FLOW_THRESHOLD
idx_unknown = (pr1 | pr2)
u[idx_unknown] = v[idx_unknown] = 0
# get max value in each direction
maxu = -999.
maxv = -999.
minu = 999.
minv = 999.
maxu = max(maxu, np.max(u))
maxv = max(maxv, np.max(v))
minu = min(minu, np.min(u))
minv = min(minv, np.min(v))
rad = np.sqrt(u ** 2 + v ** 2)
maxrad = max(-1, np.max(rad))
u = u / maxrad + np.finfo(float).eps
v = v / maxrad + np.finfo(float).eps
img = compute_color(u, v)
idx = np.repeat(idx_unknown[:, :, np.newaxis], 3, axis=2)
img[idx] = 0
return np.uint8(img)
def compute_color(u, v):
"""
compute optical flow color map
:param u: horizontal optical flow
:param v: vertical optical flow
:return:
"""
height, width = u.shape
img = np.zeros((height, width, 3))
NAN_idx = np.isnan(u) | np.isnan(v)
u[NAN_idx] = v[NAN_idx] = 0
colorwheel = make_color_wheel()
ncols = np.size(colorwheel, 0)
rad = np.sqrt(u ** 2 + v ** 2)
a = np.arctan2(-v, -u) / np.pi
fk = (a + 1) / 2 * (ncols - 1) + 1
k0 = np.floor(fk).astype(int)
k1 = k0 + 1
k1[k1 == ncols + 1] = 1
f = fk - k0
for i in range(0, np.size(colorwheel, 1)):
tmp = colorwheel[:, i]
col0 = tmp[k0 - 1] / 255
col1 = tmp[k1 - 1] / 255
col = (1 - f) * col0 + f * col1
idx = rad <= 1
col[idx] = 1 - rad[idx] * (1 - col[idx])
notidx = np.logical_not(idx)
col[notidx] *= 0.75
img[:, :, i] = np.uint8(np.floor(255 * col * (1 - NAN_idx)))
return img
def make_color_wheel():
"""
Generate color wheel according Middlebury color code
:return: Color wheel
"""
RY = 15
YG = 6
GC = 4
CB = 11
BM = 13
MR = 6
ncols = RY + YG + GC + CB + BM + MR
colorwheel = np.zeros([ncols, 3])
col = 0
# RY
colorwheel[0:RY, 0] = 255
colorwheel[0:RY, 1] = np.transpose(np.floor(255 * np.arange(0, RY) / RY))
col += RY
# YG
colorwheel[col:col + YG, 0] = 255 - np.transpose(np.floor(255 * np.arange(0, YG) / YG))
colorwheel[col:col + YG, 1] = 255
col += YG
# GC
colorwheel[col:col + GC, 1] = 255
colorwheel[col:col + GC, 2] = np.transpose(np.floor(255 * np.arange(0, GC) / GC))
col += GC
# CB
colorwheel[col:col + CB, 1] = 255 - np.transpose(np.floor(255 * np.arange(0, CB) / CB))
colorwheel[col:col + CB, 2] = 255
col += CB
# BM
colorwheel[col:col + BM, 2] = 255
colorwheel[col:col + BM, 0] = np.transpose(np.floor(255 * np.arange(0, BM) / BM))
col += + BM
# MR
colorwheel[col:col + MR, 2] = 255 - np.transpose(np.floor(255 * np.arange(0, MR) / MR))
colorwheel[col:col + MR, 0] = 255
return colorwheel
| flownet2-pytorch-master | utils/flow_utils.py |
# freda (todo) :
import os, time, sys, math
import subprocess, shutil
from os.path import *
import numpy as np
from inspect import isclass
from pytz import timezone
from datetime import datetime
import inspect
import torch
def datestr():
pacific = timezone('US/Pacific')
now = datetime.now(pacific)
return '{}{:02}{:02}_{:02}{:02}'.format(now.year, now.month, now.day, now.hour, now.minute)
def module_to_dict(module, exclude=[]):
return dict([(x, getattr(module, x)) for x in dir(module)
if isclass(getattr(module, x))
and x not in exclude
and getattr(module, x) not in exclude])
class TimerBlock:
def __init__(self, title):
print(("{}".format(title)))
def __enter__(self):
self.start = time.clock()
return self
def __exit__(self, exc_type, exc_value, traceback):
self.end = time.clock()
self.interval = self.end - self.start
if exc_type is not None:
self.log("Operation failed\n")
else:
self.log("Operation finished\n")
def log(self, string):
duration = time.clock() - self.start
units = 's'
if duration > 60:
duration = duration / 60.
units = 'm'
print((" [{:.3f}{}] {}".format(duration, units, string)))
def log2file(self, fid, string):
fid = open(fid, 'a')
fid.write("%s\n"%(string))
fid.close()
def add_arguments_for_module(parser, module, argument_for_class, default, skip_params=[], parameter_defaults={}):
argument_group = parser.add_argument_group(argument_for_class.capitalize())
module_dict = module_to_dict(module)
argument_group.add_argument('--' + argument_for_class, type=str, default=default, choices=list(module_dict.keys()))
args, unknown_args = parser.parse_known_args()
class_obj = module_dict[vars(args)[argument_for_class]]
argspec = inspect.getargspec(class_obj.__init__)
defaults = argspec.defaults[::-1] if argspec.defaults else None
args = argspec.args[::-1]
for i, arg in enumerate(args):
cmd_arg = '{}_{}'.format(argument_for_class, arg)
if arg not in skip_params + ['self', 'args']:
if arg in list(parameter_defaults.keys()):
argument_group.add_argument('--{}'.format(cmd_arg), type=type(parameter_defaults[arg]), default=parameter_defaults[arg])
elif (defaults is not None and i < len(defaults)):
argument_group.add_argument('--{}'.format(cmd_arg), type=type(defaults[i]), default=defaults[i])
else:
print(("[Warning]: non-default argument '{}' detected on class '{}'. This argument cannot be modified via the command line"
.format(arg, module.__class__.__name__)))
# We don't have a good way of dealing with inferring the type of the argument
# TODO: try creating a custom action and using ast's infer type?
# else:
# argument_group.add_argument('--{}'.format(cmd_arg), required=True)
def kwargs_from_args(args, argument_for_class):
argument_for_class = argument_for_class + '_'
return {key[len(argument_for_class):]: value for key, value in list(vars(args).items()) if argument_for_class in key and key != argument_for_class + 'class'}
def format_dictionary_of_losses(labels, values):
try:
string = ', '.join([('{}: {:' + ('.3f' if value >= 0.001 else '.1e') +'}').format(name, value) for name, value in zip(labels, values)])
except (TypeError, ValueError) as e:
print((list(zip(labels, values))))
string = '[Log Error] ' + str(e)
return string
class IteratorTimer():
def __init__(self, iterable):
self.iterable = iterable
self.iterator = self.iterable.__iter__()
def __iter__(self):
return self
def __len__(self):
return len(self.iterable)
def __next__(self):
start = time.time()
n = next(self.iterator)
self.last_duration = (time.time() - start)
return n
next = __next__
def gpumemusage():
gpu_mem = subprocess.check_output("nvidia-smi | grep MiB | cut -f 3 -d '|'", shell=True).replace(' ', '').replace('\n', '').replace('i', '')
all_stat = [float(a) for a in gpu_mem.replace('/','').split('MB')[:-1]]
gpu_mem = ''
for i in range(len(all_stat)/2):
curr, tot = all_stat[2*i], all_stat[2*i+1]
util = "%1.2f"%(100*curr/tot)+'%'
cmem = str(int(math.ceil(curr/1024.)))+'GB'
gmem = str(int(math.ceil(tot/1024.)))+'GB'
gpu_mem += util + '--' + join(cmem, gmem) + ' '
return gpu_mem
def update_hyperparameter_schedule(args, epoch, global_iteration, optimizer):
if args.schedule_lr_frequency > 0:
for param_group in optimizer.param_groups:
if (global_iteration + 1) % args.schedule_lr_frequency == 0:
param_group['lr'] /= float(args.schedule_lr_fraction)
param_group['lr'] = float(np.maximum(param_group['lr'], 0.000001))
def save_checkpoint(state, is_best, path, prefix, filename='checkpoint.pth.tar'):
prefix_save = os.path.join(path, prefix)
name = prefix_save + '_' + filename
torch.save(state, name)
if is_best:
shutil.copyfile(name, prefix_save + '_model_best.pth.tar')
| flownet2-pytorch-master | utils/tools.py |
flownet2-pytorch-master | utils/__init__.py |
|
import torch
import torch.nn as nn
import numpy as np
def parse_flownetc(modules, weights, biases):
keys = [
'conv1',
'conv2',
'conv3',
'conv_redir',
'conv3_1',
'conv4',
'conv4_1',
'conv5',
'conv5_1',
'conv6',
'conv6_1',
'deconv5',
'deconv4',
'deconv3',
'deconv2',
'Convolution1',
'Convolution2',
'Convolution3',
'Convolution4',
'Convolution5',
'upsample_flow6to5',
'upsample_flow5to4',
'upsample_flow4to3',
'upsample_flow3to2',
]
i = 0
for m in modules:
if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
weight = weights[keys[i]].copy()
bias = biases[keys[i]].copy()
if keys[i] == 'conv1':
m.weight.data[:,:,:,:] = torch.from_numpy(np.flip(weight, axis=1).copy())
m.bias.data[:] = torch.from_numpy(bias)
else:
m.weight.data[:,:,:,:] = torch.from_numpy(weight)
m.bias.data[:] = torch.from_numpy(bias)
i = i + 1
return
def parse_flownets(modules, weights, biases, param_prefix='net2_'):
keys = [
'conv1',
'conv2',
'conv3',
'conv3_1',
'conv4',
'conv4_1',
'conv5',
'conv5_1',
'conv6',
'conv6_1',
'deconv5',
'deconv4',
'deconv3',
'deconv2',
'predict_conv6',
'predict_conv5',
'predict_conv4',
'predict_conv3',
'predict_conv2',
'upsample_flow6to5',
'upsample_flow5to4',
'upsample_flow4to3',
'upsample_flow3to2',
]
for i, k in enumerate(keys):
if 'upsample' in k:
keys[i] = param_prefix + param_prefix + k
else:
keys[i] = param_prefix + k
i = 0
for m in modules:
if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
weight = weights[keys[i]].copy()
bias = biases[keys[i]].copy()
if keys[i] == param_prefix+'conv1':
m.weight.data[:,0:3,:,:] = torch.from_numpy(np.flip(weight[:,0:3,:,:], axis=1).copy())
m.weight.data[:,3:6,:,:] = torch.from_numpy(np.flip(weight[:,3:6,:,:], axis=1).copy())
m.weight.data[:,6:9,:,:] = torch.from_numpy(np.flip(weight[:,6:9,:,:], axis=1).copy())
m.weight.data[:,9::,:,:] = torch.from_numpy(weight[:,9:,:,:].copy())
if m.bias is not None:
m.bias.data[:] = torch.from_numpy(bias)
else:
m.weight.data[:,:,:,:] = torch.from_numpy(weight)
if m.bias is not None:
m.bias.data[:] = torch.from_numpy(bias)
i = i + 1
return
def parse_flownetsonly(modules, weights, biases, param_prefix=''):
keys = [
'conv1',
'conv2',
'conv3',
'conv3_1',
'conv4',
'conv4_1',
'conv5',
'conv5_1',
'conv6',
'conv6_1',
'deconv5',
'deconv4',
'deconv3',
'deconv2',
'Convolution1',
'Convolution2',
'Convolution3',
'Convolution4',
'Convolution5',
'upsample_flow6to5',
'upsample_flow5to4',
'upsample_flow4to3',
'upsample_flow3to2',
]
for i, k in enumerate(keys):
if 'upsample' in k:
keys[i] = param_prefix + param_prefix + k
else:
keys[i] = param_prefix + k
i = 0
for m in modules:
if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
weight = weights[keys[i]].copy()
bias = biases[keys[i]].copy()
if keys[i] == param_prefix+'conv1':
# print ("%s :"%(keys[i]), m.weight.size(), m.bias.size(), tf_w[keys[i]].shape[::-1])
m.weight.data[:,0:3,:,:] = torch.from_numpy(np.flip(weight[:,0:3,:,:], axis=1).copy())
m.weight.data[:,3:6,:,:] = torch.from_numpy(np.flip(weight[:,3:6,:,:], axis=1).copy())
if m.bias is not None:
m.bias.data[:] = torch.from_numpy(bias)
else:
m.weight.data[:,:,:,:] = torch.from_numpy(weight)
if m.bias is not None:
m.bias.data[:] = torch.from_numpy(bias)
i = i + 1
return
def parse_flownetsd(modules, weights, biases, param_prefix='netsd_'):
keys = [
'conv0',
'conv1',
'conv1_1',
'conv2',
'conv2_1',
'conv3',
'conv3_1',
'conv4',
'conv4_1',
'conv5',
'conv5_1',
'conv6',
'conv6_1',
'deconv5',
'deconv4',
'deconv3',
'deconv2',
'interconv5',
'interconv4',
'interconv3',
'interconv2',
'Convolution1',
'Convolution2',
'Convolution3',
'Convolution4',
'Convolution5',
'upsample_flow6to5',
'upsample_flow5to4',
'upsample_flow4to3',
'upsample_flow3to2',
]
for i, k in enumerate(keys):
keys[i] = param_prefix + k
i = 0
for m in modules:
if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
weight = weights[keys[i]].copy()
bias = biases[keys[i]].copy()
if keys[i] == param_prefix+'conv0':
m.weight.data[:,0:3,:,:] = torch.from_numpy(np.flip(weight[:,0:3,:,:], axis=1).copy())
m.weight.data[:,3:6,:,:] = torch.from_numpy(np.flip(weight[:,3:6,:,:], axis=1).copy())
if m.bias is not None:
m.bias.data[:] = torch.from_numpy(bias)
else:
m.weight.data[:,:,:,:] = torch.from_numpy(weight)
if m.bias is not None:
m.bias.data[:] = torch.from_numpy(bias)
i = i + 1
return
def parse_flownetfusion(modules, weights, biases, param_prefix='fuse_'):
keys = [
'conv0',
'conv1',
'conv1_1',
'conv2',
'conv2_1',
'deconv1',
'deconv0',
'interconv1',
'interconv0',
'_Convolution5',
'_Convolution6',
'_Convolution7',
'upsample_flow2to1',
'upsample_flow1to0',
]
for i, k in enumerate(keys):
keys[i] = param_prefix + k
i = 0
for m in modules:
if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
weight = weights[keys[i]].copy()
bias = biases[keys[i]].copy()
if keys[i] == param_prefix+'conv0':
m.weight.data[:,0:3,:,:] = torch.from_numpy(np.flip(weight[:,0:3,:,:], axis=1).copy())
m.weight.data[:,3::,:,:] = torch.from_numpy(weight[:,3:,:,:].copy())
if m.bias is not None:
m.bias.data[:] = torch.from_numpy(bias)
else:
m.weight.data[:,:,:,:] = torch.from_numpy(weight)
if m.bias is not None:
m.bias.data[:] = torch.from_numpy(bias)
i = i + 1
return
| flownet2-pytorch-master | utils/param_utils.py |
import numpy as np
from os.path import *
from scipy.misc import imread
from . import flow_utils
def read_gen(file_name):
ext = splitext(file_name)[-1]
if ext == '.png' or ext == '.jpeg' or ext == '.ppm' or ext == '.jpg':
im = imread(file_name)
if im.shape[2] > 3:
return im[:,:,:3]
else:
return im
elif ext == '.bin' or ext == '.raw':
return np.load(file_name)
elif ext == '.flo':
return flow_utils.readFlow(file_name).astype(np.float32)
return []
| flownet2-pytorch-master | utils/frame_utils.py |
'''
Portions of this code copyright 2017, Clement Pinard
'''
import torch
import torch.nn as nn
from torch.nn import init
import math
import numpy as np
from .submodules import *
'Parameter count : 38,676,504 '
class FlowNetS(nn.Module):
def __init__(self, args, input_channels = 12, batchNorm=True):
super(FlowNetS,self).__init__()
self.batchNorm = batchNorm
self.conv1 = conv(self.batchNorm, input_channels, 64, kernel_size=7, stride=2)
self.conv2 = conv(self.batchNorm, 64, 128, kernel_size=5, stride=2)
self.conv3 = conv(self.batchNorm, 128, 256, kernel_size=5, stride=2)
self.conv3_1 = conv(self.batchNorm, 256, 256)
self.conv4 = conv(self.batchNorm, 256, 512, stride=2)
self.conv4_1 = conv(self.batchNorm, 512, 512)
self.conv5 = conv(self.batchNorm, 512, 512, stride=2)
self.conv5_1 = conv(self.batchNorm, 512, 512)
self.conv6 = conv(self.batchNorm, 512, 1024, stride=2)
self.conv6_1 = conv(self.batchNorm,1024, 1024)
self.deconv5 = deconv(1024,512)
self.deconv4 = deconv(1026,256)
self.deconv3 = deconv(770,128)
self.deconv2 = deconv(386,64)
self.predict_flow6 = predict_flow(1024)
self.predict_flow5 = predict_flow(1026)
self.predict_flow4 = predict_flow(770)
self.predict_flow3 = predict_flow(386)
self.predict_flow2 = predict_flow(194)
self.upsampled_flow6_to_5 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=False)
self.upsampled_flow5_to_4 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=False)
self.upsampled_flow4_to_3 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=False)
self.upsampled_flow3_to_2 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=False)
for m in self.modules():
if isinstance(m, nn.Conv2d):
if m.bias is not None:
init.uniform_(m.bias)
init.xavier_uniform_(m.weight)
if isinstance(m, nn.ConvTranspose2d):
if m.bias is not None:
init.uniform_(m.bias)
init.xavier_uniform_(m.weight)
# init_deconv_bilinear(m.weight)
self.upsample1 = nn.Upsample(scale_factor=4, mode='bilinear')
def forward(self, x):
out_conv1 = self.conv1(x)
out_conv2 = self.conv2(out_conv1)
out_conv3 = self.conv3_1(self.conv3(out_conv2))
out_conv4 = self.conv4_1(self.conv4(out_conv3))
out_conv5 = self.conv5_1(self.conv5(out_conv4))
out_conv6 = self.conv6_1(self.conv6(out_conv5))
flow6 = self.predict_flow6(out_conv6)
flow6_up = self.upsampled_flow6_to_5(flow6)
out_deconv5 = self.deconv5(out_conv6)
concat5 = torch.cat((out_conv5,out_deconv5,flow6_up),1)
flow5 = self.predict_flow5(concat5)
flow5_up = self.upsampled_flow5_to_4(flow5)
out_deconv4 = self.deconv4(concat5)
concat4 = torch.cat((out_conv4,out_deconv4,flow5_up),1)
flow4 = self.predict_flow4(concat4)
flow4_up = self.upsampled_flow4_to_3(flow4)
out_deconv3 = self.deconv3(concat4)
concat3 = torch.cat((out_conv3,out_deconv3,flow4_up),1)
flow3 = self.predict_flow3(concat3)
flow3_up = self.upsampled_flow3_to_2(flow3)
out_deconv2 = self.deconv2(concat3)
concat2 = torch.cat((out_conv2,out_deconv2,flow3_up),1)
flow2 = self.predict_flow2(concat2)
if self.training:
return flow2,flow3,flow4,flow5,flow6
else:
return flow2,
| flownet2-pytorch-master | networks/FlowNetS.py |
import torch
import torch.nn as nn
from torch.nn import init
import math
import numpy as np
from .submodules import *
'Parameter count = 581,226'
class FlowNetFusion(nn.Module):
def __init__(self,args, batchNorm=True):
super(FlowNetFusion,self).__init__()
self.batchNorm = batchNorm
self.conv0 = conv(self.batchNorm, 11, 64)
self.conv1 = conv(self.batchNorm, 64, 64, stride=2)
self.conv1_1 = conv(self.batchNorm, 64, 128)
self.conv2 = conv(self.batchNorm, 128, 128, stride=2)
self.conv2_1 = conv(self.batchNorm, 128, 128)
self.deconv1 = deconv(128,32)
self.deconv0 = deconv(162,16)
self.inter_conv1 = i_conv(self.batchNorm, 162, 32)
self.inter_conv0 = i_conv(self.batchNorm, 82, 16)
self.predict_flow2 = predict_flow(128)
self.predict_flow1 = predict_flow(32)
self.predict_flow0 = predict_flow(16)
self.upsampled_flow2_to_1 = nn.ConvTranspose2d(2, 2, 4, 2, 1)
self.upsampled_flow1_to_0 = nn.ConvTranspose2d(2, 2, 4, 2, 1)
for m in self.modules():
if isinstance(m, nn.Conv2d):
if m.bias is not None:
init.uniform_(m.bias)
init.xavier_uniform_(m.weight)
if isinstance(m, nn.ConvTranspose2d):
if m.bias is not None:
init.uniform_(m.bias)
init.xavier_uniform_(m.weight)
# init_deconv_bilinear(m.weight)
def forward(self, x):
out_conv0 = self.conv0(x)
out_conv1 = self.conv1_1(self.conv1(out_conv0))
out_conv2 = self.conv2_1(self.conv2(out_conv1))
flow2 = self.predict_flow2(out_conv2)
flow2_up = self.upsampled_flow2_to_1(flow2)
out_deconv1 = self.deconv1(out_conv2)
concat1 = torch.cat((out_conv1,out_deconv1,flow2_up),1)
out_interconv1 = self.inter_conv1(concat1)
flow1 = self.predict_flow1(out_interconv1)
flow1_up = self.upsampled_flow1_to_0(flow1)
out_deconv0 = self.deconv0(concat1)
concat0 = torch.cat((out_conv0,out_deconv0,flow1_up),1)
out_interconv0 = self.inter_conv0(concat0)
flow0 = self.predict_flow0(out_interconv0)
return flow0
| flownet2-pytorch-master | networks/FlowNetFusion.py |
# freda (todo) :
import torch.nn as nn
import torch
import numpy as np
def conv(batchNorm, in_planes, out_planes, kernel_size=3, stride=1):
if batchNorm:
return nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=(kernel_size-1)//2, bias=False),
nn.BatchNorm2d(out_planes),
nn.LeakyReLU(0.1,inplace=True)
)
else:
return nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=(kernel_size-1)//2, bias=True),
nn.LeakyReLU(0.1,inplace=True)
)
def i_conv(batchNorm, in_planes, out_planes, kernel_size=3, stride=1, bias = True):
if batchNorm:
return nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=(kernel_size-1)//2, bias=bias),
nn.BatchNorm2d(out_planes),
)
else:
return nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=(kernel_size-1)//2, bias=bias),
)
def predict_flow(in_planes):
return nn.Conv2d(in_planes,2,kernel_size=3,stride=1,padding=1,bias=True)
def deconv(in_planes, out_planes):
return nn.Sequential(
nn.ConvTranspose2d(in_planes, out_planes, kernel_size=4, stride=2, padding=1, bias=True),
nn.LeakyReLU(0.1,inplace=True)
)
class tofp16(nn.Module):
def __init__(self):
super(tofp16, self).__init__()
def forward(self, input):
return input.half()
class tofp32(nn.Module):
def __init__(self):
super(tofp32, self).__init__()
def forward(self, input):
return input.float()
def init_deconv_bilinear(weight):
f_shape = weight.size()
heigh, width = f_shape[-2], f_shape[-1]
f = np.ceil(width/2.0)
c = (2 * f - 1 - f % 2) / (2.0 * f)
bilinear = np.zeros([heigh, width])
for x in range(width):
for y in range(heigh):
value = (1 - abs(x / f - c)) * (1 - abs(y / f - c))
bilinear[x, y] = value
weight.data.fill_(0.)
for i in range(f_shape[0]):
for j in range(f_shape[1]):
weight.data[i,j,:,:] = torch.from_numpy(bilinear)
def save_grad(grads, name):
def hook(grad):
grads[name] = grad
return hook
'''
def save_grad(grads, name):
def hook(grad):
grads[name] = grad
return hook
import torch
from channelnorm_package.modules.channelnorm import ChannelNorm
model = ChannelNorm().cuda()
grads = {}
a = 100*torch.autograd.Variable(torch.randn((1,3,5,5)).cuda(), requires_grad=True)
a.register_hook(save_grad(grads, 'a'))
b = model(a)
y = torch.mean(b)
y.backward()
'''
| flownet2-pytorch-master | networks/submodules.py |
flownet2-pytorch-master | networks/__init__.py |
|
import torch
import torch.nn as nn
from torch.nn import init
import math
import numpy as np
from .correlation_package.correlation import Correlation
from .submodules import *
'Parameter count , 39,175,298 '
class FlowNetC(nn.Module):
def __init__(self,args, batchNorm=True, div_flow = 20):
super(FlowNetC,self).__init__()
self.batchNorm = batchNorm
self.div_flow = div_flow
self.conv1 = conv(self.batchNorm, 3, 64, kernel_size=7, stride=2)
self.conv2 = conv(self.batchNorm, 64, 128, kernel_size=5, stride=2)
self.conv3 = conv(self.batchNorm, 128, 256, kernel_size=5, stride=2)
self.conv_redir = conv(self.batchNorm, 256, 32, kernel_size=1, stride=1)
if args.fp16:
self.corr = nn.Sequential(
tofp32(),
Correlation(pad_size=20, kernel_size=1, max_displacement=20, stride1=1, stride2=2, corr_multiply=1),
tofp16())
else:
self.corr = Correlation(pad_size=20, kernel_size=1, max_displacement=20, stride1=1, stride2=2, corr_multiply=1)
self.corr_activation = nn.LeakyReLU(0.1,inplace=True)
self.conv3_1 = conv(self.batchNorm, 473, 256)
self.conv4 = conv(self.batchNorm, 256, 512, stride=2)
self.conv4_1 = conv(self.batchNorm, 512, 512)
self.conv5 = conv(self.batchNorm, 512, 512, stride=2)
self.conv5_1 = conv(self.batchNorm, 512, 512)
self.conv6 = conv(self.batchNorm, 512, 1024, stride=2)
self.conv6_1 = conv(self.batchNorm,1024, 1024)
self.deconv5 = deconv(1024,512)
self.deconv4 = deconv(1026,256)
self.deconv3 = deconv(770,128)
self.deconv2 = deconv(386,64)
self.predict_flow6 = predict_flow(1024)
self.predict_flow5 = predict_flow(1026)
self.predict_flow4 = predict_flow(770)
self.predict_flow3 = predict_flow(386)
self.predict_flow2 = predict_flow(194)
self.upsampled_flow6_to_5 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=True)
self.upsampled_flow5_to_4 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=True)
self.upsampled_flow4_to_3 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=True)
self.upsampled_flow3_to_2 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=True)
for m in self.modules():
if isinstance(m, nn.Conv2d):
if m.bias is not None:
init.uniform_(m.bias)
init.xavier_uniform_(m.weight)
if isinstance(m, nn.ConvTranspose2d):
if m.bias is not None:
init.uniform_(m.bias)
init.xavier_uniform_(m.weight)
# init_deconv_bilinear(m.weight)
self.upsample1 = nn.Upsample(scale_factor=4, mode='bilinear')
def forward(self, x):
x1 = x[:,0:3,:,:]
x2 = x[:,3::,:,:]
out_conv1a = self.conv1(x1)
out_conv2a = self.conv2(out_conv1a)
out_conv3a = self.conv3(out_conv2a)
# FlownetC bottom input stream
out_conv1b = self.conv1(x2)
out_conv2b = self.conv2(out_conv1b)
out_conv3b = self.conv3(out_conv2b)
# Merge streams
out_corr = self.corr(out_conv3a, out_conv3b) # False
out_corr = self.corr_activation(out_corr)
# Redirect top input stream and concatenate
out_conv_redir = self.conv_redir(out_conv3a)
in_conv3_1 = torch.cat((out_conv_redir, out_corr), 1)
# Merged conv layers
out_conv3_1 = self.conv3_1(in_conv3_1)
out_conv4 = self.conv4_1(self.conv4(out_conv3_1))
out_conv5 = self.conv5_1(self.conv5(out_conv4))
out_conv6 = self.conv6_1(self.conv6(out_conv5))
flow6 = self.predict_flow6(out_conv6)
flow6_up = self.upsampled_flow6_to_5(flow6)
out_deconv5 = self.deconv5(out_conv6)
concat5 = torch.cat((out_conv5,out_deconv5,flow6_up),1)
flow5 = self.predict_flow5(concat5)
flow5_up = self.upsampled_flow5_to_4(flow5)
out_deconv4 = self.deconv4(concat5)
concat4 = torch.cat((out_conv4,out_deconv4,flow5_up),1)
flow4 = self.predict_flow4(concat4)
flow4_up = self.upsampled_flow4_to_3(flow4)
out_deconv3 = self.deconv3(concat4)
concat3 = torch.cat((out_conv3_1,out_deconv3,flow4_up),1)
flow3 = self.predict_flow3(concat3)
flow3_up = self.upsampled_flow3_to_2(flow3)
out_deconv2 = self.deconv2(concat3)
concat2 = torch.cat((out_conv2a,out_deconv2,flow3_up),1)
flow2 = self.predict_flow2(concat2)
if self.training:
return flow2,flow3,flow4,flow5,flow6
else:
return flow2,
| flownet2-pytorch-master | networks/FlowNetC.py |
import torch
import torch.nn as nn
from torch.nn import init
import math
import numpy as np
from .submodules import *
'Parameter count = 45,371,666'
class FlowNetSD(nn.Module):
def __init__(self, args, batchNorm=True):
super(FlowNetSD,self).__init__()
self.batchNorm = batchNorm
self.conv0 = conv(self.batchNorm, 6, 64)
self.conv1 = conv(self.batchNorm, 64, 64, stride=2)
self.conv1_1 = conv(self.batchNorm, 64, 128)
self.conv2 = conv(self.batchNorm, 128, 128, stride=2)
self.conv2_1 = conv(self.batchNorm, 128, 128)
self.conv3 = conv(self.batchNorm, 128, 256, stride=2)
self.conv3_1 = conv(self.batchNorm, 256, 256)
self.conv4 = conv(self.batchNorm, 256, 512, stride=2)
self.conv4_1 = conv(self.batchNorm, 512, 512)
self.conv5 = conv(self.batchNorm, 512, 512, stride=2)
self.conv5_1 = conv(self.batchNorm, 512, 512)
self.conv6 = conv(self.batchNorm, 512, 1024, stride=2)
self.conv6_1 = conv(self.batchNorm,1024, 1024)
self.deconv5 = deconv(1024,512)
self.deconv4 = deconv(1026,256)
self.deconv3 = deconv(770,128)
self.deconv2 = deconv(386,64)
self.inter_conv5 = i_conv(self.batchNorm, 1026, 512)
self.inter_conv4 = i_conv(self.batchNorm, 770, 256)
self.inter_conv3 = i_conv(self.batchNorm, 386, 128)
self.inter_conv2 = i_conv(self.batchNorm, 194, 64)
self.predict_flow6 = predict_flow(1024)
self.predict_flow5 = predict_flow(512)
self.predict_flow4 = predict_flow(256)
self.predict_flow3 = predict_flow(128)
self.predict_flow2 = predict_flow(64)
self.upsampled_flow6_to_5 = nn.ConvTranspose2d(2, 2, 4, 2, 1)
self.upsampled_flow5_to_4 = nn.ConvTranspose2d(2, 2, 4, 2, 1)
self.upsampled_flow4_to_3 = nn.ConvTranspose2d(2, 2, 4, 2, 1)
self.upsampled_flow3_to_2 = nn.ConvTranspose2d(2, 2, 4, 2, 1)
for m in self.modules():
if isinstance(m, nn.Conv2d):
if m.bias is not None:
init.uniform_(m.bias)
init.xavier_uniform_(m.weight)
if isinstance(m, nn.ConvTranspose2d):
if m.bias is not None:
init.uniform_(m.bias)
init.xavier_uniform_(m.weight)
# init_deconv_bilinear(m.weight)
self.upsample1 = nn.Upsample(scale_factor=4, mode='bilinear')
def forward(self, x):
out_conv0 = self.conv0(x)
out_conv1 = self.conv1_1(self.conv1(out_conv0))
out_conv2 = self.conv2_1(self.conv2(out_conv1))
out_conv3 = self.conv3_1(self.conv3(out_conv2))
out_conv4 = self.conv4_1(self.conv4(out_conv3))
out_conv5 = self.conv5_1(self.conv5(out_conv4))
out_conv6 = self.conv6_1(self.conv6(out_conv5))
flow6 = self.predict_flow6(out_conv6)
flow6_up = self.upsampled_flow6_to_5(flow6)
out_deconv5 = self.deconv5(out_conv6)
concat5 = torch.cat((out_conv5,out_deconv5,flow6_up),1)
out_interconv5 = self.inter_conv5(concat5)
flow5 = self.predict_flow5(out_interconv5)
flow5_up = self.upsampled_flow5_to_4(flow5)
out_deconv4 = self.deconv4(concat5)
concat4 = torch.cat((out_conv4,out_deconv4,flow5_up),1)
out_interconv4 = self.inter_conv4(concat4)
flow4 = self.predict_flow4(out_interconv4)
flow4_up = self.upsampled_flow4_to_3(flow4)
out_deconv3 = self.deconv3(concat4)
concat3 = torch.cat((out_conv3,out_deconv3,flow4_up),1)
out_interconv3 = self.inter_conv3(concat3)
flow3 = self.predict_flow3(out_interconv3)
flow3_up = self.upsampled_flow3_to_2(flow3)
out_deconv2 = self.deconv2(concat3)
concat2 = torch.cat((out_conv2,out_deconv2,flow3_up),1)
out_interconv2 = self.inter_conv2(concat2)
flow2 = self.predict_flow2(out_interconv2)
if self.training:
return flow2,flow3,flow4,flow5,flow6
else:
return flow2,
| flownet2-pytorch-master | networks/FlowNetSD.py |
from torch.autograd import Function, Variable
from torch.nn.modules.module import Module
import channelnorm_cuda
class ChannelNormFunction(Function):
@staticmethod
def forward(ctx, input1, norm_deg=2):
assert input1.is_contiguous()
b, _, h, w = input1.size()
output = input1.new(b, 1, h, w).zero_()
channelnorm_cuda.forward(input1, output, norm_deg)
ctx.save_for_backward(input1, output)
ctx.norm_deg = norm_deg
return output
@staticmethod
def backward(ctx, grad_output):
input1, output = ctx.saved_tensors
grad_input1 = Variable(input1.new(input1.size()).zero_())
channelnorm_cuda.backward(input1, output, grad_output.data,
grad_input1.data, ctx.norm_deg)
return grad_input1, None
class ChannelNorm(Module):
def __init__(self, norm_deg=2):
super(ChannelNorm, self).__init__()
self.norm_deg = norm_deg
def forward(self, input1):
return ChannelNormFunction.apply(input1, self.norm_deg)
| flownet2-pytorch-master | networks/channelnorm_package/channelnorm.py |
flownet2-pytorch-master | networks/channelnorm_package/__init__.py |
|
#!/usr/bin/env python3
import os
import torch
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
cxx_args = ['-std=c++11']
nvcc_args = [
'-gencode', 'arch=compute_52,code=sm_52',
'-gencode', 'arch=compute_60,code=sm_60',
'-gencode', 'arch=compute_61,code=sm_61',
'-gencode', 'arch=compute_70,code=sm_70',
'-gencode', 'arch=compute_70,code=compute_70'
]
setup(
name='channelnorm_cuda',
ext_modules=[
CUDAExtension('channelnorm_cuda', [
'channelnorm_cuda.cc',
'channelnorm_kernel.cu'
], extra_compile_args={'cxx': cxx_args, 'nvcc': nvcc_args})
],
cmdclass={
'build_ext': BuildExtension
})
| flownet2-pytorch-master | networks/channelnorm_package/setup.py |
flownet2-pytorch-master | networks/correlation_package/__init__.py |
|
#!/usr/bin/env python3
import os
import torch
from setuptools import setup, find_packages
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
cxx_args = ['-std=c++11']
nvcc_args = [
'-gencode', 'arch=compute_50,code=sm_50',
'-gencode', 'arch=compute_52,code=sm_52',
'-gencode', 'arch=compute_60,code=sm_60',
'-gencode', 'arch=compute_61,code=sm_61',
'-gencode', 'arch=compute_70,code=sm_70',
'-gencode', 'arch=compute_70,code=compute_70'
]
setup(
name='correlation_cuda',
ext_modules=[
CUDAExtension('correlation_cuda', [
'correlation_cuda.cc',
'correlation_cuda_kernel.cu'
], extra_compile_args={'cxx': cxx_args, 'nvcc': nvcc_args})
],
cmdclass={
'build_ext': BuildExtension
})
| flownet2-pytorch-master | networks/correlation_package/setup.py |
import torch
from torch.nn.modules.module import Module
from torch.autograd import Function
import correlation_cuda
class CorrelationFunction(Function):
@staticmethod
def forward(ctx, input1, input2, pad_size=3, kernel_size=3, max_displacement=20, stride1=1, stride2=2, corr_multiply=1):
ctx.save_for_backward(input1, input2)
ctx.pad_size = pad_size
ctx.kernel_size = kernel_size
ctx.max_displacement = max_displacement
ctx.stride1 = stride1
ctx.stride2 = stride2
ctx.corr_multiply = corr_multiply
with torch.cuda.device_of(input1):
rbot1 = input1.new()
rbot2 = input2.new()
output = input1.new()
correlation_cuda.forward(input1, input2, rbot1, rbot2, output,
ctx.pad_size, ctx.kernel_size, ctx.max_displacement, ctx.stride1, ctx.stride2, ctx.corr_multiply)
return output
@staticmethod
def backward(ctx, grad_output):
input1, input2 = ctx.saved_tensors
with torch.cuda.device_of(input1):
rbot1 = input1.new()
rbot2 = input2.new()
grad_input1 = input1.new()
grad_input2 = input2.new()
correlation_cuda.backward(input1, input2, rbot1, rbot2, grad_output, grad_input1, grad_input2,
ctx.pad_size, ctx.kernel_size, ctx.max_displacement, ctx.stride1, ctx.stride2, ctx.corr_multiply)
return grad_input1, grad_input2, None, None, None, None, None, None
class Correlation(Module):
def __init__(self, pad_size=0, kernel_size=0, max_displacement=0, stride1=1, stride2=2, corr_multiply=1):
super(Correlation, self).__init__()
self.pad_size = pad_size
self.kernel_size = kernel_size
self.max_displacement = max_displacement
self.stride1 = stride1
self.stride2 = stride2
self.corr_multiply = corr_multiply
def forward(self, input1, input2):
result = CorrelationFunction.apply(input1, input2, self.pad_size, self.kernel_size, self.max_displacement, self.stride1, self.stride2, self.corr_multiply)
return result
| flownet2-pytorch-master | networks/correlation_package/correlation.py |
from torch.nn.modules.module import Module
from torch.autograd import Function, Variable
import resample2d_cuda
class Resample2dFunction(Function):
@staticmethod
def forward(ctx, input1, input2, kernel_size=1, bilinear= True):
assert input1.is_contiguous()
assert input2.is_contiguous()
ctx.save_for_backward(input1, input2)
ctx.kernel_size = kernel_size
ctx.bilinear = bilinear
_, d, _, _ = input1.size()
b, _, h, w = input2.size()
output = input1.new(b, d, h, w).zero_()
resample2d_cuda.forward(input1, input2, output, kernel_size, bilinear)
return output
@staticmethod
def backward(ctx, grad_output):
grad_output = grad_output.contiguous()
assert grad_output.is_contiguous()
input1, input2 = ctx.saved_tensors
grad_input1 = Variable(input1.new(input1.size()).zero_())
grad_input2 = Variable(input1.new(input2.size()).zero_())
resample2d_cuda.backward(input1, input2, grad_output.data,
grad_input1.data, grad_input2.data,
ctx.kernel_size, ctx.bilinear)
return grad_input1, grad_input2, None, None
class Resample2d(Module):
def __init__(self, kernel_size=1, bilinear = True):
super(Resample2d, self).__init__()
self.kernel_size = kernel_size
self.bilinear = bilinear
def forward(self, input1, input2):
input1_c = input1.contiguous()
return Resample2dFunction.apply(input1_c, input2, self.kernel_size, self.bilinear)
| flownet2-pytorch-master | networks/resample2d_package/resample2d.py |
flownet2-pytorch-master | networks/resample2d_package/__init__.py |
|
#!/usr/bin/env python3
import os
import torch
from setuptools import setup
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
cxx_args = ['-std=c++11']
nvcc_args = [
'-gencode', 'arch=compute_50,code=sm_50',
'-gencode', 'arch=compute_52,code=sm_52',
'-gencode', 'arch=compute_60,code=sm_60',
'-gencode', 'arch=compute_61,code=sm_61',
'-gencode', 'arch=compute_70,code=sm_70',
'-gencode', 'arch=compute_70,code=compute_70'
]
setup(
name='resample2d_cuda',
ext_modules=[
CUDAExtension('resample2d_cuda', [
'resample2d_cuda.cc',
'resample2d_kernel.cu'
], extra_compile_args={'cxx': cxx_args, 'nvcc': nvcc_args})
],
cmdclass={
'build_ext': BuildExtension
})
| flownet2-pytorch-master | networks/resample2d_package/setup.py |
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import setuptools
import pathlib
README = (pathlib.Path(__file__).parent / "README.md").read_text()
setuptools.setup(
name="DLLogger",
version="1.0.0",
author="NVIDIA Corporation",
description="NVIDIA DLLogger - logging for Deep Learning applications",
long_description=README,
long_description_content_type="text/markdown",
url="https://github.com/NVIDIA/dllogger",
packages=["dllogger"],
install_package_data=True,
license='Apache2',
license_file='./LICENSE',
classifiers=[
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.5",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Operating System :: OS Independent",
],
python_requires=">=3.5",
)
| dllogger-master | setup.py |
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .logger import (
Backend,
Verbosity,
Logger,
default_step_format,
default_metric_format,
StdOutBackend,
JSONStreamBackend,
)
__version__ = "1.0.0"
class DLLoggerNotInitialized(Exception):
pass
class DLLLoggerAlreadyInitialized(Exception):
pass
class NotInitializedObject(object):
def __getattribute__(self, name):
raise DLLoggerNotInitialized(
"DLLogger not initialized. Initialize DLLogger with init(backends) function"
)
GLOBAL_LOGGER = NotInitializedObject()
def log(step, data, verbosity=Verbosity.DEFAULT):
GLOBAL_LOGGER.log(step, data, verbosity=verbosity)
def metadata(metric, metadata):
GLOBAL_LOGGER.metadata(metric, metadata)
def flush():
GLOBAL_LOGGER.flush()
def init(backends):
global GLOBAL_LOGGER
try:
if isinstance(GLOBAL_LOGGER, Logger):
raise DLLLoggerAlreadyInitialized()
except DLLoggerNotInitialized:
GLOBAL_LOGGER = Logger(backends)
| dllogger-master | dllogger/__init__.py |
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from abc import ABC, abstractmethod
from collections import defaultdict
from datetime import datetime
import json
import atexit
class Backend(ABC):
def __init__(self, verbosity):
self._verbosity = verbosity
@property
def verbosity(self):
return self._verbosity
@abstractmethod
def log(self, timestamp, elapsedtime, step, data):
pass
@abstractmethod
def metadata(self, timestamp, elapsedtime, metric, metadata):
pass
class Verbosity:
OFF = -1
DEFAULT = 0
VERBOSE = 1
class Logger:
def __init__(self, backends):
self.backends = backends
atexit.register(self.flush)
self.starttime = datetime.now()
def metadata(self, metric, metadata):
timestamp = datetime.now()
elapsedtime = (timestamp - self.starttime).total_seconds()
for b in self.backends:
b.metadata(timestamp, elapsedtime, metric, metadata)
def log(self, step, data, verbosity=1):
timestamp = datetime.now()
elapsedtime = (timestamp - self.starttime).total_seconds()
for b in self.backends:
if b.verbosity >= verbosity:
b.log(timestamp, elapsedtime, step, data)
def flush(self):
for b in self.backends:
b.flush()
def default_step_format(step):
return str(step)
def default_metric_format(metric, metadata, value):
unit = metadata["unit"] if "unit" in metadata.keys() else ""
format = "{" + metadata["format"] + "}" if "format" in metadata.keys() else "{}"
return "{} : {} {}".format(
metric, format.format(value) if value is not None else value, unit
)
def default_prefix_format(timestamp):
return "DLL {} - ".format(timestamp)
class StdOutBackend(Backend):
def __init__(
self,
verbosity,
step_format=default_step_format,
metric_format=default_metric_format,
prefix_format=default_prefix_format,
):
super().__init__(verbosity=verbosity)
self._metadata = defaultdict(dict)
self.step_format = step_format
self.metric_format = metric_format
self.prefix_format = prefix_format
def metadata(self, timestamp, elapsedtime, metric, metadata):
self._metadata[metric].update(metadata)
def log(self, timestamp, elapsedtime, step, data):
print(
"{}{} {}".format(
self.prefix_format(timestamp),
self.step_format(step),
" ".join(
[
self.metric_format(m, self._metadata[m], v)
for m, v in data.items()
]
),
)
)
def flush(self):
pass
class JSONStreamBackend(Backend):
def __init__(self, verbosity, filename, append=False):
super().__init__(verbosity=verbosity)
self._filename = filename
self.file = open(filename, "a" if append else "w")
atexit.register(self.file.close)
def metadata(self, timestamp, elapsedtime, metric, metadata):
self.file.write(
"DLLL {}\n".format(
json.dumps(
dict(
timestamp=str(timestamp.timestamp()),
elapsedtime=str(elapsedtime),
datetime=str(timestamp),
type="METADATA",
metric=metric,
metadata=metadata,
)
)
)
)
def log(self, timestamp, elapsedtime, step, data):
self.file.write(
"DLLL {}\n".format(
json.dumps(
dict(
timestamp=str(timestamp.timestamp()),
datetime=str(timestamp),
elapsedtime=str(elapsedtime),
type="LOG",
step=step,
data=data,
)
)
)
)
def flush(self):
self.file.flush()
| dllogger-master | dllogger/logger.py |
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dllogger import Logger, StdOutBackend, JSONStreamBackend, Verbosity
def format_step(step):
if isinstance(step, str):
return step
s = ""
if len(step) > 0:
s += "Epoch: {} ".format(step[0])
if len(step) > 1:
s += "Iteration: {} ".format(step[1])
if len(step) > 2:
s += "Validation Iteration: {} ".format(step[2])
return s
l = Logger(
[
StdOutBackend(Verbosity.DEFAULT, step_format=format_step),
JSONStreamBackend(Verbosity.VERBOSE, "tmp.json"),
]
)
# You can log metrics in separate calls
l.log(step="PARAMETER", data={"HP1": 17}, verbosity=Verbosity.DEFAULT)
l.log(step="PARAMETER", data={"HP2": 23}, verbosity=Verbosity.DEFAULT)
# or together
l.log(step="PARAMETER", data={"HP3": 1, "HP4": 2}, verbosity=Verbosity.DEFAULT)
l.metadata("loss", {"unit": "nat", "GOAL": "MINIMIZE", "STAGE": "TRAIN"})
l.metadata("val.loss", {"unit": "nat", "GOAL": "MINIMIZE", "STAGE": "VAL"})
l.metadata(
"speed",
{"unit": "speeds/s", "format": ":.3f", "GOAL": "MAXIMIZE", "STAGE": "TRAIN"},
)
for epoch in range(0, 2):
for it in range(0, 10):
l.log(
step=(epoch, it),
data={"loss": 130 / (1 + epoch * 10 + it)},
verbosity=Verbosity.DEFAULT,
)
if it % 3 == 0:
for vit in range(0, 3):
l.log(
step=(epoch, it, vit),
data={"val.loss": 230 / (1 + epoch * 10 + it + vit)},
verbosity=Verbosity.DEFAULT,
)
l.log(step=(epoch,), data={"speed": 10}, verbosity=Verbosity.DEFAULT)
l.flush()
| dllogger-master | examples/dllogger_singleton_example.py |
# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dllogger import Logger, StdOutBackend, JSONStreamBackend, Verbosity
def format_step(step):
if isinstance(step, str):
return step
s = ""
if len(step) > 0:
s += "Epoch: {} ".format(step[0])
if len(step) > 1:
s += "Iteration: {} ".format(step[1])
if len(step) > 2:
s += "Validation Iteration: {} ".format(step[2])
return s
l = Logger(
[
StdOutBackend(Verbosity.DEFAULT, step_format=format_step),
JSONStreamBackend(Verbosity.VERBOSE, "tmp.json"),
]
)
# You can log metrics in separate calls
l.log(step="PARAMETER", data={"HP1": 17}, verbosity=Verbosity.DEFAULT)
l.log(step="PARAMETER", data={"HP2": 23}, verbosity=Verbosity.DEFAULT)
# or together
l.log(step="PARAMETER", data={"HP3": 1, "HP4": 2}, verbosity=Verbosity.DEFAULT)
l.metadata("loss", {"unit": "nat", "GOAL": "MINIMIZE", "STAGE": "TRAIN"})
l.metadata("val.loss", {"unit": "nat", "GOAL": "MINIMIZE", "STAGE": "VAL"})
l.metadata(
"speed",
{"unit": "speeds/s", "format": ":.3f", "GOAL": "MAXIMIZE", "STAGE": "TRAIN"},
)
for epoch in range(0, 2):
for it in range(0, 10):
l.log(
step=(epoch, it),
data={"loss": 130 / (1 + epoch * 10 + it)},
verbosity=Verbosity.DEFAULT,
)
if it % 3 == 0:
for vit in range(0, 3):
l.log(
step=(epoch, it, vit),
data={"val.loss": 230 / (1 + epoch * 10 + it + vit)},
verbosity=Verbosity.DEFAULT,
)
l.log(step=(epoch,), data={"speed": 10}, verbosity=Verbosity.DEFAULT)
l.flush()
| dllogger-master | examples/dllogger_example.py |
import matplotlib
matplotlib.use("Agg")
import matplotlib.pylab as plt
import numpy as np
def save_figure_to_numpy(fig):
# save it to a numpy array.
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
return data
def plot_alignment_to_numpy(alignment, info=None):
fig, ax = plt.subplots(figsize=(6, 4))
im = ax.imshow(alignment, aspect='auto', origin='lower',
interpolation='none')
fig.colorbar(im, ax=ax)
xlabel = 'Decoder timestep'
if info is not None:
xlabel += '\n\n' + info
plt.xlabel(xlabel)
plt.ylabel('Encoder timestep')
plt.tight_layout()
fig.canvas.draw()
data = save_figure_to_numpy(fig)
plt.close()
return data
def plot_spectrogram_to_numpy(spectrogram):
fig, ax = plt.subplots(figsize=(12, 3))
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
interpolation='none')
plt.colorbar(im, ax=ax)
plt.xlabel("Frames")
plt.ylabel("Channels")
plt.tight_layout()
fig.canvas.draw()
data = save_figure_to_numpy(fig)
plt.close()
return data
def plot_gate_outputs_to_numpy(gate_targets, gate_outputs):
fig, ax = plt.subplots(figsize=(12, 3))
ax.scatter(range(len(gate_targets)), gate_targets, alpha=0.5,
color='green', marker='+', s=1, label='target')
ax.scatter(range(len(gate_outputs)), gate_outputs, alpha=0.5,
color='red', marker='.', s=1, label='predicted')
plt.xlabel("Frames (Green target, Red predicted)")
plt.ylabel("Gate State")
plt.tight_layout()
fig.canvas.draw()
data = save_figure_to_numpy(fig)
plt.close()
return data
| tacotron2-master | plotting_utils.py |
import tensorflow as tf
from text import symbols
def create_hparams(hparams_string=None, verbose=False):
"""Create model hyperparameters. Parse nondefault from given string."""
hparams = tf.contrib.training.HParams(
################################
# Experiment Parameters #
################################
epochs=500,
iters_per_checkpoint=1000,
seed=1234,
dynamic_loss_scaling=True,
fp16_run=False,
distributed_run=False,
dist_backend="nccl",
dist_url="tcp://localhost:54321",
cudnn_enabled=True,
cudnn_benchmark=False,
ignore_layers=['embedding.weight'],
################################
# Data Parameters #
################################
load_mel_from_disk=False,
training_files='filelists/ljs_audio_text_train_filelist.txt',
validation_files='filelists/ljs_audio_text_val_filelist.txt',
text_cleaners=['english_cleaners'],
################################
# Audio Parameters #
################################
max_wav_value=32768.0,
sampling_rate=22050,
filter_length=1024,
hop_length=256,
win_length=1024,
n_mel_channels=80,
mel_fmin=0.0,
mel_fmax=8000.0,
################################
# Model Parameters #
################################
n_symbols=len(symbols),
symbols_embedding_dim=512,
# Encoder parameters
encoder_kernel_size=5,
encoder_n_convolutions=3,
encoder_embedding_dim=512,
# Decoder parameters
n_frames_per_step=1, # currently only 1 is supported
decoder_rnn_dim=1024,
prenet_dim=256,
max_decoder_steps=1000,
gate_threshold=0.5,
p_attention_dropout=0.1,
p_decoder_dropout=0.1,
# Attention parameters
attention_rnn_dim=1024,
attention_dim=128,
# Location Layer parameters
attention_location_n_filters=32,
attention_location_kernel_size=31,
# Mel-post processing network parameters
postnet_embedding_dim=512,
postnet_kernel_size=5,
postnet_n_convolutions=5,
################################
# Optimization Hyperparameters #
################################
use_saved_learning_rate=False,
learning_rate=1e-3,
weight_decay=1e-6,
grad_clip_thresh=1.0,
batch_size=64,
mask_padding=True # set model's padded outputs to padded values
)
if hparams_string:
tf.logging.info('Parsing command line hparams: %s', hparams_string)
hparams.parse(hparams_string)
if verbose:
tf.logging.info('Final parsed hparams: %s', hparams.values())
return hparams
| tacotron2-master | hparams.py |
import torch
import numpy as np
from scipy.signal import get_window
import librosa.util as librosa_util
def window_sumsquare(window, n_frames, hop_length=200, win_length=800,
n_fft=800, dtype=np.float32, norm=None):
"""
# from librosa 0.6
Compute the sum-square envelope of a window function at a given hop length.
This is used to estimate modulation effects induced by windowing
observations in short-time fourier transforms.
Parameters
----------
window : string, tuple, number, callable, or list-like
Window specification, as in `get_window`
n_frames : int > 0
The number of analysis frames
hop_length : int > 0
The number of samples to advance between frames
win_length : [optional]
The length of the window function. By default, this matches `n_fft`.
n_fft : int > 0
The length of each analysis frame.
dtype : np.dtype
The data type of the output
Returns
-------
wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
The sum-squared envelope of the window function
"""
if win_length is None:
win_length = n_fft
n = n_fft + hop_length * (n_frames - 1)
x = np.zeros(n, dtype=dtype)
# Compute the squared window at the desired length
win_sq = get_window(window, win_length, fftbins=True)
win_sq = librosa_util.normalize(win_sq, norm=norm)**2
win_sq = librosa_util.pad_center(win_sq, n_fft)
# Fill the envelope
for i in range(n_frames):
sample = i * hop_length
x[sample:min(n, sample + n_fft)] += win_sq[:max(0, min(n_fft, n - sample))]
return x
def griffin_lim(magnitudes, stft_fn, n_iters=30):
"""
PARAMS
------
magnitudes: spectrogram magnitudes
stft_fn: STFT class with transform (STFT) and inverse (ISTFT) methods
"""
angles = np.angle(np.exp(2j * np.pi * np.random.rand(*magnitudes.size())))
angles = angles.astype(np.float32)
angles = torch.autograd.Variable(torch.from_numpy(angles))
signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
for i in range(n_iters):
_, angles = stft_fn.transform(signal)
signal = stft_fn.inverse(magnitudes, angles).squeeze(1)
return signal
def dynamic_range_compression(x, C=1, clip_val=1e-5):
"""
PARAMS
------
C: compression factor
"""
return torch.log(torch.clamp(x, min=clip_val) * C)
def dynamic_range_decompression(x, C=1):
"""
PARAMS
------
C: compression factor used to compress
"""
return torch.exp(x) / C
| tacotron2-master | audio_processing.py |
import random
import torch
from torch.utils.tensorboard import SummaryWriter
from plotting_utils import plot_alignment_to_numpy, plot_spectrogram_to_numpy
from plotting_utils import plot_gate_outputs_to_numpy
class Tacotron2Logger(SummaryWriter):
def __init__(self, logdir):
super(Tacotron2Logger, self).__init__(logdir)
def log_training(self, reduced_loss, grad_norm, learning_rate, duration,
iteration):
self.add_scalar("training.loss", reduced_loss, iteration)
self.add_scalar("grad.norm", grad_norm, iteration)
self.add_scalar("learning.rate", learning_rate, iteration)
self.add_scalar("duration", duration, iteration)
def log_validation(self, reduced_loss, model, y, y_pred, iteration):
self.add_scalar("validation.loss", reduced_loss, iteration)
_, mel_outputs, gate_outputs, alignments = y_pred
mel_targets, gate_targets = y
# plot distribution of parameters
for tag, value in model.named_parameters():
tag = tag.replace('.', '/')
self.add_histogram(tag, value.data.cpu().numpy(), iteration)
# plot alignment, mel target and predicted, gate target and predicted
idx = random.randint(0, alignments.size(0) - 1)
self.add_image(
"alignment",
plot_alignment_to_numpy(alignments[idx].data.cpu().numpy().T),
iteration, dataformats='HWC')
self.add_image(
"mel_target",
plot_spectrogram_to_numpy(mel_targets[idx].data.cpu().numpy()),
iteration, dataformats='HWC')
self.add_image(
"mel_predicted",
plot_spectrogram_to_numpy(mel_outputs[idx].data.cpu().numpy()),
iteration, dataformats='HWC')
self.add_image(
"gate",
plot_gate_outputs_to_numpy(
gate_targets[idx].data.cpu().numpy(),
torch.sigmoid(gate_outputs[idx]).data.cpu().numpy()),
iteration, dataformats='HWC')
| tacotron2-master | logger.py |
from math import sqrt
import torch
from torch.autograd import Variable
from torch import nn
from torch.nn import functional as F
from layers import ConvNorm, LinearNorm
from utils import to_gpu, get_mask_from_lengths
class LocationLayer(nn.Module):
def __init__(self, attention_n_filters, attention_kernel_size,
attention_dim):
super(LocationLayer, self).__init__()
padding = int((attention_kernel_size - 1) / 2)
self.location_conv = ConvNorm(2, attention_n_filters,
kernel_size=attention_kernel_size,
padding=padding, bias=False, stride=1,
dilation=1)
self.location_dense = LinearNorm(attention_n_filters, attention_dim,
bias=False, w_init_gain='tanh')
def forward(self, attention_weights_cat):
processed_attention = self.location_conv(attention_weights_cat)
processed_attention = processed_attention.transpose(1, 2)
processed_attention = self.location_dense(processed_attention)
return processed_attention
class Attention(nn.Module):
def __init__(self, attention_rnn_dim, embedding_dim, attention_dim,
attention_location_n_filters, attention_location_kernel_size):
super(Attention, self).__init__()
self.query_layer = LinearNorm(attention_rnn_dim, attention_dim,
bias=False, w_init_gain='tanh')
self.memory_layer = LinearNorm(embedding_dim, attention_dim, bias=False,
w_init_gain='tanh')
self.v = LinearNorm(attention_dim, 1, bias=False)
self.location_layer = LocationLayer(attention_location_n_filters,
attention_location_kernel_size,
attention_dim)
self.score_mask_value = -float("inf")
def get_alignment_energies(self, query, processed_memory,
attention_weights_cat):
"""
PARAMS
------
query: decoder output (batch, n_mel_channels * n_frames_per_step)
processed_memory: processed encoder outputs (B, T_in, attention_dim)
attention_weights_cat: cumulative and prev. att weights (B, 2, max_time)
RETURNS
-------
alignment (batch, max_time)
"""
processed_query = self.query_layer(query.unsqueeze(1))
processed_attention_weights = self.location_layer(attention_weights_cat)
energies = self.v(torch.tanh(
processed_query + processed_attention_weights + processed_memory))
energies = energies.squeeze(-1)
return energies
def forward(self, attention_hidden_state, memory, processed_memory,
attention_weights_cat, mask):
"""
PARAMS
------
attention_hidden_state: attention rnn last output
memory: encoder outputs
processed_memory: processed encoder outputs
attention_weights_cat: previous and cummulative attention weights
mask: binary mask for padded data
"""
alignment = self.get_alignment_energies(
attention_hidden_state, processed_memory, attention_weights_cat)
if mask is not None:
alignment.data.masked_fill_(mask, self.score_mask_value)
attention_weights = F.softmax(alignment, dim=1)
attention_context = torch.bmm(attention_weights.unsqueeze(1), memory)
attention_context = attention_context.squeeze(1)
return attention_context, attention_weights
class Prenet(nn.Module):
def __init__(self, in_dim, sizes):
super(Prenet, self).__init__()
in_sizes = [in_dim] + sizes[:-1]
self.layers = nn.ModuleList(
[LinearNorm(in_size, out_size, bias=False)
for (in_size, out_size) in zip(in_sizes, sizes)])
def forward(self, x):
for linear in self.layers:
x = F.dropout(F.relu(linear(x)), p=0.5, training=True)
return x
class Postnet(nn.Module):
"""Postnet
- Five 1-d convolution with 512 channels and kernel size 5
"""
def __init__(self, hparams):
super(Postnet, self).__init__()
self.convolutions = nn.ModuleList()
self.convolutions.append(
nn.Sequential(
ConvNorm(hparams.n_mel_channels, hparams.postnet_embedding_dim,
kernel_size=hparams.postnet_kernel_size, stride=1,
padding=int((hparams.postnet_kernel_size - 1) / 2),
dilation=1, w_init_gain='tanh'),
nn.BatchNorm1d(hparams.postnet_embedding_dim))
)
for i in range(1, hparams.postnet_n_convolutions - 1):
self.convolutions.append(
nn.Sequential(
ConvNorm(hparams.postnet_embedding_dim,
hparams.postnet_embedding_dim,
kernel_size=hparams.postnet_kernel_size, stride=1,
padding=int((hparams.postnet_kernel_size - 1) / 2),
dilation=1, w_init_gain='tanh'),
nn.BatchNorm1d(hparams.postnet_embedding_dim))
)
self.convolutions.append(
nn.Sequential(
ConvNorm(hparams.postnet_embedding_dim, hparams.n_mel_channels,
kernel_size=hparams.postnet_kernel_size, stride=1,
padding=int((hparams.postnet_kernel_size - 1) / 2),
dilation=1, w_init_gain='linear'),
nn.BatchNorm1d(hparams.n_mel_channels))
)
def forward(self, x):
for i in range(len(self.convolutions) - 1):
x = F.dropout(torch.tanh(self.convolutions[i](x)), 0.5, self.training)
x = F.dropout(self.convolutions[-1](x), 0.5, self.training)
return x
class Encoder(nn.Module):
"""Encoder module:
- Three 1-d convolution banks
- Bidirectional LSTM
"""
def __init__(self, hparams):
super(Encoder, self).__init__()
convolutions = []
for _ in range(hparams.encoder_n_convolutions):
conv_layer = nn.Sequential(
ConvNorm(hparams.encoder_embedding_dim,
hparams.encoder_embedding_dim,
kernel_size=hparams.encoder_kernel_size, stride=1,
padding=int((hparams.encoder_kernel_size - 1) / 2),
dilation=1, w_init_gain='relu'),
nn.BatchNorm1d(hparams.encoder_embedding_dim))
convolutions.append(conv_layer)
self.convolutions = nn.ModuleList(convolutions)
self.lstm = nn.LSTM(hparams.encoder_embedding_dim,
int(hparams.encoder_embedding_dim / 2), 1,
batch_first=True, bidirectional=True)
def forward(self, x, input_lengths):
for conv in self.convolutions:
x = F.dropout(F.relu(conv(x)), 0.5, self.training)
x = x.transpose(1, 2)
# pytorch tensor are not reversible, hence the conversion
input_lengths = input_lengths.cpu().numpy()
x = nn.utils.rnn.pack_padded_sequence(
x, input_lengths, batch_first=True)
self.lstm.flatten_parameters()
outputs, _ = self.lstm(x)
outputs, _ = nn.utils.rnn.pad_packed_sequence(
outputs, batch_first=True)
return outputs
def inference(self, x):
for conv in self.convolutions:
x = F.dropout(F.relu(conv(x)), 0.5, self.training)
x = x.transpose(1, 2)
self.lstm.flatten_parameters()
outputs, _ = self.lstm(x)
return outputs
class Decoder(nn.Module):
def __init__(self, hparams):
super(Decoder, self).__init__()
self.n_mel_channels = hparams.n_mel_channels
self.n_frames_per_step = hparams.n_frames_per_step
self.encoder_embedding_dim = hparams.encoder_embedding_dim
self.attention_rnn_dim = hparams.attention_rnn_dim
self.decoder_rnn_dim = hparams.decoder_rnn_dim
self.prenet_dim = hparams.prenet_dim
self.max_decoder_steps = hparams.max_decoder_steps
self.gate_threshold = hparams.gate_threshold
self.p_attention_dropout = hparams.p_attention_dropout
self.p_decoder_dropout = hparams.p_decoder_dropout
self.prenet = Prenet(
hparams.n_mel_channels * hparams.n_frames_per_step,
[hparams.prenet_dim, hparams.prenet_dim])
self.attention_rnn = nn.LSTMCell(
hparams.prenet_dim + hparams.encoder_embedding_dim,
hparams.attention_rnn_dim)
self.attention_layer = Attention(
hparams.attention_rnn_dim, hparams.encoder_embedding_dim,
hparams.attention_dim, hparams.attention_location_n_filters,
hparams.attention_location_kernel_size)
self.decoder_rnn = nn.LSTMCell(
hparams.attention_rnn_dim + hparams.encoder_embedding_dim,
hparams.decoder_rnn_dim, 1)
self.linear_projection = LinearNorm(
hparams.decoder_rnn_dim + hparams.encoder_embedding_dim,
hparams.n_mel_channels * hparams.n_frames_per_step)
self.gate_layer = LinearNorm(
hparams.decoder_rnn_dim + hparams.encoder_embedding_dim, 1,
bias=True, w_init_gain='sigmoid')
def get_go_frame(self, memory):
""" Gets all zeros frames to use as first decoder input
PARAMS
------
memory: decoder outputs
RETURNS
-------
decoder_input: all zeros frames
"""
B = memory.size(0)
decoder_input = Variable(memory.data.new(
B, self.n_mel_channels * self.n_frames_per_step).zero_())
return decoder_input
def initialize_decoder_states(self, memory, mask):
""" Initializes attention rnn states, decoder rnn states, attention
weights, attention cumulative weights, attention context, stores memory
and stores processed memory
PARAMS
------
memory: Encoder outputs
mask: Mask for padded data if training, expects None for inference
"""
B = memory.size(0)
MAX_TIME = memory.size(1)
self.attention_hidden = Variable(memory.data.new(
B, self.attention_rnn_dim).zero_())
self.attention_cell = Variable(memory.data.new(
B, self.attention_rnn_dim).zero_())
self.decoder_hidden = Variable(memory.data.new(
B, self.decoder_rnn_dim).zero_())
self.decoder_cell = Variable(memory.data.new(
B, self.decoder_rnn_dim).zero_())
self.attention_weights = Variable(memory.data.new(
B, MAX_TIME).zero_())
self.attention_weights_cum = Variable(memory.data.new(
B, MAX_TIME).zero_())
self.attention_context = Variable(memory.data.new(
B, self.encoder_embedding_dim).zero_())
self.memory = memory
self.processed_memory = self.attention_layer.memory_layer(memory)
self.mask = mask
def parse_decoder_inputs(self, decoder_inputs):
""" Prepares decoder inputs, i.e. mel outputs
PARAMS
------
decoder_inputs: inputs used for teacher-forced training, i.e. mel-specs
RETURNS
-------
inputs: processed decoder inputs
"""
# (B, n_mel_channels, T_out) -> (B, T_out, n_mel_channels)
decoder_inputs = decoder_inputs.transpose(1, 2)
decoder_inputs = decoder_inputs.view(
decoder_inputs.size(0),
int(decoder_inputs.size(1)/self.n_frames_per_step), -1)
# (B, T_out, n_mel_channels) -> (T_out, B, n_mel_channels)
decoder_inputs = decoder_inputs.transpose(0, 1)
return decoder_inputs
def parse_decoder_outputs(self, mel_outputs, gate_outputs, alignments):
""" Prepares decoder outputs for output
PARAMS
------
mel_outputs:
gate_outputs: gate output energies
alignments:
RETURNS
-------
mel_outputs:
gate_outpust: gate output energies
alignments:
"""
# (T_out, B) -> (B, T_out)
alignments = torch.stack(alignments).transpose(0, 1)
# (T_out, B) -> (B, T_out)
gate_outputs = torch.stack(gate_outputs).transpose(0, 1)
gate_outputs = gate_outputs.contiguous()
# (T_out, B, n_mel_channels) -> (B, T_out, n_mel_channels)
mel_outputs = torch.stack(mel_outputs).transpose(0, 1).contiguous()
# decouple frames per step
mel_outputs = mel_outputs.view(
mel_outputs.size(0), -1, self.n_mel_channels)
# (B, T_out, n_mel_channels) -> (B, n_mel_channels, T_out)
mel_outputs = mel_outputs.transpose(1, 2)
return mel_outputs, gate_outputs, alignments
def decode(self, decoder_input):
""" Decoder step using stored states, attention and memory
PARAMS
------
decoder_input: previous mel output
RETURNS
-------
mel_output:
gate_output: gate output energies
attention_weights:
"""
cell_input = torch.cat((decoder_input, self.attention_context), -1)
self.attention_hidden, self.attention_cell = self.attention_rnn(
cell_input, (self.attention_hidden, self.attention_cell))
self.attention_hidden = F.dropout(
self.attention_hidden, self.p_attention_dropout, self.training)
attention_weights_cat = torch.cat(
(self.attention_weights.unsqueeze(1),
self.attention_weights_cum.unsqueeze(1)), dim=1)
self.attention_context, self.attention_weights = self.attention_layer(
self.attention_hidden, self.memory, self.processed_memory,
attention_weights_cat, self.mask)
self.attention_weights_cum += self.attention_weights
decoder_input = torch.cat(
(self.attention_hidden, self.attention_context), -1)
self.decoder_hidden, self.decoder_cell = self.decoder_rnn(
decoder_input, (self.decoder_hidden, self.decoder_cell))
self.decoder_hidden = F.dropout(
self.decoder_hidden, self.p_decoder_dropout, self.training)
decoder_hidden_attention_context = torch.cat(
(self.decoder_hidden, self.attention_context), dim=1)
decoder_output = self.linear_projection(
decoder_hidden_attention_context)
gate_prediction = self.gate_layer(decoder_hidden_attention_context)
return decoder_output, gate_prediction, self.attention_weights
def forward(self, memory, decoder_inputs, memory_lengths):
""" Decoder forward pass for training
PARAMS
------
memory: Encoder outputs
decoder_inputs: Decoder inputs for teacher forcing. i.e. mel-specs
memory_lengths: Encoder output lengths for attention masking.
RETURNS
-------
mel_outputs: mel outputs from the decoder
gate_outputs: gate outputs from the decoder
alignments: sequence of attention weights from the decoder
"""
decoder_input = self.get_go_frame(memory).unsqueeze(0)
decoder_inputs = self.parse_decoder_inputs(decoder_inputs)
decoder_inputs = torch.cat((decoder_input, decoder_inputs), dim=0)
decoder_inputs = self.prenet(decoder_inputs)
self.initialize_decoder_states(
memory, mask=~get_mask_from_lengths(memory_lengths))
mel_outputs, gate_outputs, alignments = [], [], []
while len(mel_outputs) < decoder_inputs.size(0) - 1:
decoder_input = decoder_inputs[len(mel_outputs)]
mel_output, gate_output, attention_weights = self.decode(
decoder_input)
mel_outputs += [mel_output.squeeze(1)]
gate_outputs += [gate_output.squeeze(1)]
alignments += [attention_weights]
mel_outputs, gate_outputs, alignments = self.parse_decoder_outputs(
mel_outputs, gate_outputs, alignments)
return mel_outputs, gate_outputs, alignments
def inference(self, memory):
""" Decoder inference
PARAMS
------
memory: Encoder outputs
RETURNS
-------
mel_outputs: mel outputs from the decoder
gate_outputs: gate outputs from the decoder
alignments: sequence of attention weights from the decoder
"""
decoder_input = self.get_go_frame(memory)
self.initialize_decoder_states(memory, mask=None)
mel_outputs, gate_outputs, alignments = [], [], []
while True:
decoder_input = self.prenet(decoder_input)
mel_output, gate_output, alignment = self.decode(decoder_input)
mel_outputs += [mel_output.squeeze(1)]
gate_outputs += [gate_output]
alignments += [alignment]
if torch.sigmoid(gate_output.data) > self.gate_threshold:
break
elif len(mel_outputs) == self.max_decoder_steps:
print("Warning! Reached max decoder steps")
break
decoder_input = mel_output
mel_outputs, gate_outputs, alignments = self.parse_decoder_outputs(
mel_outputs, gate_outputs, alignments)
return mel_outputs, gate_outputs, alignments
class Tacotron2(nn.Module):
def __init__(self, hparams):
super(Tacotron2, self).__init__()
self.mask_padding = hparams.mask_padding
self.fp16_run = hparams.fp16_run
self.n_mel_channels = hparams.n_mel_channels
self.n_frames_per_step = hparams.n_frames_per_step
self.embedding = nn.Embedding(
hparams.n_symbols, hparams.symbols_embedding_dim)
std = sqrt(2.0 / (hparams.n_symbols + hparams.symbols_embedding_dim))
val = sqrt(3.0) * std # uniform bounds for std
self.embedding.weight.data.uniform_(-val, val)
self.encoder = Encoder(hparams)
self.decoder = Decoder(hparams)
self.postnet = Postnet(hparams)
def parse_batch(self, batch):
text_padded, input_lengths, mel_padded, gate_padded, \
output_lengths = batch
text_padded = to_gpu(text_padded).long()
input_lengths = to_gpu(input_lengths).long()
max_len = torch.max(input_lengths.data).item()
mel_padded = to_gpu(mel_padded).float()
gate_padded = to_gpu(gate_padded).float()
output_lengths = to_gpu(output_lengths).long()
return (
(text_padded, input_lengths, mel_padded, max_len, output_lengths),
(mel_padded, gate_padded))
def parse_output(self, outputs, output_lengths=None):
if self.mask_padding and output_lengths is not None:
mask = ~get_mask_from_lengths(output_lengths)
mask = mask.expand(self.n_mel_channels, mask.size(0), mask.size(1))
mask = mask.permute(1, 0, 2)
outputs[0].data.masked_fill_(mask, 0.0)
outputs[1].data.masked_fill_(mask, 0.0)
outputs[2].data.masked_fill_(mask[:, 0, :], 1e3) # gate energies
return outputs
def forward(self, inputs):
text_inputs, text_lengths, mels, max_len, output_lengths = inputs
text_lengths, output_lengths = text_lengths.data, output_lengths.data
embedded_inputs = self.embedding(text_inputs).transpose(1, 2)
encoder_outputs = self.encoder(embedded_inputs, text_lengths)
mel_outputs, gate_outputs, alignments = self.decoder(
encoder_outputs, mels, memory_lengths=text_lengths)
mel_outputs_postnet = self.postnet(mel_outputs)
mel_outputs_postnet = mel_outputs + mel_outputs_postnet
return self.parse_output(
[mel_outputs, mel_outputs_postnet, gate_outputs, alignments],
output_lengths)
def inference(self, inputs):
embedded_inputs = self.embedding(inputs).transpose(1, 2)
encoder_outputs = self.encoder.inference(embedded_inputs)
mel_outputs, gate_outputs, alignments = self.decoder.inference(
encoder_outputs)
mel_outputs_postnet = self.postnet(mel_outputs)
mel_outputs_postnet = mel_outputs + mel_outputs_postnet
outputs = self.parse_output(
[mel_outputs, mel_outputs_postnet, gate_outputs, alignments])
return outputs
| tacotron2-master | model.py |
"""
BSD 3-Clause License
Copyright (c) 2017, Prem Seetharaman
All rights reserved.
* Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice, this
list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from this
software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
import torch
import numpy as np
import torch.nn.functional as F
from torch.autograd import Variable
from scipy.signal import get_window
from librosa.util import pad_center, tiny
from audio_processing import window_sumsquare
class STFT(torch.nn.Module):
"""adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft"""
def __init__(self, filter_length=800, hop_length=200, win_length=800,
window='hann'):
super(STFT, self).__init__()
self.filter_length = filter_length
self.hop_length = hop_length
self.win_length = win_length
self.window = window
self.forward_transform = None
scale = self.filter_length / self.hop_length
fourier_basis = np.fft.fft(np.eye(self.filter_length))
cutoff = int((self.filter_length / 2 + 1))
fourier_basis = np.vstack([np.real(fourier_basis[:cutoff, :]),
np.imag(fourier_basis[:cutoff, :])])
forward_basis = torch.FloatTensor(fourier_basis[:, None, :])
inverse_basis = torch.FloatTensor(
np.linalg.pinv(scale * fourier_basis).T[:, None, :])
if window is not None:
assert(filter_length >= win_length)
# get window and zero center pad it to filter_length
fft_window = get_window(window, win_length, fftbins=True)
fft_window = pad_center(fft_window, filter_length)
fft_window = torch.from_numpy(fft_window).float()
# window the bases
forward_basis *= fft_window
inverse_basis *= fft_window
self.register_buffer('forward_basis', forward_basis.float())
self.register_buffer('inverse_basis', inverse_basis.float())
def transform(self, input_data):
num_batches = input_data.size(0)
num_samples = input_data.size(1)
self.num_samples = num_samples
# similar to librosa, reflect-pad the input
input_data = input_data.view(num_batches, 1, num_samples)
input_data = F.pad(
input_data.unsqueeze(1),
(int(self.filter_length / 2), int(self.filter_length / 2), 0, 0),
mode='reflect')
input_data = input_data.squeeze(1)
forward_transform = F.conv1d(
input_data,
Variable(self.forward_basis, requires_grad=False),
stride=self.hop_length,
padding=0)
cutoff = int((self.filter_length / 2) + 1)
real_part = forward_transform[:, :cutoff, :]
imag_part = forward_transform[:, cutoff:, :]
magnitude = torch.sqrt(real_part**2 + imag_part**2)
phase = torch.autograd.Variable(
torch.atan2(imag_part.data, real_part.data))
return magnitude, phase
def inverse(self, magnitude, phase):
recombine_magnitude_phase = torch.cat(
[magnitude*torch.cos(phase), magnitude*torch.sin(phase)], dim=1)
inverse_transform = F.conv_transpose1d(
recombine_magnitude_phase,
Variable(self.inverse_basis, requires_grad=False),
stride=self.hop_length,
padding=0)
if self.window is not None:
window_sum = window_sumsquare(
self.window, magnitude.size(-1), hop_length=self.hop_length,
win_length=self.win_length, n_fft=self.filter_length,
dtype=np.float32)
# remove modulation effects
approx_nonzero_indices = torch.from_numpy(
np.where(window_sum > tiny(window_sum))[0])
window_sum = torch.autograd.Variable(
torch.from_numpy(window_sum), requires_grad=False)
window_sum = window_sum.cuda() if magnitude.is_cuda else window_sum
inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices]
# scale by hop ratio
inverse_transform *= float(self.filter_length) / self.hop_length
inverse_transform = inverse_transform[:, :, int(self.filter_length/2):]
inverse_transform = inverse_transform[:, :, :-int(self.filter_length/2):]
return inverse_transform
def forward(self, input_data):
self.magnitude, self.phase = self.transform(input_data)
reconstruction = self.inverse(self.magnitude, self.phase)
return reconstruction
| tacotron2-master | stft.py |
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