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macroplacement
Unverified Commit fc4fd0a9 by Yucheng Wang Committed by GitHub
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Merge pull request #51 from TILOS-AI-Institute/plc_client-open-source 

Cython codebase + documentation
parents 49dae8c5 3f5134c3
Showing with 2670 additions and 268 deletions
CodeElements/Plc_client/README.md
# PlacementCost
`plc_client_os` is a reversed-engineered work to provide almost the same functionaility with Google's plc_client API. The codebase is available in Cython for faster runtime while being easily accessible. While Circuit Training repository does provide [documentation](https://github.com/google-research/circuit_training/blob/main/docs/PLACEMENT_COST.md) for their plc_client, we provide complete functions and detailed documentations in our codebase.
## Quick Start
Under `MACROPLACEMENT/CodeElements` directory, run the following command:
```
......@@ -11,6 +11,9 @@ sudo curl https://storage.googleapis.com/rl-infra-public/circuit-training/placem
# Run plc testbench
# python -m Plc_client.plc_client_os_test [-h] [--helpfull] --netlist NETLIST [--plc PLC] --width WIDTH --height HEIGHT --col COL --row ROW [--rpmh RPMH] [--rpmv RPMV] [--marh MARH] [--marv MARV] [--smooth SMOOTH]
# Compile Cython
python Plc_client.setup.py build_ext --inplace
# Example
python -m Plc_client.plc_client_os_test --netlist ./Plc_client/test/ariane/netlist.pb.txt\
--plc ./Plc_client/test/ariane/initial.plc\
......@@ -27,13 +30,6 @@ python -m Plc_client.plc_client_os_test --netlist ./Plc_client/test/ariane/netli
You may uncomment any available tests and even run your own test dataset. We do not handle all corner cases since during RL placement, they are unlikely to occur. Our aim here is to reproduce Google's code as much as possible and be able to plug into Circuit Training Flow.
## How to run our code in Circuit Training?
Once you have downloaded Google's Circuit Training code, replace the environment.py with environment_ct.py (**you do need to change the name of the file**). Then, copy `plc_client_os.py` under the same directory (**you should not replace it with `plc_client.py` and should not change the name of the file**).
Since Force Directed Placer for the soft macros is not implemented yet, our code is essentially running Google's `plc_client.py` in parallel with our `plc_client_os.py` but extracting input from our code only except for soft macro positions. The memory usage will double and the runtime tends to be longer. However, with this "more open sourced" version of Circuit Training, we do see comparable training quality as using Google's API.
If you wish to find any discrepancies between these outputs, toggle `DEBUG` to `True` [here](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/e634766f6aa53510c3fe8062896a6020f7ff18d1/CodeElements/Plc_client/environment_ct.py#L42) at the beginning of `environment_ct.py`. This will save all discrepancies into the corresponding folders.
## Implementation Details
For complete information on how the proxy cost is computed in our code, please refer to [Proxy Cost Documentation](https://tilos-ai-institute.github.io/MacroPlacement/Docs/ProxyCost/). Below is a quick overview of the formulation.
......@@ -97,6 +93,11 @@ $$
Notice a smoothing range can be set for congestion. This is only applied to congestion due to net routing which by counting adjacent cells and adding the averaged congestion to these adjacent cells. More details are provided in the document above.
## FD Placement
To enable FD placement, please forward to [FD Placement section](https://github.com/TILOS-AI-Institute/MacroPlacement/tree/main/CodeElements/FDPlacement) where we provide full implementation in C++ for faster runtime. Our Cython codebase is fully integrated with our FD implementation. Once you generated the FD executable `fd_placer`, move it under the same directory as your `plc_client_os.pyx` source code directory. Then, FD functionailty should be enabled without extra steps.
For Details on how FD works, we provide documentation [here](https://github.com/TILOS-AI-Institute/MacroPlacement/blob/main/CodeElements/FDPlacement/README.md).
## DISCLAIMER
**We DO NOT own the original content of placement_util_os.py, observation_extractor_os.py, environment_os.py, environment_ct.py, coordinate_descent_placer.py. All rights belong to Google Authors. These are modified version of the original code and we are including in the repo for the sake of testing. Original Code can be viewed [here](https://github.com/google-research/circuit_training/blob/main/circuit_training/environment/placement_util.py)**.
......
CodeElements/Plc_client/plc_client_os.py
......@@ -55,6 +55,10 @@ class PlacementCost(object):
# Check netlist existance
assert os.path.isfile(self.netlist_file)
# [Experimental] Net Data Structure
# nets[driver] => [list of sinks]
self.nets = {}
# Set meta information
self.init_plc = None
self.project_name = "circuit_training"
......@@ -244,6 +248,7 @@ class PlacementCost(object):
line_item = re.findall(r'\w+', line)
# putting info into data structure
if node_name == "__metadata__":
# skipping metadata header
logging.info('[INFO NETLIST PARSER] skipping invalid net input')
......@@ -293,6 +298,8 @@ class PlacementCost(object):
else:
self.net_cnt += 1
soft_macro_pin.add_sinks(input_list)
# add net
self.nets[node_name] = input_list
self.modules_w_pins.append(soft_macro_pin)
# mapping node_name ==> node idx
......@@ -350,6 +357,7 @@ class PlacementCost(object):
else:
self.net_cnt += 1
hard_macro_pin.add_sinks(input_list)
self.nets[node_name] = input_list
self.modules_w_pins.append(hard_macro_pin)
# mapping node_name ==> node idx
......@@ -380,6 +388,7 @@ class PlacementCost(object):
port.add_sinks(input_list)
# ports does not have pins so update connection immediately
port.add_connections(input_list)
self.nets[node_name] = input_list
self.modules_w_pins.append(port)
self.modules.append(port)
......@@ -662,6 +671,9 @@ class PlacementCost(object):
"""
Compute wirelength cost from wirelength
"""
if self.net_cnt == 0:
self.net_cnt = 1
if self.FLAG_UPDATE_WIRELENGTH:
self.FLAG_UPDATE_WIRELENGTH = False
return self.get_wirelength() / ((self.get_canvas_width_height()[0]\
......@@ -703,18 +715,19 @@ class PlacementCost(object):
assert (self.modules_w_pins[pin_idx].get_type() == 'MACRO_PIN' or\
self.modules_w_pins[pin_idx].get_type() == 'PORT')
except Exception:
print("[ERROR PIN POSITION] Not a MACRO PIN")
print("[ERROR PIN POSITION] Not a MACRO PIN", self.modules_w_pins[pin_idx].get_name())
exit(1)
# PORT pin pos is itself
if self.modules_w_pins[pin_idx].get_type() == 'PORT':
return self.modules_w_pins[pin_idx].get_pos()
# Retrieve node that this pin instantiated on
ref_node_idx = self.get_ref_node_id(pin_idx)
if ref_node_idx == -1:
if self.modules_w_pins[pin_idx].get_type() == 'PORT':
return self.modules_w_pins[pin_idx].get_pos()
else:
print("[ERROR PIN POSITION] Parent Node Not Found.")
exit(1)
if ref_node_idx == -1:
print("[ERROR PIN POSITION] Parent Node Not Found.")
exit(1)
# Parent node
ref_node = self.modules_w_pins[ref_node_idx]
......@@ -729,6 +742,38 @@ class PlacementCost(object):
def get_wirelength(self) -> float:
"""
Proxy HPWL computation w/ [Experimental] net
"""
total_hpwl = 0.0
for driver_pin_name in self.nets.keys():
weight_fact = 1.0
x_coord = []
y_coord = []
# extract driver pin
driver_pin_idx = self.mod_name_to_indices[driver_pin_name]
driver_pin = self.modules_w_pins[driver_pin_idx]
# extract net weight
weight_fact = driver_pin.get_weight()
x_coord.append(self.__get_pin_position(driver_pin_idx)[0])
y_coord.append(self.__get_pin_position(driver_pin_idx)[1])
# iterate through each sink
for sink_pin_name in self.nets[driver_pin_name]:
sink_pin_idx = self.mod_name_to_indices[sink_pin_name]
x_coord.append(self.__get_pin_position(sink_pin_idx)[0])
y_coord.append(self.__get_pin_position(sink_pin_idx)[1])
if x_coord:
total_hpwl += weight_fact * \
(abs(max(x_coord) - min(x_coord)) + \
abs(max(y_coord) - min(y_coord)))
return total_hpwl
def _get_wirelength(self) -> float:
"""
Proxy HPWL computation
"""
# NOTE: in pb.txt, netlist input count exceed certain threshold will be ommitted
......@@ -1929,9 +1974,6 @@ class PlacementCost(object):
return mod.get_fix_flag()
def optimize_stdcells(self):
pass
def update_node_coords(self, node_idx, x_pos, y_pos):
"""
Update Node location if node is 'MACRO', 'STDCELL', 'PORT'
......@@ -2285,7 +2327,11 @@ class PlacementCost(object):
def get_ref_node_id(self, node_idx=-1):
"""
ref_node_id is used for macro_pins. Refers to the macro it belongs to.
if input PORT, return itself
"""
if self.modules_w_pins[node_idx].get_type() == "PORT":
return node_idx
if node_idx != -1:
if node_idx in self.soft_macro_pin_indices or node_idx in self.hard_macro_pin_indices:
pin = self.modules_w_pins[node_idx]
......@@ -2321,7 +2367,9 @@ class PlacementCost(object):
def display_canvas( self,
annotate=True,
amplify=False):
amplify=False,
saveName=None,
show=True):
"""
Non-google function, For quick canvas view
"""
......@@ -2379,8 +2427,11 @@ class PlacementCost(object):
# alpha=0.5, zorder=1000, facecolor='y'))
# if annotate:
# ax.annotate(mod.get_name(), mod.get_pos(), wrap=True,color='r', weight='bold', fontsize=FONT_SIZE, ha='center', va='center')
if saveName:
plt.savefig(saveName)
if show:
plt.show()
plt.show()
plt.close('all')
'''
......@@ -2406,16 +2457,24 @@ class PlacementCost(object):
return u_x1 < v_x2 and u_x2 > v_x1 and u_y1 > v_y2 and u_y2 < v_y1
def __repulsive_force(self, repel_factor, node_i, node_j):
def __repulsive_force(self, repel_factor, mod_i_idx, mod_j_idx, with_initialization=False):
'''
Calculate repulsive force between two nodes node_i, node_j
'''
if repel_factor == 0.0:
return 0.0, 0.0
# Only exert force when modules are overlapping
# TODO: effects on PORTs
if not self.__ifOverlap(u_i=node_i, v_i=node_j):
# node_i_x, node_i_y, node_j_x, node_j_y
return 0.0, 0.0, 0.0, 0.0
print("[INFO] REPEL FORCE detects overlapping, exerting repelling")
if with_initialization:
# TODO: exerting SPRING FORCE
pass
# retrieve module instance
mod_i = self.modules_w_pins[node_i]
mod_j = self.modules_w_pins[node_j]
mod_i = self.modules_w_pins[mod_i_idx]
mod_j = self.modules_w_pins[mod_j_idx]
# retrieve module position
x_i, y_i = mod_i.get_pos()
......@@ -2425,17 +2484,35 @@ class PlacementCost(object):
x_dist = x_i - x_j
y_dist = y_i - y_j
# get dist of hypotenuse
hypo_dist = math.sqrt(x_dist**2 + y_dist**2)
# compute force in x and y direction
if hypo_dist <= 1e-10:
return math.sqrt(repel_factor), math.sqrt(repel_factor)
# get directional vector for node i
x_i2j = x_dist
xd_i2j = x_i2j / abs(x_dist)
y_i2j = y_dist
yd_i2j = y_i2j / abs(y_dist)
# get directional vector for node j
x_j2i = -1.0 * x_dist
xd_j2i = x_j2i / abs(x_dist)
y_j2i = -1.0 * y_dist
yd_j2i = y_j2i / abs(y_dist)
# detect boundaries and if driver or sink is MACRO
# TODO: consider PORT to be inmoveable as well
if self.is_node_hard_macro(mod_i_idx):
# then, mod_j is should move towards mod_i
i_force = self.__i_force(i)
# node_i_x, node_i_y, node_j_x, node_j_y
return 0.0, 0.0, i_force * xd_j2i, i_force * yd_j2i
if self.is_node_hard_macro(mod_j_idx):
# then, mod_i is should move towards mod_j
i_force = self.__i_force(i)
# node_i_x, node_i_y, node_j_x, node_j_y
return i_force * xd_i2j, i_force * yd_i2j, 0.0, 0.0
else:
f_x = repel_factor * x_dist / hypo_dist
f_y = repel_factor * y_dist / hypo_dist
return f_x, f_y
# between macro and macro, attract each to each other
# Can result in heavily overlapping
return
def __repulsive_force_hard_macro(self, repel_factor, h_node_i, s_node_j):
'''
Calculate repulsive force between hard macro and soft macro
......@@ -2465,70 +2542,75 @@ class PlacementCost(object):
else:
return 0.0, 0.0
def __attractive_force(self, io_factor, attract_factor, node_i, node_j, io_flag = True, attract_exponent = 1):
def __attractive_force(self, io_factor, attract_factor, pin_i_idx, pin_j_idx, io_flag = True, attract_exponent = 1, i = 1):
'''
Calculate repulsive force between two nodes node_i, node_j
Calculate repulsive force between two pins pin_i, pin_j
'''
# retrieve module instance
mod_i = self.modules_w_pins[node_i]
mod_j = self.modules_w_pins[node_j]
# retrieve module position
x_i, y_i = mod_i.get_pos()
x_j, y_j = mod_j.get_pos()
# get dist between x and y
x_dist = x_i - x_j - mod_i.get_height()/2 - mod_j.get_height()/2
y_dist = y_i - y_j - mod_i.get_height()/2 - mod_j.get_height()/2
# get dist of hypotenuse
hypo_dist = math.sqrt(x_dist**2 + y_dist**2)
# retrieve module position
x_i, y_i = self.__get_pin_position(pin_idx=pin_i_idx)
x_j, y_j = self.__get_pin_position(pin_idx=pin_j_idx)
# compute force in x and y direction
if hypo_dist <= 0.0 or self.__ifOverlap(u_i=node_i, v_i=node_j):
return 0.0, 0.0
# get distance
x_dist = x_i - x_j
y_dist = y_i - y_j
# if pins are close enough, dont attract futher
if abs(x_dist) <= 1e-3 and abs(y_dist) <= 1e-3:
return 0.0, 0.0, 0.0, 0.0
elif abs(x_dist) <= 1e-3:
x_i2j = 0
xd_i2j = 0
x_j2i = 0
xd_j2i = 0
y_i2j = -1.0 * (y_dist)
yd_i2j = y_i2j / abs(y_dist)
y_j2i = y_dist
yd_j2i = y_j2i / abs(y_dist)
elif abs(y_dist) <= 1e-3:
x_i2j = -1.0 * (x_dist)
xd_i2j = x_i2j / abs(x_dist)
x_j2i = x_dist
xd_j2i = x_j2i / abs(x_dist)
y_i2j = 0
yd_i2j = 0
y_j2i = 0
yd_j2i = 0
else:
if io_flag:
temp_f = io_factor * (hypo_dist ** attract_exponent)
else:
temp_f = attract_factor * (hypo_dist ** attract_exponent)
# get directional vector for pin i
x_i2j = -1.0 * (x_dist)
xd_i2j = x_i2j / abs(x_dist)
y_i2j = -1.0 * (y_dist)
yd_i2j = y_i2j / abs(y_dist)
# get directional vector for pin j
x_j2i = x_dist
xd_j2i = x_j2i / abs(x_dist)
y_j2i = y_dist
yd_j2i = y_j2i / abs(y_dist)
# TODO: consider PORT to be inmoveable as well
i_force = self.__i_force(i)
# pin_i_x, pin_i_y, pin_j_x, pin_j_y
return i_force * xd_i2j, i_force * yd_i2j, i_force * xd_j2i, i_force * yd_j2i
f_x = x_dist / hypo_dist * temp_f
f_y = y_dist / hypo_dist * temp_f
return f_x, f_y
def __centralize(self, mod_id):
def __centralize_soft_macro(self, mod_id):
'''
Pull the modules to the nearest center of the gridcell
'''
mod = self.modules_w_pins[mod_id]
mod_x, mod_y = mod.get_pos()
if self.is_node_soft_macro(mod_id):
# put everyting at center, regardless the overlapping issue
mod = self.modules_w_pins[mod_id]
mod.set_pos(self.width/2, self.height/2)
# compute grid cell col
# why / 2.0?
col = round((mod_x - self.grid_width / 2.0) / self.grid_width)
if (col < 0):
col = 0
elif col > self.grid_col - 1:
col = self.grid_col - 1
row = round((mod_y - self.grid_height / 2.0) / self.grid_height)
if (row < 0):
row = 0
elif row > self.grid_row - 1:
row = self.grid_row - 1
mod.set_pos((col + 0.5) * self.grid_width, (row + 0.5) * self.grid_height)
def __centeralize_circle(self, mod_id):
def __initialization(self):
'''
Pull the modules to a randomized unit circle in the center of the canvas
Initialize soft macros to the center
'''
r = 1 * math.sqrt(random.random())
theta = random.random() * 2 * math.pi
centerX = self.width / 2
centerY = self.height / 2
self.modules_w_pins[mod_id].set_pos(centerX + r * math.cos(theta), centerY + r * math.sin(theta))
for mod_idx in self.soft_macro_indices:
self.__centralize_soft_macro(mod_idx)
def __boundary_check(self, mod_id):
'''
......@@ -2550,188 +2632,260 @@ class PlacementCost(object):
mod_y = self.height
mod.set_pos(mod_x, mod_y)
def __fd_placement(self, io_factor, max_displacement, attract_factor, repel_factor):
def __fd_placement(self, io_factor, num_steps, max_move_distance, attract_factor, repel_factor, use_current_loc, verbose=True):
'''
Force-directed Placement for standard-cell clusters
'''
# store x/y displacement for all soft macro disp
soft_macro_disp = {}
for mod_idx in self.soft_macro_indices:
soft_macro_disp[mod_idx] = [0.0, 0.0]
def check_OOB(mod_id, x_disp, y_disp):
mod = self.modules_w_pins[mod_id]
mod_x, mod_y = mod.get_pos()
mod_height = mod.get_height()
mod_width = mod.get_width()
# print(x_disp, y_disp, mod_x, mod_y, mod_width, mod_height)
# boundary after displacement
x_max = mod_x + mod_width/2 + x_disp
y_max = mod_y + mod_height/2 + y_disp
x_min = mod_x - mod_width/2 + x_disp
y_min = mod_y - mod_height/2 + y_disp
# print(x_max, x_min, y_max, y_min)
# determine if move
if x_min <= 0.0 or x_max >= self.width:
x_disp = 0.0
if y_min <= 0.0 or y_max >= self.height:
y_disp = 0.0
return x_disp, y_disp
def getBBox(mod_id):
mod = self.modules_w_pins[mod_id]
x, y = mod.get_pos()
width = mod.get_width()
height = mod.get_height()
lx = x - width / 2.0
ly = y - height / 2.0
ux = x + width / 2.0
uy = y + height / 2.0
return lx, ly, ux, uy
def _check_overlap(mod_u, mod_v):
'''
Zhiang's implmentation
'''
u_lx, u_ly, u_ux, u_uy = getBBox(mod_u)
v_lx, v_ly, v_ux, v_uy = getBBox(mod_v)
if (u_lx >= v_ux or u_ux <= v_lx or u_ly >= v_uy or u_uy <= v_ly):
# no overlap
return None, None
else:
u_cx = (u_lx + u_ux) / 2.0
u_cy = (u_ly + u_uy) / 2.0
v_cx = (v_lx + v_ux) / 2.0
v_cy = (v_ly + v_uy) / 2.0
if u_cx == v_cx and u_cy == v_cy:
# fully overlap
x_dir = -1.0 / math.sqrt(2.0)
y_dir = -1.0 / math.sqrt(2.0)
return x_dir, y_dir
else:
x_dir = u_cx - v_cx
y_dir = u_cy - v_cy
dist = math.sqrt(x_dir * x_dir + y_dir * y_dir)
return x_dir / dist, y_dir / dist
def check_overlap(mod_u, mod_v):
u_lx, u_ly, u_ux, u_uy = getBBox(mod_u)
v_lx, v_ly, v_ux, v_uy = getBBox(mod_v)
if (u_lx >= v_ux or u_ux <= v_lx or u_ly >= v_uy or u_uy <= v_ly):
return 1, 1
else:
u_cx = (u_lx + u_ux) / 2.0
u_cy = (u_ly + u_uy) / 2.0
v_cx = (v_lx + v_ux) / 2.0
v_cy = (v_ly + v_uy) / 2.0
x_d = u_cx - v_cx
y_d = u_cy - v_cy
# set the minimum val to 1e-12
# min_dist = 1e-2
# if abs(x_d) <= min_dist:
# x_d = -1.0 * min_dist
# if abs(y_d) <= min_dist:
# y_d = -1.0 * min_dist
if x_d == 0:
x_d = 1
else:
x_d /= abs(x_d)
if y_d == 0:
y_d = 1
else:
y_d /= abs(y_d)
return x_d, y_d
def add_displace(mod_id, x_disp, y_disp):
'''
Add the displacement
'''
soft_macro_disp[mod_id][0] += x_disp
soft_macro_disp[mod_id][1] += y_disp
if mod_id in self.soft_macro_indices:
soft_macro_disp[mod_id][0] += x_disp
soft_macro_disp[mod_id][1] += y_disp
def update_location(mod_id, x_disp, y_disp):
'''
Update the displacement to the coordiante
'''
x_pos, y_pos = self.modules_w_pins[mod_id].get_pos()
# logging.info("{} {} {} {}".format(x_pos, y_pos, x_disp, y_disp))
x_disp, y_disp = check_OOB(mod_id, x_disp, y_disp)
# for debug purpose
with open('os_debug.txt', 'a+') as the_file:
the_file.write("{} {} {} {} {}\n".format(
mod_id,
x_pos + x_disp,
y_pos + y_disp,
x_disp, y_disp
))
self.modules_w_pins[mod_id].set_pos(x_pos + x_disp, y_pos + y_disp)
def piecewise_sigmoid(x, shift = 50):
if x >= 0:
return 1/(math.exp(-x+shift) + 1)
else:
return -1/(math.exp(x+shift) + 1)
##SOFT_SOFT REPEL###############################################################################################
# calculate the repulsive forces
# repulsive forces between stdcell clusters
if repel_factor != 0.0:
# temp storing the soft macro count
xr_collection = [0] * len(self.modules_w_pins)
yr_collection = [0] * len(self.modules_w_pins)
# repulsive forces between stdcell clusters and stdcell clusters
for mod_i in self.soft_macro_indices:
for mod_j in self.soft_macro_indices:
if (mod_i <= mod_j):
continue
repul_x, repul_y = self.__repulsive_force(repel_factor=repel_factor,
node_i=mod_i, node_j=mod_j)
xr_collection[mod_i] += 1.0 * repul_x
yr_collection[mod_i] += 1.0 * repul_y
xr_collection[mod_j] += -1.0 * repul_x
yr_collection[mod_j] += -1.0 * repul_y
# finding max x y displacement
max_x_disp, max_y_disp = (0.0, 0.0)
for xr, yr in zip(xr_collection, yr_collection):
if xr != 0.0:
max_x_disp = max(max_x_disp, abs(xr))
if yr != 0.0:
max_y_disp = max(max_y_disp, abs(yr))
# prevent zero division
if max_x_disp == 0.0:
max_x_disp = 1.0
if max_y_disp == 0.0:
max_y_disp = 1.0
def move_soft_macros(attract_factor, repel_factor, io_factor, max_displacement):
# map to soft macro index
for mod_idx in self.soft_macro_indices:
soft_macro_disp[mod_idx] = [0.0, 0.0]
calcAttractiveForce(attract_factor, io_factor, max_displacement)
calcRepulsiveForce(repel_factor, max_displacement)
max_x_disp = 0.0
max_y_disp = 0.0
scaling = 2.0
for mod_idx in self.soft_macro_indices:
add_displace(mod_idx, scaling * xr_collection[mod_idx] / max_x_disp, scaling * yr_collection[mod_idx] / max_y_disp)
##SOFT_HARD REPEL###############################################################################################
if repel_factor != 0.0:
# temp storing the soft macro count
xr_collection = [0] * len(self.modules_w_pins)
yr_collection = [0] * len(self.modules_w_pins)
# repulsive forces between stdcell clusters and macros
for mod_i in self.soft_macro_indices:
for mod_j in self.hard_macro_indices:
repul_x, repul_y = self.__repulsive_force_hard_macro(repel_factor=repel_factor,
h_node_i=mod_i, s_node_j=mod_j)
xr_collection[mod_i] += 1.0 * repul_x
yr_collection[mod_i] += 1.0 * repul_y
max_x_disp = max(max_x_disp, abs(soft_macro_disp[mod_idx][0]))
max_y_disp = max(max_y_disp, abs(soft_macro_disp[mod_idx][1]))
# finding max x y displacement
max_x_disp, max_y_disp = (0.0, 0.0)
for xr, yr in zip(xr_collection, yr_collection):
if xr != 0.0:
max_x_disp = max(max_x_disp, abs(xr))
if yr != 0.0:
max_y_disp = max(max_y_disp, abs(yr))
# prevent zero division
if max_x_disp == 0.0:
max_x_disp = 1.0
if max_y_disp == 0.0:
max_y_disp = 1.0
scaling = 4.0
# normalization
if max_x_disp > 0.0:
for mod_idx in self.soft_macro_indices:
soft_macro_disp[mod_idx][0] = (soft_macro_disp[mod_idx][0] / max_x_disp) * max_displacement
if max_y_disp > 0.0:
for mod_idx in self.soft_macro_indices:
soft_macro_disp[mod_idx][1] = (soft_macro_disp[mod_idx][1] / max_y_disp) * max_displacement
for mod_idx in self.soft_macro_indices:
add_displace(mod_idx, scaling * xr_collection[mod_idx] / max_x_disp, scaling * yr_collection[mod_idx] / max_y_disp)
##NET ATTRACT###################################################################################################
if attract_factor != 0.0:
# temp storing the soft macro count
xr_collection = [0] * len(self.modules_w_pins)
yr_collection = [0] * len(self.modules_w_pins)
# calculate the attractive force
# traverse each edge
# the adj_matrix is a symmetric matrix
for driver_pin_idx, driver_pin in enumerate(self.modules_w_pins):
# only for soft macro
if driver_pin_idx in self.soft_macro_pin_indices and driver_pin.get_sink():
driver_mod_idx = self.get_ref_node_id(driver_pin_idx)
for sink_pin_name in driver_pin.get_sink().keys():
sink_mod_idx = self.mod_name_to_indices[sink_pin_name]
# if overlapped, dont attract further
if self.__ifOverlap(driver_mod_idx, sink_mod_idx):
continue
attrac_x, attrac_y = self.__attractive_force(io_factor=io_factor,
attract_factor=attract_factor,
node_i=driver_mod_idx,
node_j=sink_mod_idx
)
# if overlapped, dont attract further
if self.__ifOverlap(driver_mod_idx, sink_mod_idx, attrac_x, attrac_y):
continue
update_location(mod_idx, soft_macro_disp[mod_idx][0], soft_macro_disp[mod_idx][1])
def checkPinRelativePos(pin_u, pin_v):
ux, uy = self.__get_pin_position(pin_u)
vx, vy = self.__get_pin_position(pin_v)
return -1.0 * (ux - vx), -1.0 * (uy - vy)
xr_collection[driver_mod_idx] += piecewise_sigmoid(-1.0 * attrac_x)
yr_collection[driver_mod_idx] += piecewise_sigmoid(-1.0 * attrac_y)
# finding max x y displacement
max_x_disp, max_y_disp = (0.0, 0.0)
for xr, yr in zip(xr_collection, yr_collection):
if xr != 0.0:
max_x_disp = max(max_x_disp, abs(xr))
if yr != 0.0:
max_y_disp = max(max_y_disp, abs(yr))
# prevent zero division
if max_x_disp == 0.0:
max_x_disp = 1.0
if max_y_disp == 0.0:
max_y_disp = 1.0
def check_if_pin_close(pin_u, pin_v):
ux, uy = self.__get_pin_position(pin_u)
vx, vy = self.__get_pin_position(pin_v)
# not too much attract
scaling = 0.1
for mod_idx in self.soft_macro_indices:
add_displace(mod_idx, scaling * xr_collection[mod_idx] / max_x_disp, scaling * yr_collection[mod_idx] / max_y_disp)
move_x = True
move_y = True
# needs to be determined
dist_thre = 1
if abs(ux - uy) <= dist_thre:
move_x = False
for mod_idx in soft_macro_disp.keys():
# push all the macros to the nearest center of gridcell
update_location(mod_idx, *soft_macro_disp[mod_idx])
# Moved to here to save a for loop
# Based on our understanding, the stdcell clusters can be placed
# at any place in the canvas instead of the center of gridcells
self.__boundary_check(mod_idx)
if abs(vx - vy) <= dist_thre:
move_y = False
return move_x, move_y
def calcRepulsiveForce(repel_factor, max_displacement):
macro_list = sorted(self.hard_macro_indices + self.soft_macro_indices)
for i in range(len(macro_list)):
mod_u_idx = macro_list[i]
for j in range(i + 1, len(macro_list)):
mod_v_idx = macro_list[j]
# if not self.__ifOverlap(mod_u_idx, mod_v_idx):
# # if not overlapping, dont exert force
# continue
x_d, y_d = _check_overlap(mod_u_idx, mod_v_idx)
x_disp = 0.0
y_disp = 0.0
# print("debugging overlap: ", x_d, y_d)
# No overlap
if x_d == None:
x_disp = 0.0
else:
# x_disp = repel_factor * 1.0 / x_d
x_disp = repel_factor * 1.0 * max_displacement * x_d
# No overlap
if y_d == None:
y_disp = 0.0
else:
# y_disp = repel_factor * 1.0 / y_d
y_disp = repel_factor * 1.0 * max_displacement * y_d
# print("debugging: ", x_disp, y_disp)
add_displace(mod_u_idx, x_disp, y_disp)
add_displace(mod_v_idx, -1.0 * x_disp, -1.0 * y_disp)
def calcAttractiveForce(attract_factor, io_factor, max_displacement):
for driver_pin_name in self.nets.keys():
# extract driver pin
driver_pin_idx = self.mod_name_to_indices[driver_pin_name]
driver_pin = self.modules_w_pins[driver_pin_idx]
# extract driver macro
driver_macro_idx = self.get_ref_node_id(driver_pin_idx)
# extract net weight
weight_factor = driver_pin.get_weight()
for sink_pin_name in self.nets[driver_pin_name]:
sink_pin_idx = self.mod_name_to_indices[sink_pin_name]
sink_macro_idx = self.get_ref_node_id(sink_pin_idx)
# compute directional vector
x_d, y_d = checkPinRelativePos(driver_pin_idx, sink_pin_idx)
# if connection has port
if sink_pin_idx in self.port_indices or driver_pin_idx in self.port_indices:
force = weight_factor * io_factor * attract_factor
else:
force = weight_factor * attract_factor
# only move when pin are not too close
# move_x, move_y = check_if_pin_close(driver_pin_idx, sink_pin_idx)
# x_disp = 0.0 if not move_x else force * x_d
# y_disp = 0.0 if not move_y else force * y_d
x_disp = force * x_d
y_disp = force * y_d
# add displacement to driver/sink pin
add_displace(driver_macro_idx, x_disp, y_disp)
add_displace(sink_macro_idx, -1.0 * x_disp, -1.0 * y_disp)
if use_current_loc == False:
self.__initialization()
for i in range(len(num_steps)):
if verbose:
print("[OPTIMIZING STDCELs] at num_step {}".format(i))
attractive_factor = attract_factor[i]
repulsive_factor = repel_factor[i]
num_step = num_steps[i]
max_displacement = max_move_distance[i]
for j in range(num_step):
if verbose:
print("[INFO] number of step {}".format(j))
move_soft_macros(attractive_factor, repulsive_factor, io_factor, max_displacement)
def optimize_stdcells(self, use_current_loc, move_stdcells, move_macros,
log_scale_conns, use_sizes, io_factor, num_steps,
max_move_distance, attract_factor, repel_factor):
# initialize the position for all the macros and stdcell clusters
# YW: here I will ignore centering Macros since CT placement does that
for mod_idx in self.soft_macro_indices:
self.__centeralize_circle(mod_id = mod_idx)
for epoch_id, iterations in enumerate(num_steps):
logging.info("#[OPTIMIZING STDCELs] at num_step {}:".format(str(epoch_id)))
print("[INFO] max_displaccment = ", max_move_distance[epoch_id])
print("[INFO] attractive_factor = ", attract_factor[epoch_id])
print("[INFO] repulsive_factor = ", repel_factor[epoch_id])
print("[INFO] io_factor = ", io_factor)
print("[INFO] number of iteration = ", iterations)
for iter in range(iterations):
logging.info("# iteration {}:".format(str(iter)))
self.__fd_placement(io_factor=io_factor,
max_displacement=max_move_distance[epoch_id],
attract_factor=attract_factor[epoch_id],
repel_factor=repel_factor[epoch_id])
self.save_placement('epoch_{}.plc'.format(str(epoch_id)))
self.__fd_placement(io_factor, num_steps, max_move_distance, attract_factor, repel_factor, use_current_loc, verbose=True)
# Board Entity Definition
class Port:
......@@ -2759,6 +2913,9 @@ class PlacementCost(object):
def get_width(self):
return 0
def get_weight(self):
return 1.0
def add_connection(self, module_name):
# NOTE: assume PORT names does not contain slash
ifPORT = False
......@@ -2991,9 +3148,6 @@ class PlacementCost(object):
def get_sink(self):
return self.sink
def get_weight(self):
return self.weight
def get_type(self):
return "MACRO_PIN"
......@@ -3180,4 +3334,4 @@ def main():
print("# STDCELLs : 0")
if __name__ == '__main__':
main()
\ No newline at end of file
main()
CodeElements/Plc_client/plc_client_os.pyx 0 → 100644
# distutils: language = c++
"""Open-Sourced PlacementCost client class."""
from ast import Assert
import os, io
import re
import math
from typing import Text, Tuple, overload
from absl import logging
from collections import namedtuple
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import traceback, sys
import random
import textwrap
import numpy as np
from circuit_training.grouping import meta_netlist_data_structure as mnds
from circuit_training.grouping import meta_netlist_convertor
from circuit_training.grouping import meta_netlist_util
# Compile: cd Plc_client_v2 && python3 setup.py build_ext --inplace && cd ..
# Cython
from cython cimport view
from libcpp cimport bool
from libcpp.vector cimport vector
from libc.string cimport memset
cimport numpy as cnp
cnp.import_array() # needed to initialize numpy-API
"""plc_client_os docstrings.
Open-sourced effort for plc_client and Google's API, plc_wrapper_main. This module
is used to initialize a PlacementCost object that computes the meta-information and
proxy cost function for RL agent's reward signal at the end of each placement.
Example:
For testing, please refer to plc_client_os_test.py for more information.
python3 setup.py build_ext --inplace
"""
# Block = namedtuple('Block', 'x_max y_max x_min y_min')
cdef class PlacementCost:
# str as general purpose PyObject *
cdef:
public str netlist_file
public float macro_macro_x_spacing
public float macro_macro_y_spacing
# meta information
cdef:
public meta_netlist
str init_plc
str project_name
str block_name
float width
float height
int grid_col
int grid_row
float hroutes_per_micron
float vroutes_per_micron
float smooth_range
float overlap_thres
float hrouting_alloc
float vrouting_alloc
float macro_horizontal_routing_allocation
float macro_vertical_routing_allocation
bool canvas_boundary_check
float grid_width
float grid_height
dict node_fix
dict node_placed
# store module/component count
cdef:
int port_cnt
int hard_macro_cnt
int hard_macro_pin_cnt
int soft_macro_cnt
int soft_macro_pin_cnt
int module_cnt
# indices storage
cdef:
vector[int] port_indices
vector[int] hard_macro_indices
vector[int] hard_macro_pin_indices
vector[int] soft_macro_indices
vector[int] soft_macro_pin_indices
vector[int] macro_indices
# modules look-up table
cdef:
dict mod_name_to_indices
dict macro_id_to_indices
dict port_id_to_indices
object modules_w_pins
dict hard_macros_to_inpins
dict soft_macros_to_inpins
# store nets information
cdef:
int net_cnt
dict nets
# update flags
cdef:
bool FLAG_UPDATE_WIRELENGTH
bool FLAG_UPDATE_DENSITY
bool FLAG_UPDATE_CONGESTION
bool FLAG_UPDATE_MACRO_ADJ
bool FLAG_UPDATE_MACRO_AND_CLUSTERED_PORT_ADJ
bool FLAG_UPDATE_NODE_MASK
# density
cdef:
object grid_cells
object grid_occupied
# congestion
cdef:
object V_routing_cong
object H_routing_cong
object V_macro_routing_cong
object H_macro_routing_cong
float grid_v_routes
float grid_h_routes
# fd placer
cdef:
dict soft_macro_disp
# miscellaneous
cdef:
list blockages
list placed_macro
bool use_incremental_cost
object node_mask
def __init__(self,
str netlist_file,
float macro_macro_x_spacing = 0.0,
float macro_macro_y_spacing = 0.0):
"""
Creates a PlacementCost object.
"""
print("Creates a PlacementCost object")
# str as general purpose PyObject *
# Check netlist existance
self.netlist_file = netlist_file
self.macro_macro_x_spacing = macro_macro_x_spacing
self.macro_macro_y_spacing = macro_macro_y_spacing
# use google open-sourced parser
self.meta_netlist = meta_netlist_convertor.read_netlist(self.netlist_file)
meta_netlist_util.set_canvas_width_height(self.meta_netlist, 10, 10)
# store nets information
self.net_cnt = 0
# [Experimental] Net Data Structure
# nets[driver] => [list of sinks]
self.nets = {}
# modules to index look-up table
self.mod_name_to_indices = {}
# Set meta information
self.init_plc = None
self.project_name = "circuit_training"
self.block_name = netlist_file.rsplit('/', -1)[-2]
self.hroutes_per_micron = 0.0
self.vroutes_per_micron = 0.0
self.smooth_range = 0.0
self.overlap_thres = 0.0
self.hrouting_alloc = 0.0
self.vrouting_alloc = 0.0
self.macro_horizontal_routing_allocation = 0.0
self.macro_vertical_routing_allocation = 0.0
self.canvas_boundary_check = True
# macro to pins look-up table: [MACRO_NAME] => [PIN_NAME]
self.hard_macros_to_inpins = {}
self.soft_macros_to_inpins = {}
# Placed macro
self.placed_macro = []
# not used
self.use_incremental_cost = False
# blockage
self.blockages = []
# default canvas width/height based on cell area
self.width = math.sqrt(self.get_area()/0.6)
self.height = math.sqrt(self.get_area()/0.6)
# default gridding
self.grid_col = 10
self.grid_row = 10
self.grid_width = float(self.width/self.grid_col)
self.grid_height = float(self.height/self.grid_row)
# initialize congestion map
self.V_routing_cong = np.zeros(shape=(self.grid_col * self.grid_row))
self.H_routing_cong = np.zeros(shape=(self.grid_col * self.grid_row))
self.V_macro_routing_cong = np.zeros(shape=(self.grid_col * self.grid_row))
self.H_macro_routing_cong = np.zeros(shape=(self.grid_col * self.grid_row))
# initial grid mask, flatten before output, not used
self.node_mask = np.ones(shape=(self.grid_row, self.grid_col))
# store module/component count
self.port_cnt = len(self.port_indices)
self.hard_macro_cnt = len(self.hard_macro_indices)
self.hard_macro_pin_cnt = len(self.hard_macro_pin_indices)
self.soft_macro_cnt = len(self.soft_macro_indices)
self.soft_macro_pin_cnt = len(self.soft_macro_pin_indices)
self.module_cnt = self.hard_macro_cnt + self.soft_macro_cnt + self.port_cnt
# Update flags
self.FLAG_UPDATE_WIRELENGTH = True
self.FLAG_UPDATE_DENSITY = True
self.FLAG_UPDATE_CONGESTION = True
self.FLAG_UPDATE_MACRO_ADJ = True
self.FLAG_UPDATE_MACRO_AND_CLUSTERED_PORT_ADJ = True
# FD: store x/y displacement for all soft macro disp
soft_macro_disp = {}
self.node_fix = {}
self.node_placed = {}
self.macro_id_to_indices = {}
macro_idx = 0
port_idx = 0
# read netlist list into data structures
for mod in self.meta_netlist.node:
# [MOD.NAME] => [MOD.ID]
self.mod_name_to_indices[mod.name] = mod.id
if mod.type == mnds.Type.STDCELL:
# standard cell, not used
self.std_cell_cnt += 1
elif mod.type == mnds.Type.MACRO:
# unfix macro
self.node_fix[mod.id] = False
# macro placed
self.node_placed[mod.id] = True
# [mod.id] ==> index
self.macro_id_to_indices[mod.id] = macro_idx
macro_idx += 1
if mod.soft_macro:
# soft macro
self.soft_macro_cnt += 1
self.soft_macro_indices.push_back(mod.id)
else:
# hard macro
self.hard_macro_cnt += 1
self.hard_macro_indices.push_back(mod.id)
self.macro_indices.push_back(mod.id)
elif mod.type == mnds.Type.PORT:
# fix ports
self.node_fix[mod.id] = True
# port placed
self.node_placed[mod.id] = True
# port
self.port_cnt += 1
self.port_indices.push_back(mod.id)
# self.port_id_to_indices[mod.id] = port_idx
port_idx += 1
elif mod.type == mnds.Type.MACRO_PIN:
ref_id = mod.ref_node_id
if self.meta_netlist.node[ref_id].soft_macro:
# soft macro pin
self.soft_macro_pin_cnt += 1
self.soft_macro_pin_indices.push_back(mod.id)
else:
# hard macro pin
self.hard_macro_pin_cnt += 1
self.hard_macro_pin_indices.push_back(mod.id)
# read net information
if (mod.type == mnds.Type.MACRO_PIN or mod.type == mnds.Type.PORT) and len(mod.output_indices) > 0:
self.nets[mod.id] = mod.output_indices.copy()
self.net_cnt += mod.weight
def get_netlist_obj(self):
return self.meta_netlist
def __read_plc(self, plc_pth: str):
"""
Plc file Parser
"""
# meta information
_columns = 0
_rows = 0
_width = 0.0
_height = 0.0
_area = 0.0
_block = None
_routes_per_micron_hor = 0.0
_routes_per_micron_ver = 0.0
_routes_used_by_macros_hor = 0.0
_routes_used_by_macros_ver = 0.0
_smoothing_factor = 0
_overlap_threshold = 0.0
# node information
_hard_macro_cnt = 0
_hard_macro_pin_cnt = 0
_macro_cnt = 0
_macro_pin_cnt = 0
_port_cnt = 0
_soft_macro_cnt = 0
_soft_macro_pin_cnt = 0
_stdcell_cnt = 0
# node placement
_node_plc = {}
for cnt, line in enumerate(open(plc_pth, 'r')):
line_item = re.findall(r'[0-9A-Za-z\.\-]+', line)
# skip empty lines
if len(line_item) == 0:
continue
if 'Columns' in line_item and 'Rows' in line_item:
# Columns and Rows should be defined on the same one-line
_columns = int(line_item[1])
_rows = int(line_item[3])
elif "Width" in line_item and "Height" in line_item:
# Width and Height should be defined on the same one-line
_width = float(line_item[1])
_height = float(line_item[3])
elif all(it in line_item for it in ['Area', 'stdcell', 'macros']):
# Total core area of modules
_area = float(line_item[3])
elif "Area" in line_item:
# Total core area of modules
_area = float(line_item[1])
elif "Block" in line_item:
# The block name of the testcase
_block = str(line_item[1])
elif all(it in line_item for it in\
['Routes', 'per', 'micron', 'hor', 'ver']):
# For routing congestion computation
_routes_per_micron_hor = float(line_item[4])
_routes_per_micron_ver = float(line_item[6])
elif all(it in line_item for it in\
['Routes', 'used', 'by', 'macros', 'hor', 'ver']):
# For MACRO congestion computation
_routes_used_by_macros_hor = float(line_item[5])
_routes_used_by_macros_ver = float(line_item[7])
elif all(it in line_item for it in ['Smoothing', 'factor']):
# smoothing factor for routing congestion
_smoothing_factor = float(line_item[2])
elif all(it in line_item for it in ['Overlap', 'threshold']):
# overlap
_overlap_threshold = float(line_item[2])
elif all(it in line_item for it in ['HARD', 'MACROs'])\
and len(line_item) == 3:
_hard_macro_cnt = int(line_item[2])
elif all(it in line_item for it in ['HARD', 'MACRO', 'PINs'])\
and len(line_item) == 4:
_hard_macro_pin_cnt = int(line_item[3])
elif all(it in line_item for it in ['PORTs'])\
and len(line_item) == 2:
_port_cnt = int(line_item[1])
elif all(it in line_item for it in ['SOFT', 'MACROs'])\
and len(line_item) == 3:
_soft_macro_cnt = int(line_item[2])
elif all(it in line_item for it in ['SOFT', 'MACRO', 'PINs'])\
and len(line_item) == 4:
_soft_macro_pin_cnt = int(line_item[3])
elif all(it in line_item for it in ['STDCELLs'])\
and len(line_item) == 2:
_stdcell_cnt = int(line_item[1])
elif all(it in line_item for it in ['MACROs'])\
and len(line_item) == 2:
_macro_cnt = int(line_item[1])
elif all(re.match(r'[0-9FNEWS\.\-]+', it) for it in line_item)\
and len(line_item) == 5:
# [node_index] [x] [y] [orientation] [fixed]
_node_plc[int(line_item[0])] = line_item[1:]
# return as dictionary
info_dict = { "columns":_columns,
"rows":_rows,
"width":_width,
"height":_height,
"area":_area,
"block":_block,
"routes_per_micron_hor":_routes_per_micron_hor,
"routes_per_micron_ver":_routes_per_micron_ver,
"routes_used_by_macros_hor":_routes_used_by_macros_hor,
"routes_used_by_macros_ver":_routes_used_by_macros_ver,
"smoothing_factor":_smoothing_factor,
"overlap_threshold":_overlap_threshold,
"hard_macro_cnt":_hard_macro_cnt,
"hard_macro_pin_cnt":_hard_macro_pin_cnt,
"macro_cnt":_macro_cnt,
"macro_pin_cnt":_macro_pin_cnt,
"port_cnt":_port_cnt,
"soft_macro_cnt":_soft_macro_cnt,
"soft_macro_pin_cnt":_soft_macro_pin_cnt,
"stdcell_cnt":_stdcell_cnt,
"node_plc":_node_plc
}
return info_dict
def restore_placement(self, plc_pth: str, ifInital=True, ifValidate=False, ifReadComment = False, retrieveFD = False):
"""
Read and retrieve .plc file information
NOTE: DO NOT always set self.init_plc because this function is also
used to read final placement file.
ifReadComment: By default, Google's plc_client does not extract
information from .plc comment. This is purely done in
placement_util.py. For purpose of testing, we included this option.
"""
# if plc is an initial placement
if ifInital:
self.init_plc = plc_pth
# recompute cost from new location
self.FLAG_UPDATE_CONGESTION = True
self.FLAG_UPDATE_DENSITY = True
self.FLAG_UPDATE_WIRELENGTH = True
# extracted information from .plc file
info_dict = self.__read_plc(plc_pth)
# validate netlist.pb.txt is on par with .plc
if ifValidate:
try:
assert(self.hard_macro_cnt == info_dict['hard_macro_cnt'])
assert(self.hard_macro_pin_cnt == info_dict['hard_macro_pin_cnt'])
assert(self.soft_macro_cnt == info_dict['soft_macro_cnt'])
assert(self.soft_macro_pin_cnt == info_dict['soft_macro_pin_cnt'])
assert(self.port_cnt == info_dict['port_cnt'])
except AssertionError:
_, _, tb = sys.exc_info()
traceback.print_tb(tb)
tb_info = traceback.extract_tb(tb)
_, line, _, text = tb_info[-1]
print('[ERROR NETLIST/PLC MISMATCH] at line {} in statement {}'\
.format(line, text))
exit(1)
for mod_idx in info_dict['node_plc'].keys():
mod_x = mod_y = mod_orient = mod_ifFixed = None
try:
mod_x = float(info_dict['node_plc'][mod_idx][0])
mod_y = float(info_dict['node_plc'][mod_idx][1])
mod_orient = info_dict['node_plc'][mod_idx][2]
mod_ifFixed = int(info_dict['node_plc'][mod_idx][3])
except Exception as e:
print('[ERROR PLC PARSER] %s' % str(e))
# extract mod object
mod = self.meta_netlist.node[mod_idx]
# if retrieving FD placement result, then only update soft macros
if retrieveFD:
if mod.soft_macro:
mod.coord.x = mod_x
mod.coord.y = mod_y
else:
mod.coord.x = mod_x
mod.coord.y = mod_y
if mod_orient and mod_orient != '-':
if mod_orient == "N":
mod.orientation = mnds.Orientation.N
elif mod_orient == "FN":
mod.orientation = mnds.Orientation.FN
elif mod_orient == "S":
mod.orientation = mnds.Orientation.S
elif mod_orient == "FS":
mod.orientation = mnds.Orientation.FS
elif mod_orient == "E":
mod.orientation = mnds.Orientation.E
elif mod_orient == "FE":
mod.orientation = mnds.Orientation.FE
elif mod_orient == "W":
mod.orientation = mnds.Orientation.W
elif mod_orient == "FW":
mod.orientation = mnds.Orientation.FW
if mod_ifFixed == 0:
self.node_fix[mod_idx] = False
elif mod_ifFixed == 1:
self.node_fix[mod_idx] = True
# set meta information
if ifReadComment:
print("[INFO] Retrieving Meta information from .plc comments")
self.set_canvas_size(info_dict['width'], info_dict['height'])
self.set_placement_grid(info_dict['columns'], info_dict['rows'])
self.set_block_name(info_dict['block'])
self.set_routes_per_micron(
info_dict['routes_per_micron_hor'],
info_dict['routes_per_micron_ver']
)
self.set_macro_routing_allocation(
info_dict['routes_used_by_macros_hor'],
info_dict['routes_used_by_macros_ver']
)
self.set_congestion_smooth_range(info_dict['smoothing_factor'])
self.set_overlap_threshold(info_dict['overlap_threshold'])
cpdef float get_area(self):
"""
Compute Total Module Area
"""
return self.meta_netlist.total_area
cpdef int get_hard_macros_count(self):
return self.hard_macro_cnt
cpdef int get_ports_count(self):
return self.port_cnt
cpdef int get_soft_macros_count(self):
return self.soft_macro_cnt
cpdef int get_hard_macro_pins_count(self):
return self.hard_macro_pin_cnt
cpdef int get_soft_macro_pins_count(self):
return self.soft_macro_pin_cnt
cpdef __get_pin_position(self, int pin_idx):
"""
private function for getting pin location
* PORT = its own position
* HARD MACRO PIN = ref position + offset position
* SOFT MACRO PIN = ref position
"""
try:
assert (self.meta_netlist.node[pin_idx].type in [mnds.Type.MACRO_PIN, mnds.Type.PORT])
except Exception:
print("[ERROR PIN POSITION] Not a MACRO PIN", self.meta_netlist.node[pin_idx].name)
exit(1)
cdef:
float pin_node_x_offset
float pin_node_y_offset
float temp_pin_node_x_offset
float ref_node_x
float ref_node_y
# PORT pin pos is itself
node = self.meta_netlist.node[pin_idx]
if node.type == mnds.Type.PORT:
return node.coord.x, node.coord.y
# Retrieve node that this pin instantiated on
ref_node_idx = node.ref_node_id
# if ref_node_idx == -1:
# print("[ERROR PIN POSITION] Parent Node Not Found.")
# exit(1)
# Parent node
ref_node = self.meta_netlist.node[ref_node_idx]
ref_node_x = ref_node.coord.x
ref_node_y = ref_node.coord.y
# Retrieve current pin node position
pin_node_x_offset = node.offset.x
pin_node_y_offset = node.offset.y
#macro orientation affects offset
if ref_node.orientation in [mnds.Orientation.N, mnds.Orientation.R0, mnds.Orientation.NORMAL]:
pass
elif ref_node.orientation in [mnds.Orientation.FN, mnds.Orientation.MY, mnds.Orientation.FLIP_X]:
pin_node_x_offset = -1.0 * pin_node_x_offset
elif ref_node.orientation in [mnds.Orientation.S, mnds.Orientation.R180, mnds.Orientation.FLIP_XY]:
pin_node_x_offset = -1.0 * pin_node_x_offset
pin_node_y_offset = -1.0 * pin_node_y_offset
elif ref_node.orientation in [mnds.Orientation.FS, mnds.Orientation.MX, mnds.Orientation.FLIP_Y]:
pin_node_y_offset = -1.0 * pin_node_y_offset
elif ref_node.orientation in [mnds.Orientation.E, mnds.Orientation.R270]:
temp_pin_node_x_offset = pin_node_x_offset
pin_node_x_offset = pin_node_y_offset
pin_node_y_offset = -1.0 * temp_pin_node_x_offset
elif ref_node.orientation in [mnds.Orientation.FE, mnds.Orientation.MX90, mnds.Orientation.MYR90]:
temp_pin_node_x_offset = pin_node_x_offset
pin_node_x_offset = -1.0 * pin_node_y_offset
pin_node_y_offset = -1.0 * temp_pin_node_x_offset
elif ref_node.orientation in [mnds.Orientation.W, mnds.Orientation.R90]:
temp_pin_node_x_offset = pin_node_x_offset
pin_node_x_offset = -1.0 * pin_node_y_offset
pin_node_y_offset = temp_pin_node_x_offset
elif ref_node.orientation in [mnds.Orientation.FW, mnds.Orientation.MY90, mnds.Orientation.MXR90]:
temp_pin_node_x_offset = pin_node_x_offset
pin_node_x_offset = pin_node_y_offset
pin_node_y_offset = temp_pin_node_x_offset
# Google's Plc client DOES NOT compute (node_position + pin_offset) when reading input
return (ref_node_x + pin_node_x_offset, ref_node_y + pin_node_y_offset)
cpdef float get_wirelength(self):
"""
Proxy HPWL computation w/ [Experimental] net
"""
total_hpwl = 0.0
for driver_pin_idx in self.nets.keys():
x_coord = []
y_coord = []
# extract net weight
weight_fact = self.meta_netlist.node[driver_pin_idx].weight
x_coord.append(self.__get_pin_position(driver_pin_idx)[0])
y_coord.append(self.__get_pin_position(driver_pin_idx)[1])
# iterate through each sink
for sink_pin_idx in self.nets[driver_pin_idx]:
x_coord.append(self.__get_pin_position(sink_pin_idx)[0])
y_coord.append(self.__get_pin_position(sink_pin_idx)[1])
if x_coord:
total_hpwl += weight_fact * \
(abs(max(x_coord) - min(x_coord)) + \
abs(max(y_coord) - min(y_coord)))
return total_hpwl
cpdef float get_cost(self):
"""
Compute wirelength cost from wirelength
"""
if self.FLAG_UPDATE_WIRELENGTH:
self.FLAG_UPDATE_WIRELENGTH = False
# avoid zero division
if self.net_cnt == 0:
return self.get_wirelength() / ((self.get_canvas_width_height()[0]\
+ self.get_canvas_width_height()[1]))
else:
return self.get_wirelength() / ((self.get_canvas_width_height()[0]\
+ self.get_canvas_width_height()[1]) * self.net_cnt)
def abu(self, xx, n = 0.1):
xxs = sorted(xx, reverse = True)
cnt = math.floor(len(xxs)*n)
if cnt == 0:
return max(xxs)
return sum(xxs[0:cnt])/cnt
cpdef float get_V_congestion_cost(self):
"""
compute average of top 10% of grid cell cong and take half of it
"""
occupied_cells = sorted([gc for gc in self.V_routing_cong if gc != 0.0], reverse=True)
cong_cost = 0.0
# take top 10%
cong_cnt = math.floor(len(self.V_routing_cong) * 0.1)
# if grid cell smaller than 10, take the average over occupied cells
if len(self.V_routing_cong) < 10:
cong_cost = float(sum(occupied_cells) / len(occupied_cells))
return cong_cost
idx = 0
sum_cong = 0
# take top 10%
while idx < cong_cnt and idx < len(occupied_cells):
sum_cong += occupied_cells[idx]
idx += 1
return float(sum_cong / cong_cnt)
cpdef float get_H_congestion_cost(self):
"""
compute average of top 10% of grid cell cong and take half of it
"""
occupied_cells = sorted([gc for gc in self.H_routing_cong if gc != 0.0], reverse=True)
cong_cost = 0.0
# take top 10%
cong_cnt = math.floor(len(self.H_routing_cong) * 0.1)
# if grid cell smaller than 10, take the average over occupied cells
if len(self.H_routing_cong) < 10:
cong_cost = float(sum(occupied_cells) / len(occupied_cells))
return cong_cost
idx = 0
sum_cong = 0
# take top 10%
while idx < cong_cnt and idx < len(occupied_cells):
sum_cong += occupied_cells[idx]
idx += 1
return float(sum_cong / cong_cnt)
cpdef float get_congestion_cost(self):
"""
Return congestion cost based on routing and macro placement
"""
if self.FLAG_UPDATE_CONGESTION:
self.get_routing()
# print(np.concatenate([self.V_routing_cong, self.H_routing_cong]))
return self.abu(np.concatenate([self.V_routing_cong, self.H_routing_cong]), 0.05)
cpdef (int, int) __get_grid_cell_location(self, float x_pos, float y_pos):
"""
private function: for getting grid cell row/col ranging from 0...N
"""
cdef:
int row
int col
self.grid_width = float(self.width/self.grid_col)
self.grid_height = float(self.height/self.grid_row)
row = math.floor(y_pos / self.grid_height)
col = math.floor(x_pos / self.grid_width)
return row, col
cpdef int get_grid_cell_of_node(self, float x_pos, float y_pos):
"""
Returns the node's current location in terms of grid cell index
"""
cdef:
int row
int col
row, col = self.__get_grid_cell_location(x_pos=x_pos, y_pos=y_pos)
return row * self.grid_col + col
cpdef float __overlap_area(self, object bbox_i, object bbox_j, bool return_pos=False):
"""
private function: for computing block overlapping
"""
x_min_max = min(bbox_i.maxx, bbox_j.maxx)
x_max_min = max(bbox_i.minx, bbox_j.minx)
y_min_max = min(bbox_i.maxy, bbox_j.maxy)
y_max_min = max(bbox_i.miny, bbox_j.miny)
x_diff = x_min_max - x_max_min
y_diff = y_min_max - y_max_min
if x_diff >= 0 and y_diff >= 0:
if return_pos:
return x_diff * y_diff, (x_min_max, y_min_max), (x_max_min, y_max_min)
else:
return x_diff * y_diff
return 0
cpdef __overlap_dist(self, bbox_i, bbox_j):
"""
private function: for computing block overlapping
"""
x_diff = min(bbox_i.maxx, bbox_j.maxx) - max(bbox_i.minx, bbox_j.minx)
y_diff = min(bbox_i.maxy, bbox_j.maxy) - max(bbox_i.miny, bbox_j.miny)
if x_diff > 0 and y_diff > 0:
return x_diff, y_diff
return 0, 0
cpdef void __add_module_to_grid_cells(self, float mod_x, float mod_y, float mod_w, float mod_h):
"""
private function: for add module to density grid cells
"""
# Two corners
ur_x = mod_x + (mod_w/2)
ur_y = mod_y + (mod_h/2)
bl_x = mod_x - (mod_w/2)
bl_y = mod_y - (mod_h/2)
# construct block based on current module
module_block = mnds.BoundingBox( minx=mod_x - (mod_w/2),
maxx=mod_x + (mod_w/2),
miny=mod_y - (mod_h/2),
maxy=mod_y + (mod_h/2))
# Only need two corners of a grid cell
ur_row, ur_col = self.__get_grid_cell_location(ur_x, ur_y)
bl_row, bl_col = self.__get_grid_cell_location(bl_x, bl_y)
# check if out of bound
if ur_row >= 0 and ur_col >= 0:
if bl_row < 0:
bl_row = 0
if bl_col < 0:
bl_col = 0
else:
# OOB, skip module
return
if bl_row >= 0 and bl_col >= 0:
if ur_row > self.grid_row - 1:
ur_row = self.grid_row - 1
if ur_col > self.grid_col - 1:
ur_col = self.grid_col - 1
else:
# OOB, skip module
return
for r_i in range(bl_row, ur_row + 1):
for c_i in range(bl_col, ur_col + 1):
# construct block based on current cell row/col
grid_cell_block = mnds.BoundingBox( minx=c_i * self.grid_width,
maxx=(c_i + 1) * self.grid_width,
miny=r_i * self.grid_height,
maxy=(r_i + 1) * self.grid_height)
self.grid_occupied[self.grid_col * r_i + c_i] += \
self.__overlap_area(grid_cell_block, module_block)
cpdef get_grid_cells_density(self):
"""
compute density for all grid cells
"""
# by default grid row/col is 10/10
self.grid_width = float(self.width/self.grid_col)
self.grid_height = float(self.height/self.grid_row)
grid_area = self.grid_width * self.grid_height
self.grid_occupied = [0] * (self.grid_col * self.grid_row)
self.grid_cells = [0] * (self.grid_col * self.grid_row)
for node_idx in self.macro_indices:
# extract module information
node = self.meta_netlist.node[node_idx]
# skipping unplaced module
# if not module.get_placed_flag():
# continue
node_h = node.dimension.height
node_w = node.dimension.width
node_x = node.coord.x
node_y = node.coord.y
self.__add_module_to_grid_cells(
mod_x=node_x,
mod_y=node_y,
mod_h=node_h,
mod_w=node_w
)
for i, gcell in enumerate(self.grid_occupied):
self.grid_cells[i] = gcell / grid_area
return self.grid_cells
cpdef float get_density_cost(self):
"""
compute average of top 10% of grid cell density and take half of it
"""
if self.FLAG_UPDATE_DENSITY:
self.get_grid_cells_density()
self.FLAG_UPDATE_DENSITY=False
occupied_cells = sorted([gc for gc in self.grid_cells if gc != 0.0], reverse=True)
density_cost = 0.0
# take top 10%
density_cnt = math.floor(len(self.grid_cells) * 0.1)
# if grid cell smaller than 10, take the average over occupied cells
if len(self.grid_cells) < 10:
density_cost = float(sum(occupied_cells) / len(occupied_cells))
return 0.5 * density_cost
idx = 0
sum_density = 0
# take top 10%
while idx < density_cnt and idx < len(occupied_cells):
sum_density += occupied_cells[idx]
idx += 1
return 0.5 * float(sum_density / density_cnt)
cpdef bool set_canvas_size(self, float width, float height):
"""
Set canvas size
"""
self.width = width
self.height = height
# Flag updates
self.FLAG_UPDATE_CONGESTION = True
self.FLAG_UPDATE_DENSITY = True
self.FLAG_UPDATE_MACRO_AND_CLUSTERED_PORT_ADJ = True
self.grid_width = float(self.width/self.grid_col)
self.grid_height = float(self.height/self.grid_row)
return True
cpdef get_canvas_width_height(self):
"""
Return canvas size
"""
return self.width, self.height
cpdef bool set_placement_grid(self, grid_col:int, grid_row:int):
"""
Set grid col/row
"""
print("#[PLACEMENT GRID] Col: %d, Row: %d" % (grid_col, grid_row))
self.grid_col = grid_col
self.grid_row = grid_row
# Flag updates
self.FLAG_UPDATE_CONGESTION = True
self.FLAG_UPDATE_DENSITY = True
self.FLAG_UPDATE_MACRO_AND_CLUSTERED_PORT_ADJ = True
self.V_routing_cong = np.zeros(shape=(self.grid_col * self.grid_row))
self.H_routing_cong = np.zeros(shape=(self.grid_col * self.grid_row))
self.V_macro_routing_cong = np.zeros(shape=(self.grid_col * self.grid_row))
self.H_macro_routing_cong = np.zeros(shape=(self.grid_col * self.grid_row))
self.grid_width = float(self.width/self.grid_col)
self.grid_height = float(self.height/self.grid_row)
return True
cpdef get_grid_num_columns_rows(self):
"""
Return grid col/row
"""
return self.grid_col, self.grid_row
cpdef list get_macro_indices(self):
"""
Return all macro indices
"""
return self.macro_indices
cpdef void set_project_name(self, str project_name):
"""
Set Project name
"""
self.project_name = project_name
cpdef str get_project_name(self):
"""
Return Project name
"""
return self.project_name
cpdef void set_block_name(self, str block_name):
"""
Return Block name
"""
self.block_name = block_name
cpdef str get_block_name(self):
"""
Return Block name
"""
return self.block_name
cpdef void set_routes_per_micron(self, float hroutes_per_micron,
float vroutes_per_micron):
"""
Set Routes per Micron
"""
print("#[ROUTES PER MICRON] Hor: %.2f, Ver: %.2f" % (hroutes_per_micron,
vroutes_per_micron))
# Flag updates
self.FLAG_UPDATE_CONGESTION = True
self.hroutes_per_micron = hroutes_per_micron
self.vroutes_per_micron = vroutes_per_micron
cpdef get_routes_per_micron(self):
"""
Return Routes per Micron
"""
return self.hroutes_per_micron, self.vroutes_per_micron
cpdef void set_congestion_smooth_range(self, float smooth_range):
"""
Set congestion smooth range
"""
print("#[CONGESTION SMOOTH RANGE] Smooth Range: %d" % (smooth_range))
# Flag updates
self.FLAG_UPDATE_CONGESTION = True
self.smooth_range = math.floor(smooth_range)
cpdef float get_congestion_smooth_range(self):
"""
Return congestion smooth range
"""
return self.smooth_range
cpdef void set_overlap_threshold(self, float overlap_thres):
"""
Set Overlap Threshold
"""
print("#[OVERLAP THRESHOLD] Threshold: %.4f" % (overlap_thres))
self.overlap_thres = overlap_thres
cpdef float get_overlap_threshold(self):
"""
Return Overlap Threshold
"""
return self.overlap_thres
cpdef void set_canvas_boundary_check(self, bool ifCheck):
"""
boundary_check: Do a boundary check during node placement.
"""
self.canvas_boundary_check = ifCheck
cpdef bool get_canvas_boundary_check(self):
"""
return canvas_boundary_check
"""
return self.canvas_boundary_check
cpdef void set_macro_routing_allocation(self,
float hrouting_alloc,
float vrouting_alloc):
"""
Set Vertical/Horizontal Macro Allocation
"""
# Flag updates
self.FLAG_UPDATE_CONGESTION = True
self.hrouting_alloc = hrouting_alloc
self.vrouting_alloc = vrouting_alloc
cpdef get_macro_routing_allocation(self):
"""
Return Vertical/Horizontal Macro Allocation
"""
return self.hrouting_alloc, self.vrouting_alloc
def __two_pin_net_routing(self, source_gcell, node_gcells, weight):
"""
private function: Routing between 2-pin nets
"""
temp_gcell = list(node_gcells)
if temp_gcell[0] == source_gcell:
sink_gcell = temp_gcell[1]
else:
sink_gcell = temp_gcell[0]
# y
row_min = min(sink_gcell[0], source_gcell[0])
row_max = max(sink_gcell[0], source_gcell[0])
# x
col_min = min(sink_gcell[1], source_gcell[1])
col_max = max(sink_gcell[1], source_gcell[1])
# H routing
for col_idx in range(col_min, col_max, 1):
col = col_idx
row = source_gcell[0]
self.H_routing_cong[row * self.grid_col + col] += weight
# V routing
for row_idx in range(row_min, row_max, 1):
row = row_idx
col = sink_gcell[1]
self.V_routing_cong[row * self.grid_col + col] += weight
def __l_routing(self, node_gcells, weight):
"""
private function: L_shape routing in 3-pin nets
"""
node_gcells.sort(key = lambda x: (x[1], x[0]))
y1, x1 = node_gcells[0]
y2, x2 = node_gcells[1]
y3, x3 = node_gcells[2]
# H routing (x1, y1) to (x2, y1)
for col in range(x1, x2):
row = y1
self.H_routing_cong[row * self.grid_col + col] += weight
# H routing (x2, y2) to (x2, y3)
for col in range(x2,x3):
row = y2
self.H_routing_cong[row * self.grid_col + col] += weight
# V routing (x2, min(y1, y2)) to (x2, max(y1, y2))
for row in range(min(y1, y2), max(y1, y2)):
col = x2
self.V_routing_cong[row * self.grid_col + col] += weight
# V routing (x3, min(y2, y3)) to (x3, max(y2, y3))
for row in range(min(y2, y3), max(y2, y3)):
col = x3
self.V_routing_cong[row * self.grid_col + col] += weight
def __t_routing(self, node_gcells, weight):
"""
private function: T_shape routing in 3-pin nets
"""
node_gcells.sort()
y1, x1 = node_gcells[0]
y2, x2 = node_gcells[1]
y3, x3 = node_gcells[2]
xmin = min(x1, x2, x3)
xmax = max(x1, x2, x3)
# H routing (xmin, y2) to (xmax, y2)
for col in range(xmin, xmax):
row = y2
self.H_routing_cong[row * self.grid_col + col] += weight
# V routing (x1, y1) to (x1, y2)
for row in range(min(y1, y2), max(y1, y2)):
col = x1
self.V_routing_cong[row * self.grid_col + col] += weight
# V routing (x3, y3) to (x3, y2)
for row in range(min(y2, y3), max(y2, y3)):
col = x3
self.V_routing_cong[row * self.grid_col + col] += weight
def __three_pin_net_routing(self, node_gcells, weight):
"""
private_function: Routing Scheme for 3-pin nets
"""
temp_gcell = list(node_gcells)
## Sorted based on X
temp_gcell.sort(key = lambda x: (x[1], x[0]))
y1, x1 = temp_gcell[0]
y2, x2 = temp_gcell[1]
y3, x3 = temp_gcell[2]
if x1 < x2 and x2 < x3 and min(y1, y3) < y2 and max(y1, y3) > y2:
self.__l_routing(temp_gcell, weight)
elif x2 == x3 and x1 < x2 and y1 < min(y2, y3):
for col_idx in range(x1,x2,1):
row = y1
col = col_idx
self.H_routing_cong[row * self.grid_col + col] += weight
for row_idx in range(y1, max(y2,y3)):
col = x2
row = row_idx
self.V_routing_cong[row * self.grid_col + col] += weight
elif y2 == y3:
for col in range(x1, x2):
row = y1
self.H_routing_cong[row * self.grid_col + col] += weight
for col in range(x2, x3):
row = y2
self.H_routing_cong[row * self.grid_col + col] += weight
for row in range(min(y2, y1), max(y2, y1)):
col = x2
self.V_routing_cong[row * self.grid_col + col] += weight
else:
self.__t_routing(temp_gcell, weight)
cpdef void __macro_route_over_grid_cell(self, float mod_x, float mod_y, float mod_w, float mod_h):
"""
private function for add module to grid cells
"""
# Two corners
ur_x = mod_x + (mod_w/2)
ur_y = mod_y + (mod_h/2)
bl_x = mod_x - (mod_w/2)
bl_y = mod_y - (mod_h/2)
# construct block based on current module
module_block = mnds.BoundingBox( minx=mod_x - (mod_w/2),
maxx=mod_x + (mod_w/2),
miny=mod_y - (mod_h/2),
maxy=mod_y + (mod_h/2))
# Only need two corners of a grid cell
ur_row, ur_col = self.__get_grid_cell_location(ur_x, ur_y)
bl_row, bl_col = self.__get_grid_cell_location(bl_x, bl_y)
# check if out of bound
if ur_row >= 0 and ur_col >= 0:
if bl_row < 0:
bl_row = 0
if bl_col < 0:
bl_col = 0
else:
# OOB, skip module
return
if bl_row >= 0 and bl_col >= 0:
if ur_row > self.grid_row - 1:
ur_row = self.grid_row - 1
if ur_col > self.grid_col - 1:
ur_col = self.grid_col - 1
else:
# OOB, skip module
return
if_PARTIAL_OVERLAP_VERTICAL = False
if_PARTIAL_OVERLAP_HORIZONTAL = False
for r_i in range(bl_row, ur_row + 1):
for c_i in range(bl_col, ur_col + 1):
# construct block based on current cell row/col
grid_cell_block = mnds.BoundingBox(
maxx= (c_i + 1) * self.grid_width,
maxy= (r_i + 1) * self.grid_height,
minx= c_i * self.grid_width,
miny= r_i * self.grid_height
)
x_dist, y_dist = self.__overlap_dist(module_block, grid_cell_block)
if ur_row != bl_row:
if (r_i == bl_row and abs(y_dist - self.grid_height) > 1e-5) or (r_i == ur_row and abs(y_dist - self.grid_height) > 1e-5):
if_PARTIAL_OVERLAP_VERTICAL = True
if ur_col != bl_col:
if (c_i == bl_col and abs(x_dist - self.grid_width) > 1e-5) or (c_i == ur_col and abs(x_dist - self.grid_width) > 1e-5):
if_PARTIAL_OVERLAP_HORIZONTAL = True
self.V_macro_routing_cong[r_i * self.grid_col + c_i] += x_dist * self.vrouting_alloc
self.H_macro_routing_cong[r_i * self.grid_col + c_i] += y_dist * self.hrouting_alloc
if if_PARTIAL_OVERLAP_VERTICAL:
for r_i in range(ur_row, ur_row + 1):
for c_i in range(bl_col, ur_col + 1):
grid_cell_block = mnds.BoundingBox(
maxx= (c_i + 1) * self.grid_width,
maxy= (r_i + 1) * self.grid_height,
minx= c_i * self.grid_width,
miny= r_i * self.grid_height
)
x_dist, y_dist = self.__overlap_dist(module_block, grid_cell_block)
self.V_macro_routing_cong[r_i * self.grid_col + c_i] -= x_dist * self.vrouting_alloc
if if_PARTIAL_OVERLAP_HORIZONTAL:
for r_i in range(bl_row, ur_row + 1):
for c_i in range(ur_col, ur_col + 1):
grid_cell_block = mnds.BoundingBox(
maxx= (c_i + 1) * self.grid_width,
maxy= (r_i + 1) * self.grid_height,
minx= c_i * self.grid_width,
miny= r_i * self.grid_height
)
x_dist, y_dist = self.__overlap_dist(module_block, grid_cell_block)
self.H_macro_routing_cong[r_i * self.grid_col + c_i] -= y_dist * self.hrouting_alloc
cpdef list __split_net(self, source_gcell, node_gcells):
"""
private function: Split >3 pin net into multiple two-pin nets
"""
splitted_netlist = []
for node_gcell in node_gcells:
if node_gcell != source_gcell:
splitted_netlist.append({source_gcell, node_gcell})
return splitted_netlist
cpdef get_vertical_routing_congestion(self):
"""
Return Vertical Routing Congestion
"""
if self.FLAG_UPDATE_CONGESTION:
self.get_routing()
return self.V_routing_cong
cpdef get_horizontal_routing_congestion(self):
"""
Return Horizontal Routing Congestion
"""
if self.FLAG_UPDATE_CONGESTION:
self.get_routing()
return self.H_routing_cong
cpdef void get_routing(self):
"""
H/V Routing Before Computing Routing Congestions
"""
if self.FLAG_UPDATE_CONGESTION:
self.grid_width = float(self.width/self.grid_col)
self.grid_height = float(self.height/self.grid_row)
self.grid_v_routes = self.grid_width * self.vroutes_per_micron
self.grid_h_routes = self.grid_height * self.hroutes_per_micron
# reset grid
self.H_routing_cong = np.zeros(shape = self.grid_col * self.grid_row)
self.V_routing_cong = np.zeros(shape = self.grid_col * self.grid_row)
self.H_macro_routing_cong = np.zeros(shape = self.grid_col * self.grid_row)
self.V_macro_routing_cong = np.zeros(shape = self.grid_col * self.grid_row)
self.FLAG_UPDATE_CONGESTION = False
for mod in self.meta_netlist.node:
curr_type = mod.type
# bounding box data structure
node_gcells = set()
source_gcell = None
weight = mod.weight
# NOTE: connection only defined on PORT, soft/hard macro pins
if curr_type == mnds.Type.PORT and mod.output_indices:
# add source grid location
source_gcell = self.__get_grid_cell_location(mod.coord.x, mod.coord.y)
node_gcells.add(self.__get_grid_cell_location(mod.coord.x, mod.coord.y))
for sink_idx in mod.output_indices:
# retrieve grid location
node_gcells.add(
self.__get_grid_cell_location(
self.__get_pin_position(sink_idx)[0],
self.__get_pin_position(sink_idx)[1]))
elif curr_type == mnds.Type.MACRO_PIN and mod.output_indices:
# add source position
mod_idx = mod.id
node_gcells.add(self.__get_grid_cell_location(
self.__get_pin_position(mod_idx)[0],
self.__get_pin_position(mod_idx)[1]))
source_gcell = self.__get_grid_cell_location(
self.__get_pin_position(mod_idx)[0],
self.__get_pin_position(mod_idx)[1])
for sink_idx in mod.output_indices:
# retrieve grid location
node_gcells.add(
self.__get_grid_cell_location(
self.__get_pin_position(sink_idx)[0],
self.__get_pin_position(sink_idx)[1]
))
# hard macro for macro routing congesiton
elif curr_type == mnds.Type.MACRO and not mod.soft_macro:
module_h = mod.dimension.height
module_w = mod.dimension.width
module_x = mod.coord.x
module_y = mod.coord.y
# compute overlap
self.__macro_route_over_grid_cell(module_x, module_y, module_w, module_h)
if len(node_gcells) == 2:
self.__two_pin_net_routing(source_gcell=source_gcell,node_gcells=node_gcells, weight=weight)
elif len(node_gcells) == 3:
self.__three_pin_net_routing(node_gcells=node_gcells, weight=weight)
elif len(node_gcells) > 3:
for curr_net in self.__split_net(source_gcell=source_gcell, node_gcells=node_gcells):
self.__two_pin_net_routing(source_gcell=source_gcell, node_gcells=curr_net, weight=weight)
# normalize routing congestion
for idx, v_gcell in enumerate(self.V_routing_cong):
self.V_routing_cong[idx] = float(v_gcell / self.grid_v_routes)
for idx, h_gcell in enumerate(self.H_routing_cong):
self.H_routing_cong[idx] = float(h_gcell / self.grid_h_routes)
for idx, v_gcell in enumerate(self.V_macro_routing_cong):
self.V_macro_routing_cong[idx] = float(v_gcell / self.grid_v_routes)
for idx, h_gcell in enumerate(self.H_macro_routing_cong):
self.H_macro_routing_cong[idx] = float(h_gcell / self.grid_h_routes)
self.__smooth_routing_cong()
# sum up routing congestion with macro congestion
self.V_routing_cong = [sum(x) for x in zip(self.V_routing_cong, self.V_macro_routing_cong)]
self.H_routing_cong = [sum(x) for x in zip(self.H_routing_cong, self.H_macro_routing_cong)]
cpdef void __smooth_routing_cong(self):
"""
Smoothing V/H Routing congestion
"""
temp_V_routing_cong = np.zeros(shape = self.grid_col * self.grid_row)
temp_H_routing_cong = np.zeros(shape = self.grid_col * self.grid_row)
# v routing cong
for row in range(self.grid_row):
for col in range(self.grid_col):
lp = col - self.smooth_range
if lp < 0:
lp = 0
rp = col + self.smooth_range
if rp >= self.grid_col:
rp = self.grid_col - 1
gcell_cnt = rp - lp + 1
val = self.V_routing_cong[row * self.grid_col + col] / gcell_cnt
for ptr in range(<int>lp, <int>rp + 1, 1):
temp_V_routing_cong[row * self.grid_col + ptr] += val
self.V_routing_cong = temp_V_routing_cong
# h routing cong
for row in range(self.grid_row):
for col in range(self.grid_col):
lp = row - self.smooth_range
if lp < 0:
lp = 0
up = row + self.smooth_range
if up >= self.grid_row:
up = self.grid_row - 1
gcell_cnt = up - lp + 1
val = self.H_routing_cong[row * self.grid_col + col] / gcell_cnt
for ptr in range(<int>lp, <int>up + 1, 1):
temp_H_routing_cong[ptr * self.grid_col + col] += val
self.H_routing_cong = temp_H_routing_cong
cpdef bool is_node_soft_macro(self, int node_idx):
"""
Return if node is a soft macro
"""
try:
return self.meta_netlist.node[node_idx].soft_macro
except IndexError:
print("[ERROR INDEX OUT OF RANGE] Can not process index at {}".format(node_idx))
exit(1)
cpdef bool is_node_hard_macro(self, int node_idx):
"""
Return if node is a hard macro
"""
try:
return self.meta_netlist.node[node_idx].type == mnds.Type.MACRO \
and not self.meta_netlist.node[node_idx].soft_macro
except IndexError:
print("[ERROR INDEX OUT OF RANGE] Can not process index at {}".format(node_idx))
exit(1)
cpdef str get_node_name(self, int node_idx):
"""
Return node name based on given node index
"""
try:
return self.meta_netlist.node[node_idx].name
except Exception:
print("[ERROR NODE INDEX] Node not found!")
exit(1)
cpdef get_node_mask(self, node_idx: int):
"""
Return node mask based on given node
All legal positions must satisfy:
- No Out-of-Bound
- No Overlapping with previously placed MACROs
"""
mod = self.meta_netlist.node[node_idx]
canvas_block = mnds.BoundingBox(minx=0,
maxx=self.width,
miny=0,
maxy=self.height)
if mod.type in [mnds.Type.PORT, mnds.Type.MACRO_PIN]:
mod_w = 1e-3
mod_h = 1e-3
else:
mod_w = mod.dimension.width
mod_h = mod.dimension.height
temp_node_mask = np.array([1] * (self.grid_col * self.grid_row))\
.reshape(self.grid_row, self.grid_col)
self.grid_width = float(self.width/self.grid_col)
self.grid_height = float(self.height/self.grid_row)
for i in range(self.grid_row):
for j in range(self.grid_col):
# try every location
# construct block based on current module dimenstion
temp_x = j * self.grid_width + (self.grid_width/2)
temp_y = i * self.grid_height + (self.grid_height/2)
mod_block = mnds.BoundingBox(minx=temp_x - (mod_w/2),
maxx=temp_x + (mod_w/2),
miny=temp_y - (mod_h/2),
maxy=temp_y + (mod_h/2))
# check OOB
if abs(self.__overlap_area(
bbox_i=canvas_block, bbox_j=mod_block) - (mod_w*mod_h)) > 1e-8:
temp_node_mask[i][j] = 0
else:
# check overlapping
for pmod_idx in self.placed_macro:
pmod = self.modules_w_pins[pmod_idx]
if not self.node_placed[pmod_idx]:
continue
p_x, p_y = pmod.coord.x, pmod.coord.y
p_w = pmod.dimension.width
p_h = pmod.dimension.height
pmod_block = mnds.BoundingBox(maxx=p_x + (p_w/2),
maxy=p_y + (p_h/2),
minx=p_x - (p_w/2),
miny=p_y - (p_h/2))
# if overlap with placed module
if self.__overlap_area(bbox_i=pmod_block, bbox_j=mod_block) > 0:
temp_node_mask[i][j] = 0
return temp_node_mask.flatten()
cpdef get_node_mask_by_name(self, str node_name):
node_idx = self.mod_name_to_indices[node_name]
return self.get_node_mask(node_idx)
cpdef str get_node_type(self, int node_idx):
"""
Return node type
"""
try:
mod = self.meta_netlist.node[node_idx]
if mod.type == mnds.Type.STDCELL:
# standard cell, not used
return "STDCELL"
elif mod.type == mnds.Type.MACRO:
return "MACRO"
elif mod.type == mnds.Type.PORT:
# port
return "PORT"
elif mod.type == mnds.Type.MACRO_PIN:
return "MACRO_PIN"
except IndexError:
# NOTE: Google's API return NONE if out of range
print("[WARNING INDEX OUT OF RANGE] Can not process index at {}".format(node_idx))
return None
cpdef get_node_width_height(self, int node_idx):
"""
Return node dimension
"""
node = self.meta_netlist.node[node_idx]
return node.width, node.height
cpdef void make_soft_macros_square(self):
"""
make soft macros as squares
"""
for mod_idx in self.soft_macro_indices:
mod = self.meta_netlist.node[mod_idx]
mod_area = mod.dimension.width * mod.dimension.height
mod.dimension.width = math.sqrt(mod_area)
mod.dimension.height = math.sqrt(mod_area)
cpdef void set_use_incremental_cost(self, bool use_incremental_cost):
"""
NOT IMPLEMENTED
"""
self.use_incremental_cost = use_incremental_cost
cpdef bool get_use_incremental_cost(self):
"""
NOT IMPLEMENTED
"""
return self.use_incremental_cost
cpdef list get_macro_adjacency(self):
"""
Compute Adjacency Matrix
"""
# NOTE: in pb.txt, netlist input count exceed certain threshold will be ommitted
#[MACRO][macro]
cdef:
int macro_cnt
int row_idx
int col_idx
list macro_adj
if self.FLAG_UPDATE_MACRO_ADJ:
# do some update
self.FLAG_UPDATE_MACRO_ADJ = False
# adjacency matrix: [MACRO] X [MACRO]
macro_cnt = self.hard_macro_cnt + self.soft_macro_cnt
macro_adj = [0] * (macro_cnt) * (macro_cnt)
for pin in self.meta_netlist.node:
if pin.type == mnds.Type.MACRO_PIN:
# row index should be order of discovery of the reference node
row_idx = self.macro_id_to_indices[pin.ref_node_id]
# col index should be from the ref node of output_indices
for output_idx in pin.output_indices:
output_pin = self.meta_netlist.node[output_idx]
# only between MACRO_PINs
if output_pin.type == mnds.Type.MACRO_PIN:
col_idx = self.macro_id_to_indices[output_pin.ref_node_id]
macro_adj[row_idx * macro_cnt + col_idx] += 1.0 * pin.weight
macro_adj[col_idx * macro_cnt + row_idx] += 1.0 * pin.weight
return macro_adj
cdef get_macro_and_clustered_port_adjacency(self):
"""
Compute Adjacency Matrix (Unclustered PORTs)
if module is a PORT, assign it to nearest cell location even if OOB
"""
cdef:
int row
int col
int row_idx
int col_idx
list macro_adj
list cell_location
dict port_to_rc = {}
dict rc_to_clustered_port_id = {}
list rc = []
int clustered_port_cnt = 0
if self.FLAG_UPDATE_MACRO_ADJ:
# do some update
self.FLAG_UPDATE_MACRO_ADJ = False
# adjacency matrix: [MACRO + clustered ports] X [MACRO + clustered ports]
macro_cnt = self.hard_macro_cnt + self.soft_macro_cnt
#[Grid Cell] => [PORT]
for port_idx in self.port_indices:
port = self.meta_netlist.node[port_idx]
x_pos, y_pos = port.coord.x, port.coord.y
row, col = self.__get_grid_cell_location(x_pos=x_pos, y_pos=y_pos)
# prevent OOB
if row >= self.grid_row:
row = self.grid_row - 1
if row < 0:
row = 0
if col >= self.grid_col:
col = self.grid_col - 1
if col < 0:
col = 0
if (row, col) not in rc:
# new cluster
clustered_port_cnt += 1
rc.append((row, col))
# [PORT.id] => [Row, Col]
port_to_rc[port.id] = (row, col)
# sort by col, this function cannot be used under cpdef
rc = sorted(rc, key=lambda tup: tup[1])
macro_adj = [0] * (macro_cnt + clustered_port_cnt) * (macro_cnt + clustered_port_cnt)
cell_location = [0] * clustered_port_cnt
for v, k in enumerate(rc):
# add cell location
cell_location[v] = k[0] * self.grid_col + k[1]
# [rc] => [cluster id]
rc_to_clustered_port_id[k] = v
for pin in self.meta_netlist.node:
if pin.type == mnds.Type.MACRO_PIN:
# row index should be order of discovery of the reference node
row_idx = self.macro_id_to_indices[pin.ref_node_id]
# col index should be from the ref node of output_indices
for output_idx in pin.output_indices:
output_pin = self.meta_netlist.node[output_idx]
# only between MACRO_PINs
if output_pin.type == mnds.Type.MACRO_PIN:
col_idx = self.macro_id_to_indices[output_pin.ref_node_id]
macro_adj[row_idx * (macro_cnt + clustered_port_cnt) + col_idx] += 1.0 * pin.weight
macro_adj[col_idx * (macro_cnt + clustered_port_cnt) + row_idx] += 1.0 * pin.weight
elif output_pin.type == mnds.Type.PORT:
col_idx = rc_to_clustered_port_id[port_to_rc[output_pin.id]]
# relocate to after macros
col_idx += macro_cnt
macro_adj[row_idx * (macro_cnt + clustered_port_cnt) + col_idx] += 1.0 * pin.weight
macro_adj[col_idx * (macro_cnt + clustered_port_cnt) + row_idx] += 1.0 * pin.weight
elif pin.type == mnds.Type.PORT:
# row index should be order of discovery of the reference node
row_idx = rc_to_clustered_port_id[port_to_rc[pin.id]]
# relocate to after macros
row_idx += macro_cnt
# col index should be from the ref node of output_indices
for output_idx in pin.output_indices:
output_pin = self.meta_netlist.node[output_idx]
# only between MACRO_PINs
if output_pin.type == mnds.Type.MACRO_PIN:
col_idx = self.macro_id_to_indices[output_pin.ref_node_id]
macro_adj[row_idx * (macro_cnt + clustered_port_cnt) + col_idx] += 1.0 * pin.weight
macro_adj[col_idx * (macro_cnt + clustered_port_cnt) + row_idx] += 1.0 * pin.weight
elif output_pin.type == mnds.Type.PORT:
col_idx = self.port_id_to_indices[output_pin.id]
# relocate to after macros
col_idx += macro_cnt
macro_adj[row_idx * (macro_cnt + clustered_port_cnt) + col_idx] += 1.0 * pin.weight
macro_adj[col_idx * (macro_cnt + clustered_port_cnt) + row_idx] += 1.0 * pin.weight
return macro_adj, sorted(cell_location)
cpdef void unfix_node_coord(self, int node_idx):
"""
Unfix a module
"""
cdef:
object mod = None
try:
mod = self.meta_netlist.node[node_idx]
assert mod.type in [mnds.Type.MACRO, mnds.Type.PORT]
except AssertionError:
print("[ERROR UNFIX NODE] Found {}. Only 'MACRO', ".format(mod.type)
+"'PORT' are considered to be fixable nodes")
exit(1)
except Exception:
print("[ERROR UNFIX NODE] Could not find module by node index")
exit(1)
self.node_fix[node_idx] = False
cpdef void fix_node_coord(self, int node_idx):
"""
Fix a module
"""
cdef:
object mod = None
try:
mod = self.meta_netlist.node[node_idx]
assert mod.type in [mnds.Type.MACRO, mnds.Type.PORT]
except AssertionError:
print("[ERROR UNFIX NODE] Found {}. Only 'MACRO', ".format(mod.type)
+"'PORT' are considered to be fixable nodes")
exit(1)
except Exception:
print("[ERROR FIX NODE] Could not find module by node index")
exit(1)
self.node_fix[node_idx] = True
cpdef bool is_node_fixed(self, int node_idx):
"""
Return if a node is fixed
"""
cdef:
object mod = None
try:
mod = self.meta_netlist.node[node_idx]
assert mod.type in [mnds.Type.MACRO, mnds.Type.PORT]
except AssertionError:
print("[ERROR UNFIX NODE] Found {}. Only 'MACRO', ".format(mod.type)
+"'PORT' are considered to be fixable nodes")
exit(1)
except Exception:
print("[ERROR NODE FIXED] Could not find module by node index")
exit(1)
return self.node_fix[node_idx]
cpdef void update_node_coords(self, int node_idx, float x_pos, float y_pos):
"""
Update Node location if node is 'MACRO', 'STDCELL', 'PORT'
"""
mod = None
try:
mod = self.meta_netlist.node[node_idx]
assert mod.type in [mnds.Type.MACRO, mnds.Type.PORT]
except AssertionError:
print("[ERROR UNFIX NODE] Found {}. Only 'MACRO', ".format(mod.type)
+"'PORT' are considered to be placable nodes")
exit(1)
except Exception:
print("[ERROR NODE LOCATION] Could not find module by node index")
exit(1)
# only update if node is not fixed
if not self.node_fix[node_idx]:
mod.coord.x = x_pos
mod.coord.y = y_pos
cpdef void update_node_coords_by_name(self, str node_name, float x_pos, float y_pos):
"""
Update Node location if node is 'MACRO', 'STDCELL', 'PORT'
"""
node_idx = self.mod_name_to_indices[node_name]
self.update_node_coords(node_idx=node_idx, x_pos=x_pos, y_pos=y_pos)
cpdef update_macro_orientation(self, int node_idx, str orientation):
"""
Update macro orientation if node is 'MACRO'
"""
mod = None
try:
mod = self.meta_netlist.node[node_idx]
assert mod.type == mnds.Type.MACRO
except AssertionError:
print("[ERROR MACRO ORIENTATION] Found {}. Only 'MACRO'".format(mod.type)
+" are considered to be ORIENTED")
exit(1)
except Exception:
print("[ERROR MACRO ORIENTATION] Could not find module by node index {}".format(node_idx))
exit(1)
mod.orientation = mnds.Orientation[orientation]
cpdef update_macro_orientation_by_name(self, str node_name, str orientation):
"""
Update macro orientation if node is 'MACRO'
"""
node_idx = self.mod_name_to_indices[node_name]
self.update_macro_orientation(node_idx=node_idx, orientation=orientation)
def update_port_sides(self):
pass
def snap_ports_to_edges(self):
pass
cpdef (float, float) get_node_location(self, int node_idx):
"""
Return Node location if node is 'MACRO', 'STDCELL', 'PORT'
"""
mod = None
try:
mod = self.meta_netlist.node[node_idx]
assert mod.type in [mnds.Type.MACRO, mnds.Type.PORT]
except AssertionError:
print("[ERROR NODE LOCATION] Found {}. Only 'MACRO', 'STDCELL'".format(mod.type)
+"'PORT' are considered to be placable nodes")
exit(1)
except Exception:
print("[ERROR NODE PLACED] Could not find module by node index")
exit(1)
return mod.coord.x, mod.coord.y
cpdef list get_node_locations(self, list node_indices):
"""
Returns the (x, y) location of a list of nodes
"""
cdef:
int node_idx
list node_locations = []
for node_idx in node_indices:
node_locations.append(self.get_node_location(node_idx))
return node_locations
cpdef get_macro_orientation(self, int node_idx):
"""
Returns the orientation of the given node
"""
mod = None
try:
mod = self.meta_netlist.node[node_idx]
assert mod.type == mnds.Type.MACRO
except AssertionError:
print("[ERROR MACRO ORIENTATION] Found {}. Only 'MACRO'".format(mod.type)
+" are considered to be ORIENTED")
exit(1)
except Exception:
print("[ERROR MACRO ORIENTATION] Could not find module by node index")
exit(1)
return mod.orientation
def place_node(self, node_idx, grid_cell_idx):
"""
Place the node into the center of the given grid_cell
"""
mod = self.meta_netlist.node[node_idx]
# TODO: add check valid clause
if not self.node_fix[node_idx]:
x_pos, y_pos = self.__get_grid_cell_position(grid_cell_idx)
mod.coord.x = x_pos
mod.coord.y = y_pos
self.node_placed[node_idx] = True
# update flag
self.FLAG_UPDATE_CONGESTION = True
self.FLAG_UPDATE_DENSITY = True
self.FLAG_UPDATE_WIRELENGTH = True
def place_node_by_name(self, node_name, grid_cell_idx):
"""
Place the node into the center of the given grid_cell
"""
node_idx = self.mod_name_to_indices[node_name]
self.place_node(node_idx=node_idx, grid_cell_idx=grid_cell_idx)
def unplace_node(self, node_idx):
"""
Set the node's ifPlaced flag to False if not fixed node
"""
mod = self.meta_netlist.node[node_idx]
if not self.node_fix[node_idx]:
if mod.type == mnds.Type.MACRO:
self.node_placed[node_idx] = False
# update flag
self.FLAG_UPDATE_CONGESTION = True
self.FLAG_UPDATE_DENSITY = True
self.FLAG_UPDATE_WIRELENGTH = True
else:
print("[WARNING UNPLACE NODE] Trying to unplace a fixed node")
def unplace_all_nodes(self):
"""
Set all ifPlaced flag to False except for fixed nodes
"""
for port_idx in sorted(self.port_indices):
if self.node_fix:
continue
self.node_placed[port_idx] = False
for mod_idx in sorted(self.macro_indices):
if self.node_fix:
continue
self.node_placed[mod_idx] = False
# update flag
self.FLAG_UPDATE_CONGESTION = True
self.FLAG_UPDATE_DENSITY = True
self.FLAG_UPDATE_WIRELENGTH = True
cpdef bool is_node_placed(self, int node_idx):
"""
return if node is placed
"""
return self.node_placed[node_idx]
def disconnect_nets(self):
pass
cpdef str get_source_filename(self):
"""
return netlist path
"""
return self.netlist_file
cpdef list get_blockages(self):
"""
NOT IMPLEMENTED
"""
return self.blockages
cpdef create_blockage(self, minx, miny, maxx, maxy, blockage_rate):
"""
NOT IMPLEMENTED
"""
self.blockages.append([minx, miny, maxx, maxy, blockage_rate])
pass
cpdef int get_ref_node_id(self, int node_idx):
"""
ref_node_id is used for macro_pins. Refers to the macro it belongs to
"""
if self.meta_netlist.node[node_idx].type == mnds.Type.PORT:
return node_idx
else:
return self.meta_netlist.node[node_idx].ref_node_id
cpdef list get_fan_outs_of_node(self, int node_idx):
"""
Returns the vector of node indices that are driven by given node
"""
return self.meta_netlist.node[node_idx].output_indices
cpdef void save_placement(self, str filename, info=""):
"""
When writing out info line-by-line, add a "#" at front
"""
with open(filename, 'w+') as f:
for line in info.split('\n'):
f.write("# " + line + '\n')
# if first, no \newline
HEADER = True
# PORT
for port_idx in self.port_indices:
port = self.meta_netlist.node[port_idx]
if HEADER:
f.write("{} {:g} {:g} {} {}".format(port_idx,\
port.coord.x, port.coord.y, "-",
"1" if self.node_fix[port_idx] else "0"))
HEADER = False
else:
f.write("\n{} {:g} {:g} {} {}".format(port_idx,\
port.coord.x, port.coord.y, "-",
"1" if self.node_fix[port_idx] else "0"))
# MACRO
for mod_idx in self.macro_indices:
# [node_index] [x] [y] [orientation] [fixed]
mod = self.meta_netlist.node[mod_idx]
if HEADER:
f.write("{} {:g} {:g} {} {}".format(mod_idx,\
mod.coord.x, mod.coord.y,
mnds.Orientation(mod.orientation.value).name if mod.orientation else "-",
"1" if self.node_fix[mod_idx] else "0"))
HEADER = False
else:
f.write("\n{} {:g} {:g} {} {}".format(mod_idx,\
mod.coord.x, mod.coord.y,
mnds.Orientation(mod.orientation.value).name if mod.orientation else "-",
"1" if self.node_fix[mod_idx] else "0"))
cpdef void __cache_placement(self, str filename):
"""
private function: used to cache placement file for FD executable
"""
cols, rows = self.get_grid_num_columns_rows()
width, height = self.get_canvas_width_height()
hor_routes, ver_routes = self.get_routes_per_micron()
hor_macro_alloc, ver_macro_alloc = self.get_macro_routing_allocation()
smooth = self.get_congestion_smooth_range()
info = textwrap.dedent("""\
Placement file for Circuit Training
Columns : {cols} Rows : {rows}
Width : {width:.3f} Height : {height:.3f}
Area : {area}
Wirelength : {wl:.3f}
Wirelength cost : {wlc:.4f}
Congestion cost : {cong:.4f}
Density cost : {density:.4f}
Project : {project}
Block : {block_name}
Routes per micron, hor : {hor_routes:.3f} ver : {ver_routes:.3f}
Routes used by macros, hor : {hor_macro_alloc:.3f} ver : {ver_macro_alloc:.3f}
Smoothing factor : {smooth}
Overlap threshold : {overlap_threshold}
""".format(
cols=cols,
rows=rows,
width=width,
height=height,
area=self.get_area(),
wl=self.get_wirelength(),
wlc=self.get_cost(),
cong=self.get_congestion_cost(),
density=self.get_density_cost(),
project=self.get_project_name(),
block_name=self.get_block_name(),
hor_routes=hor_routes,
ver_routes=ver_routes,
hor_macro_alloc=hor_macro_alloc,
ver_macro_alloc=ver_macro_alloc,
smooth=smooth,
overlap_threshold=self.get_overlap_threshold()))
self.save_placement(filename=filename, info=info)
def display_canvas( self,
annotate=True,
amplify=False,
saveName=None,
show=True):
"""
Non-google function, For quick canvas view
"""
#define Matplotlib figure and axis
fig, ax = plt.subplots(figsize=(8,8), dpi=50)
if amplify:
PORT_SIZE = 4
FONT_SIZE = 10
PIN_SIZE = 4
else:
PORT_SIZE = 2
FONT_SIZE = 5
PIN_SIZE = 2
# Plt config
ax.margins(x=0.05, y=0.05)
ax.set_aspect('equal', adjustable='box')
# Construct grid
x, y = np.meshgrid(np.linspace(0, self.width, self.grid_col + 1),\
np.linspace(0, self.height, self.grid_row + 1))
ax.plot(x, y, c='b', alpha=0.1) # use plot, not scatter
ax.plot(np.transpose(x), np.transpose(y), c='b', alpha=0.2) # add this here
# Construct module blocks
for mod in self.meta_netlist.node:
if mod.type == mnds.Type.PORT and self.node_placed[mod.id]:
plt.plot((mod.coord.x, mod.coord.y),'ro', markersize=PORT_SIZE)
elif mod.type == mnds.Type.MACRO and self.node_placed[mod.id]:
if not mod.soft_macro:
# hard macro
ax.add_patch(Rectangle((mod.coord.x - mod.dimension.width/2, mod.coord.y - mod.dimension.height/2),\
mod.dimension.width, mod.dimension.height,\
alpha=0.5, zorder=1000, facecolor='b', edgecolor='darkblue'))
if annotate:
ax.annotate(mod.name, (mod.coord.x, mod.coord.y), wrap=True,color='r', weight='bold', fontsize=FONT_SIZE, ha='center', va='center')
else:
# soft macro
ax.add_patch(Rectangle((mod.coord.x - mod.dimension.width/2, mod.coord.y - mod.dimension.height/2),\
mod.dimension.width, mod.dimension.height,\
alpha=0.5, zorder=1000, facecolor='y'))
if annotate:
ax.annotate(mod.name, (mod.coord.x, mod.coord.y), wrap=True,color='r', weight='bold', fontsize=FONT_SIZE, ha='center', va='center')
elif mod.type == mnds.Type.MACRO_PIN:
macro = self.meta_netlist.node[mod.ref_node_id]
if self.node_placed[macro.id]:
plt.plot(*self.__get_pin_position(mod.id),'bo', markersize=PIN_SIZE)
if saveName:
plt.savefig(saveName)
if show:
plt.show()
plt.close('all')
'''
FD Placement below shares the same functionality as the FDPlacement/fd_placement.py
'''
def optimize_stdcells(self, use_current_loc, move_stdcells, move_macros,
log_scale_conns, use_sizes, io_factor, num_steps,
max_move_distance, attract_factor, repel_factor):
cache_plc = self.init_plc + ".cache"
self.__cache_placement(cache_plc)
os.system("./fd_placer {} {}".format(self.netlist_file, cache_plc))
fd_plc = self.init_plc + ".cache.new"
self.restore_placement(fd_plc, retrieveFD=True)
print("[INFO] FD ran successfully")
# self.__fd_placement(io_factor, num_steps, max_move_distance, attract_factor, repel_factor, use_current_loc, verbose=True)
CodeElements/Plc_client/plc_client_os_test.py
......@@ -308,15 +308,14 @@ class PlacementCostTest():
# self.plc.make_soft_macros_square()
self.plc.set_placement_grid(self.GRID_COL, self.GRID_ROW)
# self.plc.make_soft_macros_square() # in effect
self.plc.make_soft_macros_square() # in effect
self.plc.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
self.plc_os.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
self.plc_os.set_placement_grid(self.GRID_COL, self.GRID_ROW)
# self.plc.make_soft_macros_square()
# self.plc_os.make_soft_macros_square()
self.plc_os.make_soft_macros_square()
# [IGNORE] create_blockage must be defined BEFORE set_canvas_size
# and set_placement_grid in order to be considered on the canvas
......@@ -867,7 +866,7 @@ class PlacementCostTest():
self.plc_util_os.display_canvas(annotate=False)
def test_fd(self):
def test_google_fd(self):
print("############################ TEST GOOGLE's FD Placer ############################")
self.plc_util = placement_util.create_placement_cost(
plc_client=plc_client,
......@@ -875,33 +874,94 @@ class PlacementCostTest():
init_placement=self.PLC_PATH
)
self.plc_util.set_routes_per_micron(self.RPMH, self.RPMV)
self.plc_util.set_macro_routing_allocation(self.MARH, self.MARV)
self.plc_util.set_congestion_smooth_range(self.SMOOTH)
self.plc_util.make_soft_macros_square()
self.plc_util.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
self.plc_util.set_placement_grid(self.GRID_COL, self.GRID_ROW)
self.plc_util_os = placement_util.create_placement_cost(
plc_client=plc_client_os,
netlist_file=self.NETLIST_PATH,
init_placement=self.PLC_PATH
)
self.plc_util.set_routes_per_micron(self.RPMH, self.RPMV)
self.plc_util_os.set_routes_per_micron(self.RPMH, self.RPMV)
self.plc_util.set_macro_routing_allocation(self.MARH, self.MARV)
self.plc_util_os.set_macro_routing_allocation(self.MARH, self.MARV)
self.plc_util.set_congestion_smooth_range(self.SMOOTH)
self.plc_util_os.set_congestion_smooth_range(self.SMOOTH)
self.plc_util.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
self.plc_util.set_placement_grid(self.GRID_COL, self.GRID_ROW)
self.plc_util_os.make_soft_macros_square()
self.plc_util_os.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
self.plc_util_os.set_placement_grid(self.GRID_COL, self.GRID_ROW)
placement_util.fd_placement_schedule(self.plc_util)
## For final result
# placement_util.fd_placement_schedule(self.plc_util,
# num_steps=(100,100),
# io_factor=1.0,
# move_distance_factors=(1.0,1.0),
# attract_factor=(100.0,100.0),
# repel_factor=(0.0,0.0),
# use_current_loc=True,
# move_macros=False)
# for node_index in placement_util.nodes_of_types(self.plc_util, ['MACRO']):
# x_pos, y_pos = self.plc_util.get_node_location(node_index)
# print(x_pos, y_pos)
# self.plc_util_os.set_soft_macro_position(node_index, x_pos, y_pos)
# self.plc_util_os.display_canvas(annotate=False, amplify=False)
# For Step-by-step result
temp_pos_collection= [[0,0]] * 10
for node_index in placement_util.nodes_of_types(self.plc_util, ['MACRO']):
x_pos, y_pos = self.plc_util.get_node_location(node_index)
if self.plc_util.is_node_soft_macro(node_index):
print(0, node_index,
x_pos,
y_pos,
x_pos - temp_pos_collection[node_index][0],
y_pos - temp_pos_collection[node_index][1])
temp_pos_collection[node_index] = [x_pos, y_pos]
self.plc_util_os.set_soft_macro_position(node_index, x_pos, y_pos)
self.plc_util_os.display_canvas(annotate=False, amplify=False)
# Step by step iteration
for i in range(1, 41):
# resetting the placement util
self.plc_util = placement_util.create_placement_cost(
plc_client=plc_client,
netlist_file=self.NETLIST_PATH,
init_placement=self.PLC_PATH
)
self.plc_util.set_routes_per_micron(self.RPMH, self.RPMV)
self.plc_util.set_macro_routing_allocation(self.MARH, self.MARV)
self.plc_util.set_congestion_smooth_range(self.SMOOTH)
self.plc_util.make_soft_macros_square()
self.plc_util.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
self.plc_util.set_placement_grid(self.GRID_COL, self.GRID_ROW)
placement_util.fd_placement_schedule(
self.plc_util,
num_steps=(i,),
io_factor=1.0,
move_distance_factors=(1.0,),
attract_factor=(100.0,),
repel_factor=(100.0,),
use_current_loc=True,
move_macros=False)
# sync with plc_util_os for visualization
for node_index in placement_util.nodes_of_types(self.plc_util, ['MACRO']):
x_pos, y_pos = self.plc_util.get_node_location(node_index)
# print(temp_pos_collection)
if self.plc_util.is_node_soft_macro(node_index):
print(i, node_index,
x_pos,
y_pos,
x_pos - temp_pos_collection[node_index][0],
y_pos - temp_pos_collection[node_index][1])
temp_pos_collection[node_index] = [x_pos, y_pos]
self.plc_util_os.set_soft_macro_position(node_index, x_pos, y_pos)
self.plc_util_os.display_canvas(annotate=True, amplify=True, saveName=str(i))
def test_environment(self):
print("############################ TEST ENVIRONMENT ############################")
......@@ -951,8 +1011,6 @@ class PlacementCostTest():
env_os._plc.display_canvas(annotate=False)
exit(1)
# check observation state
obs_gl = env._get_obs()
obs_os = env_os._get_obs()
......@@ -974,6 +1032,7 @@ class PlacementCostTest():
def test_fd_placement(self):
print("############################ TEST FDPLACEMENT ############################")
# test FD placement in plc_client_os
self.plc_util_os = placement_util.create_placement_cost(
plc_client=plc_client_os,
netlist_file=self.NETLIST_PATH,
......@@ -989,9 +1048,42 @@ class PlacementCostTest():
self.plc_util_os.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
self.plc_util_os.set_placement_grid(self.GRID_COL, self.GRID_ROW)
placement_util.fd_placement_schedule(self.plc_util_os)
self.plc_util_os.display_canvas(annotate=False, amplify=False)
# placement_util.fd_placement_schedule(self.plc_util_os)
for i in range(1, 41):
placement_util.fd_placement_schedule(
self.plc_util_os,
num_steps=(i,),
io_factor=1.0,
move_distance_factors=(1.0,),
attract_factor=(100.0,),
repel_factor=(100.0,),
use_current_loc=False,
move_macros=False)
self.plc_util_os.display_canvas(annotate=True, amplify=True, saveName=str(i))
# test FD placement under FDPlacement folder
# print(os.getcwd())
# os.system("cd FDPlacement/ && python3 fd_placement_exp.py --netlist .{} \
# --width {} --height {} --col {} --row {} && cd -".format(
# self.NETLIST_PATH, self.CANVAS_WIDTH,
# self.CANVAS_HEIGHT, self.GRID_COL, self.GRID_ROW
# )
# )
# self.plc_util_os = placement_util.create_placement_cost(
# plc_client=plc_client_os,
# netlist_file=self.NETLIST_PATH + ".final",
# )
# print(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
# self.plc_util_os.set_canvas_size(self.CANVAS_WIDTH, self.CANVAS_HEIGHT)
# self.plc_util_os.set_placement_grid(self.GRID_COL, self.GRID_ROW)
# self.plc_util_os.make_soft_macros_square()
# print(self.plc_util_os.get_canvas_width_height())
# self.plc_util_os.display_canvas(annotate=True, amplify=True)
# os.chdir()
def parse_flags(argv):
parser = argparse_flags.ArgumentParser(
......@@ -1059,9 +1151,9 @@ def main(args):
# PCT.test_miscellaneous()
# PCT.test_observation_extractor()
# PCT.view_canvas()
# PCT.test_fd()
PCT.test_google_fd() # python3 -m Plc_client.plc_client_os_test --netlist ./Plc_client/test/1P1M2m/netlist.pb.txt --width 500 --height 500 --col 10 --row 10
# PCT.test_environment()
PCT.test_fd_placement()
# PCT.test_fd_placement()
if __name__ == '__main__':
......
CodeElements/Plc_client/setup.py 0 → 100644
# setup.py
from distutils.core import setup
from Cython.Build import cythonize
setup(
ext_modules=cythonize(
"plc_client_os.pyx", compiler_directives={"language_level": "3"}
)
)
\ No newline at end of file
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