def algorithm(A):
B = hcl.bitcast(A, hcl.Float(32))
return B
def graph_6D():
V_f = hcl.placeholder(tuple(g.pts_each_dim), name="V_f", dtype=hcl.Float())
V_init = hcl.placeholder(tuple(g.pts_each_dim),
name="V_init",
dtype=hcl.Float())
l0 = hcl.placeholder(tuple(g.pts_each_dim), name="l0", dtype=hcl.Float())
t = hcl.placeholder((2, ), name="t", dtype=hcl.Float())
# Positions vector
x1 = hcl.placeholder((g.pts_each_dim[0], ), name="x1", dtype=hcl.Float())
x2 = hcl.placeholder((g.pts_each_dim[1], ), name="x2", dtype=hcl.Float())
x3 = hcl.placeholder((g.pts_each_dim[2], ), name="x3", dtype=hcl.Float())
x4 = hcl.placeholder((g.pts_each_dim[3], ), name="x4", dtype=hcl.Float())
x5 = hcl.placeholder((g.pts_each_dim[4], ), name="x5", dtype=hcl.Float())
x6 = hcl.placeholder((g.pts_each_dim[5], ), name="x6", dtype=hcl.Float())
def graph_create(V_new, V_init, x1, x2, x3, x4, x5, x6, t, l0):
# Specify intermediate tensors
deriv_diff1 = hcl.compute(V_init.shape, lambda *x: 0, "deriv_diff1")
deriv_diff2 = hcl.compute(V_init.shape, lambda *x: 0, "deriv_diff2")
deriv_diff3 = hcl.compute(V_init.shape, lambda *x: 0, "deriv_diff3")
deriv_diff4 = hcl.compute(V_init.shape, lambda *x: 0, "deriv_diff4")
deriv_diff5 = hcl.compute(V_init.shape, lambda *x: 0, "deriv_diff5")
deriv_diff6 = hcl.compute(V_init.shape, lambda *x: 0, "deriv_diff6")
# Maximum derivative for each dim
max_deriv1 = hcl.scalar(-1e9, "max_deriv1")
max_deriv2 = hcl.scalar(-1e9, "max_deriv2")
max_deriv3 = hcl.scalar(-1e9, "max_deriv3")
max_deriv4 = hcl.scalar(-1e9, "max_deriv4")
max_deriv5 = hcl.scalar(-1e9, "max_deriv5")
max_deriv6 = hcl.scalar(-1e9, "max_deriv6")
# Min derivative for each dim
min_deriv1 = hcl.scalar(1e9, "min_deriv1")
min_deriv2 = hcl.scalar(1e9, "min_deriv2")
min_deriv3 = hcl.scalar(1e9, "min_deriv3")
min_deriv4 = hcl.scalar(1e9, "min_deriv4")
min_deriv5 = hcl.scalar(1e9, "min_deriv5")
min_deriv6 = hcl.scalar(1e9, "min_deriv6")
# These variables are used to dissipation calculation
max_alpha1 = hcl.scalar(-1e9, "max_alpha1")
max_alpha2 = hcl.scalar(-1e9, "max_alpha2")
max_alpha3 = hcl.scalar(-1e9, "max_alpha3")
max_alpha4 = hcl.scalar(-1e9, "max_alpha4")
max_alpha5 = hcl.scalar(-1e9, "max_alpha5")
max_alpha6 = hcl.scalar(-1e9, "max_alpha6")
def step_bound(): # Function to calculate time step
stepBoundInv = hcl.scalar(0, "stepBoundInv")
stepBound = hcl.scalar(0, "stepBound")
stepBoundInv[0] = max_alpha1[0] / g.dx[0] + max_alpha2[0] / g.dx[1] + max_alpha3[0] / g.dx[2] + max_alpha4[
0] / g.dx[3] \
+ max_alpha5[0] / g.dx[4] + max_alpha6[0] / g.dx[5]
stepBound[0] = 0.8 / stepBoundInv[0]
with hcl.if_(stepBound > t[1] - t[0]):
stepBound[0] = t[1] - t[0]
# Update the lower time ranges
t[0] = t[0] + stepBound[0]
# t[0] = min_deriv2[0]
return stepBound[0]
def maxVWithV0(i, j, k, l, m, n): # Take the max
with hcl.if_(V_new[i, j, k, l, m, n] < l0[i, j, k, l, m, n]):
V_new[i, j, k, l, m, n] = l0[i, j, k, l, m, n]
# Max(V, g )
def maxVWithCStraint(i, j, k, l, m, n):
with hcl.if_(V_new[i, j, k, l, m, n] < 5.0):
V_new[i, j, k, l, m, n] = 1.0
# Min with V_before
def minVWithVInit(i, j, k, l, m, n):
with hcl.if_(V_new[i, j, k, l, m, n] > V_init[i, j, k, l, m, n]):
V_new[i, j, k, l, m, n] = V_init[i, j, k, l, m, n]
# Calculate Hamiltonian for every grid point in V_init
with hcl.Stage("Hamiltonian"):
with hcl.for_(0, V_init.shape[0], name="i") as i:
with hcl.for_(0, V_init.shape[1], name="j") as j:
with hcl.for_(0, V_init.shape[2], name="k") as k:
with hcl.for_(0, V_init.shape[3], name="l") as l:
with hcl.for_(0, V_init.shape[4], name="m") as m:
with hcl.for_(0, V_init.shape[5],
name="n") as n:
# Variables to calculate dV_dx
dV_dx1_L = hcl.scalar(0, "dV_dx1_L")
dV_dx1_R = hcl.scalar(0, "dV_dx1_R")
dV_dx1 = hcl.scalar(0, "dV_dx1")
dV_dx2_L = hcl.scalar(0, "dV_dx2_L")
dV_dx2_R = hcl.scalar(0, "dV_dx2_R")
dV_dx2 = hcl.scalar(0, "dV_dx2")
dV_dx3_L = hcl.scalar(0, "dV_dx3_L")
dV_dx3_R = hcl.scalar(0, "dV_dx3_R")
dV_dx3 = hcl.scalar(0, "dV_dx3")
dV_dx4_L = hcl.scalar(0, "dV_dx4_L")
dV_dx4_R = hcl.scalar(0, "dV_dx4_R")
dV_dx4 = hcl.scalar(0, "dV_dx4")
dV_dx5_L = hcl.scalar(0, "dV_dx5_L")
dV_dx5_R = hcl.scalar(0, "dV_dx5_R")
dV_dx5 = hcl.scalar(0, "dV_dx5")
dV_dx6_L = hcl.scalar(0, "dV_dx6_L")
dV_dx6_R = hcl.scalar(0, "dV_dx6_R")
dV_dx6 = hcl.scalar(0, "dV_dx6")
# No tensor slice operation
# dV_dx_L[0], dV_dx_R[0] = spa_derivX(i, j, k)
dV_dx1_L[0], dV_dx1_R[0] = spa_derivX1_6d(
i, j, k, l, m, n, V_init, g)
dV_dx2_L[0], dV_dx2_R[0] = spa_derivX2_6d(
i, j, k, l, m, n, V_init, g)
dV_dx3_L[0], dV_dx3_R[0] = spa_derivX3_6d(
i, j, k, l, m, n, V_init, g)
dV_dx4_L[0], dV_dx4_R[0] = spa_derivX4_6d(
i, j, k, l, m, n, V_init, g)
dV_dx5_L[0], dV_dx5_R[0] = spa_derivX5_6d(
i, j, k, l, m, n, V_init, g)
dV_dx6_L[0], dV_dx6_R[0] = spa_derivX6_6d(
i, j, k, l, m, n, V_init, g)
# Saves spatial derivative diff into tables
deriv_diff1[i, j, k, l, m,
n] = dV_dx1_R[0] - dV_dx1_L[0]
deriv_diff2[i, j, k, l, m,
n] = dV_dx2_R[0] - dV_dx2_L[0]
deriv_diff3[i, j, k, l, m,
n] = dV_dx3_R[0] - dV_dx3_L[0]
deriv_diff4[i, j, k, l, m,
n] = dV_dx4_R[0] - dV_dx4_L[0]
deriv_diff5[i, j, k, l, m,
n] = dV_dx5_R[0] - dV_dx5_L[0]
deriv_diff6[i, j, k, l, m,
n] = dV_dx6_R[0] - dV_dx6_L[0]
# Calculate average gradient
dV_dx1[0] = (dV_dx1_L + dV_dx1_R) / 2
dV_dx2[0] = (dV_dx2_L + dV_dx2_R) / 2
dV_dx3[0] = (dV_dx3_L + dV_dx3_R) / 2
dV_dx4[0] = (dV_dx4_L + dV_dx4_R) / 2
dV_dx5[0] = (dV_dx5_L + dV_dx5_R) / 2
dV_dx6[0] = (dV_dx6_L + dV_dx6_R) / 2
# Find optimal control
uOpt = my_object.opt_ctrl(
t, (x1[i], x2[j], x3[k], x4[l], x5[m],
x6[n]),
(dV_dx1[0], dV_dx2[0], dV_dx3[0],
dV_dx4[0], dV_dx5[0], dV_dx6[0]))
# Find optimal disturbance
dOpt = my_object.optDstb(
(dV_dx1[0], dV_dx2[0], dV_dx3[0],
dV_dx4[0], dV_dx5[0], dV_dx6[0]))
# Find rates of changes based on dynamics equation
dx1_dt, dx2_dt, dx3_dt, dx4_dt, dx5_dt, dx6_dt = my_object.dynamics(
t, (x1[i], x2[j], x3[k], x4[l], x5[m],
x6[n]), uOpt, dOpt)
# Calculate Hamiltonian terms:
V_new[i, j, k, l, m,
n] = -(dx1_dt * dV_dx1[0] +
dx2_dt * dV_dx2[0] +
dx3_dt * dV_dx3[0] +
dx4_dt * dV_dx4[0] +
dx5_dt * dV_dx5[0] +
dx6_dt * dV_dx6[0])
# Get derivMin
with hcl.if_(dV_dx1_L[0] < min_deriv1[0]):
min_deriv1[0] = dV_dx1_L[0]
with hcl.if_(dV_dx1_R[0] < min_deriv1[0]):
min_deriv1[0] = dV_dx1_R[0]
with hcl.if_(dV_dx2_L[0] < min_deriv2[0]):
min_deriv2[0] = dV_dx2_L[0]
with hcl.if_(dV_dx2_R[0] < min_deriv2[0]):
min_deriv2[0] = dV_dx2_R[0]
with hcl.if_(dV_dx3_L[0] < min_deriv3[0]):
min_deriv3[0] = dV_dx3_L[0]
with hcl.if_(dV_dx3_R[0] < min_deriv3[0]):
min_deriv3[0] = dV_dx3_R[0]
with hcl.if_(dV_dx4_L[0] < min_deriv4[0]):
min_deriv4[0] = dV_dx4_L[0]
with hcl.if_(dV_dx4_R[0] < min_deriv4[0]):
min_deriv4[0] = dV_dx4_R[0]
with hcl.if_(dV_dx5_L[0] < min_deriv5[0]):
min_deriv5[0] = dV_dx5_L[0]
with hcl.if_(dV_dx5_R[0] < min_deriv5[0]):
min_deriv5[0] = dV_dx5_R[0]
with hcl.if_(dV_dx6_L[0] < min_deriv6[0]):
min_deriv6[0] = dV_dx6_L[0]
with hcl.if_(dV_dx6_R[0] < min_deriv6[0]):
min_deriv6[0] = dV_dx6_R[0]
# Get derivMax
with hcl.if_(dV_dx1_L[0] > max_deriv1[0]):
max_deriv1[0] = dV_dx1_L[0]
with hcl.if_(dV_dx1_R[0] > max_deriv1[0]):
max_deriv1[0] = dV_dx1_R[0]
with hcl.if_(dV_dx2_L[0] > max_deriv2[0]):
max_deriv2[0] = dV_dx2_L[0]
with hcl.if_(dV_dx2_R[0] > max_deriv2[0]):
max_deriv2[0] = dV_dx2_R[0]
with hcl.if_(dV_dx3_L[0] > max_deriv3[0]):
max_deriv3[0] = dV_dx3_L[0]
with hcl.if_(dV_dx3_R[0] > max_deriv3[0]):
max_deriv3[0] = dV_dx3_R[0]
with hcl.if_(dV_dx4_L[0] > max_deriv4[0]):
max_deriv4[0] = dV_dx4_L[0]
with hcl.if_(dV_dx4_R[0] > max_deriv4[0]):
max_deriv4[0] = dV_dx4_R[0]
with hcl.if_(dV_dx5_L[0] > max_deriv5[0]):
max_deriv5[0] = dV_dx5_L[0]
with hcl.if_(dV_dx5_R[0] > max_deriv5[0]):
max_deriv5[0] = dV_dx5_R[0]
with hcl.if_(dV_dx6_L[0] > max_deriv6[0]):
max_deriv6[0] = dV_dx6_L[0]
with hcl.if_(dV_dx6_R[0] > max_deriv6[0]):
max_deriv6[0] = dV_dx6_R[0]
# Calculate dissipation amount
with hcl.Stage("Dissipation"):
# uOptL1 = hcl.scalar(0, "uOptL1")
# uOptL2 = hcl.scalar(0, "uOptL2")
# uOptL3 = hcl.scalar(0, "uOptL3")
# uOptL4 = hcl.scalar(0, "uOptL4")
# uOptU1 = hcl.scalar(0, "uOptU1")
# uOptU2 = hcl.scalar(0, "uOptU2")
# uOptU3 = hcl.scalar(0, "uOptU3")
# uOptU4 = hcl.scalar(0, "uOptU4")
# dOptL1 = hcl.scalar(0, "dOptL1")
# dOptL2 = hcl.scalar(0, "dOptL2")
# dOptL3 = hcl.scalar(0, "dOptL3")
# dOptL4 = hcl.scalar(0, "dOptL4")
# dOptU1 = hcl.scalar(0, "dOptU1")
# dOptU2 = hcl.scalar(0, "dOptU2")
# dOptU3 = hcl.scalar(0, "dOptU3")
# dOptU4 = hcl.scalar(0, "dOptU4")
# Storing alphas
alpha1 = hcl.scalar(0, "alpha1")
alpha2 = hcl.scalar(0, "alpha2")
alpha3 = hcl.scalar(0, "alpha3")
alpha4 = hcl.scalar(0, "alpha4")
alpha5 = hcl.scalar(0, "alpha5")
alpha6 = hcl.scalar(0, "alpha6")
# Find LOWER BOUND optimal disturbance
dOptL = my_object.optDstb(
(min_deriv1[0], min_deriv2[0], min_deriv3[0], min_deriv4[0],
min_deriv5[0], min_deriv6[0]))
# Find UPPER BOUND optimal disturbance
dOptU = my_object.optDstb(
(max_deriv1[0], max_deriv2[0], max_deriv3[0], max_deriv4[0],
max_deriv5[0], max_deriv6[0]))
with hcl.for_(0, V_init.shape[0], name="i") as i:
with hcl.for_(0, V_init.shape[1], name="j") as j:
with hcl.for_(0, V_init.shape[2], name="k") as k:
with hcl.for_(0, V_init.shape[3], name="l") as l:
with hcl.for_(0, V_init.shape[4], name="m") as m:
with hcl.for_(0, V_init.shape[5],
name="n") as n:
dx_LL1 = hcl.scalar(0, "dx_LL1")
dx_LL2 = hcl.scalar(0, "dx_LL2")
dx_LL3 = hcl.scalar(0, "dx_LL3")
dx_LL4 = hcl.scalar(0, "dx_LL4")
dx_LL5 = hcl.scalar(0, "dx_LL5")
dx_LL6 = hcl.scalar(0, "dx_LL6")
dx_UL1 = hcl.scalar(0, "dx_UL1")
dx_UL2 = hcl.scalar(0, "dx_UL2")
dx_UL3 = hcl.scalar(0, "dx_UL3")
dx_UL4 = hcl.scalar(0, "dx_UL4")
dx_UL5 = hcl.scalar(0, "dx_UL5")
dx_UL6 = hcl.scalar(0, "dx_UL6")
#
dx_LU1 = hcl.scalar(0, "dx_LU1")
dx_LU2 = hcl.scalar(0, "dx_LU2")
dx_LU3 = hcl.scalar(0, "dx_LU3")
dx_LU4 = hcl.scalar(0, "dx_LU4")
dx_LU5 = hcl.scalar(0, "dx_LU5")
dx_LU6 = hcl.scalar(0, "dx_LU6")
dx_UU1 = hcl.scalar(0, "dx_UU1")
dx_UU2 = hcl.scalar(0, "dx_UU2")
dx_UU3 = hcl.scalar(0, "dx_UU3")
dx_UU4 = hcl.scalar(0, "dx_UU4")
dx_UU5 = hcl.scalar(0, "dx_UU5")
dx_UU6 = hcl.scalar(0, "dx_UU6")
# Find LOWER BOUND optimal control
uOptL = my_object.opt_ctrl(
t, (x1[i], x2[j], x3[k], x4[l], x5[m],
x6[n]),
(min_deriv1[0], min_deriv2[0],
min_deriv3[0], min_deriv4[0],
min_deriv5[0], min_deriv6[0]))
# Find UPPER BOUND optimal control
uOptU = my_object.opt_ctrl(
t, (x1[i], x2[j], x3[k], x4[l], x5[m],
x6[n]),
(max_deriv1[0], max_deriv2[0],
max_deriv3[0], max_deriv4[0],
max_deriv5[0], max_deriv6[0]))
# Get upper bound and lower bound rates of changes
dx_LL1[0], dx_LL2[0], dx_LL3[0], dx_LL4[
0], dx_LL5[0], dx_LL6[
0] = my_object.dynamics(
t, (x1[i], x2[j], x3[k], x4[l],
x5[m], x6[n]), uOptL,
dOptL)
# Get absolute value of each
dx_LL1[0] = my_abs(dx_LL1[0])
dx_LL2[0] = my_abs(dx_LL2[0])
dx_LL3[0] = my_abs(dx_LL3[0])
dx_LL4[0] = my_abs(dx_LL4[0])
dx_LL5[0] = my_abs(dx_LL5[0])
dx_LL6[0] = my_abs(dx_LL6[0])
dx_UL1[0], dx_UL2[0], dx_UL3[0], dx_UL4[
0], dx_UL5[0], dx_UL6[
0] = my_object.dynamics(
t, (x1[i], x2[j], x3[k], x4[l],
x5[m], x6[n]), uOptU,
dOptL)
# Get absolute value of each
dx_UL1[0] = my_abs(dx_UL1[0])
dx_UL2[0] = my_abs(dx_UL2[0])
dx_UL3[0] = my_abs(dx_UL3[0])
dx_UL4[0] = my_abs(dx_UL4[0])
dx_UL5[0] = my_abs(dx_UL5[0])
dx_UL6[0] = my_abs(dx_UL6[0])
# Set maximum alphas
alpha1[0] = my_max(dx_UL1[0], dx_LL1[0])
alpha2[0] = my_max(dx_UL2[0], dx_LL2[0])
alpha3[0] = my_max(dx_UL3[0], dx_LL3[0])
alpha4[0] = my_max(dx_UL4[0], dx_LL4[0])
alpha5[0] = my_max(dx_UL5[0], dx_LL5[0])
alpha6[0] = my_max(dx_UL6[0], dx_LL6[0])
dx_LU1[0], dx_LU2[0], dx_LU3[0], dx_LU4[
0], dx_LU5[0], dx_LU6[
0] = my_object.dynamics(
t, (x1[i], x2[j], x3[k], x4[l],
x5[m], x6[n]), uOptL,
dOptU)
# Get absolute value of each
dx_LU1[0] = my_abs(dx_LU1[0])
dx_LU2[0] = my_abs(dx_LU2[0])
dx_LU3[0] = my_abs(dx_LU3[0])
dx_LU4[0] = my_abs(dx_LU4[0])
dx_LU5[0] = my_abs(dx_LU5[0])
dx_LU6[0] = my_abs(dx_LU6[0])
alpha1[0] = my_max(alpha1[0], dx_LU1[0])
alpha2[0] = my_max(alpha2[0], dx_LU2[0])
alpha3[0] = my_max(alpha3[0], dx_LU3[0])
alpha4[0] = my_max(alpha4[0], dx_LU4[0])
alpha5[0] = my_max(alpha5[0], dx_LU5[0])
alpha6[0] = my_max(alpha6[0], dx_LU6[0])
dx_UU1[0], dx_UU2[0], dx_UU3[0], dx_UU4[
0], dx_UU5[0], dx_UU6[
0] = my_object.dynamics(
t, (x1[i], x2[j], x3[k], x4[l],
x5[m], x6[n]), uOptU,
dOptU)
dx_UU1[0] = my_abs(dx_UU1[0])
dx_UU2[0] = my_abs(dx_UU2[0])
dx_UU3[0] = my_abs(dx_UU3[0])
dx_UU4[0] = my_abs(dx_UU4[0])
dx_UU5[0] = my_abs(dx_UU5[0])
dx_UU6[0] = my_abs(dx_UU6[0])
alpha1[0] = my_max(alpha1[0], dx_UU1[0])
alpha2[0] = my_max(alpha2[0], dx_UU2[0])
alpha3[0] = my_max(alpha3[0], dx_UU3[0])
alpha4[0] = my_max(alpha4[0], dx_UU4[0])
alpha5[0] = my_max(alpha5[0], dx_UU5[0])
alpha6[0] = my_max(alpha6[0], dx_UU6[0])
diss = hcl.scalar(0, "diss")
diss[0] = 0.5 * (deriv_diff1[i, j, k, l, m, n] * alpha1[0] + deriv_diff2[
i, j, k, l, m, n] * alpha2[0] \
+ deriv_diff3[i, j, k, l, m, n] * alpha3[0] + deriv_diff4[
i, j, k, l, m, n] * alpha4[0] \
+ deriv_diff5[i, j, k, l, m, n] * alpha5[0] + deriv_diff6[
i, j, k, l, m, n] * alpha6[0])
# Finally
V_new[i, j, k, l, m,
n] = -(V_new[i, j, k, l, m, n] -
diss[0])
# Get maximum alphas in each dimension
# Calculate alphas
with hcl.if_(alpha1 > max_alpha1):
max_alpha1[0] = alpha1[0]
with hcl.if_(alpha2 > max_alpha2):
max_alpha2[0] = alpha2[0]
with hcl.if_(alpha3 > max_alpha3):
max_alpha3[0] = alpha3[0]
with hcl.if_(alpha4 > max_alpha4):
max_alpha4[0] = alpha4[0]
with hcl.if_(alpha5 > max_alpha5):
max_alpha5[0] = alpha5[0]
with hcl.if_(alpha6 > max_alpha6):
max_alpha6[0] = alpha6[0]
# Determine time step
delta_t = hcl.compute((1, ), lambda x: step_bound(), name="delta_t")
# hcl.update(t, lambda x: t[x] + delta_t[x])
# Integrate
# if compMethod == 'HJ_PDE':
result = hcl.update(
V_new, lambda i, j, k, l, m, n: V_init[i, j, k, l, m, n] + V_new[
i, j, k, l, m, n] * delta_t[0])
if compMethod == 'maxVWithV0':
result = hcl.update(
V_new, lambda i, j, k, l, m, n: maxVWithV0(i, j, k, l, m, n))
if compMethod == 'maxVWithCStraint':
result = hcl.update(
V_new,
lambda i, j, k, l, m, n: maxVWithCStraint(i, j, k, l, m, n))
if compMethod == 'minVWithVInit':
result = hcl.update(
V_new,
lambda i, j, k, l, m, n: minVWithVInit(i, j, k, l, m, n))
# Copy V_new to V_init
hcl.update(V_init, lambda i, j, k, l, m, n: V_new[i, j, k, l, m, n])
return result
s = hcl.create_schedule([V_f, V_init, x1, x2, x3, x4, x5, x6, t, l0],
graph_create)
##################### CODE OPTIMIZATION HERE ###########################
print("Optimizing\n")
# Accessing the hamiltonian and dissipation stage
s_H = graph_create.Hamiltonian
s_D = graph_create.Dissipation
# Thread parallelize hamiltonian and dissipation
s[s_H].parallel(s_H.i)
s[s_D].parallel(s_D.i)
# Inspect IR
# if args.llvm:
# print(hcl.lower(s))
# Return executable
return (hcl.build(s))
def relay_parser(model, shape, frontend='keras', dtype=hcl.Float()):
hcl.init(dtype)
defined_inputs = {}
node_map = {}
for item in shape:
defined_inputs[item] = None
if frontend == 'keras':
try:
keras_model = keras.models.load_model(model)
except BaseException:
keras_model = model
module, params = relay_front.from_keras(keras_model, shape)
if debug_mode:
print(module)
body = module.functions[module.global_var_map_["main"]]
build_node_map(body.body, True, node_map, [0])
def gen_call(node, name, opname):
args = []
var = []
type_dict = {name: Call}
env = {}
for arg in node.args:
temp_var, temp_type, temp_env = parse_rec(arg)
if isinstance(arg, Var):
var.append(temp_var[0])
var = partial_flatten(var)
args.append(temp_env[fst(temp_var[0])])
elif isinstance(arg, Constant):
var.append(temp_var)
var = partial_flatten(var)
args.append(temp_env[temp_var[0]])
env.update(temp_env)
elif isinstance(arg, Call):
var.append(temp_var)
var = partial_flatten(var)
args.append(temp_env[temp_var[-1]][0])
env.update(temp_env)
elif isinstance(arg, TupleGetItem):
if temp_env[temp_var[-1]][1] == Var:
item, item_name, temp_type, temp_env, inst_var = get_item(
temp_env[temp_var[-1]])
var.append(inst_var)
var = partial_flatten(var)
args.append(item)
env.update(temp_env)
else:
args.append(temp_env[temp_var[-1]][0])
var.append(temp_var)
var = partial_flatten(var)
env.update(temp_env)
elif isinstance(arg, Tuple):
tup_var = temp_var[-1]
temp_var = partial_flatten(temp_var)
var.append(temp_var)
var = partial_flatten(var)
tup_env = {}
t_name = temp_env[tup_var][0]
t_res = temp_env[tup_var][1]
t_dict = temp_env[tup_var][2]
t_env = temp_env[tup_var][3]
tup_env[tup_var] = (t_name, t_res, t_dict, t_env)
args.append(tup_var)
env.update(tup_env)
update_if(env, t_env)
type_dict.update(temp_type)
var.append(name)
kwargs = {}
for i in range(len(args)):
if hasattr(args[i], "name"):
if args[i].name in var:
env[args[i].name] = args[i]
if opname in _attrib:
for attr in _attrib[opname]:
kwargs[attr] = getattr(node.attrs, attr)
env[name] = (name, _convert_map[opname], tuple(args), kwargs)
else:
env[name] = (name, tuple(args))
if isinstance(node.op, Function):
temp_var, temp_type, temp_env = parse_rec(node.op)
var.append(opname)
type_dict.update({opname: Function})
env[opname] = (temp_var, temp_type, temp_env)
return var, type_dict, env
def parse_rec(node, init=False, global_env={}):
if isinstance(node, (Call, Tuple)):
if node_map[node][1] > 0:
name = "%" + str(node_map[node][0])
var = [name]
type_dict = {}
env = {}
env[name] = global_env[name]
return var, type_dict, env
else:
node_map[node][1] += 1
if isinstance(node, Function):
name = "%" + str(len(node_map))
if debug_mode:
print("Function: ", name)
var = [name]
type_dict = {name: Function}
env = {}
temp_var, temp_type, temp_env = parse_rec(node.body, global_env)
if init:
var = temp_var
type_dict = temp_type
env = temp_env
else:
env = update_if(
env, {
name: (full_flatten(temp_var), temp_type, temp_env,
len(node_map))
})
elif isinstance(node, Var):
name = node.name_hint
var = [name]
type_dict = {name: Var}
ty = node.type_annotation
env = {}
if node.name_hint in shape:
dtype = ty.dtype
if defined_inputs[name] is None:
env[name] = hcl.placeholder(shape[name], name, dtype)
defined_inputs[name] = env[name]
else:
env[name] = defined_inputs[name]
else:
env[name] = get_type(ty, name)
if debug_mode:
print("Var: " + name)
elif isinstance(node, Constant):
name = "con(" + str(node.data) + ")"
if debug_mode:
print("Constant: " + name)
var = [name]
type_dict = {name: Constant}
env = {}
env[name] = hcl.scalar(float(node.data.asnumpy()))
elif isinstance(node, TupleGetItem):
index = node.index
tup = node.tuple_value
if isinstance(tup, Var):
var_name = tup.vid.name_hint
name = "get_" + var_name + "_" + str(index)
ty = tup.type_annotation
var = [name]
type_dict = {name: TupleGetItem}
env = {}
env[name] = (name, Var, get_type(ty, var_name), index)
elif isinstance(tup, Call):
name = '%' + str(node_map[tup][0])
get_name = 'get' + str(node_map[tup][0]) + "_" + str(index)
if not hasattr(tup.op, "name"):
opname = '%' + str(node_map[tup][0] - 1)
else:
opname = tup.op.name
var, type_dict, env = gen_call(tup, name, opname)
var.append(get_name)
type_dict.update({get_name: TupleGetItem})
env[get_name] = (get_name, TupleGetItem, name, index)
if debug_mode:
print("TupleGet: " + get_name)
elif isinstance(node, Let):
name = node.var.vid.name_hint
if debug_mode:
print("Let: " + name)
var = [name]
type_dict = {name: Let}
env = {}
args = []
kwargs = {}
ty = node.var.type_annotation
bind_var = get_type(ty, name)
value = node.value
temp_var, temp_type, temp_env = parse_rec(value)
if isinstance(value, Var):
env = update_if(env, {
name:
(Var, bind_var, temp_type, temp_env[fst(temp_var[0])])
})
elif isinstance(value, Function):
env = update_if(env, {
name: (Function, bind_var, temp_var, temp_type, temp_env)
})
elif isinstance(value, Tuple):
env = update_if(
env,
{name: (Tuple, bind_var, temp_var, temp_type, temp_env)})
elif isinstance(value, TupleGetItem):
item, get_name, get_type_, get_env, _ = get_item(
temp_env[temp_var[0]])
temp_var = [get_name]
temp_type = {get_name: get_type_}
temp_env = {get_name: item}
env = update_if(
env, {
name: (get_type_[get_name], bind_var, temp_var,
temp_type, temp_env)
})
elif isinstance(value, Call):
if not hasattr(value.op, "name"):
opname = "%" + str(node_map[tup][0])
else:
opname = value.op.name
args = temp_env[temp_var[-1]][0]
env = update_if(env, temp_env)
for i in range(len(args)):
if hasattr(args[i], "name"):
if args[i].name in temp_var:
env[args[i].name] = args[i]
if opname in _attrib:
for attr in _attrib[opname]:
kwargs[attr] = getattr(value.attrs, attr)
env[name] = (Call, bind_var, temp_var, temp_type, temp_env)
type_dict = update_if(type_dict, temp_type)
temp_var, temp_type, temp_env = parse_rec(node.body)
var.append(temp_var)
type_dict = update_if(type_dict, temp_type)
env = update_if(env, temp_env)
elif isinstance(node, If):
print("If not instantiated yet")
elif isinstance(node, Tuple):
name = "%" + str(node_map[node][0])
if debug_mode:
print("Tuple: " + name)
var = []
type_dict = {name: Tuple}
env = {}
tup_type_dict = {}
tup_res = []
tup = []
tup_env = {}
for field in node.fields:
temp_var, temp_type, temp_env = parse_rec(field)
tup.append(temp_var)
tup_res.append(temp_var[-1])
tup_type_dict.update(temp_type)
tup_env.update(temp_env)
var.append(tup)
var.append([name])
var = partial_flatten(var)
update_if(type_dict, tup_type_dict)
env.update(tup_env)
env[name] = (name, tup_res, tup_type_dict, tup_env)
elif isinstance(node, Call):
if not hasattr(node.op, "name"):
opname = '%' + str(node_map[node][0])
else:
opname = node.op.name
name = '%' + str(node_map[node][0])
if debug_mode:
print("Call " + name + ":" + opname)
var, type_dict, env = gen_call(node, name, opname)
if not isinstance(node, Function):
global_env[name] = env[name]
return var, type_dict, env
out_var, out_type, out_env = parse_rec(body, True)
return out_var, out_type, out_env, params
def value_iteration_6D(MDP_obj):
def solve_Vopt(Vopt, actions, intermeds, trans, interpV, gamma, epsilon,
iVals, sVals, bounds, goal, ptsEachDim, count, maxIters,
useNN):
reSweep = hcl.scalar(1, "reSweep")
oldV = hcl.scalar(0, "oldV")
newV = hcl.scalar(0, "newV")
with hcl.while_(hcl.and_(reSweep[0] == 1, count[0] < maxIters[0])):
reSweep[0] = 0
# Perform value iteration by sweeping in direction 1
with hcl.Stage("Sweep_1"):
with hcl.for_(0, Vopt.shape[0], name="i") as i:
with hcl.for_(0, Vopt.shape[1], name="j") as j:
with hcl.for_(0, Vopt.shape[2], name="k") as k:
with hcl.for_(0, Vopt.shape[3], name="l") as l:
with hcl.for_(0, Vopt.shape[4], name="m") as m:
with hcl.for_(0, Vopt.shape[5],
name="n") as n:
oldV[0] = Vopt[i, j, k, l, m, n]
updateVopt(MDP_obj, i, j, k, l, m, n,
iVals, sVals, actions, Vopt,
intermeds, trans, interpV,
gamma, bounds, goal,
ptsEachDim, useNN)
newV[0] = Vopt[i, j, k, l, m, n]
evaluateConvergence(
newV, oldV, epsilon, reSweep)
count[0] += 1
# Perform value iteration by sweeping in direction 2
with hcl.Stage("Sweep_2"):
with hcl.if_(useNN[0] == 1):
with hcl.for_(1, Vopt.shape[0] + 1, name="i") as i:
with hcl.for_(1, Vopt.shape[1] + 1, name="j") as j:
with hcl.for_(1, Vopt.shape[2] + 1, name="k") as k:
with hcl.for_(0, Vopt.shape[3], name="l") as l:
with hcl.for_(0, Vopt.shape[4],
name="m") as m:
with hcl.for_(0,
Vopt.shape[5],
name="n") as n:
i2 = Vopt.shape[0] - i
j2 = Vopt.shape[1] - j
k2 = Vopt.shape[2] - k
oldV[0] = Vopt[i2, j2, k2, l, m, n]
updateVopt(MDP_obj, i2, j2, k2, l,
m, n, iVals, sVals,
actions, Vopt,
intermeds, trans,
interpV, gamma, bounds,
goal, ptsEachDim, useNN)
newV[0] = Vopt[i2, j2, k2, l, m, n]
evaluateConvergence(
newV, oldV, epsilon, reSweep)
count[0] += 1
# Perform value iteration by sweeping in direction 3
with hcl.Stage("Sweep_3"):
with hcl.if_(useNN[0] == 1):
with hcl.for_(1, Vopt.shape[0] + 1, name="i") as i:
with hcl.for_(0, Vopt.shape[1], name="j") as j:
with hcl.for_(0, Vopt.shape[2], name="k") as k:
with hcl.for_(0, Vopt.shape[3], name="l") as l:
with hcl.for_(0, Vopt.shape[4],
name="m") as m:
with hcl.for_(0,
Vopt.shape[5],
name="n") as n:
i2 = Vopt.shape[0] - i
oldV[0] = Vopt[i2, j, k, l, m, n]
updateVopt(MDP_obj, i2, j, k, l, m,
n, iVals, sVals,
actions, Vopt,
intermeds, trans,
interpV, gamma, bounds,
goal, ptsEachDim, useNN)
newV[0] = Vopt[i2, j, k, l, m, n]
evaluateConvergence(
newV, oldV, epsilon, reSweep)
count[0] += 1
# Perform value iteration by sweeping in direction 4
with hcl.Stage("Sweep_4"):
with hcl.if_(useNN[0] == 1):
with hcl.for_(0, Vopt.shape[0], name="i") as i:
with hcl.for_(1, Vopt.shape[1] + 1, name="j") as j:
with hcl.for_(0, Vopt.shape[2], name="k") as k:
with hcl.for_(0, Vopt.shape[3], name="l") as l:
with hcl.for_(0, Vopt.shape[4],
name="m") as m:
with hcl.for_(0,
Vopt.shape[5],
name="n") as n:
j2 = Vopt.shape[1] - j
oldV[0] = Vopt[i, j2, k, l, m, n]
updateVopt(MDP_obj, i, j2, k, l, m,
n, iVals, sVals,
actions, Vopt,
intermeds, trans,
interpV, gamma, bounds,
goal, ptsEachDim, useNN)
newV[0] = Vopt[i, j2, k, l, m, n]
evaluateConvergence(
newV, oldV, epsilon, reSweep)
count[0] += 1
# Perform value iteration by sweeping in direction 5
with hcl.Stage("Sweep_5"):
with hcl.if_(useNN[0] == 1):
with hcl.for_(0, Vopt.shape[0], name="i") as i:
with hcl.for_(0, Vopt.shape[1], name="j") as j:
with hcl.for_(1, Vopt.shape[2] + 1, name="k") as k:
with hcl.for_(0, Vopt.shape[3], name="l") as l:
with hcl.for_(0, Vopt.shape[4],
name="m") as m:
with hcl.for_(0,
Vopt.shape[5],
name="n") as n:
k2 = Vopt.shape[2] - k
oldV[0] = Vopt[i, j, k2, l, m, n]
updateVopt(MDP_obj, i, j, k2, l, m,
n, iVals, sVals,
actions, Vopt,
intermeds, trans,
interpV, gamma, bounds,
goal, ptsEachDim, useNN)
newV[0] = Vopt[i, j, k2, l, m, n]
evaluateConvergence(
newV, oldV, epsilon, reSweep)
count[0] += 1
# Perform value iteration by sweeping in direction 6
with hcl.Stage("Sweep_6"):
with hcl.if_(useNN[0] == 1):
with hcl.for_(1, Vopt.shape[0] + 1, name="i") as i:
with hcl.for_(1, Vopt.shape[1] + 1, name="j") as j:
with hcl.for_(0, Vopt.shape[2], name="k") as k:
with hcl.for_(0, Vopt.shape[3], name="l") as l:
with hcl.for_(0, Vopt.shape[4],
name="m") as m:
with hcl.for_(0,
Vopt.shape[5],
name="n") as n:
i2 = Vopt.shape[0] - i
j2 = Vopt.shape[1] - j
oldV[0] = Vopt[i2, j2, k, l, m, n]
updateVopt(MDP_obj, i2, j2, k, l,
m, n, iVals, sVals,
actions, Vopt,
intermeds, trans,
interpV, gamma, bounds,
goal, ptsEachDim, useNN)
newV[0] = Vopt[i2, j2, k, l, m, n]
evaluateConvergence(
newV, oldV, epsilon, reSweep)
count[0] += 1
# Perform value iteration by sweeping in direction 7
with hcl.Stage("Sweep_7"):
with hcl.if_(useNN[0] == 1):
with hcl.for_(1, Vopt.shape[0] + 1, name="i") as i:
with hcl.for_(0, Vopt.shape[1], name="j") as j:
with hcl.for_(1, Vopt.shape[2] + 1, name="k") as k:
with hcl.for_(0, Vopt.shape[3], name="l") as l:
with hcl.for_(0, Vopt.shape[4],
name="m") as m:
with hcl.for_(0,
Vopt.shape[5],
name="n") as n:
i2 = Vopt.shape[0] - i
k2 = Vopt.shape[2] - k
oldV[0] = Vopt[i2, j, k2, l, m, n]
updateVopt(MDP_obj, i2, j, k2, l,
m, n, iVals, sVals,
actions, Vopt,
intermeds, trans,
interpV, gamma, bounds,
goal, ptsEachDim, useNN)
newV[0] = Vopt[i2, j, k2, l, m, n]
evaluateConvergence(
newV, oldV, epsilon, reSweep)
count[0] += 1
# Perform value iteration by sweeping in direction 8
with hcl.Stage("Sweep_8"):
with hcl.if_(useNN[0] == 1):
with hcl.for_(0, Vopt.shape[0], name="i") as i:
with hcl.for_(1, Vopt.shape[1] + 1, name="j") as j:
with hcl.for_(1, Vopt.shape[2] + 1, name="k") as k:
with hcl.for_(0, Vopt.shape[3], name="l") as l:
with hcl.for_(0, Vopt.shape[4],
name="m") as m:
with hcl.for_(0,
Vopt.shape[5],
name="n") as n:
j2 = Vopt.shape[1] - j
k2 = Vopt.shape[2] - k
oldV[0] = Vopt[i, j2, k2, l, m, n]
updateVopt(MDP_obj, i, j2, k2, l,
m, n, iVals, sVals,
actions, Vopt,
intermeds, trans,
interpV, gamma, bounds,
goal, ptsEachDim, useNN)
newV[0] = Vopt[i, j2, k2, l, m, n]
evaluateConvergence(
newV, oldV, epsilon, reSweep)
count[0] += 1
###################################### SETUP PLACEHOLDERS ######################################
# Initialize the HCL environment
hcl.init()
hcl.config.init_dtype = hcl.Float()
# NOTE: trans is a tensor with size = maximum number of transitions
# NOTE: intermeds must have size [# possible actions]
# NOTE: transition must have size [# possible outcomes, #state dimensions + 1]
Vopt = hcl.placeholder(tuple(MDP_obj._ptsEachDim),
name="Vopt",
dtype=hcl.Float())
gamma = hcl.placeholder((0, ), "gamma")
count = hcl.placeholder((0, ), "count")
maxIters = hcl.placeholder((0, ), "maxIters")
epsilon = hcl.placeholder((0, ), "epsilon")
actions = hcl.placeholder(tuple(MDP_obj._actions.shape),
name="actions",
dtype=hcl.Float())
intermeds = hcl.placeholder(tuple([MDP_obj._actions.shape[0]]),
name="intermeds",
dtype=hcl.Float())
trans = hcl.placeholder(tuple(MDP_obj._trans.shape),
name="successors",
dtype=hcl.Float())
bounds = hcl.placeholder(tuple(MDP_obj._bounds.shape),
name="bounds",
dtype=hcl.Float())
goal = hcl.placeholder(tuple(MDP_obj._goal.shape),
name="goal",
dtype=hcl.Float())
ptsEachDim = hcl.placeholder(tuple([6]),
name="ptsEachDim",
dtype=hcl.Float())
sVals = hcl.placeholder(tuple([6]), name="sVals", dtype=hcl.Float())
iVals = hcl.placeholder(tuple([6]), name="iVals", dtype=hcl.Float())
interpV = hcl.placeholder((0, ), "interpols")
useNN = hcl.placeholder((0, ), "useNN")
# Create a static schedule -- graph
s = hcl.create_schedule([
Vopt, actions, intermeds, trans, interpV, gamma, epsilon, iVals, sVals,
bounds, goal, ptsEachDim, count, maxIters, useNN
], solve_Vopt)
# Use this graph and build an executable
return hcl.build(s, target="llvm")
from collections import OrderedDict
import heterocl as hcl
import heterocl.tvm as tvm
import numpy as np
dtype = hcl.Float()
sum = hcl.reducer(0, lambda x, y: x + y, dtype)
max = hcl.reducer(-1, lambda x, y: tvm.make.Max(x, y), dtype)
def simplify(expr):
return tvm.ir_pass.Simplify(expr) if isinstance(expr,
tvm.expr.Expr) else expr
#def pad(data, pad_before, pad_after=None, pad_value=0.0):
# n = len(data.shape)
# pad_after = pad_after if pad_after else pad_before
# out_shape = tuple(
# tvm.ir_pass.Simplify(
# (data.shape[i] + tvm.const(pad_before[i]) + tvm.const(pad_after[i]))) for i in range(n))
# def _pad(*indices):
# not_zero = []
# index_tuple = []
# for i in range(n):
# if equal_const_int(pad_before[i], 0) and equal_const_int(pad_after[i], 0):
# index_tuple.append(indices[i])
# else:
# index_tuple.append(indices[i] - pad_before[i])
# not_zero.append(indices[i] >= pad_before[i])
#heterocl Canny Filter
from PIL import Image
import heterocl as hcl
import numpy as np
import math
import imageio
hcl.init(init_dtype=hcl.Float())
#image path
path = "home.jpg"
img = Image.open(path)
width, height = img.size
#initialize placeholders
A = hcl.placeholder((height, width, 3), "A", dtype=hcl.Float())
Gx = hcl.placeholder((3, 3), "Gx", dtype=hcl.Float())
Gy = hcl.placeholder((3, 3), "Gy", dtype=hcl.Float())
hcl_A = hcl.asarray(np.asarray(img))
hcl_Gx = hcl.asarray(np.array([[1, 0, -1], [2, 0, -2], [1, 0, -1]]))
hcl_Gy = hcl.asarray(np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]))
#output
hcl_img = hcl.asarray(np.zeros((height, width)))
#=======================================sobel_algo============================================
def sobel(A, Gx, Gy):
B = hcl.compute((height, width),
def graph_3D_2nd_ENO(my_object, g, compMethod):
V_f = hcl.placeholder(tuple(g.pts_each_dim), name="V_f", dtype=hcl.Float())
V_init = hcl.placeholder(tuple(g.pts_each_dim),
name="V_init",
dtype=hcl.Float())
l0 = hcl.placeholder(tuple(g.pts_each_dim), name="l0", dtype=hcl.Float())
t = hcl.placeholder((2, ), name="t", dtype=hcl.Float())
probe = hcl.placeholder(tuple(g.pts_each_dim),
name="probe",
dtype=hcl.Float())
#my_object = dynamics_obj
#g = grid
# Positions vector
x1 = hcl.placeholder((g.pts_each_dim[0], ), name="x1", dtype=hcl.Float())
x2 = hcl.placeholder((g.pts_each_dim[1], ), name="x2", dtype=hcl.Float())
x3 = hcl.placeholder((g.pts_each_dim[2], ), name="x3", dtype=hcl.Float())
def graph_create(V_new, V_init, x1, x2, x3, t, l0):
# Specify intermediate tensors
deriv_diff1 = hcl.compute(V_init.shape, lambda *x: 0, "deriv_diff1")
deriv_diff2 = hcl.compute(V_init.shape, lambda *x: 0, "deriv_diff2")
deriv_diff3 = hcl.compute(V_init.shape, lambda *x: 0, "deriv_diff3")
# Maximum derivative for each dim
max_deriv1 = hcl.scalar(-1e9, "max_deriv1")
max_deriv2 = hcl.scalar(-1e9, "max_deriv2")
max_deriv3 = hcl.scalar(-1e9, "max_deriv3")
# Min derivative for each dim
min_deriv1 = hcl.scalar(1e9, "min_deriv1")
min_deriv2 = hcl.scalar(1e9, "min_deriv2")
min_deriv3 = hcl.scalar(1e9, "min_deriv3")
# These variables are used to dissipation calculation
max_alpha1 = hcl.scalar(-1e9, "max_alpha1")
max_alpha2 = hcl.scalar(-1e9, "max_alpha2")
max_alpha3 = hcl.scalar(-1e9, "max_alpha3")
def step_bound(): # Function to calculate time step
stepBoundInv = hcl.scalar(0, "stepBoundInv")
stepBound = hcl.scalar(0, "stepBound")
stepBoundInv[0] = max_alpha1[0] / g.dx[0] + max_alpha2[0] / g.dx[
1] + max_alpha3[0] / g.dx[2]
stepBound[0] = 0.8 / stepBoundInv[0]
with hcl.if_(stepBound > t[1] - t[0]):
stepBound[0] = t[1] - t[0]
t[0] = t[0] + stepBound[0]
return stepBound[0]
# Min with V_before
def minVWithVInit(i, j, k):
with hcl.if_(V_new[i, j, k] > V_init[i, j, k]):
V_new[i, j, k] = V_init[i, j, k]
def maxVWithVInit(i, j, k):
with hcl.if_(V_new[i, j, k] < V_init[i, j, k]):
V_new[i, j, k] = V_init[i, j, k]
def maxVWithV0(i, j, k): # Take the max
with hcl.if_(V_new[i, j, k] < l0[i, j, k]):
V_new[i, j, k] = l0[i, j, k]
def minVWithV0(i, j, k):
with hcl.if_(V_new[i, j, k] > l0[i, j, k]):
V_new[i, j, k] = l0[i, j, k]
# Calculate Hamiltonian for every grid point in V_init
with hcl.Stage("Hamiltonian"):
with hcl.for_(
0, V_init.shape[0], name="i"
) as i: # Plus 1 as for loop count stops at V_init.shape[0]
with hcl.for_(0, V_init.shape[1], name="j") as j:
with hcl.for_(0, V_init.shape[2], name="k") as k:
# Variables to calculate dV_dx
dV_dx_L = hcl.scalar(0, "dV_dx_L")
dV_dx_R = hcl.scalar(0, "dV_dx_R")
dV_dx = hcl.scalar(0, "dV_dx")
# Variables to calculate dV_dy
dV_dy_L = hcl.scalar(0, "dV_dy_L")
dV_dy_R = hcl.scalar(0, "dV_dy_R")
dV_dy = hcl.scalar(0, "dV_dy")
# Variables to calculate dV_dtheta
dV_dT_L = hcl.scalar(0, "dV_dT_L")
dV_dT_R = hcl.scalar(0, "dV_dT_R")
dV_dT = hcl.scalar(0, "dV_dT")
# Variables to keep track of dynamics
# dx_dt = hcl.scalar(0, "dx_dt")
# dy_dt = hcl.scalar(0, "dy_dt")
# dtheta_dt = hcl.scalar(0, "dtheta_dt")
# No tensor slice operation
dV_dx_L[0], dV_dx_R[0] = spa_derivX(i, j, k, V_init, g)
dV_dy_L[0], dV_dy_R[0] = spa_derivY(i, j, k, V_init, g)
dV_dT_L[0], dV_dT_R[0] = spa_derivT(i, j, k, V_init, g)
# Saves spatial derivative diff into tables
deriv_diff1[i, j, k] = dV_dx_R[0] - dV_dx_L[0]
deriv_diff2[i, j, k] = dV_dy_R[0] - dV_dy_L[0]
deriv_diff3[i, j, k] = dV_dT_R[0] - dV_dT_L[0]
# Calculate average gradient
dV_dx[0] = (dV_dx_L + dV_dx_R) / 2
dV_dy[0] = (dV_dy_L + dV_dy_R) / 2
dV_dT[0] = (dV_dT_L + dV_dT_R) / 2
# Use method of DubinsCar to solve optimal control instead
uOpt = my_object.opt_ctrl(
t, (x1[i], x2[j], x3[k]),
(dV_dx[0], dV_dy[0], dV_dT[0]))
dOpt = my_object.optDstb(
t, (x1[i], x2[j], x3[k]),
(dV_dx[0], dV_dy[0], dV_dT[0]))
# Calculate dynamical rates of changes
dx_dt, dy_dt, dtheta_dt = my_object.dynamics(
t, (x1[i], x2[j], x3[k]), uOpt, dOpt)
# Calculate Hamiltonian terms:
V_new[i, j,
k] = -(dx_dt * dV_dx[0] + dy_dt * dV_dy[0] +
dtheta_dt * dV_dT[0])
#probe[i, j, k] = V_new[i, j, k]
# Get derivMin
with hcl.if_(dV_dx_L[0] < min_deriv1[0]):
min_deriv1[0] = dV_dx_L[0]
with hcl.if_(dV_dx_R[0] < min_deriv1[0]):
min_deriv1[0] = dV_dx_R[0]
with hcl.if_(dV_dy_L[0] < min_deriv2[0]):
min_deriv2[0] = dV_dy_L[0]
with hcl.if_(dV_dy_R[0] < min_deriv2[0]):
min_deriv2[0] = dV_dy_R[0]
with hcl.if_(dV_dT_L[0] < min_deriv3[0]):
min_deriv3[0] = dV_dT_L[0]
with hcl.if_(dV_dT_R[0] < min_deriv3[0]):
min_deriv3[0] = dV_dT_R[0]
# Get derivMax
with hcl.if_(dV_dx_L[0] > max_deriv1[0]):
max_deriv1[0] = dV_dx_L[0]
with hcl.if_(dV_dx_R[0] > max_deriv1[0]):
max_deriv1[0] = dV_dx_R[0]
with hcl.if_(dV_dy_L[0] > max_deriv2[0]):
max_deriv2[0] = dV_dy_L[0]
with hcl.if_(dV_dy_R[0] > max_deriv2[0]):
max_deriv2[0] = dV_dy_R[0]
with hcl.if_(dV_dT_L[0] > max_deriv3[0]):
max_deriv3[0] = dV_dT_L[0]
with hcl.if_(dV_dT_R[0] > max_deriv3[0]):
max_deriv3[0] = dV_dT_R[0]
# Calculate the dissipation
with hcl.Stage("Dissipation"):
# Storing alphas
dOptL1 = hcl.scalar(0, "dOptL1")
dOptL2 = hcl.scalar(0, "dOptL2")
dOptL3 = hcl.scalar(0, "dOptL3")
# Find UPPER BOUND optimal disturbance
dOptU1 = hcl.scalar(0, "dOptU1")
dOptU2 = hcl.scalar(0, "dOptU2")
dOptU3 = hcl.scalar(0, "dOptU3")
alpha1 = hcl.scalar(0, "alpha1")
alpha2 = hcl.scalar(0, "alpha2")
alpha3 = hcl.scalar(0, "alpha3")
# Lower bound optimal control
uOptL1 = hcl.scalar(0, "uOptL1")
uOptL2 = hcl.scalar(0, "uOptL2")
uOptL3 = hcl.scalar(0, "uOptL3")
# Find UPPER BOUND optimal disturbance
uOptU1 = hcl.scalar(0, "uOptU1")
uOptU2 = hcl.scalar(0, "uOptU2")
uOptU3 = hcl.scalar(0, "uOptU3")
with hcl.for_(0, V_init.shape[0], name="i") as i:
with hcl.for_(0, V_init.shape[1], name="j") as j:
with hcl.for_(0, V_init.shape[2], name="k") as k:
dx_LL1 = hcl.scalar(0, "dx_LL1")
dx_LL2 = hcl.scalar(0, "dx_LL2")
dx_LL3 = hcl.scalar(0, "dx_LL3")
dx_UL1 = hcl.scalar(0, "dx_UL1")
dx_UL2 = hcl.scalar(0, "dx_UL2")
dx_UL3 = hcl.scalar(0, "dx_UL3")
dx_UU1 = hcl.scalar(0, "dx_UU1")
dx_UU2 = hcl.scalar(0, "dx_UU2")
dx_UU3 = hcl.scalar(0, "dx_UU3")
dx_LU1 = hcl.scalar(0, "dx_LU1")
dx_LU2 = hcl.scalar(0, "dx_LU2")
dx_LU3 = hcl.scalar(0, "dx_LU3")
# Find LOWER BOUND optimal disturbance
dOptL1[0], dOptL2[0], dOptL3[0] = my_object.optDstb(t, (x1[i], x2[j], x3[k]),\
(min_deriv1[0], min_deriv2[0],min_deriv3[0]))
# probe[i, j, k] = dOptL1[0] + dOptL2[0] + dOptL3[0]
dOptU1[0], dOptU2[0], dOptU3[0] = my_object.optDstb(t, (x1[i], x2[j], x3[k]),\
(max_deriv1[0], max_deriv2[0],max_deriv3[0]))
# probe[i, j, k] = dOptL1[0] + dOptL2[0] + dOptL3[0]
# Find LOWER BOUND optimal control
uOptL1[0], uOptL2[0], uOptL3[0] = my_object.opt_ctrl(t, (x1[i], x2[j], x3[k]), \
(min_deriv1[0], min_deriv2[0],min_deriv3[0]))
# probe[i, j, k] = uOptL1[0] + uOptL2[0] + uOptL3[0]
# Find UPPER BOUND optimal control
uOptU1[0], uOptU2[0], uOptU3[0] = my_object.opt_ctrl(
t, (x1[i], x2[j], x3[k]),
(max_deriv1[0], max_deriv2[0], max_deriv3[0]))
#probe[i, j, k] = uOptU1[0] + uOptU2[0] + uOptU3[0]
# Find magnitude of rates of changes
dx_LL1[0], dx_LL2[0], dx_LL3[0] = my_object.dynamics(t, (x1[i], x2[j], x3[k]),
(uOptL1[0], uOptL2[0], uOptL3[0]), \
(dOptL1[0], dOptL2[0], dOptL3[0]))
dx_LL1[0] = my_abs(dx_LL1[0])
dx_LL2[0] = my_abs(dx_LL2[0])
dx_LL3[0] = my_abs(dx_LL3[0])
#probe[i, j, k] = dx_LL1[0] + dx_LL2[0] + dx_LL3[0]
dx_LU1[0], dx_LU2[0], dx_LU3[0] = my_object.dynamics(t, (x1[i], x2[j], x3[k]),
(uOptL1[0], uOptL2[0], uOptL3[0]), \
(dOptU1[0], dOptU2[0], dOptU3[0]))
dx_LU1[0] = my_abs(dx_LU1[0])
dx_LU2[0] = my_abs(dx_LU2[0])
dx_LU3[0] = my_abs(dx_LU3[0])
# Calculate alpha
alpha1[0] = my_max(dx_LL1[0], dx_LU1[0])
alpha2[0] = my_max(dx_LL2[0], dx_LU2[0])
alpha3[0] = my_max(dx_LL3[0], dx_LU3[0])
# dx_UL3 = hcl.scalar(0, "dx_UL3")
# dx_UL1[0], dx_UL2[0], dx_UL3[0], dx_UL4[0] = my_object.dynamics(t, (x1[i], x2[j], x3[k], x4[l]),
# uOptU, dOptL)
dx_UL1[0], dx_UL2[0], dx_UL3[0]= my_object.dynamics(t, (x1[i], x2[j], x3[k]),\
(uOptU1[0], uOptU2[0], uOptU3[0]), \
(dOptL1[0], dOptL2[0], dOptL3[0]))
dx_UL1[0] = my_abs(dx_UL1[0])
dx_UL2[0] = my_abs(dx_UL2[0])
dx_UL3[0] = my_abs(dx_UL3[0])
# Calculate alpha
alpha1[0] = my_max(alpha1[0], dx_UL1[0])
alpha2[0] = my_max(alpha2[0], dx_UL2[0])
alpha3[0] = my_max(alpha3[0], dx_UL3[0])
dx_UU1[0], dx_UU2[0], dx_UU3[0] = my_object.dynamics(t, (x1[i], x2[j], x3[k]),
(uOptU1[0], uOptU2[0], uOptU3[0]),\
(dOptU1[0], dOptU2[0], dOptU3[0]))
dx_UU1[0] = my_abs(dx_UU1[0])
dx_UU2[0] = my_abs(dx_UU2[0])
dx_UU3[0] = my_abs(dx_UU3[0])
# Calculate alpha
alpha1[0] = my_max(alpha1[0], dx_UU1[0])
alpha2[0] = my_max(alpha2[0], dx_UU2[0])
alpha3[0] = my_max(alpha3[0], dx_UU3[0])
#probe[i, j, k] = alpha1[0] + alpha2[0] + alpha3[0]
diss = hcl.scalar(0, "diss")
diss[0] = 0.5 * (deriv_diff1[i, j, k] * alpha1[0] +
deriv_diff2[i, j, k] * alpha2[0] +
deriv_diff3[i, j, k] * alpha3[0])
# probe[i, j, k] = alpha2[0]
# Finally
V_new[i, j, k] = -(V_new[i, j, k] - diss[0])
# probe[i, j, k] = alpha3[0]
# Get maximum alphas in each dimension
# Calculate alphas
with hcl.if_(alpha1[0] > max_alpha1[0]):
max_alpha1[0] = alpha1[0]
with hcl.if_(alpha2[0] > max_alpha2[0]):
max_alpha2[0] = alpha2[0]
with hcl.if_(alpha3[0] > max_alpha3[0]):
max_alpha3[0] = alpha3[0]
# Determine time step
delta_t = hcl.compute((1, ), lambda x: step_bound(), name="delta_t")
# Integrate
result = hcl.update(
V_new,
lambda i, j, k: V_init[i, j, k] + V_new[i, j, k] * delta_t[0])
# Different computation method check
if compMethod == 'maxVWithV0':
result = hcl.update(V_new, lambda i, j, k: maxVWithV0(i, j, k))
if compMethod == 'minVWithV0':
result = hcl.update(V_new, lambda i, j, k: minVWithV0(i, j, k))
if compMethod == 'minVWithVInit':
result = hcl.update(V_new, lambda i, j, k: minVWithVInit(i, j, k))
if compMethod == 'maxVWithVInit':
result = hcl.update(V_new, lambda i, j, k: maxVWithVInit(i, j, k))
# Copy V_new to V_init
hcl.update(V_init, lambda i, j, k: V_new[i, j, k])
return result
s = hcl.create_schedule([V_f, V_init, x1, x2, x3, t, l0], graph_create)
##################### CODE OPTIMIZATION HERE ###########################
print("Optimizing\n")
# Accessing the hamiltonian and dissipation stage
s_H = graph_create.Hamiltonian
s_D = graph_create.Dissipation
# Thread parallelize hamiltonian and dissipation
s[s_H].parallel(s_H.i)
s[s_D].parallel(s_D.i)
# Inspect IR
# if args.llvm:
#print(hcl.lower(s))
# Return executable
return (hcl.build(s))
""" import heterocl as hcl ############################################################################## # Data Types Supported by HeteroCL # -------------------------------- # HeteroCL supports both bit-accurate data types and floating points. We show # some examples below. If no argument is provided, the default bitwidth for # each type is 32. hcl.Int(15) # 15-bit signed integer hcl.UInt(24) # 24-bit unsigned integer hcl.Fixed(13, 5) # 13-bit signed fixed point with 5 fractional bits hcl.UFixed(44, 30) # 44-bit unsigned fixed point with 30 fractional bits hcl.Float(32) # single-precision floating point hcl.Float(64) # double-precision floating point ############################################################################## # These data types can be used in ``hcl.init`` to set the default data type hcl.init(hcl.Float()) ############################################################################## # Data Type Customization # ----------------------- # Another important hardware customization is data type customization, which # can be data quantization or downsizing a data type. Data quantization has # been proved to improve hardware efficiency in many accelerators. In HeteroCL, # to apply data type customization, we need to use ``hcl.create_scheme``,
def top(
input,
filter,
bias,
):
f_conv = hcl.compute(
(final_extent_0, final_extent_1, final_extent_2, final_extent_3),
lambda x, y, z, w: 0,
name="f_conv",
dtype=hcl.Float(bits=32))
with hcl.Stage("f_conv"):
with hcl.for_(final_min_3, final_extent_3,
name="f_conv_s0_n") as f_conv_s0_n:
with hcl.for_(final_min_2, final_extent_2,
name="f_conv_s0_z") as f_conv_s0_z:
with hcl.for_(final_min_1, final_extent_1,
name="f_conv_s0_y") as f_conv_s0_y:
with hcl.for_(final_min_0,
final_extent_0,
name="f_conv_s0_x") as f_conv_s0_x:
f_conv[f_conv_s0_x, f_conv_s0_y, f_conv_s0_z,
f_conv_s0_n] = bias[f_conv_s0_z]
with hcl.for_(final_min_3, final_extent_3,
name="f_conv_s1_n") as f_conv_s1_n:
with hcl.for_(final_min_2, final_extent_2,
name="f_conv_s1_z") as f_conv_s1_z:
with hcl.for_(final_min_1, final_extent_1,
name="f_conv_s1_y") as f_conv_s1_y:
with hcl.for_(final_min_0,
final_extent_0,
name="f_conv_s1_x") as f_conv_s1_x:
with hcl.for_(0, 32,
name="f_conv_s1_r__z") as f_conv_s1_r__z:
with hcl.for_(
0, 3,
name="f_conv_s1_r__y") as f_conv_s1_r__y:
with hcl.for_(0, 3, name="f_conv_s1_r__x"
) as f_conv_s1_r__x:
f_conv[
f_conv_s1_x, f_conv_s1_y, f_conv_s1_z,
f_conv_s1_n] = (
f_conv[f_conv_s1_x, f_conv_s1_y,
f_conv_s1_z, f_conv_s1_n] +
(filter[
f_conv_s1_r__x, f_conv_s1_r__y,
f_conv_s1_r__z, f_conv_s1_z] *
input[
(f_conv_s1_r__x +
f_conv_s1_x),
(f_conv_s1_r__y +
f_conv_s1_y), f_conv_s1_r__z,
f_conv_s1_n]))
f_relu = hcl.compute(
(final_extent_0, final_extent_1, final_extent_2, final_extent_3),
lambda x, y, z, w: 0,
name="f_relu",
dtype=hcl.Float(bits=32))
with hcl.Stage("f_relu"):
with hcl.for_(final_min_3, final_extent_3,
name="f_relu_s0_n") as f_relu_s0_n:
with hcl.for_(final_min_2, final_extent_2,
name="f_relu_s0_z") as f_relu_s0_z:
with hcl.for_(final_min_1, final_extent_1,
name="f_relu_s0_y") as f_relu_s0_y:
with hcl.for_(final_min_0,
final_extent_0,
name="f_relu_s0_x") as f_relu_s0_x:
f_relu[f_relu_s0_x, f_relu_s0_y, f_relu_s0_z,
f_relu_s0_n] = hcl.select(
f_conv[f_relu_s0_x, f_relu_s0_y,
f_relu_s0_z, f_relu_s0_n] > hcl.cast(
dtype=hcl.Float(bits=32),
expr=0.000000),
f_conv[f_relu_s0_x, f_relu_s0_y,
f_relu_s0_z, f_relu_s0_n],
hcl.cast(dtype=hcl.Float(bits=32),
expr=0.000000))
final = hcl.compute((64, 64, 32, 4),
lambda x, y, z, w: 0,
name="final",
dtype=hcl.Float(bits=32))
with hcl.Stage("final"):
with hcl.for_(final_min_3, final_extent_3,
name="final_s0_n") as final_s0_n:
with hcl.for_(final_min_2, final_extent_2,
name="final_s0_z") as final_s0_z:
with hcl.for_(final_min_1, final_extent_1,
name="final_s0_y") as final_s0_y:
with hcl.for_(final_min_0,
final_extent_0,
name="final_s0_x") as final_s0_x:
final[final_s0_x, final_s0_y, final_s0_z,
final_s0_n] = f_relu[final_s0_x, final_s0_y,
final_s0_z, final_s0_n]
return final
def graph_4D(my_object, g, compMethod, accuracy):
V_f = hcl.placeholder(tuple(g.pts_each_dim), name="V_f", dtype=hcl.Float())
V_init = hcl.placeholder(tuple(g.pts_each_dim),
name="V_init",
dtype=hcl.Float())
l0 = hcl.placeholder(tuple(g.pts_each_dim), name="l0", dtype=hcl.Float())
t = hcl.placeholder((2, ), name="t", dtype=hcl.Float())
probe = hcl.placeholder(tuple(g.pts_each_dim),
name="probe",
dtype=hcl.Float())
# Positions vector
x1 = hcl.placeholder((g.pts_each_dim[0], ), name="x1", dtype=hcl.Float())
x2 = hcl.placeholder((g.pts_each_dim[1], ), name="x2", dtype=hcl.Float())
x3 = hcl.placeholder((g.pts_each_dim[2], ), name="x3", dtype=hcl.Float())
x4 = hcl.placeholder((g.pts_each_dim[3], ), name="x4", dtype=hcl.Float())
def graph_create(V_new, V_init, x1, x2, x3, x4, t, l0, probe):
# Specify intermediate tensors
deriv_diff1 = hcl.compute(V_init.shape, lambda *x: 0, "deriv_diff1")
deriv_diff2 = hcl.compute(V_init.shape, lambda *x: 0, "deriv_diff2")
deriv_diff3 = hcl.compute(V_init.shape, lambda *x: 0, "deriv_diff3")
deriv_diff4 = hcl.compute(V_init.shape, lambda *x: 0, "deriv_diff4")
# Maximum derivative for each dim
max_deriv1 = hcl.scalar(-1e9, "max_deriv1")
max_deriv2 = hcl.scalar(-1e9, "max_deriv2")
max_deriv3 = hcl.scalar(-1e9, "max_deriv3")
max_deriv4 = hcl.scalar(-1e9, "max_deriv4")
# Min derivative for each dim
min_deriv1 = hcl.scalar(1e9, "min_deriv1")
min_deriv2 = hcl.scalar(1e9, "min_deriv2")
min_deriv3 = hcl.scalar(1e9, "min_deriv3")
min_deriv4 = hcl.scalar(1e9, "min_deriv4")
# These variables are used to dissipation calculation
max_alpha1 = hcl.scalar(-1e9, "max_alpha1")
max_alpha2 = hcl.scalar(-1e9, "max_alpha2")
max_alpha3 = hcl.scalar(-1e9, "max_alpha3")
max_alpha4 = hcl.scalar(-1e9, "max_alpha4")
def step_bound(): # Function to calculate time step
stepBoundInv = hcl.scalar(0, "stepBoundInv")
stepBound = hcl.scalar(0, "stepBound")
stepBoundInv[0] = max_alpha1[0] / g.dx[0] + max_alpha2[0] / g.dx[1] + max_alpha3[0] / g.dx[2] + max_alpha4[0] / \
g.dx[3]
stepBound[0] = 0.8 / stepBoundInv[0]
with hcl.if_(stepBound > t[1] - t[0]):
stepBound[0] = t[1] - t[0]
# Update the lower time ranges
t[0] = t[0] + stepBound[0]
# t[0] = min_deriv2[0]
return stepBound[0]
# Min with V_before
def minVWithVInit(i, j, k, l):
with hcl.if_(V_new[i, j, k, l] > V_init[i, j, k, l]):
V_new[i, j, k, l] = V_init[i, j, k, l]
def maxVWithVInit(i, j, k, l):
with hcl.if_(V_new[i, j, k, l] < V_init[i, j, k, l]):
V_new[i, j, k, l] = V_init[i, j, k, l]
def maxVWithV0(i, j, k, l): # Take the max
with hcl.if_(V_new[i, j, k, l] < l0[i, j, k, l]):
V_new[i, j, k, l] = l0[i, j, k, l]
def minVWithV0(i, j, k, l):
with hcl.if_(V_new[i, j, k, l] > l0[i, j, k, l]):
V_new[i, j, k, l] = l0[i, j, k, l]
# Calculate Hamiltonian for every grid point in V_init
with hcl.Stage("Hamiltonian"):
with hcl.for_(0, V_init.shape[0], name="i") as i:
with hcl.for_(0, V_init.shape[1], name="j") as j:
with hcl.for_(0, V_init.shape[2], name="k") as k:
with hcl.for_(0, V_init.shape[3], name="l") as l:
# Variables to calculate dV_dx
dV_dx1_L = hcl.scalar(0, "dV_dx1_L")
dV_dx1_R = hcl.scalar(0, "dV_dx1_R")
dV_dx1 = hcl.scalar(0, "dV_dx1")
dV_dx2_L = hcl.scalar(0, "dV_dx2_L")
dV_dx2_R = hcl.scalar(0, "dV_dx2_R")
dV_dx2 = hcl.scalar(0, "dV_dx2")
dV_dx3_L = hcl.scalar(0, "dV_dx3_L")
dV_dx3_R = hcl.scalar(0, "dV_dx3_R")
dV_dx3 = hcl.scalar(0, "dV_dx3")
dV_dx4_L = hcl.scalar(0, "dV_dx4_L")
dV_dx4_R = hcl.scalar(0, "dV_dx4_R")
dV_dx4 = hcl.scalar(0, "dV_dx4")
# No tensor slice operation
# dV_dx_L[0], dV_dx_R[0] = spa_derivX(i, j, k)
if accuracy == "low":
dV_dx1_L[0], dV_dx1_R[0] = spa_derivX1_4d(
i, j, k, l, V_init, g)
dV_dx2_L[0], dV_dx2_R[0] = spa_derivX2_4d(
i, j, k, l, V_init, g)
dV_dx3_L[0], dV_dx3_R[0] = spa_derivX3_4d(
i, j, k, l, V_init, g)
dV_dx4_L[0], dV_dx4_R[0] = spa_derivX4_4d(
i, j, k, l, V_init, g)
if accuracy == "high":
dV_dx1_L[0], dV_dx1_R[0] = secondOrderX1_4d(
i, j, k, l, V_init, g)
dV_dx2_L[0], dV_dx2_R[0] = secondOrderX2_4d(
i, j, k, l, V_init, g)
dV_dx3_L[0], dV_dx3_R[0] = secondOrderX3_4d(
i, j, k, l, V_init, g)
dV_dx4_L[0], dV_dx4_R[0] = secondOrderX4_4d(
i, j, k, l, V_init, g)
# Saves spatial derivative diff into tables
deriv_diff1[i, j, k, l] = dV_dx1_R[0] - dV_dx1_L[0]
deriv_diff2[i, j, k, l] = dV_dx2_R[0] - dV_dx2_L[0]
deriv_diff3[i, j, k, l] = dV_dx3_R[0] - dV_dx3_L[0]
deriv_diff4[i, j, k, l] = dV_dx4_R[0] - dV_dx4_L[0]
# Calculate average gradient
dV_dx1[0] = (dV_dx1_L + dV_dx1_R) / 2
dV_dx2[0] = (dV_dx2_L + dV_dx2_R) / 2
dV_dx3[0] = (dV_dx3_L + dV_dx3_R) / 2
dV_dx4[0] = (dV_dx4_L + dV_dx4_R) / 2
#probe[i,j,k,l] = dV_dx2[0]
# Find optimal control
uOpt = my_object.opt_ctrl(
t, (x1[i], x2[j], x3[k], x4[l]),
(dV_dx1[0], dV_dx2[0], dV_dx3[0], dV_dx4[0]))
# Find optimal disturbance
dOpt = my_object.opt_dstb(
t, (x1[i], x2[j], x3[k], x4[l]),
(dV_dx1[0], dV_dx2[0], dV_dx3[0], dV_dx4[0]))
# Find rates of changes based on dynamics equation
dx1_dt, dx2_dt, dx3_dt, dx4_dt = my_object.dynamics(
t, (x1[i], x2[j], x3[k], x4[l]), uOpt, dOpt)
# Calculate Hamiltonian terms:
V_new[i, j, k, l] = -(
dx1_dt * dV_dx1[0] + dx2_dt * dV_dx2[0] +
dx3_dt * dV_dx3[0] + dx4_dt * dV_dx4[0])
# Debugging
# V_new[i, j, k, l] = dV_dx2[0]
probe[i, j, k, l] = V_init[i, j, k, l]
# Get derivMin
with hcl.if_(dV_dx1_L[0] < min_deriv1[0]):
min_deriv1[0] = dV_dx1_L[0]
with hcl.if_(dV_dx1_R[0] < min_deriv1[0]):
min_deriv1[0] = dV_dx1_R[0]
with hcl.if_(dV_dx2_L[0] < min_deriv2[0]):
min_deriv2[0] = dV_dx2_L[0]
with hcl.if_(dV_dx2_R[0] < min_deriv2[0]):
min_deriv2[0] = dV_dx2_R[0]
with hcl.if_(dV_dx3_L[0] < min_deriv3[0]):
min_deriv3[0] = dV_dx3_L[0]
with hcl.if_(dV_dx3_R[0] < min_deriv3[0]):
min_deriv3[0] = dV_dx3_R[0]
with hcl.if_(dV_dx4_L[0] < min_deriv4[0]):
min_deriv4[0] = dV_dx4_L[0]
with hcl.if_(dV_dx4_R[0] < min_deriv4[0]):
min_deriv4[0] = dV_dx4_R[0]
# Get derivMax
with hcl.if_(dV_dx1_L[0] > max_deriv1[0]):
max_deriv1[0] = dV_dx1_L[0]
with hcl.if_(dV_dx1_R[0] > max_deriv1[0]):
max_deriv1[0] = dV_dx1_R[0]
with hcl.if_(dV_dx2_L[0] > max_deriv2[0]):
max_deriv2[0] = dV_dx2_L[0]
with hcl.if_(dV_dx2_R[0] > max_deriv2[0]):
max_deriv2[0] = dV_dx2_R[0]
with hcl.if_(dV_dx3_L[0] > max_deriv3[0]):
max_deriv3[0] = dV_dx3_L[0]
with hcl.if_(dV_dx3_R[0] > max_deriv3[0]):
max_deriv3[0] = dV_dx3_R[0]
with hcl.if_(dV_dx4_L[0] > max_deriv4[0]):
max_deriv4[0] = dV_dx4_L[0]
with hcl.if_(dV_dx4_R[0] > max_deriv4[0]):
max_deriv4[0] = dV_dx4_R[0]
# Calculate dissipation amount
with hcl.Stage("Dissipation"):
# Storing alphas
dOptL1 = hcl.scalar(0, "dOptL1")
dOptL2 = hcl.scalar(0, "dOptL2")
dOptL3 = hcl.scalar(0, "dOptL3")
dOptL4 = hcl.scalar(0, "dOptL4")
# Find UPPER BOUND optimal disturbance
dOptU1 = hcl.scalar(0, "dOptL1")
dOptU2 = hcl.scalar(0, "dOptL2")
dOptU3 = hcl.scalar(0, "dOptL3")
dOptU4 = hcl.scalar(0, "dOptL4")
alpha1 = hcl.scalar(0, "alpha1")
alpha2 = hcl.scalar(0, "alpha2")
alpha3 = hcl.scalar(0, "alpha3")
alpha4 = hcl.scalar(0, "alpha4")
"""
NOTE: If optimal adversarial disturbance is not dependent on states
, the below approximate LOWER/UPPER BOUND optimal disturbance is accurate.
If that's not the case, move the next two statements into the nested loops and modify the states passed in
as my_object.opt_dstb(t, (x1[i], x2[j], x3[k], x4[l], ...), ...).
The reason we don't have this line in the nested loop by default is to avoid redundant computations
for certain systems where disturbance are not dependent on states.
In general, dissipation amount can just be approximates.
"""
dOptL1[0], dOptL2[0], dOptL3[0], dOptL4[0] = my_object.opt_dstb(t, (x1[0], x2[0], x3[0], x4[0]),
(min_deriv1[0], min_deriv2[0], \
min_deriv3[0], min_deriv4[0]))
dOptU1[0], dOptL2[0], dOptL3[0], dOptL4[0] = my_object.opt_dstb(t, (x1[0], x2[0], x3[0], x4[0]),
(max_deriv1[0], max_deriv2[0], \
max_deriv3[0], max_deriv4[0]))
uOptL1 = hcl.scalar(0, "uOptL1")
uOptL2 = hcl.scalar(0, "uOptL2")
uOptL3 = hcl.scalar(0, "uOptL3")
uOptL4 = hcl.scalar(0, "uOptL4")
# Find UPPER BOUND optimal disturbance
uOptU1 = hcl.scalar(0, "uOptU1")
uOptU2 = hcl.scalar(0, "uOptU2")
uOptU3 = hcl.scalar(0, "uOptU3")
uOptU4 = hcl.scalar(0, "uOptU4")
with hcl.for_(0, V_init.shape[0], name="i") as i:
with hcl.for_(0, V_init.shape[1], name="j") as j:
with hcl.for_(0, V_init.shape[2], name="k") as k:
with hcl.for_(0, V_init.shape[3], name="l") as l:
dx_LL1 = hcl.scalar(0, "dx_LL1")
dx_LL2 = hcl.scalar(0, "dx_LL2")
dx_LL3 = hcl.scalar(0, "dx_LL3")
dx_LL4 = hcl.scalar(0, "dx_LL4")
dx_UL1 = hcl.scalar(0, "dx_UL1")
dx_UL2 = hcl.scalar(0, "dx_UL2")
dx_UL3 = hcl.scalar(0, "dx_UL3")
dx_UL4 = hcl.scalar(0, "dx_UL4")
dx_UU1 = hcl.scalar(0, "dx_UU1")
dx_UU2 = hcl.scalar(0, "dx_UU2")
dx_UU3 = hcl.scalar(0, "dx_UU3")
dx_UU4 = hcl.scalar(0, "dx_UU4")
dx_LU1 = hcl.scalar(0, "dx_LU1")
dx_LU2 = hcl.scalar(0, "dx_LU2")
dx_LU3 = hcl.scalar(0, "dx_LU3")
dx_LU4 = hcl.scalar(0, "dx_LU4")
# dOptL1[0], dOptL2[0], dOptL3[0], dOptL4[0] = my_object.opt_dstb(t, (x1[i], x2[j], x3[k], x4[l]),
# (min_deriv1[0], min_deriv2[0], \
# min_deriv3[0], min_deriv4[0]))
# dOptU1[0], dOptL2[0], dOptL3[0], dOptL4[0] = my_object.opt_dstb(t, (x1[i], x2[j], x3[k], x4[l]),
# (max_deriv1[0], max_deriv2[0], \
# max_deriv3[0], max_deriv4[0]))
# Find LOWER BOUND optimal control
uOptL1[0], uOptL2[0], uOptL3[0], uOptL4[
0] = my_object.opt_ctrl(
t, (x1[i], x2[j], x3[k], x4[l]),
(min_deriv1[0], min_deriv2[0],
min_deriv3[0], min_deriv4[0]))
# Find UPPER BOUND optimal control
uOptU1[0], uOptU2[0], uOptU3[0], uOptU4[
0] = my_object.opt_ctrl(
t, (x1[i], x2[j], x3[k], x4[l]),
(max_deriv1[0], max_deriv2[0],
max_deriv3[0], max_deriv4[0]))
# Find magnitude of rates of changes
dx_LL1[0], dx_LL2[0], dx_LL3[0], dx_LL4[0] = my_object.dynamics(t, (x1[i], x2[j], x3[k], x4[l]),
(uOptL1[0], uOptL2[0],uOptL3[0], uOptL4[0]),\
(dOptL1[0], dOptL2[0], dOptL3[0], dOptL4[0]))
dx_LL1[0] = my_abs(dx_LL1[0])
dx_LL2[0] = my_abs(dx_LL2[0])
dx_LL3[0] = my_abs(dx_LL3[0])
dx_LL4[0] = my_abs(dx_LL4[0])
dx_LU1[0], dx_LU2[0], dx_LU3[0], dx_LU4[0] = my_object.dynamics(t, (x1[i], x2[j], x3[k], x4[l]),
(uOptL1[0], uOptL2[0],uOptL3[0], uOptL4[0]), \
(dOptU1[0], dOptU2[0], dOptU3[0], dOptU4[0]))
dx_LU1[0] = my_abs(dx_LU1[0])
dx_LU2[0] = my_abs(dx_LU2[0])
dx_LU3[0] = my_abs(dx_LU3[0])
dx_LU4[0] = my_abs(dx_LU4[0])
# Calculate alpha
alpha1[0] = my_max(dx_LL1[0], dx_LU1[0])
alpha2[0] = my_max(dx_LL2[0], dx_LU2[0])
alpha3[0] = my_max(dx_LL3[0], dx_LU3[0])
alpha4[0] = my_max(dx_LL4[0], dx_LU4[0])
dx_UL1[0], dx_UL2[0], dx_UL3[0], dx_UL4[0] = my_object.dynamics(t, (x1[i], x2[j], x3[k], x4[l]),\
(uOptU1[0], uOptU2[0], uOptU3[0], uOptU4[0]), \
(dOptL1[0], dOptL2[0], dOptL3[0], dOptL4[0]))
dx_UL1[0] = my_abs(dx_UL1[0])
dx_UL2[0] = my_abs(dx_UL2[0])
dx_UL3[0] = my_abs(dx_UL3[0])
dx_UL4[0] = my_abs(dx_UL4[0])
# Calculate alpha
alpha1[0] = my_max(alpha1[0], dx_UL1[0])
alpha2[0] = my_max(alpha2[0], dx_UL2[0])
alpha3[0] = my_max(alpha3[0], dx_UL3[0])
alpha4[0] = my_max(alpha4[0], dx_UL4[0])
dx_UU1[0], dx_UU2[0], dx_UU3[0], dx_UU4[0] = my_object.dynamics(t, (x1[i], x2[j], x3[k], x4[l]),
(uOptU1[0], uOptU2[0], uOptU3[0], uOptU4[0]),\
(dOptU1[0], dOptU2[0], dOptU3[0], dOptU4[0]))
dx_UU1[0] = my_abs(dx_UU1[0])
dx_UU2[0] = my_abs(dx_UU2[0])
dx_UU3[0] = my_abs(dx_UU3[0])
dx_UU4[0] = my_abs(dx_UU4[0])
# Calculate alphas
alpha1[0] = my_max(alpha1[0], dx_UU1[0])
alpha2[0] = my_max(alpha2[0], dx_UU2[0])
alpha3[0] = my_max(alpha3[0], dx_UU3[0])
alpha4[0] = my_max(alpha4[0], dx_UU4[0])
diss = hcl.scalar(0, "diss")
diss[0] = 0.5 * (
deriv_diff1[i, j, k, l] * alpha1[0] +
deriv_diff2[i, j, k, l] * alpha2[0] +
deriv_diff3[i, j, k, l] * alpha3[0] +
deriv_diff4[i, j, k, l] * alpha4[0])
#probe[i, j, k, l] = alpha1[0]
# Finally
V_new[i, j, k, l] = -(V_new[i, j, k, l] - diss[0])
# Get maximum alphas in each dimension
with hcl.if_(alpha1[0] > max_alpha1[0]):
max_alpha1[0] = alpha1[0]
with hcl.if_(alpha2[0] > max_alpha2[0]):
max_alpha2[0] = alpha2[0]
with hcl.if_(alpha3[0] > max_alpha3[0]):
max_alpha3[0] = alpha3[0]
with hcl.if_(alpha4[0] > max_alpha4[0]):
max_alpha4[0] = alpha4[0]
# Determine time step
delta_t = hcl.compute((1, ), lambda x: step_bound(), name="delta_t")
# Integrate
result = hcl.update(
V_new, lambda i, j, k, l: V_init[i, j, k, l] + V_new[i, j, k, l] *
delta_t[0])
# Different computation method check
if compMethod == 'maxVWithV0':
result = hcl.update(V_new,
lambda i, j, k, l: maxVWithV0(i, j, k, l))
if compMethod == 'minVWithV0':
result = hcl.update(V_new,
lambda i, j, k, l: minVWithV0(i, j, k, l))
if compMethod == 'minVWithVInit':
result = hcl.update(V_new,
lambda i, j, k, l: minVWithVInit(i, j, k, l))
if compMethod == 'maxVWithVInit':
result = hcl.update(V_new,
lambda i, j, k, l: maxVWithVInit(i, j, k, l))
# Copy V_new to V_init
hcl.update(V_init, lambda i, j, k, l: V_new[i, j, k, l])
return result
s = hcl.create_schedule([V_f, V_init, x1, x2, x3, x4, t, l0, probe],
graph_create)
##################### CODE OPTIMIZATION HERE ###########################
print("Optimizing\n")
# Accessing the hamiltonian and dissipation stage
s_H = graph_create.Hamiltonian
s_D = graph_create.Dissipation
# Thread parallelize hamiltonian and dissipation
s[s_H].parallel(s_H.i)
s[s_D].parallel(s_D.i)
# Inspect IR
# if args.llvm:
# print(hcl.lower(s))
# Return executable
return (hcl.build(s))
final_extent_0,
name="final_s0_x") as final_s0_x:
final[final_s0_x, final_s0_y, final_s0_z,
final_s0_n] = f_relu[final_s0_x, final_s0_y,
final_s0_z, final_s0_n]
return final
input = hcl.placeholder((
67,
67,
32,
4,
),
name="input",
dtype=hcl.Float(bits=32))
filter = hcl.placeholder((
3,
3,
32,
32,
),
name="filter",
dtype=hcl.Float(bits=32))
bias = hcl.placeholder((32, ), name="bias", dtype=hcl.Float(bits=32))
s = hcl.create_schedule([
input,
filter,
bias,
], top)
f = hcl.build(s)
def main():
################### PARSING ARGUMENTS FROM USERS #####################
parser = ArgumentParser()
parser.add_argument("-p", "--plot", default=False, type=bool)
args = parser.parse_args()
hcl.init()
hcl.config.init_dtype = hcl.Float()
################## DATA SHAPE PREPARATION FOR GRAPH FORMATION ####################
V_f = hcl.placeholder(tuple(g.pts_each_dim), name="V_f", dtype=hcl.Float())
V_init = hcl.placeholder(tuple(g.pts_each_dim),
name="V_init",
dtype=hcl.Float())
x = hcl.placeholder((4, g.pts_each_dim[0]), name="x", dtype=hcl.Float())
t = hcl.placeholder((1, ), name="t", dtype=hcl.Float())
# Deriv diff tensor
deriv_diff1 = hcl.Tensor((tuple(g.pts_each_dim)), name="deriv_diff1")
deriv_diff2 = hcl.Tensor((tuple(g.pts_each_dim)), name="deriv_diff2")
deriv_diff3 = hcl.Tensor((tuple(g.pts_each_dim)), name="deriv_diff3")
deriv_diff4 = hcl.Tensor((tuple(g.pts_each_dim)), name="deriv_diff4")
################# INITIALIZE DATA TO BE INPUT INTO GRAPH ##########################
V_0 = hcl.asarray(my_shape)
V_1 = hcl.asarray(np.zeros(tuple(g.pts_each_dim)))
t_minh = hcl.asarray(np.zeros(1))
x = np.zeros(4, g.pts_each_dim[0])
for i in range(0, 4):
for j in range(0, g.pts_each_dim[i]):
x[i, j] = g.xs[i][j]
# Convert x to hcl array type
x = hcl.asarray(x)
##################### CREATE SCHEDULE##############
# Create schedule
s = hcl.create_schedule([V_f, V_init, x, t], graph_4D)
# Inspect the LLVM code
print(hcl.lower(s))
##################### CODE OPTIMIZATION HERE ###########################
# Accessing the hamiltonian stage
s_H = graph_4D.Hamiltonian
#
################ GET EXECUTABLE AND USE THE EXECUTABLE ############
# Get executable
solve_pde = hcl.build(s)
# Variables used for timing
execution_time = 0
lookback_time = 0
while lookback_time <= lookback_length:
# Start timing
start = time.time()
# Printing some info
#print("Look back time is (s): {:.5f}".format(lookback_time))
# Run the execution and pass input into graph
solve_pde(V_1, V_0, list_theta, t_minh)
if lookback_time != 0: # Exclude first time of the computation
execution_time += time.time() - start
lookback_time += np.asscalar(t_minh.asnumpy())
# Some information printing
#print(t_minh)
print(
"Computational time to integrate (s): {:.5f}".format(time.time() -
start))
V_1 = V_1.asnumpy()
V_1 = np.swapaxes(V_1, 0, 2)
#V = np.swapaxes(V, 1,2)
#probe = probe.asnumpy()
#probe = np.swapaxes(probe, 0, 2)
#probe = np.swapaxes(probe, 1, 2)
#print(V)
#V_1 = V_1.asnumpy()
# Time info printing
print("Total kernel time (s): {:.5f}".format(execution_time))
print("Finished solving\n")
##################### PLOTTING #####################
if args.plot:
print("Plotting beautiful plots. Please wait\n")
fig = go.Figure(
data=go.Isosurface(x=g.mg_X.flatten(),
y=g.mg_Y.flatten(),
z=g.mg_T.flatten(),
value=V_1.flatten(),
colorscale='jet',
isomin=0,
surface_count=1,
isomax=0,
caps=dict(x_show=True, y_show=True)))
fig.show()
print("Please check the plot on your browser.")
import heterocl as hcl
import numpy as np
from gemm_main import *
dtypes = [hcl.Int(32), hcl.Float(), hcl.Fixed(32, 16)]
for dtype in dtypes:
time_gemm(dtype, 10, 10, 10, 'vhls_csim')
def sobel():
hcl.init(init_dtype=hcl.Float())
path = pathlib.Path(__file__).parent.absolute()
path = str(path) + '/test_report_data/rose-grayscale.jpg'
img = imageio.imread(path)
height, width, rgb = img.shape
imgF = hcl.placeholder((height, width, 3), "Image")
Gx = hcl.placeholder((3, 3), "Gx")
Gy = hcl.placeholder((3, 3), "Gy")
def sobel_kernel(imgF, Gx, Gy):
def pad(x, y, z):
out = hcl.scalar(0, "out")
with hcl.if_(hcl.and_(x > 0, y > 0)):
out.v = imgF[x - 1, y - 1, z]
with hcl.else_():
out.v = 0
return out.v
P = hcl.compute((height + 2, width + 2, 3),
lambda x, y, z: pad(x, y, z), "P")
A = hcl.compute((height + 2, width + 2),
lambda x, y: P[x][y][0] + P[x][y][1] + P[x][y][2], "A")
r = hcl.reduce_axis(0, 3)
c = hcl.reduce_axis(0, 3)
resX = hcl.compute((height, width), lambda x, y: hcl.sum(
A[x + r, y + c] * Gx[r, c], axis=[r, c], name="sum1"), "X")
t = hcl.reduce_axis(0, 3)
g = hcl.reduce_axis(0, 3)
resY = hcl.compute((height, width), lambda x, y: hcl.sum(
A[x + t, y + g] * Gy[t, g], axis=[t, g], name="sum2"), "Y")
R = hcl.compute((height, width), lambda x, y: hcl.sqrt(resX[x][
y] * resX[x][y] + resY[x][y] * resY[x][y]), "R")
norm = hcl.scalar(255 / 4328)
return hcl.compute((height, width), lambda x, y: R[x][y] * norm.v, "F")
s = hcl.create_schedule([imgF, Gx, Gy], sobel_kernel)
sA = sobel_kernel.A
sX = sobel_kernel.X
sY = sobel_kernel.Y
LBX = s.reuse_at(sA._op, s[sX], sX.axis[0], "LBX")
LBY = s.reuse_at(sA._op, s[sY], sY.axis[0], "LBY")
WBX = s.reuse_at(LBX, s[sX], sX.axis[1], "WBX")
WBY = s.reuse_at(LBY, s[sY], sY.axis[1], "WBY")
s.partition(LBX, dim=1)
s.partition(LBY, dim=1)
s.partition(WBX)
s.partition(WBY)
s.partition(Gx)
s.partition(Gy)
sP = sobel_kernel.P
sR = sobel_kernel.R
sF = sobel_kernel.F
s[sX].pipeline(sX.axis[1])
s[sY].pipeline(sY.axis[1])
s[sR].pipeline(sR.axis[1])
s[sF].pipeline(sF.axis[1])
target = hcl.platform.zc706
target.config(compile="vivado_hls", mode="csyn")
hcl_img = hcl.asarray(img)
hcl_Gx = hcl.asarray(np.array([[-1, -2, -1], [0, 0, 0], [1, 2, 1]]))
hcl_Gy = hcl.asarray(np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]))
hcl_F = hcl.asarray(np.zeros((height, width)))
f = hcl.build(s, target)
f(hcl_img, hcl_Gx, hcl_Gy, hcl_F)
return f.report()
def HJSolver(dynamics_obj, grid, init_value, tau, compMethod, plot_option):
print("Welcome to optimized_dp \n")
################### PARSING ARGUMENTS FROM USERS #####################
parser = ArgumentParser()
parser.add_argument("-p", "--plot", default=True, type=bool)
# # Print out LLVM option only
# parser.add_argument("-l", "--llvm", default=False, type=bool)
args = parser.parse_args()
hcl.init()
hcl.config.init_dtype = hcl.Float(32)
################# INITIALIZE DATA TO BE INPUT INTO EXECUTABLE ##########################
print("Initializing\n")
V_0 = hcl.asarray(init_value)
V_1 = hcl.asarray(np.zeros(tuple(grid.pts_each_dim)))
l0 = hcl.asarray(init_value)
probe = hcl.asarray(np.zeros(tuple(grid.pts_each_dim)))
#obstacle = hcl.asarray(cstraint_values)
list_x1 = np.reshape(grid.vs[0], grid.pts_each_dim[0])
list_x2 = np.reshape(grid.vs[1], grid.pts_each_dim[1])
list_x3 = np.reshape(grid.vs[2], grid.pts_each_dim[2])
if grid.dims >= 4:
list_x4 = np.reshape(grid.vs[3], grid.pts_each_dim[3])
if grid.dims >= 5:
list_x5 = np.reshape(grid.vs[4], grid.pts_each_dim[4])
if grid.dims >= 6:
list_x6 = np.reshape(grid.vs[5], grid.pts_each_dim[5])
# Convert to hcl array type
list_x1 = hcl.asarray(list_x1)
list_x2 = hcl.asarray(list_x2)
list_x3 = hcl.asarray(list_x3)
if grid.dims >= 4:
list_x4 = hcl.asarray(list_x4)
if grid.dims >= 5:
list_x5 = hcl.asarray(list_x5)
if grid.dims >= 6:
list_x6 = hcl.asarray(list_x6)
# Get executable
if grid.dims == 3:
solve_pde = graph_3D(dynamics_obj, grid, compMethod)
if grid.dims == 4:
solve_pde = graph_4D(dynamics_obj, grid, compMethod)
if grid.dims == 5:
solve_pde = graph_5D(dynamics_obj, grid, compMethod)
if grid.dims == 6:
solve_pde = graph_6D(dynamics_obj, grid, compMethod)
# Print out code for different backend
#print(solve_pde)
################ USE THE EXECUTABLE ############
# Variables used for timing
execution_time = 0
iter = 0
tNow = tau[0]
for i in range(1, len(tau)):
#tNow = tau[i-1]
t_minh = hcl.asarray(np.array((tNow, tau[i])))
while tNow <= tau[i] - 1e-4:
tmp_arr = V_0.asnumpy()
# Start timing
iter += 1
start = time.time()
print("Started running\n")
# Run the execution and pass input into graph
if grid.dims == 3:
solve_pde(V_1, V_0, list_x1, list_x2, list_x3, t_minh, l0)
if grid.dims == 4:
solve_pde(V_1, V_0, list_x1, list_x2, list_x3, list_x4, t_minh,
l0, probe)
if grid.dims == 5:
solve_pde(V_1, V_0, list_x1, list_x2, list_x3, list_x4,
list_x5, t_minh, l0)
if grid.dims == 6:
solve_pde(V_1, V_0, list_x1, list_x2, list_x3, list_x4,
list_x5, list_x6, t_minh, l0)
tNow = np.asscalar((t_minh.asnumpy())[0])
# Calculate computation time
execution_time += time.time() - start
# Some information printing
print(t_minh)
print("Computational time to integrate (s): {:.5f}".format(
time.time() - start))
# Time info printing
print("Total kernel time (s): {:.5f}".format(execution_time))
print("Finished solving\n")
##################### PLOTTING #####################
if args.plot:
# plot Value table when speed is maximum
plot_isosurface(grid, V_1.asnumpy(), plot_option)