def freduce(x, Y):
with hcl.for_(0, Y.shape[0]) as i:
with hcl.if_(x < Y[i]):
with hcl.for_(Y.shape[0]-1, i, -1) as j:
Y[j] = Y[j-1]
Y[i] = x
hcl.break_()
def loop_kernel(labels):
# assign cluster
with hcl.for_(0, N, name="n") as n:
min_dist = hcl.scalar(100000)
new_label = hcl.scalar(labels[n])
with hcl.for_(0, K) as k:
dist = hcl.scalar(0)
with hcl.for_(0, dim) as d:
dist_ = hcl.scalar(points[n, d] - means[k, d], "temp")
dist.v += dist_.v * dist_.v
with hcl.if_(dist.v < min_dist.v):
min_dist.v = dist.v
new_label[0] = k
labels[n] = new_label
# update mean
num_k = hcl.compute((K, ), lambda x: 0, "num_k")
sum_k = hcl.compute((K, dim), lambda x, y: 0, "sum_k")
def calc_sum(n):
num_k[labels[n]] += 1
with hcl.for_(0, dim) as d:
sum_k[labels[n], d] += points[n, d]
hcl.mutate((N, ), lambda n: calc_sum(n), "calc_sum")
hcl.update(means, lambda k, d: sum_k[k, d] // num_k[k],
"update_mean")
def fft(X_real, X_imag, IndexTable, F_real, F_imag):
L = X_real.shape[0]
if np.log2(L) % 1 > 0:
raise ValueError("Length of input vector (1d tensor) must be power of 2")
num_stages = int(np.log2(L))
# bit reverse permutation
hcl.update(F_real, lambda i: X_real[IndexTable[i]], name='F_real_update')
hcl.update(F_imag, lambda i: X_imag[IndexTable[i]], name='F_imag_update')
with hcl.Stage("Out"):
one = hcl.scalar(1, dtype="int32")
with hcl.for_(0, num_stages) as stage:
DFTpts = one[0] << (stage + 1)
numBF = DFTpts / 2
e = -2 * np.pi / DFTpts
a = hcl.scalar(0)
with hcl.for_(0, numBF) as j:
c = hcl.scalar(hcl.cos(a[0]))
s = hcl.scalar(hcl.sin(a[0]))
a[0] = a[0] + e
with hcl.for_(j, L + DFTpts - 1, DFTpts) as i:
i_lower = i + numBF
temp_r = hcl.scalar(F_real[i_lower] * c - F_imag[i_lower] * s)
temp_i = hcl.scalar(F_imag[i_lower] * c + F_real[i_lower] * s)
F_real[i_lower] = F_real[i] - temp_r[0]
F_imag[i_lower] = F_imag[i] - temp_i[0]
F_real[i] = F_real[i] + temp_r[0]
F_imag[i] = F_imag[i] + temp_i[0]
def absolute(A, B):
with hcl.for_(0, A.shape[0], name="x") as x:
with hcl.for_(0, A.shape[1], name="y") as y:
with hcl.if_(A[x, y] >= 0):
B[x, y] = A[x, y]
with hcl.else_():
B[x, y] = -A[x, y]
def kernel(A):
with hcl.Stage():
with hcl.for_(0, 10) as i:
with hcl.for_(0, 10) as j:
with hcl.if_(j >= i):
hcl.break_()
A[i] += j
def freduce(x, Y):
with hcl.for_(0, 10) as i:
with hcl.if_(x < Y[i]):
with hcl.for_(9, i, -1) as j:
Y[j] = Y[j-1]
Y[i] = x
hcl.break_()
def learn(k, hdTrainData, prototype, prototypeCounter):
#Find samples that have the label k
match = hcl.compute(
hdTrainData.shape,
lambda x, y: hcl.select(trainLabels[x] == k, hdTrainData[x][y], 0),
"match")
#Record the number of these samples
with hcl.for_(0, hdTrainData.shape[0]) as a:
with hcl.if_(trainLabels[a] == k):
max[k] += 1
#Do hdc sum on these samples' hdv
r = hcl.reduce_axis(0, hdTrainData.shape[0], 'r')
result = hcl.compute((hdTrainData.shape[1], ),
lambda y: hcl.sum(match[r][y], axis=r), "result")
#Do the binary voting
sum1 = hcl.compute((hdTrainData.shape[1], ), lambda x: 0, "sum1")
with hcl.if_(max[k] % 2 == 0):
hcl.update(
sum1, lambda x: hcl.select(
result[x] + rdv3[k][x] - max[k] / 2 > 0, 1, 0))
with hcl.else_():
hcl.update(sum1,
lambda x: hcl.select(result[x] - max[k] / 2 > 0, 1, 0))
#Push the binary sum to prototype and the original sum to prototypeCounter
with hcl.for_(0, hdTrainData.shape[1]) as t:
prototype[k][t] = sum1[t]
prototypeCounter[k][t] = result[t]
def func(data):
out = hcl.compute((4, 4), lambda x, y: 0, "out", dtype)
with hcl.Stage("S"):
with hcl.for_(0, 4, name="i") as i:
with hcl.for_(0, 4, name="j") as j:
out[i, j] = data[i, j] + 1
return out
def update(l, prototype, prototypeCounter, max):
hcl.print((l + 1),
"%d:Use hard examples to update the prototype counters.\n")
###data preparation
distance = hcl.compute((hdTrainData.shape[1], ), lambda x: 0,
'distance')
hamming_dist = hcl.compute((numClasses, ), lambda x: 0, "hamming_dist")
m = hcl.reduce_axis(0, hdTrainData.shape[1], "m")
###
with hcl.for_(0, hdTrainData.shape[0]) as i:
with hcl.for_(0, numClasses) as n:
#Do hdc multiplication(XOR) on sample[i]'s hdv and prototype[n]'s hdv (elementwise on the high-bit data)
hcl.update(distance,
lambda x: hdTrainData[i][x] ^ prototype[n][x])
#Calculate the hamming distance of the two vectors by adding 1s
hamming_dist[n] = hcl.sum(distance[m], axis=m)
#Find the one having the least hamming distance and choose it's label as the predicted label
pred = hcl.scalar(0, 'pred')
with hcl.for_(0, hamming_dist.shape[0]) as j:
with hcl.if_(hamming_dist[j] < hamming_dist[pred]):
pred.v = j
#Adjust the proto vectors by adding the sample vector on its label proto hdv and substrct it on its predicted proto hdv
with hcl.if_(pred.v != trainLabels[i]):
max[trainLabels[i]] += 1
max[pred] -= 1
with hcl.for_(0, hdTrainData.shape[1]) as m:
prototypeCounter[trainLabels[i]][m] += hdTrainData[i][m]
prototypeCounter[pred][m] -= hdTrainData[i][m]
with hcl.if_(max[trainLabels[i]] % 2 == 0):
with hcl.if_(prototypeCounter[trainLabels[i]][m] -
max[trainLabels[i]] / 2 == 0):
prototype[trainLabels[i]][m] &= 1
with hcl.else_():
prototype[trainLabels[i]][m] = hcl.select(
prototypeCounter[trainLabels[i]][m] -
max[trainLabels[i]] / 2 > 0, 1, 0)
with hcl.if_(max[pred] % 2 == 0):
with hcl.if_(prototypeCounter[pred][m] -
max[pred] / 2 == 0):
prototype[pred][m] &= 1
with hcl.else_():
prototype[pred][m] = hcl.select(
prototypeCounter[pred][m] - max[pred] / 2 > 0, 1,
0)
#print the accuracy
hcl.mutate(
(1, ),
lambda x: test_hdc_accu(prototype, hdTrainData, trainLabels, 1),
'training_update')
hcl.mutate(
(1, ),
lambda x: test_hdc_accu(prototype, hdTestData, testLabels, 2),
'testing_update')
def _mvpodd_reduce(*args):
"""compute {1, -1} dot product on packed data."""
temp = hcl.local(0, name='mvpodd_acc', dtype=hcl.Int(64))
with hcl.for_(0, in_block_num) as o:
with hcl.for_(0, block_size) as i:
temp[0] += tvm.popcount(d_packed[args[0], i+block_size*o] ^ w_packed[args[1], i+block_size*o])
temp[0] = ppac_config.elem_num - temp[0]*2
return temp[0]
def kernel(A, B):
C = hcl.compute(A.shape, lambda *args : 0, "C")
with hcl.Stage("stage"):
with hcl.for_(0, 10, name="i") as i:
with hcl.for_(0, 32, name="j") as j:
B[i, j] = A[i, j] + B[i, j]
C[i, j] = 2 * B[i, j]
return C
def update(l, prototype, prototypeCounter, max):
hcl.print((l+1),"%d:Use hard examples to update the prototype counters.\n")
###data preparation
distance = hcl.compute((in_train.shape[1],), lambda x: 0, 'distance', dtype=hcl.UInt(in_bw))
pre_dist = hcl.compute((in_train.shape[1],), lambda x: 0, "pre_dist")
hamming_dist = hcl.compute((numClasses,), lambda x: 0, "hamming_dist")
m = hcl.reduce_axis(0, in_train.shape[1], "m")
###
with hcl.for_(0, in_train.shape[0]) as i:
hcl.print((i),"%d suc\n")
# pack_proto = hcl.pack(prototype, axis=1, dtype=hcl.UInt(in_bw), name="pack_proto")
with hcl.for_(0, numClasses) as n:
#Do hdc multiplication(XOR) on sample[i]'s hdv and prototype[n]'s hdv (elementwise on the high-bit data)
hcl.update(distance, lambda x: in_train[i][x] ^ prototype[n][x])
#Calculate the hamming distance of the two vectors by adding 1s
hcl.update(pre_dist, lambda x: popcount(distance[x]))
hcl.print((),"sum of 1s suc")
hamming_dist[n] = hcl.sum(pre_dist[m], axis=m)
#Find the one having the least hamming distance and choose it's label as the predicted label
pred = hcl.scalar(0, 'pred')
with hcl.for_(0, hamming_dist.shape[0]) as j:
with hcl.if_(hamming_dist[j] < hamming_dist[pred]):
pred.v = j
#Adjust the proto vectors by adding the sample vector on its label proto hdv and substrct it on its predicted proto hdv
with hcl.if_(pred.v != trainLabels[i]):
max[trainLabels[i]] += 1
max[pred] -= 1
with hcl.for_(0, in_train.shape[1]) as m:
with hcl.for_(0, in_bw) as bit:
# with hcl.if_(in_train[i][m][bit] == 1):
# ###########
# prototypeCounter[trainLabels[i]][m*in_bw+bit] += 1
# prototypeCounter[pred][m*in_bw+bit] -= 1
prototypeCounter[trainLabels[i]][m*in_bw+bit] += in_train[i][m][bit]
prototypeCounter[pred][m*in_bw+bit] -= in_train[i][m][bit]
with hcl.if_(max[trainLabels[i]] % 2 == 0):
with hcl.if_(prototypeCounter[trainLabels[i]][m*in_bw+bit] - max[trainLabels[i]]/2 == 0):
prototype[trainLabels[i]][m][bit] &= 1
with hcl.else_():
prototype[trainLabels[i]][m][bit] = hcl.select(prototypeCounter[trainLabels[i]][m*in_bw+bit] - max[trainLabels[i]]/2 > 0, 1, 0)
with hcl.if_(max[pred] % 2 == 0):
with hcl.if_(prototypeCounter[pred][m*in_bw+bit] - max[pred]/2 == 0):
prototype[pred][m][bit] &= 1
with hcl.else_():
prototype[pred][m][bit] = hcl.select(prototypeCounter[pred][m*in_bw+bit] - max[pred]/2 > 0, 1, 0)
#print the accuracy
hcl.mutate((1,), lambda x: test_hdc_accu(prototype, in_train, trainLabels, 1), 'training_update')
hcl.mutate((1,), lambda x: test_hdc_accu(prototype, in_test, testLabels, 2), 'testing_update')
def knn_vote(labels, max_label):
max_vote = hcl.scalar(0)
votes = hcl.compute((10, ), lambda x: 0, "votes")
with hcl.for_(0, K_CONST) as i:
votes[labels[i]] += 1
with hcl.for_(0, 10) as i:
with hcl.if_(votes[i] > max_vote.v):
max_vote.v = votes[i]
max_label[0] = i
def test_hdc_accu(proto, pack_data, labels, type):
#pack the prototype
pack_proto = hcl.pack(proto,
axis=1,
dtype=hcl.UInt(bw),
name="pack_proto")
###data preparation
distance1 = hcl.compute((pack_data.shape[1], ),
lambda x: 0,
'distance1',
dtype=hcl.UInt(bw))
pre_hamming = hcl.compute((pack_data.shape[1], ), lambda x: 0,
"pre_hamming")
hamming_dist1 = hcl.compute((numClasses, ), lambda x: 0,
"hamming_dist1")
m1 = hcl.reduce_axis(0, pack_data.shape[1], "m1")
correct1 = hcl.scalar(0, 'correct1')
###
with hcl.for_(0, pack_data.shape[0]) as i:
hcl.print((i), "%d suc\n")
with hcl.for_(0, numClasses) as n:
#Do hdc multiplication(XOR) on sample[i]'s hdv and prototype[n]'s hdv (elementwise on the high-bit data)
hcl.update(distance1,
lambda x: pack_data[i][x] ^ pack_proto[n][x])
#Calculate the hamming distance of the two vectors by adding 1s
hcl.update(pre_hamming, lambda x: popcount(distance1[x]))
hcl.print((), "sum of 1s suc")
###########################seg fault
hamming_dist1[n] = hcl.sum(pre_hamming[m1], axis=m1)
#Find the one having the least hamming distance and choose it's label as the predicted label
pred1 = hcl.scalar(0, 'pred1')
with hcl.for_(0, hamming_dist1.shape[0]) as j:
with hcl.if_(hamming_dist1[j] < hamming_dist1[pred1]):
pred1.v = j
with hcl.if_(pred1.v == labels[i]):
correct1.v += 1
#Print the accuracy
all1 = hcl.scalar(pack_data.shape[0], "all1", dtype=hcl.Float(32))
accuracy1 = hcl.compute((1, ),
lambda x: correct1.v / all1.v * 100,
"accuracy1",
dtype=hcl.Float(32))
with hcl.if_(type == 1):
hcl.print((correct1, pack_data.shape[0], accuracy1[0]),
"Training accu: %d/%d (%.2f%%)\n")
with hcl.else_():
hcl.print((correct1, pack_data.shape[0], accuracy1[0]),
"Testing accu: %d/%d (%.2f%%)\n")
def updateVopt(i, j, k, l, m, n, o, iVals, sVals, actions, Vopt, intermeds,
trans, interpV, gamma, bounds, goal, ptsEachDim, useNN):
p = hcl.scalar(0, "p")
with hcl.for_(0, actions.shape[0], name="a") as a:
# set iVals equal to (i,j,k,l,m,n,o) and sVals equal to the corresponding state values (si,sj,sk,sl,sm,sn,so)
updateStateVals(i, j, k, l, m, n, o, iVals, sVals, bounds, ptsEachDim)
# call the transition function to obtain the outcome(s) of action a from state (si,sj,sk,sl,sm,sn,so)
UD.transition(sVals, actions[a], bounds, trans, goal)
# initialize the value of the action Q value with the immediate reward of taking that action
intermeds[a] = UD.reward(sVals, actions[a], bounds, goal, trans)
# add the value of each possible successor state to the Q value
with hcl.for_(0, trans.shape[0], name="si") as si:
p[0] = trans[si, 0]
sVals[0] = trans[si, 1]
sVals[1] = trans[si, 2]
sVals[2] = trans[si, 3]
sVals[3] = trans[si, 4]
sVals[4] = trans[si, 5]
sVals[5] = trans[si, 6]
sVals[6] = trans[si, 7]
# Nearest neighbour
with hcl.if_(useNN[0] == 1):
# convert the state values of the successor state (si,sj,sk,sl,sm,sn,so) into indeces (ia,ja,ka,la,ma,na,oa)
stateToIndex(sVals, iVals, bounds, ptsEachDim)
# if (ia,ja,ka,la,ma,na,oa) is within the state space, add its discounted value to the Q value
with hcl.if_(
hcl.and_(iVals[0] < Vopt.shape[0],
iVals[1] < Vopt.shape[1],
iVals[2] < Vopt.shape[2])):
with hcl.if_(
hcl.and_(iVals[3] < Vopt.shape[3],
iVals[4] < Vopt.shape[4],
iVals[5] < Vopt.shape[5],
iVals[6] < Vopt.shape[6])):
with hcl.if_(
hcl.and_(iVals[0] >= 0, iVals[1] >= 0,
iVals[2] >= 0, iVals[3] >= 0,
iVals[4] >= 0, iVals[5] >= 0,
iVals[6] >= 0)):
intermeds[a] += (
gamma[0] *
(p[0] *
Vopt[iVals[0], iVals[1], iVals[2], iVals[3],
iVals[4], iVals[5], iVals[6]]))
# maximize over each Q value to obtain the optimal value
Vopt[i, j, k, l, m, n, o] = -1000000
with hcl.for_(0, intermeds.shape[0], name="r") as r:
with hcl.if_(Vopt[i, j, k, l, m, n, o] < intermeds[r]):
Vopt[i, j, k, l, m, n, o] = intermeds[r]
def kernel(matrix_1, matrix_2):
return_matrix = hcl.compute((m,k), lambda x, y: matrix_1[x,y] + matrix_2[x,y], "return_matrix")
with hcl.for_(0, 7, name="for_loop") as f:
hcl.assert_(matrix_2[f,2] == 0, "assert message in the first for loop") #assert true
hcl.print(0, "in the first for loop\n") #should be printed
with hcl.for_(0, 7, name="for_loop") as f:
hcl.assert_(matrix_2[f,2] != 0, "assert message in the second for loop") #assert false
hcl.print(0, "in the second for loop\n") #should not be printed
hcl.print(0, "this should not be printed\n") #should not be printed
return return_matrix
def updateVopt(obj, i, j, k, iVals, sVals, actions, Vopt, intermeds, trans,
interpV, gamma, bounds, goal, ptsEachDim, useNN, fillVal):
p = hcl.scalar(0, "p")
with hcl.for_(0, actions.shape[0], name="a") as a:
# set iVals equal to (i,j,k) and sVals equal to the corresponding state values (si,sj,sk)
updateStateVals(i, j, k, iVals, sVals, bounds, ptsEachDim)
# call the transition function to obtain the outcome(s) of action a from state (si,sj,sk)
obj.transition(sVals, actions[a], bounds, trans, goal)
# initialize the value of the action using the immediate reward of taking that action
intermeds[a] = obj.reward(sVals, actions[a], bounds, goal, trans)
Vopt[i, j, k] = intermeds[a]
# add the value of each possible successor state to the estimated value of taking action a
with hcl.for_(0, trans.shape[0], name="si") as si:
p[0] = trans[si, 0]
sVals[0] = trans[si, 1]
sVals[1] = trans[si, 2]
sVals[2] = trans[si, 3]
# Nearest neighbour
with hcl.if_(useNN[0] == 1):
# convert the state values of the successor state (si,sj,sk) into indeces (ia,ij,ik)
stateToIndex(sVals, iVals, bounds, ptsEachDim)
# if (ia, ij, ik) is within the state space, add its discounted value to action a
with hcl.if_(
hcl.and_(iVals[0] < Vopt.shape[0],
iVals[1] < Vopt.shape[1],
iVals[2] < Vopt.shape[2])):
with hcl.if_(
hcl.and_(iVals[0] >= 0, iVals[1] >= 0,
iVals[2] >= 0)):
intermeds[a] += (
gamma[0] *
(p[0] * Vopt[iVals[0], iVals[1], iVals[2]]))
# Linear interpolation
with hcl.if_(useNN[0] == 0):
# if (sia, sja, ska) is within the state space, add its discounted value to action a
with hcl.if_(
hcl.and_(sVals[0] <= bounds[0, 1],
sVals[1] <= bounds[1, 1],
sVals[2] <= bounds[2, 1])):
with hcl.if_(
hcl.and_(sVals[0] >= bounds[0, 0],
sVals[1] >= bounds[1, 0],
sVals[2] >= bounds[2, 0])):
stateToIndexInterpolants(Vopt, sVals, bounds,
ptsEachDim, interpV, fillVal)
intermeds[a] += (gamma[0] * (p[0] * interpV[0]))
# maximize over each possible action in intermeds to obtain the optimal value
with hcl.for_(0, intermeds.shape[0], name="r") as r:
with hcl.if_(Vopt[i, j, k] < intermeds[r]):
Vopt[i, j, k] = intermeds[r]
def updateQopt(i, j, k, a, iVals, sVals, Qopt, actions, intermeds, trans,
interpV, gamma, bounds, goal, ptsEachDim, useNN, fillVal):
r = hcl.scalar(0, "r")
p = hcl.scalar(0, "p")
# set iVals equal to (i,j,k) and sVals equal to the corresponding state values at (i,j,k)
updateStateVals(i, j, k, iVals, sVals, bounds, ptsEachDim)
# call the transition function to obtain the outcome(s) of action a from state (si,sj,sk)
transition(sVals, actions[a], bounds, trans, goal)
# initialize Qopt[i,j,k,a] with the immediate reward
r[0] = reward(sVals, actions[a], bounds, goal, trans)
Qopt[i, j, k, a] = r[0]
# maximize over successor Q-values
with hcl.for_(0, trans.shape[0], name="si") as si:
p[0] = trans[si, 0]
sVals[0] = trans[si, 1]
sVals[1] = trans[si, 2]
sVals[2] = trans[si, 3]
# Nearest neighbour
with hcl.if_(useNN[0] == 1):
# obtain the nearest neighbour successor state
stateToIndex(sVals, iVals, bounds, ptsEachDim)
# maximize over successor state Q-values
with hcl.if_(
hcl.and_(iVals[0] < Qopt.shape[0],
iVals[1] < Qopt.shape[1],
iVals[2] < Qopt.shape[2])):
with hcl.if_(
hcl.and_(iVals[0] >= 0, iVals[1] >= 0, iVals[2] >= 0)):
with hcl.for_(0, actions.shape[0], name="a_") as a_:
with hcl.if_(
(r[0] +
(gamma[0] *
(p[0] * Qopt[iVals[0], iVals[1], iVals[2], a_]))
) > Qopt[i, j, k, a]):
Qopt[i, j, k, a] = r[0] + (gamma[0] * (
p[0] * Qopt[iVals[0], iVals[1], iVals[2], a_]))
# Linear interpolation
with hcl.if_(useNN[0] == 0):
with hcl.if_(
hcl.and_(sVals[0] <= bounds[0, 1],
sVals[1] <= bounds[1, 1],
sVals[2] <= bounds[2, 1])):
with hcl.if_(
hcl.and_(sVals[0] >= bounds[0, 0],
sVals[1] >= bounds[1, 0],
sVals[2] >= bounds[2, 0])):
stateToIndexInterpolants(Qopt, sVals, actions, bounds,
ptsEachDim, interpV, fillVal)
Qopt[i, j, k, a] += (gamma[0] * (p[0] * interpV[0]))
r[0] += Qopt[i, j, k, a]
def kernel_digit_rec(training_set, test_set, result):
with hcl.for_(0, NUM_TEST) as t:
dists = hcl.compute((K_CONST, ), lambda x: 0, "dists")
labels = hcl.compute((K_CONST, ), lambda x: 0, "labels")
test = get_data(test_set, t)
with hcl.for_(0, NUM_TRAINING) as i:
training = get_data(training_set, i)
label = hcl.compute((1, ), lambda x: 0, "label")
update_knn(training, test, dists, labels, label)
max_label = hcl.compute((1, ), lambda x: 0, "max_label")
knn_vote(labels, max_label)
def genpack(nn, cc, hh, ww):
out = hcl.scalar(0, name=name + "_pack", dtype=hcl.UInt(bitwidth))
with hcl.for_(0, bitwidth) as k:
out[0][(k + 1):k] = hcl.select(
data[nn, cc * bitwidth + k, hh, ww] + alpha[cc * bitwidth + k]
> 0, 1, 0)
return out[0]
def mul(A, B, x):
temp = hcl.scalar(0)
with hcl.for_(0, x) as i:
hcl.assert_(x < 5, "assert in for")
temp[0] += add(A, B, x)
hcl.print(0, "in for\n")
hcl.return_(temp[0])
def kernel(matrix_1, matrix_2):
first_matrix = hcl.compute((m, k), lambda x, y : matrix_1[x, y] + matrix_2[x, y], "first_matrix")
return_matrix = hcl.compute((m, k), lambda x, y : matrix_1[x, y] + matrix_2[x, y] + 7, "return_matrix")
ax = hcl.scalar(0)
with hcl.while_(ax.v < 3):
matrix_A = hcl.compute((m, k), lambda x, y : matrix_1[x, y] + matrix_2[x, y] + 7, "matrix_A")
with hcl.for_(0, 2, name="for_loop_in") as h:
matrix_B = hcl.compute((m, k), lambda x, y : matrix_1[x, y] + matrix_2[x, y] + 8, "matrix_B")
with hcl.if_(matrix_1[0, 2] >= 0):
matrix_C = hcl.compute((m, k), lambda x, y : matrix_1[x, x] + matrix_2[x, x] + 9, "matrix_C")
hcl.assert_(matrix_1[0, 0]> 0, "assert message in the if statement %d", matrix_C[0, 0])
matrix_D = hcl.compute((m, k), lambda x, y : matrix_1[x, x] + matrix_2[x, x] + 9, "matrix_D")
hcl.print(0, "in if statement\n")
hcl.assert_(matrix_1[0, 0]> 1, "assert message for loop")
matrix_F = hcl.compute((m, k), lambda x, y : matrix_1[x, y] + matrix_2[x, y] + 8, "matrix_F")
hcl.print(0, "in for loop\n")
hcl.assert_(matrix_1[0, 0]> 2, "assert error, matrix_A[1, 1]: %d matrix_A[2, 1]: %d matrix_A[3, 1]: %d", [matrix_A[1, 1], matrix_A[2, 1], matrix_A[3, 1]])
hcl.print(0, "in the while loop\n")
ax.v = ax.v + 1
hcl.assert_(matrix_1[0, 0]> 3, "assert message end")
matrix_E = hcl.compute((m, k), lambda x, y : matrix_1[x, y] + matrix_2[x, y] + 10, "matrix_E")
hcl.print(0, "this should not be printed\n")
return return_matrix
def kernel(A, B, C, O):
dtype_xyz = hcl.Struct({
"x": hcl.Int(),
"y": hcl.Int(),
"z": hcl.Int()
})
dtype_out = hcl.Struct({
"v0": hcl.Int(),
"v1": hcl.Int(),
"v2": hcl.Int(),
"v3": hcl.Int(),
"v4": hcl.Int(),
"v5": hcl.Int()
})
D = hcl.compute(A.shape, lambda x: (A[x], B[x], C[x]), dtype=dtype_xyz)
E = hcl.compute(A.shape,
lambda x:
(D[x].x * D[x].x, D[x].y * D[x].y, D[x].z * D[x].z, D[
x].x * D[x].y, D[x].y * D[x].z, D[x].x * D[x].z),
dtype=dtype_out)
with hcl.Stage():
with hcl.for_(0, 100) as i:
for j in range(0, 6):
O[i][j] = E[i].__getattr__("v" + str(j))
def kernel(A, B, C):
with hcl.Stage("S"):
with hcl.for_(0, 10) as i:
# set the LSB of B to be the same as A
B[i][0] = A[i][0]
# set the lower 4-bit of C
C[i][4:0] = A[i]
def update_knn(dist, knn_mat, i, j):
max_id = hcl.local(0, "max_id")
with hcl.for_(0, 3) as k:
with hcl.if_(knn_mat[i][k] > knn_mat[i][max_id[0]]):
max_id[0] = k
with hcl.if_(dist[i][j] < knn_mat[i][max_id[0]]):
knn_mat[i][max_id[0]] = dist[i][j]
def update_knn(dist, knn_mat, i, j):
max_id = hcl.scalar(0, "max_id")
with hcl.for_(0, 3) as k:
with hcl.if_(knn_mat[i][k] > knn_mat[i][max_id.v]):
max_id.v = k
with hcl.if_(dist[i][j] < knn_mat[i][max_id.v]):
knn_mat[i][max_id.v] = dist[i][j]
def add(a, b, c):
with hcl.for_(0, 10) as i:
a[i] = 0
hcl.assert_(i < 10, "assert error 1")
d = hcl.compute(a.shape, lambda *x: a[x] + b[x])
hcl.assert_(a[0] == 0, "assert error 2")
hcl.update(c, lambda *x: d[x] + 1)
hcl.assert_(a[0] == 0, "assert error 3")
def simple_compute(a, A):
B = hcl.compute(A.shape, lambda x, y: A[x, y], "B")
index = 2
with hcl.for_(0, A.shape[0], name="i") as i:
with hcl.for_(0, A.shape[1], name="j") as j:
ind = np.array([index, j])
with hcl.if_(A[ind] > 10):
ind = ind + np.array([0, 1])
ind = ind % np.array([3, 3])
ind = tuple(ind)
B[i, j] = B[i, j] - 1000
with hcl.else_():
ind = ind - np.array([0, 1])
ind = ind % np.array([3, 3])
ind = tuple(ind)
B[i, j] = B[i, j] + 1000
return B
def algorithm(A, B):
@hcl.def_([A.shape, B.shape, ()])
def update_B(A, B, x):
B[x] = A[x] + 1
with hcl.Stage():
with hcl.for_(0, 10) as i:
update_B(A, B, i)
def find_max(A, len_):
max_ = hcl.local(A[0], "max")
act_ = hcl.local(0, "act")
with hcl.for_(0, len_) as i:
with hcl.if_(A[i] > max_[0]):
max_[0] = A[i]
act_[0] = i
return max_[0], act_[0]
