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test_schedule_memory.py
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test_schedule_memory.py
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import heterocl as hcl
import numpy as np
def test_reuse_blur_x():
hcl.init()
A = hcl.placeholder((10, 10))
B = hcl.compute((10, 8), lambda y, x: A[y, x] + A[y, x+1] + A[y, x+2])
s = hcl.create_schedule([A, B])
RB = s.reuse_at(A, s[B], B.axis[1])
f = hcl.build(s)
np_A = np.random.randint(0, 10, size=(10, 10))
np_B = np.zeros((10, 8), dtype="int")
np_C = np.zeros((10, 8), dtype="int")
for y in range(0, 10):
for x in range(0, 8):
np_C[y][x] = np_A[y][x] + np_A[y][x+1] + np_A[y][x+2]
hcl_A = hcl.asarray(np_A)
hcl_B = hcl.asarray(np_B)
f(hcl_A, hcl_B)
np_B = hcl_B.asnumpy()
assert np.array_equal(np_B, np_C)
def test_reuse_blur_x_tensor():
hcl.init()
A = hcl.placeholder((10, 10))
X = hcl.compute((10, 10), lambda y, x: A[y, x])
B = hcl.compute((10, 8), lambda y, x: X[y, x] + X[y, x+1] + X[y, x+2])
s = hcl.create_schedule([A, B])
RB = s.reuse_at(X, s[B], B.axis[1])
f = hcl.build(s)
np_A = np.random.randint(0, 10, size=(10, 10))
np_B = np.zeros((10, 8), dtype="int")
np_C = np.zeros((10, 8), dtype="int")
for y in range(0, 10):
for x in range(0, 8):
np_C[y][x] = np_A[y][x] + np_A[y][x+1] + np_A[y][x+2]
hcl_A = hcl.asarray(np_A)
hcl_B = hcl.asarray(np_B)
f(hcl_A, hcl_B)
np_B = hcl_B.asnumpy()
assert np.array_equal(np_B, np_C)
def test_reuse_blur_y():
hcl.init()
A = hcl.placeholder((10, 10))
B = hcl.compute((8, 10), lambda y, x: A[y, x] + A[y+1, x] + A[y+2, x])
s = hcl.create_schedule([A, B])
RB = s.reuse_at(A, s[B], B.axis[0])
f = hcl.build(s)
np_A = np.random.randint(0, 10, size=(10, 10))
np_B = np.zeros((8, 10), dtype="int")
np_C = np.zeros((8, 10), dtype="int")
for y in range(0, 8):
for x in range(0, 10):
np_C[y][x] = np_A[y][x] + np_A[y+1][x] + np_A[y+2][x]
hcl_A = hcl.asarray(np_A)
hcl_B = hcl.asarray(np_B)
f(hcl_A, hcl_B)
np_B = hcl_B.asnumpy()
assert np.array_equal(np_B, np_C)
def test_reuse_blur_x_y():
hcl.init()
A = hcl.placeholder((10, 10), "A")
B = hcl.compute((8, 8), lambda y, x: A[y, x] + A[y+1, x+1] + A[y+2, x+2], "B")
s = hcl.create_schedule([A, B])
RB_y = s.reuse_at(A, s[B], B.axis[0], "RB_y")
RB_x = s.reuse_at(RB_y, s[B], B.axis[1], "RB_x")
f = hcl.build(s)
np_A = np.random.randint(0, 10, size=(10, 10))
np_B = np.zeros((8, 8), dtype="int")
np_C = np.zeros((8, 8), dtype="int")
for y in range(0, 8):
for x in range(0, 8):
np_C[y][x] = np_A[y][x] + np_A[y+1][x+1] + np_A[y+2][x+2]
hcl_A = hcl.asarray(np_A)
hcl_B = hcl.asarray(np_B)
f(hcl_A, hcl_B)
np_B = hcl_B.asnumpy()
assert np.array_equal(np_B, np_C)
def test_reuse_blur_x_3D():
hcl.init()
A = hcl.placeholder((10, 10, 2))
B = hcl.compute((10, 8, 2), lambda y, x, c: A[y, x, c] + A[y, x+1, c] + A[y, x+2, c])
s = hcl.create_schedule([A, B])
RB = s.reuse_at(A, s[B], B.axis[1])
f = hcl.build(s)
np_A = np.random.randint(0, 10, size=(10, 10, 2))
np_B = np.zeros((10, 8, 2), dtype="int")
np_C = np.zeros((10, 8, 2), dtype="int")
for y in range(0, 10):
for x in range(0, 8):
for c in range(0, 2):
np_C[y][x][c] = np_A[y][x][c] + np_A[y][x+1][c] + np_A[y][x+2][c]
hcl_A = hcl.asarray(np_A)
hcl_B = hcl.asarray(np_B)
f(hcl_A, hcl_B)
np_B = hcl_B.asnumpy()
assert np.array_equal(np_B, np_C)
def test_reuse_blur_y_3D():
hcl.init()
A = hcl.placeholder((10, 10, 2))
B = hcl.compute((8, 10, 2), lambda y, x, c: A[y, x, c] + A[y+1, x, c] + A[y+2, x, c])
s = hcl.create_schedule([A, B])
RB = s.reuse_at(A, s[B], B.axis[0])
f = hcl.build(s)
np_A = np.random.randint(0, 10, size=(10, 10, 2))
np_B = np.zeros((8, 10, 2), dtype="int")
np_C = np.zeros((8, 10, 2), dtype="int")
for y in range(0, 8):
for x in range(0, 10):
for c in range(0, 2):
np_C[y][x][c] = np_A[y][x][c] + np_A[y+1][x][c] + np_A[y+2][x][c]
hcl_A = hcl.asarray(np_A)
hcl_B = hcl.asarray(np_B)
f(hcl_A, hcl_B)
np_B = hcl_B.asnumpy()
assert np.array_equal(np_B, np_C)
def test_reuse_blur_x_y_3D():
hcl.init()
A = hcl.placeholder((10, 10, 2), "A")
B = hcl.compute((8, 8, 2), lambda y, x, c: A[y, x, c] + A[y+1, x+1, c] + A[y+2, x+2, c], "B")
s = hcl.create_schedule([A, B])
RB_y = s.reuse_at(A, s[B], B.axis[0], "RB_y")
RB_x = s.reuse_at(RB_y, s[B], B.axis[1], "RB_x")
f = hcl.build(s)
np_A = np.random.randint(0, 10, size=(10, 10, 2))
np_B = np.zeros((8, 8, 2), dtype="int")
np_C = np.zeros((8, 8, 2), dtype="int")
for y in range(0, 8):
for x in range(0, 8):
for c in range(0, 2):
np_C[y][x][c] = np_A[y][x][c] + np_A[y+1][x+1][c] + np_A[y+2][x+2][c]
hcl_A = hcl.asarray(np_A)
hcl_B = hcl.asarray(np_B)
f(hcl_A, hcl_B)
np_B = hcl_B.asnumpy()
assert np.array_equal(np_B, np_C)
def test_reuse_blur_x_y_z_3D():
hcl.init()
A = hcl.placeholder((10, 8, 6), "A")
B = hcl.compute((8, 6, 4), lambda y, x, z: A[y, x, z] + A[y+1, x+1, z+1] + A[y+2, x+2, z+2], "B")
s = hcl.create_schedule([A, B])
RB_y = s.reuse_at(A, s[B], B.axis[0], "RB_y")
RB_x = s.reuse_at(RB_y, s[B], B.axis[1], "RB_x")
RB_z = s.reuse_at(RB_x, s[B], B.axis[2], "RB_z")
f = hcl.build(s)
np_A = np.random.randint(0, 10, size=(10, 8, 6))
np_B = np.zeros((8, 6, 4), dtype="int")
np_C = np.zeros((8, 6, 4), dtype="int")
for y in range(0, 8):
for x in range(0, 6):
for z in range(0, 4):
np_C[y][x][z] = np_A[y][x][z] + np_A[y+1][x+1][z+1] + np_A[y+2][x+2][z+2]
hcl_A = hcl.asarray(np_A)
hcl_B = hcl.asarray(np_B)
f(hcl_A, hcl_B)
np_B = hcl_B.asnumpy()
assert np.array_equal(np_B, np_C)
def test_conv2D_lb():
hcl.init()
A = hcl.placeholder((10, 10))
r = hcl.reduce_axis(0, 3)
c = hcl.reduce_axis(0, 3)
B = hcl.compute((8, 8), lambda y, x: hcl.sum(A[y+r, x+c], axis=[r, c]))
s = hcl.create_schedule([A, B])
LB = s.reuse_at(A, s[B], B.axis[0])
f = hcl.build(s)
np_A = np.random.randint(0, 10, size=(10, 10))
np_B = np.zeros((8, 8), dtype="int")
np_C = np.zeros((8, 8), dtype="int")
for y in range(0, 8):
for x in range(0, 8):
for r in range(0, 3):
for c in range(0, 3):
np_C[y][x] += np_A[y+r][x+c]
hcl_A = hcl.asarray(np_A)
hcl_B = hcl.asarray(np_B)
f(hcl_A, hcl_B)
np_B = hcl_B.asnumpy()
assert np.array_equal(np_B, np_C)
def test_conv2D_wb():
hcl.init()
A = hcl.placeholder((10, 10))
r = hcl.reduce_axis(0, 3)
c = hcl.reduce_axis(0, 3)
B = hcl.compute((8, 8), lambda y, x: hcl.sum(A[y+r, x+c], axis=[r, c]))
s = hcl.create_schedule([A, B])
WB = s.reuse_at(A, s[B], B.axis[1])
f = hcl.build(s)
np_A = np.random.randint(0, 10, size=(10, 10))
np_B = np.zeros((8, 8), dtype="int")
np_C = np.zeros((8, 8), dtype="int")
for y in range(0, 8):
for x in range(0, 8):
for r in range(0, 3):
for c in range(0, 3):
np_C[y][x] += np_A[y+r][x+c]
hcl_A = hcl.asarray(np_A)
hcl_B = hcl.asarray(np_B)
f(hcl_A, hcl_B)
np_B = hcl_B.asnumpy()
assert np.array_equal(np_B, np_C)
def test_conv2D_lb_wb():
hcl.init()
A = hcl.placeholder((10, 10))
r = hcl.reduce_axis(0, 3)
c = hcl.reduce_axis(0, 3)
B = hcl.compute((8, 8), lambda y, x: hcl.sum(A[y+r, x+c], axis=[r, c]))
s = hcl.create_schedule([A, B])
LB = s.reuse_at(A, s[B], B.axis[0])
WB = s.reuse_at(LB, s[B], B.axis[1])
f = hcl.build(s)
np_A = np.random.randint(0, 10, size=(10, 10))
np_B = np.zeros((8, 8), dtype="int")
np_C = np.zeros((8, 8), dtype="int")
for y in range(0, 8):
for x in range(0, 8):
for r in range(0, 3):
for c in range(0, 3):
np_C[y][x] += np_A[y+r][x+c]
hcl_A = hcl.asarray(np_A)
hcl_B = hcl.asarray(np_B)
f(hcl_A, hcl_B)
np_B = hcl_B.asnumpy()
assert np.array_equal(np_B, np_C)
def test_partition_basic():
hcl.init()
A = hcl.placeholder((10, 10), "A")
B = hcl.compute(A.shape, lambda x, y: A[x, y], "B")
s = hcl.create_schedule([A, B])
s.partition(A)
ir = str(hcl.lower(s))
assert "partition variable=A" in ir
def test_partition_type():
hcl.init()
A = hcl.placeholder((10, 10), "A")
B = hcl.compute(A.shape, lambda x, y: A[x, y], "B")
s1 = hcl.create_schedule([A, B])
s1.partition(A)
ir = str(hcl.lower(s1))
assert "partition variable=A complete" in ir
s1 = hcl.create_schedule([A, B])
s1.partition(A, hcl.Partition.Block)
ir = str(hcl.lower(s1))
assert "partition variable=A block" in ir
s1 = hcl.create_schedule([A, B])
s1.partition(A, hcl.Partition.Cyclic)
ir = str(hcl.lower(s1))
assert "partition variable=A cyclic" in ir
def test_partition_dim_factor():
hcl.init()
A = hcl.placeholder((10, 10), "A")
B = hcl.compute(A.shape, lambda x, y: A[x, y], "B")
s = hcl.create_schedule([A, B])
s.partition(A, dim=1, factor=2)
ir = str(hcl.lower(s))
assert "partition variable=A complete factor=2 dim=1" in ir
def test_reshape():
hcl.init()
A = hcl.placeholder((10, 10), "A")
B = hcl.compute(A.shape, lambda x, y: A[x, y], "B")
C = hcl.compute(A.shape, lambda x, y: B[x, y], "C")
s = hcl.create_schedule([A, C])
s.reshape(B, (2, 5, 2, 5))
ir = str(hcl.lower(s))
assert "allocate B[int32 * 2 * 5 * 2 * 5]" in ir
def test_conv2D_lb_wb_schedule():
hcl.init()
A = hcl.placeholder((10, 10))
r = hcl.reduce_axis(0, 3)
c = hcl.reduce_axis(0, 3)
B = hcl.compute((8, 8), lambda y, x: hcl.sum(A[y+r, x+c], axis=[r, c]))
s = hcl.create_schedule([A, B])
xo, xi = s[B].split(B.axis[1], 4)
s[B].reorder(xo, B.axis[0], xi)
LB = s.reuse_at(A, s[B], B.axis[0])
WB = s.reuse_at(LB, s[B], xi)
s.partition(LB, dim=2)
s.partition(WB)
s.reshape(B, (8, 2, 4))
s[B].pipeline(B.axis[0])
f = hcl.build(s)
np_A = np.random.randint(0, 10, size=(10, 10))
np_B = np.zeros((8, 2, 4), dtype="int")
np_C = np.zeros((8, 2, 4), dtype="int")
for y in range(0, 8):
for xo in range(0, 2):
for xi in range(0, 4):
for r in range(0, 3):
for c in range(0, 3):
np_C[y][xo][xi] += np_A[y+r][xi+xo*4+c]
hcl_A = hcl.asarray(np_A)
hcl_B = hcl.asarray(np_B)
f(hcl_A, hcl_B)
np_B = hcl_B.asnumpy()
assert np.array_equal(np_B, np_C)