def test_extern_ops():
hcl.init()
A = hcl.placeholder((10, 32), "A")
def kernel(A):
B = hcl.compute(A.shape, lambda *args: A[args] + 1, "B")
C = hcl.compute(A.shape, lambda *args: B[args] + 1, "C")
D = hcl.compute(A.shape, lambda *args: C[args] * 2, "D")
return D
target = hcl.Platform.aws_f1
s = hcl.create_schedule([A], kernel)
s.to(kernel.B, target.xcel)
s.to(kernel.C, target.host)
code = str(hcl.lower(s))
print(code)
assert "test(B, C)" in code
def inner_loop_tile():
hcl.init()
A = hcl.placeholder((10, 32), "A")
def kernel(A):
C = hcl.compute(A.shape, lambda *args: A[args] * 4, "C")
return C
target = hcl.platform.aws_f1
s = hcl.create_schedule([A], kernel)
stage = kernel.C
yo, yi = s[stage].split(stage.axis[0], factor=3)
xo, xi = s[stage].split(stage.axis[1], factor=3)
s.to(kernel.C, target.xcel, axis=1)
code = str(hcl.lower(s))
assert "test(args.outer, C, A)" in code
def move_outputs():
hcl.init()
A = hcl.placeholder((10, 32), "A")
def kernel(A):
B = hcl.compute(A.shape, lambda i, j: A[i, j] * 2, "B")
hcl.update(B, lambda i, j: B[i, j] + 1, "update1")
hcl.update(B, lambda i, j: B[i, j] * 2, "update2")
return B
target = hcl.platform.aws_f1
s = hcl.create_schedule([A], kernel)
s.to(A, target.xcel)
s.to(kernel.update1.B, target.host)
code = str(hcl.lower(s))
assert "test(A.channel, B.update.channel)" in code
def inter_stage_fork():
hcl.init()
A = hcl.placeholder((10, 32), "A")
B = hcl.placeholder((10, 32), "B")
def kernel(A, B):
C = hcl.compute(A.shape, lambda i, j: A[i, j] + B[i, j], "C")
D = hcl.compute(C.shape, lambda i, j: C[i, j] + 1, "D")
E = hcl.compute(C.shape, lambda i, j: C[i, j] * 2, "E")
return D, E
target = hcl.Platform.aws_f1
s = hcl.create_schedule([A, B], kernel)
s.to(kernel.C, [kernel.D, kernel.E])
code = str(hcl.lower(s))
assert "allocate C.pipe.1[int32 * 10 * 32]" in code
assert "allocate C.pipe.2[int32 * 10 * 32]" in code
def test_inter_stage():
A = hcl.placeholder((10, 32), "A")
B = hcl.placeholder((10, 32), "B")
def kernel(A, B):
C = hcl.compute(A.shape,
lambda i, j: A[i][j] + B[i][j], "C")
D = hcl.compute(C.shape,
lambda i, j: C[i][j], "D")
return D
target = hcl.platform.aws_f1
s = hcl.create_schedule([A, B], kernel)
s.to(kernel.C, s[kernel.D])
code = str(hcl.lower(s))
assert "C.pipe1.write" in code
assert "C.pipe1.read" in code
def test_multiple_subgraph():
hcl.init()
A = hcl.placeholder((10, 32), "A")
B = hcl.placeholder((10, 32), "B")
def kernel(A, B):
C = hcl.compute(A.shape, lambda i, j: A[i,j] + 1, "C")
D = hcl.compute(C.shape, lambda i, j: B[i,j] + 1, "D")
return hcl.compute(C.shape, lambda i, j: C[i,j] + D[i,j], "E")
target = hcl.Platform.aws_f1
s = hcl.create_schedule([A, B], kernel)
s.to([A, B], target.xcel)
s.to([kernel.E], target.host)
code = str(hcl.lower(s))
assert "io attr: \"B\"" in code
assert "io attr: \"A\"" in code
assert "io attr: \"E\"" in code
def inter_stage_join():
hcl.init()
A = hcl.placeholder((10, 32), "A")
B = hcl.placeholder((10, 32), "B")
def kernel(A, B):
C = hcl.compute(A.shape, lambda i, j: 0, "C")
hcl.update(C, lambda i, j: A[i,j] + 1, "s1")
hcl.update(C, lambda i, j: B[i,j] * 2, "s2")
return hcl.compute(C.shape, lambda *args: C[args] + 3, "ret")
target = hcl.platform.aws_f1
s = hcl.create_schedule([A, B], kernel)
s.join([kernel.s1.C, kernel.s2.C], kernel.ret.C)
code = str(hcl.lower(s))
assert "C.pipe1.read" in code
assert "C.pipe2.write" in code
def test_reuse_at_with_streaming():
hcl.init()
A = hcl.placeholder((10, 10), name="A")
def kernel(A):
B = hcl.compute((10, 10), lambda y, x: A[y, x], "B")
C = hcl.compute(
(10, 8), lambda y, x: B[y, x] + B[y, x + 1] + B[y, x + 2], "C")
return C
s = hcl.create_schedule([A], kernel)
target = hcl.platform.zc706
target.config(compile="vivado_hls", mode="csim")
B_ = s.to(kernel.B, target.xcel)
RB = s.reuse_at(B_, s[kernel.C], kernel.C.axis[1])
s.to(kernel.C, target.host)
print(hcl.lower(s))
def get_relay_model(model,
shape={},
frontend='keras',
dtype=hcl.Float(),
in_params=None):
"""
Parameters
----------
model :
A machine learning framework model
shape : dict
The model's input shape
frontend : str
The machine learning framework the model comes from
dtype : heterocl type
The model's preferred data type
in_params :
The input parameters of the model if not included in the model
"""
out_var, out_type, out_env, params = relay_parser(model, shape, frontend)
out_var = full_flatten(out_var)
_param = gen_params(out_type, out_env)
v_param = [holder for holder in _param if ("_param" in holder.name)]
v_input = [holder for holder in _param if ("input" in holder.name)]
v_param.sort(
key=lambda x: int(''.join(filter(lambda i: i.isdigit(), x.name))))
v_input.sort(
key=lambda x: int(''.join(filter(lambda i: i.isdigit(), x.name))))
_param = partial_flatten([v_input, v_param])
func = gen_func(_param, out_var, out_type, out_env)
_inputs = []
if params is None:
params = in_params
for var in params:
_inputs.append(hcl.asarray(params[var].asnumpy()))
s = hcl.create_schedule(_param, func)
if debug_mode:
print(hcl.lower(s))
return hcl.build(s), _inputs
def test_merge_kernel_stages():
hcl.init()
A = hcl.placeholder((10, 32), "A")
B = hcl.placeholder((10, 32), "B")
def kernel(A, B):
C = hcl.compute(A.shape, lambda i, j: 0, "C")
hcl.update(C, lambda i, j: A[i, j] + 1, "s1")
hcl.update(C, lambda i, j: B[i, j] * 2, "s2")
return hcl.compute(C.shape, lambda *args: C[args] + 3, "ret")
target = hcl.platform.aws_f1
s = hcl.create_schedule([A, B], kernel)
A_, B_ = s.to([A, B], target.xcel)
ret_ = s.to(kernel.ret, target.host)
kernel = s.duplicate(inputs=[A_, B_], outputs=[ret_])
print(hcl.lower(s))
def test_compute_at_blur_x_with_data_placement():
hcl.init()
A = hcl.placeholder((10, 10), name="A")
def kernel(A):
B = hcl.compute((10, 8), lambda y, x: A[y, x] + A[y, x+1] + A[y, x+2],name="B")
C = hcl.compute((10, 8), lambda y, x: B[y, x], name="C")
D = hcl.compute((10, 8), lambda y, x: C[y, x], name="D")
return D
s = hcl.create_schedule([A], kernel)
target = hcl.Platform.xilinx_zc706
target.config(compiler="vivado_hls",mode="csim")
s[kernel.B].compute_at(s[kernel.C], kernel.C.axis[1])
s.to(kernel.C, target.xcel)
s.to(kernel.D, target.host)
code = str(hcl.lower(s))
assert "test(D, C)" in code
def test_reuse_blur_x_with_data_placement():
hcl.init()
A = hcl.placeholder((10, 10), name="A")
def kernel(A):
B = hcl.compute((10, 8), lambda y, x: A[y,x] + A[y,x+1] + A[y,x+2],name="B")
C = hcl.compute((10, 8), lambda y, x: B[y,x], name="C")
return C
s = hcl.create_schedule([A], kernel)
kernel_B = kernel.B
target = hcl.Platform.xilinx_zc706
target.config(compiler="vivado_hls",mode="csim")
# RB = s.reuse_at(A, s[kernel_B], kernel_B.axis[1])
s.to(kernel.B, target.xcel)
s.to(kernel.C, target.host)
print(hcl.lower(s))
f = hcl.build(s, target)
def test_compute_at_with_reuse_2D():
hcl.init()
A = hcl.compute((10, 10), lambda y, x: x + y, "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([B])
s[A].compute_at(s[B], B.axis[1])
ir = hcl.lower(s)
assert "allocate A[int32 * 3 * 3]" in str(ir)
f = hcl.build(s)
a_np = np.fromfunction(lambda i, j: i + j, A.shape, dtype="int")
b_np = np.zeros(B.shape, dtype="int")
c_np = np.zeros(B.shape, dtype="int")
for y in range(0, 8):
for x in range(0, 8):
c_np[y][x] = a_np[y][x] + a_np[y+1][x+1] + a_np[y+2][x+2]
b_hcl = hcl.asarray(b_np)
f(b_hcl)
np.testing.assert_array_equal(c_np, b_hcl.asnumpy())
def test_mixed_stream():
A = hcl.placeholder((10, 32), "A")
B = hcl.placeholder((10, 32), "B")
def kernel(A, B):
C = hcl.compute(A.shape, lambda i, j: A[i][j] + B[i][j], "C")
D = hcl.compute(C.shape, lambda i, j: C[i][j], "D")
return D
target = hcl.Platform.aws_f1
s = hcl.create_schedule([A, B], kernel)
s.to([A, B], target.xcel)
s.to(kernel.D, target.host)
s.to(kernel.C, s[kernel.D])
code = str(hcl.lower(s))
assert "test(A, B, D)" in code
assert "allocate C.pipe.1[int32 * 10 * 32]" in code
def test_stages_one_to_many():
A = hcl.placeholder((10, 32), "A")
B = hcl.placeholder((10, 32), "B")
def kernel(A, B):
C = hcl.compute(A.shape, lambda i, j: A[i][j] + B[i][j], "C")
D = hcl.compute(C.shape, lambda i, j: C[i][j] + 1, "D")
E = hcl.compute(C.shape, lambda i, j: C[i][j] * 2, "E")
return D, E
target = hcl.Platform.aws_f1
s = hcl.create_schedule([A, B], kernel)
s.to(kernel.C, s[kernel.D])
s.to(kernel.C, s[kernel.E])
code = str(hcl.lower(s))
print(code)
assert "allocate C.pipe.1[int32 * 10 * 32]" in code
assert "allocate C.pipe.2[int32 * 10 * 32]" in code
def test_tutorial():
hcl.init()
A = hcl.placeholder((6, 6), "A")
F = hcl.placeholder((3, 3), "F")
def kernel(A, F):
r = hcl.reduce_axis(0, 3)
c = hcl.reduce_axis(0, 3)
return hcl.compute(
(4, 4),
lambda y, x: hcl.sum(A[y + r, x + c] * F[r, c], axis=[r, c]), "B")
# s = hcl.create_schedule([A, F], kernel)
# print(hcl.lower(s))
s_x = hcl.create_schedule([A, F], kernel)
WB = s_x.reuse_at(A, s_x[kernel.B], kernel.B.axis[1], "WB")
print(hcl.lower(s_x))
def test_move_inputs():
hcl.init()
A = hcl.placeholder((10, 32), "A")
B = hcl.placeholder((10, 32), "B")
C = hcl.placeholder((10, 32), "C")
D = hcl.compute(A.shape, lambda i, j: A[i][j] + B[i][j], "D")
E = hcl.compute(C.shape, lambda i, j: C[i][j] * D[i][j], "E")
F = hcl.compute(C.shape, lambda i, j: E[i][j] + 1, "F")
target = hcl.platform.aws_f1
s = hcl.create_schedule([A, B, C, D, E, F])
s.to([A, B, C], target.xcel)
s.to(E, target.host)
code = str(hcl.lower(s))
pattern = "test({}.channel, {}.channel, {}.channel, E.channel)"
combination = [
pattern.format(*_) for _ in list(permutations(["A", "B", "C"]))
]
assert any([_ in code for _ in combination])
def test_compute_at_complex_num_axis():
hcl.init()
A = hcl.placeholder((10, 20, 30), name="A")
B = hcl.compute(A.shape, lambda i, j, m: A[i, j, m] * 2, name="B")
C = hcl.compute(B.shape, lambda ii, jj, mm: B[ii, jj, mm] + 1, name="C")
D = hcl.compute(C.shape, lambda iii, jjj, mmm: C[iii, jjj, mmm] % 3, name="D")
s = hcl.create_schedule([A, D])
s[B].compute_at(s[C], 1)
s[C].compute_at(s[D], 2)
ir = hcl.lower(s)
assert "allocate B[int32 * 1 * 1 * 30]" in str(ir)
assert "allocate C[int32 * 1 * 1 * 1]" in str(ir)
f = hcl.build(s)
a_np = np.random.randint(low=0, high=100, size=A.shape)
a_hcl = hcl.asarray(a_np)
d_hcl = hcl.asarray(np.zeros(D.shape), dtype="int32")
f(a_hcl, d_hcl)
d_np = (a_np * 2 + 1) % 3
np.testing.assert_allclose(d_np, d_hcl.asnumpy())
def test_stencil_multi_stencil():
A = hcl.placeholder((10, 10), "A")
def kernel(A):
B = hcl.compute((10, 8),
lambda y, x: A[y, x] + A[y, x + 1] + A[y, x + 2], "B")
C = hcl.compute((8, 8),
lambda y, x: B[y, x] + B[y + 1, x] + B[y + 2, x], "C")
s = hcl.create_schedule(A, kernel)
s[kernel.B].stencil(burst_width=256, unroll_factor=4)
s[kernel.C].stencil(burst_width=128, unroll_factor=8)
ir = str(hcl.lower(s))
assert "stencil burst_width=256 unroll_factor=4 num_iteration=1" in ir
assert "inputs=[A]" in ir
assert "outputs=[B]" in ir
assert "stencil burst_width=128 unroll_factor=8 num_iteration=1" in ir
assert "inputs=[B]" in ir
assert "outputs=[C]" in ir
def test_dtype_struct():
hcl.init()
A = hcl.placeholder((100, ), dtype=hcl.Int(8))
B = hcl.placeholder((100, ), dtype=hcl.Fixed(13, 11))
C = hcl.placeholder((100, ), dtype=hcl.Float())
def kernel(A, B, C):
stype = hcl.Struct({
"fa": hcl.Int(8),
"fb": hcl.Fixed(13, 11),
"fc": hcl.Float()
})
D = hcl.compute(A.shape, lambda x: (A[x], B[x], C[x]), dtype=stype)
E = hcl.compute(A.shape, lambda x: D[x].fa, dtype=hcl.Int(8))
F = hcl.compute(A.shape, lambda x: D[x].fb, dtype=hcl.Fixed(13, 11))
G = hcl.compute(A.shape, lambda x: D[x].fc, dtype=hcl.Float())
# Check the data type
assert D[0].fa.dtype == "int8"
assert D[0].fb.dtype == "fixed13_11"
assert D[0].fc.dtype == "float32"
return E, F, G
s = hcl.create_schedule([A, B, C], kernel)
print(hcl.lower(s))
f = hcl.build(s)
np_A = np.random.randint(0, 500, size=100) - 250
np_B = np.random.rand(100) - 0.5
np_C = np.random.rand(100) - 0.5
np_E = np.zeros(100)
np_F = np.zeros(100)
np_G = np.zeros(100)
hcl_A = hcl.asarray(np_A, dtype=hcl.Int(8))
hcl_B = hcl.asarray(np_B, dtype=hcl.Fixed(13, 11))
hcl_C = hcl.asarray(np_C, dtype=hcl.Float())
hcl_E = hcl.asarray(np_E, dtype=hcl.Int(8))
hcl_F = hcl.asarray(np_F, dtype=hcl.Fixed(13, 11))
hcl_G = hcl.asarray(np_G, dtype=hcl.Float())
f(hcl_A, hcl_B, hcl_C, hcl_E, hcl_F, hcl_G)
assert np.allclose(hcl_A.asnumpy(), hcl_E.asnumpy())
assert np.allclose(hcl_B.asnumpy(), hcl_F.asnumpy())
assert np.allclose(hcl_C.asnumpy(), hcl_G.asnumpy())
def systolic(m=16, k=16, n=16, dtype=hcl.Int(), target=None):
hcl.init(dtype)
dim_x, dim_y = 16, 16
A = hcl.placeholder((m, k), dtype=dtype, name="A")
B = hcl.placeholder((k, n), dtype=dtype, name="B")
def kernel(A, B):
localA = hcl.compute((m, k - 1), lambda *args: 0, "localA")
localB = hcl.compute((k - 1, n), lambda *args: 0, "localB")
output = hcl.compute((m, n), lambda *args: 0, "output")
def update(k, y, x):
localA[y, x] = hcl.select(x > 0, localA[y, x - 1], A[y, k])
localB[y, x] = hcl.select(y > 0, localB[y - 1, x], B[k, x])
output[y, x] = hcl.select(
k == 0, 0, output[y, x]) + localA[y, x] * localB[y, x]
hcl.mutate((m, dim_y, dim_x),
lambda k, y, x: update(k, y, x),
name="update")
return output
s = hcl.create_schedule([A, B], kernel)
k = kernel.update
s[k].pipeline(k.axis[0])
# self loopback streaming
s.to(k.localA, kernel.update)
s.to(k.localB, kernel.update)
# move to xcel scope
if not host_only:
s.to([A, B], target.xcel)
s.to(k.output, target.host)
print(hcl.lower(s))
f = hcl.build(s, target=target)
return f
def test_one_stage_on_dev():
hcl.init()
dtype = hcl.Float()
M = 64
K = 64
N = 64
A = hcl.placeholder((M, K), "A", dtype=dtype)
B = hcl.placeholder((K, N), "B", dtype=dtype)
k = hcl.reduce_axis(0, K)
def kernel(A, B):
C = hcl.compute((M, N), lambda x, y: hcl.sum(A[x, k] * B[k, y], axis=k, dtype=dtype), "C", dtype=dtype)
return C
target = hcl.Platform.xilinx_zc706
target.config(compiler="vivado_hls", mode="csyn", project="gemm")
s = hcl.create_schedule([A, B], kernel)
s.to([A, B],target.xcel)
s.to(kernel.C,target.host)
print(hcl.lower(s))
def test_mixed_stream():
A = hcl.placeholder((10, 32), "A")
B = hcl.placeholder((10, 32), "B")
def kernel(A, B):
C = hcl.compute(A.shape, lambda i, j: A[i][j] + B[i][j], "C")
D = hcl.compute(C.shape, lambda i, j: C[i][j], "D")
return D
target = hcl.platform.aws_f1
s = hcl.create_schedule([A, B], kernel)
s.to([A, B], target.xcel)
s.to(kernel.D, target.host)
s.to(kernel.C, s[kernel.D])
code = str(hcl.lower(s))
pattern = "test({}.channel, {}.channel, D.channel)"
combination = [pattern.format(*_) for _ in list(permutations(["A", "B"]))]
cond = any([_ in code for _ in combination])
assert cond, code
assert "C.pipe1.write" in code
assert "C.pipe1.read" in code
def _test_sim(length):
hcl.init(hcl.Int())
A = hcl.placeholder((length, ), name="A")
B = hcl.placeholder((length, ), name="B")
def math_func(A, B):
res = hlib.ip.vadd_rtl(A, B, length)
return hcl.compute(A.shape, lambda *args: res[args] * 2, "out")
target = hcl.platform.aws_f1
s = hcl.create_schedule([A, B], math_func)
s.to([A, B], target.xcel)
s.to(math_func.out, target.host)
# test ir correctness
ir = str(hcl.lower(s))
pattern = "test({}.channel, {}.channel, out.channel)"
combination = [
pattern.format(*_) for _ in list(permutations(["A", "B"]))
]
assert any([_ in ir for _ in combination])
def _test_invalid_stream_pattern():
A = hcl.placeholder((10,), "A")
def kernel(A):
B = hcl.compute(A.shape,
lambda i: A[i] + 1, "B")
C = hcl.compute(B.shape,
lambda i: hcl.select(i < 9, B[i] + B[i+1], B[i]),"C")
return C
target = hcl.Platform.aws_f1
s = hcl.create_schedule([A], kernel)
s.to([A], target.xcel)
s.to(kernel.C, target.host)
s.to(kernel.B, s[kernel.C])
passed = False
try:
code = str(hcl.lower(s))
passed = True
except:
assert not passed
def test_inter_kernel_channels():
hcl.init()
A = hcl.placeholder((10, 32), "A")
C = hcl.placeholder((10, 32), "C")
def kernel(A, C):
B = hcl.compute((10, 32), lambda *args: 0, "B")
@hcl.def_([(10, 32), (10, 32)])
def add(A, B):
hcl.update(B, lambda *args: A[args] + 1)
@hcl.def_([(10, 32), (10, 32)])
def mul(B, C):
hcl.update(C, lambda *args: B[args] * 2)
add(A, B)
mul(B, C)
s = hcl.create_schedule([A, C], kernel)
s.to(kernel.mul.B, kernel.add.B, fifo_depth=10)
code = str(hcl.lower(s))
print(code)
def test_stencil_stream():
hcl.init()
A = hcl.placeholder((10, 10), "A")
def stencil(A):
B = hcl.compute((10, 8), lambda y, x: A[y, x] + A[y, x+1] + A[y, x+2], "B")
C = hcl.compute((8, 8), lambda y, x: B[y, x] + B[y+1, x] + B[y+2, x], "C")
return C
target = hcl.Platform.aws_f1
target.config(compiler="vitis", mode="debug", backend="vhls")
s = hcl.create_schedule([A], stencil)
# create stencil node
s[stencil.B].stencil(burst_width=256, unroll_factor=4)
s[stencil.C].stencil(burst_width=128, unroll_factor=8)
# compute offloading to FPGA
s.to(A, target.xcel, mode=hcl.IO.DMA)
s.to(stencil.C, target.host, mode=hcl.IO.Stream)
code = hcl.lower(s)
assert "C[0].write" in str(code)
def test_kernel():
hcl.init()
A = hcl.placeholder((10, 32), "A")
B = hcl.placeholder((10, 32), "B")
def kernel(A, B):
C = hcl.compute((10, 32), lambda *args: 10)
@hcl.def_([(10, 32), (10, 32)])
def add(A, B):
hcl.update(B, lambda *args: A[args] + 1)
@hcl.def_([(10, 32), (10, 32)])
def mul(B, C):
hcl.update(C, lambda *args: B[args] * 2)
add(A, B)
mul(B, C)
s = hcl.create_schedule([A, B], kernel)
s.to(B, s[kernel.mul], s[kernel.add])
code = str(hcl.lower(s))
assert "c_buf_1.write" in code
assert "c_buf_1.read" in code
def conv():
image = hcl.placeholder((batch_size, 1, 256, 256), "input_image")
k1 = hcl.placeholder((1, 1, 3, 3), "kernel_1")
k2 = hcl.placeholder((1, 1, 3, 3), "kernel_2")
def kernel(input_image, kernel_1, kernel_2):
# return tensor required (cannot do def_())
interm_shape = (1, 1, 254, 254)
output_shape = (1, 1, 252, 252)
# make compute wrapped in hcl def
module1 = hcl.def_([input_image.shape, kernel_1.shape, interm_shape],
name="conv1")(hlib.nn.conv2d_nchw_imp)
module2 = hcl.def_([interm_shape, kernel_2.shape, output_shape],
name="conv2")(hlib.nn.conv2d_nchw_imp)
conv1 = hcl.compute(interm_shape, lambda *args: 0)
conv2 = hcl.compute(output_shape, lambda *args: 0)
module1(input_image, kernel_1, conv1)
module2(conv1, kernel_2, conv2)
# derivative module for normalization
return hcl.compute(output_shape,
lambda *args: conv2[args],
name="derv")
s = hcl.create_schedule([image, k1, k2], kernel)
# data moved to local
i0, k10, k20 = s.to([image, k1, k2], target.fpga)
# s.to([i0, k10], s[kernel.conv1])
# s.to([k20], s[kernel.conv2])
s.to(kernel.derv, target.cpu)
# create stream channel between modules
print(type(target.fpga), hcl.lower(s))
return hcl.build(s, target)
def test_extract_subgraph(combine=False):
hcl.init()
A = hcl.placeholder((10, 32), "A")
B = hcl.placeholder((10, 32), "B")
def kernel(A, B):
C = hcl.compute(A.shape, lambda i, j: 0, "C")
hcl.update(C, lambda i, j: A[i, j] + 1, "s1")
hcl.update(C, lambda i, j: B[i, j] * 2, "s2")
return hcl.compute(C.shape, lambda *args: C[args] + 3, "ret")
target = hcl.platform.aws_f1
s = hcl.create_schedule([A, B], kernel)
A_, B_ = s.to([A, B], target.xcel)
ret_ = s.to(kernel.ret, target.host)
# combine and split
if combine == True:
# merge the channel stages into
s[A_].compute_at(s[B_], 1)
s[B_].compute_at(s[kernel.C], 1)
# merge stages from top to bottom
s[kernel.C].compute_at(s[kernel.s1], kernel.s1.axis[1])
s[kernel.s1].compute_at(s[kernel.s2], kernel.s2.axis[1])
s[kernel.s2].compute_at(s[kernel.ret], kernel.ret.axis[1])
ret_s = s.placement[kernel.ret.name][0]
s[kernel.ret].compute_at(ret_s, ret_s.op.axis[1])
# split along the first axis
ret_s.split(ret_s.op.axis[0], factor=2)
nodes = s.subgraph(inputs=[A_, B_], outputs=[ret_])
code = str(hcl.lower(s))