def test_codegen_dense():
if skip_codegen_test():
return
np.random.seed(0)
dtype = "float32"
trials = [
[(1, 128), (16, 128), 16, True],
[(1, 128), (16, 128), 16, False],
[(32, 32), (32, 32), 32, True],
[(32, 32), (32, 32), 32, False],
[(1, 64), (1, 64), 1, True],
[(1, 64), (1, 64), 1, False],
[(11, 2), (2, 2), 2, True],
[(11, 2), (2, 2), 2, False],
]
for shape, weight_shape, units, composite in trials:
inputs = {"a"}
args = (shape, weight_shape, units, dtype)
func, params = _get_model(*args,
var_names=iter(inputs),
has_bias=composite)
exp_codegen = _get_expected_codegen(*args, has_bias=composite)
verify_codegen(func, exp_codegen)
def test_codegen_global_pooling():
if skip_codegen_test():
return
fp32_dtype = ("float32", -127, 128)
uint8_dtype = ("uint8", 0, 255)
trials = [
["nn.global_max_pool2d", fp32_dtype, (8, 8, 16)],
["nn.global_max_pool2d", fp32_dtype, (9, 9, 16)],
["nn.global_max_pool2d", fp32_dtype, (8, 8, 16)],
["nn.global_max_pool2d", uint8_dtype, (8, 8, 16)],
["nn.global_max_pool2d", uint8_dtype, (9, 9, 16)],
["nn.global_avg_pool2d", fp32_dtype, (8, 8, 16)],
["nn.global_avg_pool2d", fp32_dtype, (8, 8, 16)],
["nn.global_avg_pool2d", fp32_dtype, (9, 9, 16)],
["nn.global_avg_pool2d", uint8_dtype, (8, 8, 16)],
["nn.global_avg_pool2d", uint8_dtype, (8, 8, 16)],
]
for typef, (dtype, low, high), input_shape in trials:
shape = (1, *input_shape)
inputs = {"a"}
args = (shape, dtype, typef)
func = _get_global_pooling_model(*args, iter(inputs))
exp_codegen = _get_expected_global_pooling_codegen(*args)
verify_codegen(func, exp_codegen, 1)
def test_codegen_conv2d():
if skip_codegen_test():
return
dtype = "float32"
trials = [
# Normal convolution
[2, 2, (1, 1), (1, 1), (1, 1), 4, (10, 10, 14), (False, False, False), False],
[2, 1, (2, 2), (1, 1), (1, 1), 7, (12, 15, 16), (False, False, True), False],
[3, 3, (2, 1), (1, 1), (1, 1), 4, (10, 10, 14), (False, True, False), False],
[3, 3, (1, 1), (1, 1), (1, 1), 16, (12, 15, 16), (False, False, False), False],
[5, 5, (1, 1), (2, 2), (1, 1), 4, (10, 10, 14), (True, False, False), False],
[1, 3, (1, 1), (1, 1), (1, 1), 7, (20, 20, 20), (False, False, True), False],
[2, 2, (2, 2), (1, 1), (1, 1), 4, (20, 20, 20), (False, True, False), False],
[5, 5, (1, 1), (2, 2), (1, 1), 4, (10, 10, 14), (True, False, False), False],
[3, 3, (2, 1), (1, 1), (1, 1), 7, (20, 20, 20), (False, False, False), False],
[3, 3, (1, 1), (2, 2), (1, 1), 16, (10, 10, 14), (False, True, True), False],
# Depth-wise convolution
[3, 3, (1, 1), (1, 1), (1, 1), 20, (20, 20, 20), (False, False, True), True],
[5, 5, (2, 2), (1, 1), (1, 1), 20, (20, 20, 20), (False, True, False), True],
[3, 3, (2, 2), (2, 2), (1, 1), 14, (10, 10, 14), (True, False, False), True],
[5, 5, (0, 0), (1, 1), (1, 1), 20, (20, 20, 20), (False, False, False), True],
[3, 3, (1, 1), (2, 2), (1, 1), 14, (10, 10, 14), (False, True, True), True],
]
for (
kernel_h,
kernel_w,
pad,
stride,
dilation,
out_channels,
shape,
composite,
is_depthwise,
) in trials:
shape = (1, *shape)
if is_depthwise:
groups = shape[3]
else:
groups = 1
inputs = {"a"}
args = (shape, kernel_h, kernel_w, pad, stride, dilation, groups, dtype, out_channels)
func, params = _get_model(
*args,
var_names=iter(inputs),
has_pad=composite[0],
has_bias=composite[1],
has_activation=composite[2],
)
exp_codegen = _get_expected_codegen(
*args, has_bias=composite[1], has_activation=composite[2]
)
verify_codegen(func, exp_codegen, 1)
def test_codegen_add():
if skip_codegen_test():
return
inputs = {"a", "b"}
for dtype, op_name, op, qnn_params in [
("float32", "add", relay.add, {}),
("uint8", "qnn.add", relay.qnn.op.add, _qnn_params),
]:
for shape in [(1, 1), (2, 2, 2), (3, 3, 3, 3)]:
func = _get_model(shape, dtype, iter(inputs), op, qnn_params)
exp_codegen = _get_expected_codegen(shape, dtype, op_name, qnn_params)
verify_codegen(func, exp_codegen, 1)
def test_codegen_concatenate():
if skip_codegen_test():
return
shape_a = [1, 234, 234, 256]
shape_b = [2, 234, 234, 256]
shape_c = [3, 234, 234, 256]
axis = 0
inputs = {"a", "b", "c"}
for dtype in ["float32"]:
args = (shape_a, shape_b, shape_c, axis, dtype)
func = _get_model(*args, iter(inputs))
exp_codegen = _get_expected_codegen(*args)
verify_codegen(func, exp_codegen, 1, disabled_ops=[])
def test_codegen_qnn_dense():
if skip_codegen_test():
return
np.random.seed(0)
dtype = "uint8"
trials = [
[(1, 2), (2, 2), 2, True],
[(1, 2), (2, 2), 2, False],
[(4, 4), (4, 4), 4, True],
[(4, 4), (4, 4), 4, False],
[(16, 16), (4, 16), 4, True],
[(16, 16), (4, 16), 4, False],
[(1, 128), (16, 128), 16, True],
[(1, 128), (16, 128), 16, False],
[(32, 32), (32, 32), 32, True],
[(32, 32), (32, 32), 32, False],
[(1, 64), (1, 64), 1, True],
[(1, 64), (1, 64), 1, False],
]
for shape, weight_shape, units, composite in trials:
inputs = {"a"}
args = (shape, weight_shape, units, dtype)
input_zp = 100
input_sc = 0.5
kernel_zp = 25
kernel_sc = 0.03
output_zp, output_sc = _get_qnn_params(input_zp, input_sc, kernel_zp,
kernel_sc, weight_shape[0],
weight_shape[1])
func, params = _get_qnn_model(
*args,
var_names=iter(inputs),
input_zp=input_zp,
input_sc=input_sc,
kernel_zp=kernel_zp,
kernel_sc=kernel_sc,
output_zp=output_zp,
output_sc=output_sc,
has_bias=composite,
)
exp_codegen = _get_expected_codegen(*args, has_bias=composite)
verify_codegen(func, exp_codegen)
def test_codegen_qnn_dense():
if skip_codegen_test():
return
np.random.seed(0)
dtype = ["uint8"]
shape = [(1, (1, 128), (16, 128), 16), (1, (32, 32), (32, 32), 32),
(0, (1, 64), (1, 64), 1)]
composite = [False, True]
trials = generate_trials([dtype, shape, composite], 3)
for dtype, (acl_partitions, shape, weight_shape,
units), composite in trials:
inputs = {"a"}
args = (shape, weight_shape, units, dtype)
input_zp = 100
input_sc = 0.5
kernel_zp = 25
kernel_sc = 0.03
output_zp, output_sc = _get_qnn_params(input_zp, input_sc, kernel_zp,
kernel_sc, weight_shape[0],
weight_shape[1])
func, params = _get_qnn_model(
*args,
var_names=iter(inputs),
input_zp=input_zp,
input_sc=input_sc,
kernel_zp=kernel_zp,
kernel_sc=kernel_sc,
output_zp=output_zp,
output_sc=output_sc,
has_bias=composite,
)
exp_codegen = _get_expected_codegen(*args, has_bias=composite)
verify_codegen(func, exp_codegen, acl_partitions,
2 - 2 * acl_partitions)
def test_codegen_dense():
if skip_codegen_test():
return
np.random.seed(0)
dtype = ["float32"]
shape = [(1, (1, 128), (16, 128), 16), (1, (32, 32), (32, 32), 32),
(0, (1, 64), (1, 64), 1)]
composite = [False, True]
trials = generate_trials([dtype, shape, composite], 3)
for dtype, (acl_partitions, shape, weight_shape,
units), composite in trials:
inputs = {"a"}
args = (shape, weight_shape, units, dtype)
func, params = _get_model(*args,
var_names=iter(inputs),
has_bias=composite)
exp_codegen = _get_expected_codegen(*args, has_bias=composite)
verify_codegen(func, exp_codegen, acl_partitions, 1 - acl_partitions)
def test_codegen_pooling():
if skip_codegen_test():
return
fp32_dtype = ("float32", -127, 128)
uint8_dtype = ("uint8", 0, 255)
# fmt: off
trials = [
[
"nn.max_pool2d",
fp32_dtype,
(2, 2),
(2, 2),
(1, 1),
(0, 0),
False,
True,
(16, 16, 16),
(0, 1),
],
[
"nn.max_pool2d",
fp32_dtype,
(3, 3),
(2, 2),
(1, 1),
(1, 1),
True,
True,
(15, 15, 16),
(0, 1),
],
[
"nn.max_pool2d",
fp32_dtype,
(2, 2),
(2, 2),
(1, 1),
(0, 1),
False,
False,
(16, 16, 16),
(0, 1),
],
[
"nn.max_pool2d",
uint8_dtype,
(3, 3),
(2, 2),
(1, 1),
(0, 1),
False,
False,
(16, 16, 16),
(0, 1),
],
[
"nn.max_pool2d",
uint8_dtype,
(2, 2),
(2, 2),
(1, 1),
(1, 1),
True,
True,
(15, 15, 16),
(0, 1),
],
[
"nn.max_pool2d",
uint8_dtype,
(2, 2),
(2, 2),
(3, 2),
(1, 1),
True,
True,
(15, 15, 16),
(1, 0),
],
[
"nn.avg_pool2d",
fp32_dtype,
(2, 2),
(2, 2),
(1, 1),
(1, 1),
False,
False,
(16, 16, 16),
(0, 1),
],
[
"nn.avg_pool2d",
fp32_dtype,
(2, 2),
(2, 2),
(1, 1),
(1, 1),
False,
False,
(16, 16, 16),
(0, 1),
],
[
"nn.avg_pool2d",
fp32_dtype,
(2, 2),
(2, 2),
(1, 1),
(0, 0),
False,
True,
(16, 16, 16),
(0, 1),
],
[
"nn.avg_pool2d",
fp32_dtype,
(3, 3),
(2, 2),
(3, 2),
(0, 1),
True,
False,
(15, 15, 16),
(1, 0),
],
[
"nn.avg_pool2d",
uint8_dtype,
(2, 2),
(2, 2),
(1, 1),
(1, 1),
False,
True,
(16, 16, 16),
(0, 1),
],
[
"nn.avg_pool2d",
uint8_dtype,
(3, 3),
(2, 2),
(1, 1),
(0, 1),
False,
False,
(16, 16, 16),
(0, 1),
],
[
"nn.l2_pool2d",
fp32_dtype,
(2, 2),
(2, 2),
(1, 1),
(0, 1),
True,
False,
(15, 15, 16),
(0, 1),
],
[
"nn.l2_pool2d",
fp32_dtype,
(3, 3),
(2, 2),
(1, 1),
(0, 0),
False,
False,
(16, 16, 16),
(0, 1),
],
[
"nn.l2_pool2d",
fp32_dtype,
(2, 2),
(2, 2),
(1, 1),
(1, 1),
False,
True,
(15, 15, 16),
(0, 1),
],
]
# fmt: on
for (
typef,
(dtype, low, high),
size,
stride,
dilation,
pad,
ceil_mode,
count_include_pad,
input_shape,
(tvm_ops, acl_partitions),
) in trials:
shape = (1, *input_shape)
inputs = {"a"}
args = (shape, dtype, typef, size, stride, dilation, pad, False, False)
func = _get_pooling_model(*args, iter(inputs))
exp_codegen = _get_expected_pooling_codegen(*args)
verify_codegen(func, exp_codegen, acl_partitions, tvm_ops)
