def test_invalid_parameters(
in_dtype,
kernel_dtype,
kernel_zero_point,
padding,
):
ifm_shape = (1, 28, 28, 12)
out_channels = 2
input_scale = 1
input_zero_point = 24
kernel_scale = [0.11, 0.0237]
in_min, in_max = get_range_for_dtype_str(in_dtype)
kernel_layout = "HWIO"
kernel_shape = [3, 3, ifm_shape[3], out_channels]
output_scale, output_zero_point = get_conv2d_qnn_params(
kernel_shape,
input_scale,
input_zero_point,
kernel_scale,
kernel_zero_point,
in_dtype,
kernel_dtype,
in_dtype,
False,
)
model, params = make_model(
shape=ifm_shape,
kernel_shape=kernel_shape,
input_zero_point=input_zero_point,
input_scale=input_scale,
kernel_zero_point=kernel_zero_point,
kernel_scale=kernel_scale,
output_zero_point=output_zero_point,
output_scale=output_scale,
padding=padding,
strides=(1, 1),
dilation=(1, 1),
groups=1,
dtype=in_dtype,
kernel_dtype=kernel_dtype,
out_channels=out_channels,
weight_format=kernel_layout,
enable_bias=True,
relu_type="NONE",
)
orig_mod = make_module(model)
cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod, params)
# validate pattern matching
attrs = [
cmsisnn_mod[var.name_hint].attrs
for var in cmsisnn_mod.get_global_vars()
if cmsisnn_mod[var.name_hint].attrs
]
assert not any(attrs), "No function should have an external attribute."
def test_invalid_parameters(
in_dtype,
kernel_dtype,
kernel_zero_point,
):
ifm_shape = (1, 28, 28, 12)
out_channels = 2
input_scale = 1
input_zero_point = 24
kernel_scale = [0.11, 0.0237]
in_min, in_max = get_range_for_dtype_str(in_dtype)
kernel_layout = "HWIO"
kernel_shape = [3, 3, ifm_shape[3], out_channels]
output_scale, output_zero_point = get_conv2d_qnn_params(
kernel_shape,
input_scale,
input_zero_point,
kernel_scale,
kernel_zero_point,
in_dtype,
kernel_dtype,
in_dtype,
False,
)
model, params = make_model(
shape=ifm_shape,
kernel_shape=kernel_shape,
input_zero_point=input_zero_point,
input_scale=input_scale,
kernel_zero_point=kernel_zero_point,
kernel_scale=kernel_scale,
output_zero_point=output_zero_point,
output_scale=output_scale,
padding="SAME",
strides=(1, 1),
dilation=(1, 1),
groups=1,
dtype=in_dtype,
kernel_dtype=kernel_dtype,
out_channels=out_channels,
weight_format=kernel_layout,
enable_bias=True,
relu_type="NONE",
)
orig_mod = make_module(model)
cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod, params)
assert_no_external_function(cmsisnn_mod)
def test_invalid_parameters(
in_dtype,
kernel_dtype,
kernel_zero_point,
):
in_shape = (2, 28)
out_channels = 2
input_scale = 1
input_zero_point = 24
kernel_scale = [0.11, 0.0237]
in_min, in_max = get_range_for_dtype_str(in_dtype)
kernel_shape = [out_channels, in_shape[1]]
conv2d_kernel_shape = [1, 1, kernel_shape[0], kernel_shape[1]]
output_scale, output_zero_point = get_conv2d_qnn_params(
conv2d_kernel_shape,
input_scale,
input_zero_point,
kernel_scale,
kernel_zero_point,
in_dtype,
kernel_dtype,
in_dtype,
)
model, params = make_model(
in_shape=in_shape,
kernel_shape=kernel_shape,
input_zero_point=input_zero_point,
kernel_zero_point=kernel_zero_point,
input_scale=input_scale,
kernel_scale=kernel_scale,
output_zero_point=output_zero_point,
output_scale=output_scale,
dtype=in_dtype,
kernel_dtype=kernel_dtype,
out_channels=out_channels,
enable_bias=True,
)
orig_mod = make_module(model)
cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod, params)
# validate pattern matching
attrs = [
cmsisnn_mod[var.name_hint].attrs
for var in cmsisnn_mod.get_global_vars()
if cmsisnn_mod[var.name_hint].attrs
]
assert not any(attrs), "No function should have an external attribute."
def test_invalid_parameters(
in_dtype,
kernel_dtype,
kernel_zero_point,
):
in_shape = (2, 28)
out_channels = 2
input_scale = 1
input_zero_point = 24
kernel_scale = [0.11, 0.0237]
in_min, in_max = get_range_for_dtype_str(in_dtype)
kernel_shape = [out_channels, in_shape[1]]
conv2d_kernel_shape = [1, 1, kernel_shape[0], kernel_shape[1]]
output_scale, output_zero_point = get_conv2d_qnn_params(
conv2d_kernel_shape,
input_scale,
input_zero_point,
kernel_scale,
kernel_zero_point,
in_dtype,
kernel_dtype,
in_dtype,
)
model, params = make_model(
in_shape=in_shape,
kernel_shape=kernel_shape,
input_zero_point=input_zero_point,
kernel_zero_point=kernel_zero_point,
input_scale=input_scale,
kernel_scale=kernel_scale,
output_zero_point=output_zero_point,
output_scale=output_scale,
dtype=in_dtype,
kernel_dtype=kernel_dtype,
out_channels=out_channels,
enable_bias=True,
)
orig_mod = make_module(model)
cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod, params)
# validate pattern matching
assert_no_external_function(cmsisnn_mod)
def test_op_int8(
enable_bias,
input_zero_point,
input_scale,
kernel_scale,
out_channels,
):
ifm_shape = (1, 28, 28, 3)
padding = "VALID"
strides = (1, 1)
dilation = (1, 1)
kernel_size = (3, 3)
kernel_zero_point = 0
groups = 1
weight_format = "HWIO"
kernel_h = kernel_size[0]
kernel_w = kernel_size[1]
dtype = "int8"
relu_type = "RELU"
in_min, in_max = get_range_for_dtype_str(dtype)
weight_shape = (kernel_h, kernel_w, ifm_shape[3] // groups, out_channels)
output_scale, output_zero_point = get_conv2d_qnn_params(
weight_shape,
input_scale,
input_zero_point,
kernel_scale,
kernel_zero_point,
dtype,
dtype,
dtype,
False,
)
model, params = make_model(
ifm_shape,
weight_shape,
input_zero_point,
input_scale,
kernel_zero_point,
kernel_scale,
output_zero_point,
output_scale,
padding,
strides,
dilation,
groups,
dtype,
dtype,
out_channels,
weight_format,
enable_bias,
relu_type,
)
mod = make_module(model)
cmsisnn_mod = cmsisnn.partition_for_cmsisnn(mod, params)
multiplier_array = []
shift_array = []
for i in range(out_channels):
multiplier, shift = quantize_scale(input_scale * kernel_scale[i] /
output_scale)
multiplier_array.append(multiplier)
shift_array.append(shift)
CheckGeneratedConstants(enable_bias, multiplier_array,
shift_array).visit_function(cmsisnn_mod["main"])
def test_depthwise_int8(
ifm_shape,
kernel_size,
padding,
strides,
dilation,
enable_bias,
relu_type,
input_zero_point,
input_scale,
kernel_scale,
out_channels,
depth_multiplier,
):
interface_api = "c"
use_unpacked_api = True
test_runner = AOT_CORSTONE300_RUNNER
dtype = "int8"
groups = 1
weight_format = "HWIO"
kernel_h = kernel_size[0]
kernel_w = kernel_size[1]
kernel_shape = (kernel_h, kernel_w, ifm_shape[3] // groups, out_channels)
kernel_zero_point = 0
in_min, in_max = get_range_for_dtype_str(dtype)
groups = ifm_shape[3]
weight_format = "HWOI"
kernel_shape = (kernel_h, kernel_w, ifm_shape[3], depth_multiplier)
out_channels = ifm_shape[3] * depth_multiplier
ks_len = len(kernel_scale)
kernel_scale = [kernel_scale[i % ks_len] for i in range(out_channels)]
output_scale, output_zero_point = get_conv2d_qnn_params(
kernel_shape,
input_scale,
input_zero_point,
kernel_scale,
kernel_zero_point,
dtype,
dtype,
dtype,
True,
)
model, params = make_model(
ifm_shape,
kernel_shape,
input_zero_point,
input_scale,
kernel_zero_point,
kernel_scale,
output_zero_point,
output_scale,
padding,
strides,
dilation,
groups,
dtype,
dtype,
out_channels,
weight_format,
enable_bias,
relu_type,
)
orig_mod = make_module(model)
cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod, params)
# validate pattern matching
assert_partitioned_function(orig_mod, cmsisnn_mod)
# validate the output
rng = np.random.default_rng(12345)
inputs = {"input": rng.integers(in_min, high=in_max, size=ifm_shape, dtype=dtype)}
output_list = generate_ref_data(orig_mod["main"], inputs, params)
compile_and_run(
AOTTestModel(
module=cmsisnn_mod,
inputs=inputs,
outputs=output_list,
params=params,
output_tolerance=1,
),
test_runner,
interface_api,
use_unpacked_api,
)
def test_op_int8(
in_shape,
enable_bias,
input_zero_point,
input_scale,
kernel_scale,
out_channels,
relu_type,
):
interface_api = "c"
use_unpacked_api = True
test_runner = AOT_USMP_CORSTONE300_RUNNER
dtype = "int8"
kernel_zero_point = 0
kernel_shape = [out_channels, in_shape[1]]
conv2d_kernel_shape = (1, 1, kernel_shape[0], kernel_shape[1])
in_min, in_max = get_range_for_dtype_str(dtype)
output_scale, output_zero_point = get_conv2d_qnn_params(
conv2d_kernel_shape,
input_scale,
input_zero_point,
kernel_scale,
kernel_zero_point,
dtype,
)
model, params = make_model(
in_shape,
kernel_shape,
input_zero_point,
kernel_zero_point,
input_scale,
kernel_scale,
output_zero_point,
output_scale,
dtype,
dtype,
out_channels,
enable_bias,
)
orig_mod = make_module(model)
cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod, params)
# validate pattern matching
assert_partitioned_function(orig_mod, cmsisnn_mod)
# validate the output
rng = np.random.default_rng(12345)
inputs = {
"input": rng.integers(in_min, high=in_max, size=in_shape, dtype=dtype)
}
output_list = generate_ref_data(orig_mod["main"], inputs, params)
compile_and_run(
AOTTestModel(
module=cmsisnn_mod,
inputs=inputs,
outputs=output_list,
params=params,
output_tolerance=1,
),
test_runner,
interface_api,
use_unpacked_api,
)
def test_depthwise_int8(
ifm_shape,
kernel_size,
padding,
strides,
dilation,
enable_bias,
relu_type,
input_zero_point,
input_scale,
kernel_scale,
out_channels,
depth_multiplier,
):
interface_api = "c"
use_unpacked_api = True
test_runner = AOT_CORSTONE300_RUNNER
dtype = "int8"
groups = 1
weight_format = "HWIO"
kernel_h = kernel_size[0]
kernel_w = kernel_size[1]
kernel_shape = (kernel_h, kernel_w, ifm_shape[3] // groups, out_channels)
kernel_zero_point = 0
in_min, in_max = get_range_for_dtype_str(dtype)
groups = ifm_shape[3]
weight_format = "HWOI"
kernel_shape = (kernel_h, kernel_w, ifm_shape[3], depth_multiplier)
out_channels = ifm_shape[3] * depth_multiplier
ks_len = len(kernel_scale)
kernel_scale = [kernel_scale[i % ks_len] for i in range(out_channels)]
output_scale, output_zero_point = get_conv2d_qnn_params(
kernel_shape,
input_scale,
input_zero_point,
kernel_scale,
kernel_zero_point,
dtype,
dtype,
dtype,
True,
)
model, params = make_model(
ifm_shape,
kernel_shape,
input_zero_point,
input_scale,
kernel_zero_point,
kernel_scale,
output_zero_point,
output_scale,
padding,
strides,
dilation,
groups,
dtype,
dtype,
out_channels,
weight_format,
enable_bias,
relu_type,
)
orig_mod = make_module(model)
cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod, params)
# validate pattern matching
attrs = [
cmsisnn_mod[var.name_hint].attrs
for var in cmsisnn_mod.get_global_vars()
if cmsisnn_mod[var.name_hint].attrs
]
assert any(attrs), "At least one function with external attributes was expected."
compilers = [
key == "Compiler" and value == "cmsis-nn" for attr in attrs for key, value in attr.items()
]
assert any(compilers), "Module does not contain function for cmsis-nn target."
assert count_num_calls(orig_mod) == count_num_calls(
cmsisnn_mod
), "Number of calls changed during partitioning"
# validate the output
rng = np.random.default_rng(12345)
inputs = {"input": rng.integers(in_min, high=in_max, size=ifm_shape, dtype=dtype)}
output_list = generate_ref_data(orig_mod["main"], inputs, params)
compile_and_run(
AOTTestModel(
module=cmsisnn_mod,
inputs=inputs,
outputs=output_list,
params=params,
output_tolerance=1,
),
test_runner,
interface_api,
use_unpacked_api,
)
def test_op_int8(
in_shape,
enable_bias,
input_zero_point,
input_scale,
kernel_scale,
out_channels,
relu_type,
):
interface_api = "c"
use_unpacked_api = True
test_runner = AOT_CORSTONE300_RUNNER
dtype = "int8"
kernel_zero_point = 0
kernel_shape = [out_channels, in_shape[1]]
conv2d_kernel_shape = (1, 1, kernel_shape[0], kernel_shape[1])
in_min, in_max = get_range_for_dtype_str(dtype)
output_scale, output_zero_point = get_conv2d_qnn_params(
conv2d_kernel_shape,
input_scale,
input_zero_point,
kernel_scale,
kernel_zero_point,
dtype,
)
model, params = make_model(
in_shape,
kernel_shape,
input_zero_point,
kernel_zero_point,
input_scale,
kernel_scale,
output_zero_point,
output_scale,
dtype,
dtype,
out_channels,
enable_bias,
)
orig_mod = make_module(model)
cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod, params)
# validate pattern matching
attrs = [
cmsisnn_mod[var.name_hint].attrs
for var in cmsisnn_mod.get_global_vars()
if cmsisnn_mod[var.name_hint].attrs
]
assert any(
attrs), "At least one function with external attributes was expected."
compilers = [
key == "Compiler" and value == "cmsis-nn" for attr in attrs
for key, value in attr.items()
]
assert any(
compilers), "Module does not contain function for cmsisnn target."
assert count_num_calls(orig_mod) == count_num_calls(
cmsisnn_mod), "Number of calls changed during partitioning"
# validate the output
rng = np.random.default_rng(12345)
inputs = {
"input": rng.integers(in_min, high=in_max, size=in_shape, dtype=dtype)
}
output_list = generate_ref_data(orig_mod["main"], inputs, params)
compile_and_run(
AOTTestModel(
module=cmsisnn_mod,
inputs=inputs,
outputs=output_list,
params=params,
output_tolerance=1,
),
test_runner,
interface_api,
use_unpacked_api,
)
