def test_op_int8(zero_point, scale):
interface_api = "c"
use_unpacked_api = True
test_runner = AOT_USMP_CORSTONE300_RUNNER
dtype = "int8"
shape = [1, 16, 16, 3]
model = make_model(shape, dtype, dtype, zero_point, scale)
orig_mod = make_module(model)
cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod)
# validate pattern matching
assert_partitioned_function(orig_mod, cmsisnn_mod)
# validate the output
in_min, in_max = get_range_for_dtype_str(dtype)
np.random.seed(0)
input_data = np.random.randint(in_min,
high=in_max,
size=shape,
dtype=dtype)
inputs = {"in0": input_data}
params = {}
output_list = generate_ref_data(orig_mod["main"], inputs, params)
compile_and_run(
AOTTestModel(module=cmsisnn_mod,
inputs=inputs,
outputs=output_list,
params=params),
test_runner,
interface_api,
use_unpacked_api,
)
def test_conv2d_int8_tflite(ifm_shape, kernel_shape, strides, dilation,
padding, activation):
interface_api = "c"
use_unpacked_api = True
test_runner = AOT_USMP_CORSTONE300_RUNNER
dtype = "int8"
tflite_model, relay_mod, params = create_conv2d_tflite_relay_models(
ifm_shape, kernel_shape, strides, dilation, padding, activation, dtype)
cmsisnn_mod = cmsisnn.partition_for_cmsisnn(relay_mod, params)
# validate pattern matching
assert_partitioned_function(relay_mod, cmsisnn_mod)
# validate CMSIS-NN output against TFLite output
input_map, output_map, output_tolerance = generate_ref_data_tflite(
tflite_model)
compile_and_run(
AOTTestModel(
module=cmsisnn_mod,
inputs=input_map,
outputs=output_map,
params=params,
output_tolerance=output_tolerance,
),
test_runner,
interface_api,
use_unpacked_api,
)
def test_op_int8(
in_shape,
pool_size,
strides,
padding,
relu_type,
pool_type,
zero_point,
scale,
):
interface_api = "c"
use_unpacked_api = True
test_runner = AOT_USMP_CORSTONE300_RUNNER
dtype = "int8"
model = make_model(
pool_type,
in_shape,
pool_size,
strides,
padding,
dtype,
scale,
zero_point,
relu_type,
)
orig_mod = make_module(model)
cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod)
# validate pattern matching
assert_partitioned_function(orig_mod, cmsisnn_mod)
# validate the output
in_min, in_max = get_range_for_dtype_str(dtype)
np.random.seed(0)
inputs = {
"input":
np.random.randint(in_min, high=in_max, size=in_shape, dtype="int8"),
}
output_list = generate_ref_data(orig_mod["main"], inputs)
compile_and_run(
AOTTestModel(
module=cmsisnn_mod,
inputs=inputs,
outputs=output_list,
params=None,
output_tolerance=1,
),
test_runner,
interface_api,
use_unpacked_api,
)
def test_constant_input_int8(op, input_0, input_1):
interface_api = "c"
use_unpacked_api = True
test_runner = AOT_USMP_CORSTONE300_RUNNER
dtype = "int8"
shape = [1, 16, 16, 3]
input_0_scale = 0.256
input_0_zero_point = 33
input_1_scale = 0.128
input_1_zero_point = -24
model = make_model(
op,
input_0,
input_1,
input_0_scale,
input_0_zero_point,
input_1_scale,
input_1_zero_point,
)
orig_mod = make_module(model)
cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod)
# validate pattern matching
assert_partitioned_function(orig_mod, cmsisnn_mod)
# validate the output
in_min, in_max = get_range_for_dtype_str(dtype)
inputs = {}
if isinstance(input_0, tvm.relay.expr.Var):
inputs.update({
"input_0":
np.random.randint(in_min, high=in_max, size=shape, dtype=dtype)
})
if isinstance(input_1, tvm.relay.expr.Var):
inputs.update({
"input_1":
np.random.randint(in_min, high=in_max, size=shape, dtype=dtype)
})
output_list = generate_ref_data(orig_mod["main"], inputs)
compile_and_run(
AOTTestModel(
module=cmsisnn_mod,
inputs=inputs,
outputs=output_list,
output_tolerance=1,
),
test_runner,
interface_api,
use_unpacked_api,
)
def test_op_int8(op, relu_type, input_0_scale, input_0_zero_point,
input_1_scale, input_1_zero_point):
interface_api = "c"
use_unpacked_api = True
test_runner = AOT_USMP_CORSTONE300_RUNNER
dtype = "int8"
shape = [1, 16, 16, 3]
model = make_model(
op,
generate_variable("input_0"),
generate_variable("input_1"),
input_0_scale,
input_0_zero_point,
input_1_scale,
input_1_zero_point,
relu_type,
)
orig_mod = make_module(model)
cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod)
# validate pattern matching
assert_partitioned_function(orig_mod, cmsisnn_mod)
# validate the output
in_min, in_max = get_range_for_dtype_str(dtype)
inputs = {
"input_0": np.random.randint(in_min,
high=in_max,
size=shape,
dtype=dtype),
"input_1": np.random.randint(in_min,
high=in_max,
size=shape,
dtype=dtype),
}
output_list = generate_ref_data(orig_mod["main"], inputs)
compile_and_run(
AOTTestModel(
module=cmsisnn_mod,
inputs=inputs,
outputs=output_list,
output_tolerance=1,
),
test_runner,
interface_api,
use_unpacked_api,
)
def test_conv2d_int8_tflite(ifm_shape, kernel_shape, strides, dilation,
padding, activation):
interface_api = "c"
use_unpacked_api = True
test_runner = AOT_USMP_CORSTONE300_RUNNER
dtype = "int8"
from tvm.relay.testing.tflite import TFLiteModel
tfl_model = TFLiteModel(dtype)
conv2d_function = tfl_model.create_conv2d_single(kernel_shape, strides,
padding, dilation,
activation)
tfl_model.create_tflite_model(conv2d_function, [ifm_shape])
relay_mod, relay_params = tfl_model.convert_to_relay()
cmsisnn_mod = cmsisnn.partition_for_cmsisnn(relay_mod, relay_params)
# validate pattern matching
assert_partitioned_function(relay_mod, cmsisnn_mod)
# validate CMSIS-NN output against TFLite output
input_map, output_map, output_tolerance = tfl_model.generate_reference_data(
)
compile_and_run(
AOTTestModel(
module=cmsisnn_mod,
inputs=input_map,
outputs=output_map,
params=relay_params,
output_tolerance=output_tolerance,
),
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
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,
)
