def test_conv2d_inline_reshape(trial):
def _get_func(ifm_shape, reshaped, ifm_layout):
ifm = relay.var("ifm", shape=ifm_shape, dtype="int8")
ifm_reshaped = relay.reshape(ifm, reshaped)
conv = make_ethosu_conv2d(
ifm_reshaped,
reshaped[3],
16,
(3, 3),
(1, 1),
(1, 1),
(1, 1),
activation="NONE",
ifm_layout=ifm_layout,
)
func = relay.Function(relay.analysis.free_vars(conv), conv)
func = run_opt_pass(func, relay.transform.InferType())
return func
reference_mod = trial[0]
params = trial[1:]
func = _get_func(*params)
mod, _ = lower_to_tir(func, cascader=total_cascader((1, 4, 6, 16)))
script = mod.script(show_meta=True)
mod = tvm.script.from_source(script)
tvm.ir.assert_structural_equal(mod["main"], reference_mod["main"], True)
def test_weight_stream():
def _cascader(cached_func, const_dict, sch):
weight = cached_func.inputs[1]
scale_bias = cached_func.inputs[2]
out = cached_func.outputs[0]
conv_compute = Convolution2DCompute.from_output(out)
co = conv_compute.split(sch, 3, 10)
cache_weight = sch.cache_read(weight, "global", [conv_compute.conv2d])
cache_scale_bias = sch.cache_read(scale_bias, "global", [conv_compute.conv2d])
sch[cache_weight].compute_at(sch[out], co)
sch[cache_scale_bias].compute_at(sch[out], co)
def _get_func():
ifm = relay.var("ifm", shape=(1, 16, 16, 32), dtype="int8")
conv = make_ethosu_conv2d(
ifm,
32,
16,
(1, 1),
(0, 0),
(1, 1),
(1, 1),
)
func = relay.Function(relay.analysis.free_vars(conv), conv)
func = run_opt_pass(func, relay.transform.InferType())
return func
func = _get_func()
mod, _ = lower_to_tir(func, cascader=_cascader)
script = mod.script(show_meta=True)
test_mod = tvm.script.from_source(script)
reference_mod = WeightStream
tvm.ir.assert_structural_equal(test_mod["main"], reference_mod["main"], True)
def test_mixed_read():
def _planner(cached_func, const_dict, sch):
weight = cached_func.inputs[4]
scale_bias = cached_func.inputs[5]
out = cached_func.outputs[0]
conv_compute = Convolution2DCompute.from_output(out)
co = conv_compute.split(sch, 3, 2)
cache_weight = sch.cache_read(weight, "global", [conv_compute.conv2d])
cache_scale_bias = sch.cache_read(scale_bias, "global",
[conv_compute.conv2d])
sch[cache_weight].compute_at(sch[out], co)
sch[cache_scale_bias].compute_at(sch[out], co)
def _get_func():
ifm = relay.var("ifm", shape=(1, 16, 16, 32), dtype="int8")
conv1 = make_ethosu_conv2d(
ifm,
32,
16,
(1, 1),
(0, 0),
(1, 1),
(1, 1),
)
conv2 = make_ethosu_conv2d(
conv1,
16,
8,
(1, 1),
(0, 0),
(1, 1),
(1, 1),
)
func = relay.Function(relay.analysis.free_vars(conv2), conv2)
func = run_opt_pass(func, relay.transform.InferType())
return func
func = _get_func()
mod, consts = lower_to_tir(func, cascader=_planner)
script = mod.script(show_meta=True)
test_mod = tvm.script.from_source(script)
reference_mod = MixedRead
tvm.ir.assert_structural_equal(test_mod["main"], reference_mod["main"],
True)
reference_const_sizes = [
592,
160,
80,
32,
80,
32,
80,
32,
80,
32,
]
test_const_size = [value.size for value in list(consts.values())]
assert reference_const_sizes == test_const_size
def _compile(ext_func):
"""
This is the main wrapper that accepts an external
relay function and runs all the passes to lower it down
to command stream
Parameters
----------
ext_func : tvm.relay.function.Function
The partitioned relay function
Returns
-------
cs : str
An hex string of the bytes of command stream
encoded_constants : str
An hex string of the bytes that includes concat'd
encoded weights, encoded biases and scales.
scratch_size : int
The size of the scratch buffer needed.
"""
mod = tvm.IRModule()
mod["main"] = ext_func
mod = LegalizeEthosU()(mod)
mod = relay.transform.InferType()(mod)
# We are currently using copy_constants scheduler In the long run,
# this should be a single intelligent and a composite scheduler
# that can perform scheduling based on user inputs such as
# scratch memory size.
tir_mod, params = lower_to_tir(mod["main"], copy_constants())
cmms, encoded_constants, scratch_size = tir_to_cs_translator.translate(
tir_mod, params)
return cmms, encoded_constants, scratch_size
def test_pooling_single(
ifm_shape,
ofm_channels,
ifm_layout,
ofm_layout,
pooling_type,
activation,
rounding_mode,
upscale,
):
pool_shape = (3, 2)
strides = (1, 2)
# When strides are not (1, 1) it is possible to create invalid
# padding configurations. It is possible to construct a pooling
# operation with invalid padding, but the compiler will account
# for this and adjust the padding accordingly, leading to a
# mismatch between the expected and actual result. Therefore,
# hardcoded padding values are used for each case.
padding = (1, 1, 1, 0) if upscale == "NONE" else (0, 0, 0, 0)
ifm = relay.var("ifm", shape=ifm_shape, dtype="int8")
pooling = make_ethosu_pooling(
ifm,
pooling_type,
pool_shape,
ofm_channels,
strides,
padding,
activation,
ifm_layout,
ofm_layout,
rounding_mode,
upscale,
)
func = relay.Function(relay.analysis.free_vars(pooling), pooling)
func = run_opt_pass(func, relay.transform.InferType())
mod, _ = lower_to_tir(func)
data = []
def _visit(stmt):
if isinstance(stmt, tvm.tir.Call):
data.append(get_pooling_args(stmt))
tvm.tir.stmt_functor.post_order_visit(mod["main"].body, _visit)
serial_pooling = _create_serial_pooling(
ifm_shape,
ofm_channels,
ifm_layout,
ofm_layout,
pool_shape,
pooling_type,
strides,
padding,
activation,
rounding_mode,
upscale,
)
assert data[0] == ["ethosu_pooling"] + list(serial_pooling)
def test_lower_to_tir_arg_count(relay_function, arg_count):
mod = tvm.IRModule()
mod["main"] = relay_function()
mod = relay.transform.InferType()(mod)
tir_mod = lower_to_tir(mod["main"])[0]
primfunc = tir_mod["main"]
assert len(primfunc.params) == arg_count
def test_concat():
def _get_func():
ifm1 = relay.var("ifm1", shape=(1, 8, 12, 16), dtype="int8")
ifm2 = relay.var("ifm2", shape=(1, 8, 10, 16), dtype="int8")
conv1 = make_ethosu_conv2d(ifm1, 16, 16, (3, 3), (1, 1), (1, 1),
(1, 1))
conv2 = make_ethosu_conv2d(ifm2, 16, 16, (3, 3), (1, 1), (1, 1),
(1, 1))
conc1 = relay.concatenate((conv1, conv2), axis=2)
conv3 = make_ethosu_conv2d(conc1, 16, 16, (3, 3), (1, 1), (1, 1),
(1, 1))
conv4 = make_ethosu_conv2d(conv2, 16, 16, (3, 3), (1, 1), (1, 1),
(1, 1))
conc2 = relay.concatenate((conv3, conv4), axis=2)
func = relay.Function(relay.analysis.free_vars(conc2), conc2)
func = run_opt_pass(func, relay.transform.InferType())
return func
func = _get_func()
mod, _ = lower_to_tir(func)
script = mod.script(show_meta=True)
test_mod = tvm.script.from_source(script)
reference_mod = ReferenceModule
tvm.ir.assert_structural_equal(test_mod["main"], reference_mod["main"],
True)
def test_lower_to_tir():
data = relay.var("data", shape=(1, 1, 1, 1024), dtype="uint8")
weight = relay.var("weight", shape=(1, 1, 1024, 1001), dtype="int8")
p2 = relay.var("p2", shape=(1, 1, 1, 1), dtype="int32")
conv = relay.nn.conv2d(
data,
weight,
kernel_size=(1, 1),
data_layout="NHWC",
kernel_layout="HWIO",
out_dtype="int32",
)
tile = relay.tile(p2, reps=(1, 1, 1, 1001))
subtract = relay.subtract(conv, tile)
func = subtract
expr = relay.Function(relay.analysis.free_vars(func), func)
mod = tvm.IRModule.from_expr(expr)
mod = relay.transform.InferType()(mod)
lower_to_tir(mod["main"])
def test_conv2d_big_pad():
def _get_func():
ifm_shape = (1, 2, 2, 8)
ifm = relay.var("ifm", shape=ifm_shape, dtype="int8")
conv = make_ethosu_conv2d(ifm, ifm_shape[3], 16, (1, 1), (7, 7), (1, 1), (1, 1), "NHWC")
func = relay.Function(relay.analysis.free_vars(conv), conv)
func = run_opt_pass(func, relay.transform.InferType())
return func
func = _get_func()
mod, _ = lower_to_tir(func, cascader=total_cascader((1, 4, 4, 16)))
def test_weight_stream_only():
def _planner(cached_func, const_dict, sch):
weights = cached_func.inputs[1]
bias = cached_func.inputs[2]
out = cached_func.outputs[0]
conv_compute = Convolution2DCompute.from_output(out)
co = conv_compute.split(sch, 3, 2)
cache_weights = sch.cache_read(weights, "global",
[conv_compute.conv2d])
cache_bias = sch.cache_read(bias, "global", [conv_compute.conv2d])
sch[cache_weights].compute_at(sch[out], co)
sch[cache_bias].compute_at(sch[out], co)
def _get_func():
ifm = relay.var("ifm", shape=(1, 16, 16, 32), dtype="int8")
conv = make_ethosu_conv2d(
ifm,
32,
8,
(1, 1),
(0, 0),
(1, 1),
(1, 1),
)
func = relay.Function(relay.analysis.free_vars(conv), conv)
func = run_opt_pass(func, relay.transform.InferType())
return func
func = _get_func()
mod, consts = lower_to_tir(func, cascader=_planner)
script = mod.script(show_meta=True)
test_mod = tvm.script.from_source(script)
reference_mod = WeightStreamOnly
tvm.ir.assert_structural_equal(test_mod["main"], reference_mod["main"],
True)
reference_const_sizes = {
2: 128,
3: 32,
4: 112,
5: 32,
6: 112,
7: 32,
8: 112,
9: 32
}
test_const_sizes = {}
for key, value in consts.items():
test_const_sizes[key] = len(value)
assert reference_const_sizes == test_const_sizes
def test_schedule_diamond_graph():
ifm_a = relay.var("IFM_A", shape=(1, 56, 56, 96), dtype="int8")
conv_a = make_ethosu_conv2d(ifm_a, 96, 24, (1, 1), (0, 0), (1, 1), (1, 1))
conv_b = make_ethosu_conv2d(conv_a, 24, 24, (1, 1), (0, 0), (1, 1), (1, 1))
add = make_ethosu_binary_elementwise(conv_a, conv_b, 24, 24, "ADD", "int8")
func = relay.Function(relay.analysis.free_vars(add), add)
func = run_opt_pass(func, relay.transform.InferType())
test_mod, _ = lower_to_tir(func, copy_constants())
reference_mod = DiamondGraphTir
tvm.ir.assert_structural_equal(test_mod["main"], reference_mod["main"],
True)
def test_pooling_single(
ifm_shape,
ofm_channels,
ifm_layout,
ofm_layout,
pooling_type,
activation,
rounding_mode,
):
pool_shape = (3, 2)
strides = (1, 2)
padding = (1, 1, 1, 0)
ifm = relay.var("ifm", shape=ifm_shape, dtype="int8")
pooling = make_ethosu_pooling(
ifm,
pooling_type,
pool_shape,
ofm_channels,
strides,
padding,
activation,
ifm_layout,
ofm_layout,
rounding_mode,
)
func = relay.Function(relay.analysis.free_vars(pooling), pooling)
func = run_opt_pass(func, relay.transform.InferType())
mod, _ = lower_to_tir(func)
data = []
def _visit(stmt):
if isinstance(stmt, tvm.tir.Call):
data.append(get_pooling_args(stmt))
tvm.tir.stmt_functor.post_order_visit(mod["main"].body, _visit)
serial_pooling = _create_serial_pooling(
ifm_shape,
ofm_channels,
ifm_layout,
ofm_layout,
pool_shape,
pooling_type,
strides,
padding,
activation,
rounding_mode,
)
assert data[0] == ["ethosu_pooling"] + list(serial_pooling)
def test_conv2d_double_cascade(trial):
def _get_func(
ifm_shape,
ifm_channels,
mid_channels,
ofm_channels,
kernel_shape,
padding,
strides,
dilation,
layout,
):
ifm = relay.var("ifm", shape=ifm_shape, dtype="int8")
conv1 = make_ethosu_conv2d(
ifm,
ifm_channels,
mid_channels,
kernel_shape,
padding,
strides,
dilation,
"NONE",
layout,
layout,
)
conv2 = make_ethosu_conv2d(
conv1,
mid_channels,
ofm_channels,
kernel_shape,
padding,
strides,
dilation,
"NONE",
layout,
layout,
)
func = relay.Function(relay.analysis.free_vars(conv2), conv2)
func = run_opt_pass(func, relay.transform.InferType())
return func
reference_mod = trial[0]
params = trial[1:]
func = _get_func(*params[:-1])
mod, _ = lower_to_tir(func, cascader=total_cascader(params[-1]))
script = tvm.script.asscript(mod, True)
mod = tvm.script.from_source(script)
tvm.ir.assert_structural_equal(mod["main"], reference_mod["main"], True)
def test_constant_as_input():
"""Test to check that constants specified as inputs aren't
interpreted as an encoded constant."""
def get_graph():
dtype = "uint8"
ifm = relay.var("ifm", shape=(1, 16, 16, 32), dtype=dtype)
conv1 = make_ethosu_conv2d(
ifm,
32,
16,
(1, 1),
(0, 0),
(1, 1),
(1, 1),
)
scalar = relay.const(np.ones((1, 1, 1, 1), dtype=dtype), dtype=dtype)
add1 = make_ethosu_binary_elementwise(conv1,
scalar,
ifm_channels=32,
ifm2_channels=1,
operator_type="ADD",
ofm_dtype=dtype)
func = relay.Function(relay.analysis.free_vars(add1), add1)
func = run_opt_pass(func, relay.transform.InferType())
return func
tir_mod, params = lower_to_tir(get_graph(), copy_constants())
# Check tile address for the scalar constant input hasn't been
# overwritten.
extern_calls = tir_mod["main"].body.body.body.body.body
binary_elementwise = extern_calls[-1].value
args = binary_elementwise.args
reason = "Tile address overwritten"
assert args[26] == 0, reason
assert args[27] == 0, reason
assert args[28] == 0, reason
# More generally, check compiles successfully to make sure
# nothing else was overrwritten.
# With Target Hooks the TIR module needs a target attached
# and lowered via make unpacked API.
tir_mod["main"] = tir_mod["main"].with_attr("target",
tvm.target.Target("ethos-u"))
tir_mod = tvm.tir.transform.MakeUnpackedAPI()(tir_mod)
tir_to_cs_translator.translate(tir_mod, params)
def test_conv2d_inline_copy(trial):
def _get_func(ifm_shape, lower, upper, ofm_channels=16):
ifm = relay.var("ifm", shape=ifm_shape, dtype="int8")
sliced = relay.strided_slice(ifm, lower, upper)
conv = make_ethosu_conv2d(
sliced, upper[3] - lower[3], ofm_channels, (3, 3), (1, 1), (1, 1), (1, 1)
)
func = relay.Function(relay.analysis.free_vars(conv), conv)
func = run_opt_pass(func, relay.transform.InferType())
return func
reference_mod = trial[0]
params = trial[1:]
func = _get_func(*params)
mod, _ = lower_to_tir(func)
script = tvm.script.asscript(mod, True)
mod = tvm.script.from_source(script)
tvm.ir.assert_structural_equal(mod["main"], reference_mod["main"], True)
def test_direct_read_only():
def _get_func():
ifm = relay.var("ifm", shape=(1, 16, 16, 32), dtype="int8")
conv1 = make_ethosu_conv2d(
ifm,
32,
16,
(1, 1),
(0, 0),
(1, 1),
(1, 1),
)
conv2 = make_ethosu_conv2d(
conv1,
16,
8,
(1, 1),
(0, 0),
(1, 1),
(1, 1),
)
func = relay.Function(relay.analysis.free_vars(conv2), conv2)
func = run_opt_pass(func, relay.transform.InferType())
return func
func = _get_func()
mod, consts = lower_to_tir(func)
script = mod.script(show_meta=True)
test_mod = tvm.script.from_source(script)
reference_mod = DirectReadOnly
tvm.ir.assert_structural_equal(test_mod["main"], reference_mod["main"],
True)
reference_const_sizes = {1: 592, 2: 160, 3: 160, 4: 80}
test_const_sizes = {}
for key, value in consts.items():
test_const_sizes[key] = len(value)
assert reference_const_sizes == test_const_sizes
def relay_to_tir_func(ext_func: relay.Function) -> tvm.tir.PrimFunc:
"""
This is the hook for python-based lowering of relay function
that gets offloaded to the microNPU.
Parameters
----------
ext_func : relay.Function
This is the partitioned relay function
Returns
-------
primfunc : tir.PrimFunc
This returns the scheduled PrimFunc
"""
assert len(ext_func.params) == 1
input_size = util.calculate_size_bytes(ext_func.params[0])
output_size = util.calculate_size_bytes(ext_func.body)
mod = tvm.IRModule()
mod["main"] = ext_func
mod = LegalizeEthosU()(mod)
mod = LUTsOptimizer()(mod)
mod = LayoutOptimizer()(mod)
mod = relay.transform.InferType()(mod)
# We are currently using copy_constants scheduler In the long run,
# this should be a single intelligent and a composite scheduler
# that can perform scheduling based on user inputs such as
# scratch memory size.
tir_mod, const_dict = lower_to_tir(mod["main"], copy_constants())
for idx in const_dict.keys():
const_dict[idx] = tvm.nd.array(const_dict[idx])
primfunc = tir_mod["main"]
primfunc = primfunc.with_attr("global_symbol",
ext_func.attrs["global_symbol"])
primfunc = primfunc.with_attr("ethos-u.constants", const_dict)
primfunc = primfunc.with_attr("ethos-u.input_size", input_size)
primfunc = primfunc.with_attr("ethos-u.output_size", output_size)
return primfunc
def test_copy():
def _get_func():
data = relay.var("data", shape=(1, 16, 16, 32), dtype="int8")
conv = make_ethosu_conv2d(
data,
32,
8,
(1, 1),
(0, 0),
(1, 1),
(1, 1),
)
func = relay.Function(relay.analysis.free_vars(conv), conv)
func = run_opt_pass(func, relay.transform.InferType())
return func
func = _get_func()
mod, _ = lower_to_tir(func, cascader=copy_constants())
script = mod.script(show_meta=True)
test_mod = tvm.script.from_source(script)
reference_mod = ReferenceModule
tvm.ir.assert_structural_equal(test_mod["main"], reference_mod["main"], True)
def test_depthwise_conv2d_single(trial):
def _get_func(
ifm_shape,
channels,
kernel_shape,
padding,
strides,
dilation,
activation,
ifm_layout,
ofm_layout,
):
ifm = relay.var("ifm", shape=ifm_shape, dtype="int8")
depthwise = make_ethosu_depthwise_conv2d(
ifm,
channels,
kernel_shape,
padding,
strides,
dilation,
activation,
ifm_layout,
ofm_layout,
)
func = relay.Function(relay.analysis.free_vars(depthwise), depthwise)
func = run_opt_pass(func, relay.transform.InferType())
return func
func = _get_func(*trial)
mod, _ = lower_to_tir(func)
data = []
def _visit(stmt):
if isinstance(stmt, tvm.tir.Call):
data.append(get_convolutional_args(stmt, remove_constants=True))
tvm.tir.stmt_functor.post_order_visit(mod["main"].body, _visit)
(
ifm_shape,
channels,
kernel_shape,
padding,
strides,
dilation,
activation,
ifm_layout,
ofm_layout,
) = trial
dilated_kernel_h = (kernel_shape[0] - 1) * dilation[0] + 1
dilated_kernel_w = (kernel_shape[1] - 1) * dilation[1] + 1
if ifm_layout == "NHWC":
ifm_stride_c = 1
ifm_stride_w = ifm_shape[3]
ifm_stride_h = ifm_shape[2] * ifm_shape[3]
ofm_height = (ifm_shape[1] - dilated_kernel_h + padding[0] +
padding[0]) // strides[0] + 1
ofm_width = (ifm_shape[2] - dilated_kernel_w + padding[1] +
padding[1]) // strides[1] + 1
else:
ifm_stride_w = 16
ifm_stride_c = 16 * ifm_shape[3]
ifm_stride_h = 16 * ifm_shape[2] * ifm_shape[3]
ofm_height = (ifm_shape[1] - dilated_kernel_h + padding[0] +
padding[0]) // strides[0] + 1
ofm_width = (ifm_shape[3] - dilated_kernel_w + padding[1] +
padding[1]) // strides[1] + 1
if ofm_layout == "NHWC":
ofm_stride_c = 1
ofm_stride_w = channels if ofm_width > 1 else 1
ofm_stride_h = channels * ofm_width if ofm_height > 1 else 1
else:
ofm_stride_w = 16
ofm_stride_c = 16 * ofm_width
ofm_stride_h = 16 * ofm_width * ((channels - 1) // 16 + 1)
answer = [
"int8",
ifm_shape[1],
ifm_shape[2] if ifm_layout == "NHWC" else ifm_shape[3],
channels,
ifm_shape[1],
0,
ifm_shape[2] if ifm_layout == "NHWC" else ifm_shape[3],
0,
0,
0,
0,
0.6,
11,
ifm_layout,
ifm_stride_h,
ifm_stride_w,
ifm_stride_c,
"int8",
ofm_height,
ofm_width,
channels,
ofm_height,
0,
ofm_width,
0,
0,
0,
0,
0.26,
15,
ofm_layout,
ofm_stride_h,
ofm_stride_w,
ofm_stride_c,
kernel_shape[1],
kernel_shape[0],
strides[1],
strides[0],
dilation[1],
dilation[0],
13,
padding[0],
padding[1],
padding[0],
padding[1],
activation,
15 if activation == "CLIP" else 0,
105 if activation == "CLIP" else 0,
"NONE",
]
assert data[0] == answer, data[0]
def test_identity(ifm_shape):
ifm = relay.var("ifm", shape=ifm_shape, dtype="int8")
identity = make_ethosu_identity(ifm)
func = relay.Function(relay.analysis.free_vars(identity), identity)
func = run_opt_pass(func, relay.transform.InferType())
mod, _ = lower_to_tir(func)
data = []
def _visit(stmt):
if isinstance(stmt, tvm.tir.Call):
data.append(get_pooling_args(stmt))
# Construct the ifm shape that the initial ifm shape gets legalized into
ref_ifm_shape = ifm_shape
if len(ref_ifm_shape) < 4:
ref_ifm_shape = [1] + ref_ifm_shape
while len(ref_ifm_shape) < 4:
ref_ifm_shape.append(1)
tvm.tir.stmt_functor.post_order_visit(mod["main"].body, _visit)
ifm_stride_c = 1
ifm_stride_w = ref_ifm_shape[3]
ifm_stride_h = ref_ifm_shape[2] * ref_ifm_shape[3]
ofm_height = ref_ifm_shape[1]
ofm_width = ref_ifm_shape[2]
ofm_channels = ref_ifm_shape[3]
ofm_stride_c = 1
ofm_stride_w = ofm_channels if ofm_width > 1 else 1
ofm_stride_h = ofm_channels * ofm_width if ofm_height > 1 else 1
# The identity operator TIR gets converted into serial pooling
serial_pooling = spec.SerialPooling(
ifm=spec.SerialFeatureMap(
data_type="int8",
height=ref_ifm_shape[1],
width=ref_ifm_shape[2],
channels=ofm_channels,
tile_height_0=ref_ifm_shape[1],
tile_height_1=0,
tile_width_0=ref_ifm_shape[2],
tile_address_0=0,
tile_address_1=0,
tile_address_2=0,
tile_address_3=0,
scale=1.0,
zero_point=0,
layout="NHWC",
stride_h=ifm_stride_h,
stride_w=ifm_stride_w,
stride_c=ifm_stride_c,
),
ofm=spec.SerialFeatureMap(
data_type="int8",
height=ofm_height,
width=ofm_width,
channels=ofm_channels,
tile_height_0=ofm_height,
tile_height_1=0,
tile_width_0=ofm_width,
tile_address_0=0,
tile_address_1=0,
tile_address_2=0,
tile_address_3=0,
scale=1.0,
zero_point=0,
layout="NHWC",
stride_h=ofm_stride_h,
stride_w=ofm_stride_w,
stride_c=ofm_stride_c,
),
pooling_type="AVG",
pool_shape=spec.SerialKernel(1, 1, 1, 1, 1, 1),
padding=spec.SerialPadding(0, 0, 0, 0),
activation=spec.SerialActivation(op="NONE", clip_min=0, clip_max=0),
upscale="NONE",
)
assert data[0] == ["ethosu_identity"] + list(serial_pooling)
def test_binary_elementwise_single(
ifm_shape,
ifm2_shape,
ifm_channels,
ifm2_channels,
ifm_layout,
ofm_layout,
operator_type,
activation,
):
dtype = "int8"
ifm = relay.var("ifm", shape=ifm_shape, dtype=dtype)
ifm2 = relay.var("ifm2", shape=ifm2_shape, dtype=dtype)
binary_elementwise = make_ethosu_binary_elementwise(
ifm,
ifm2,
ifm_channels,
ifm2_channels,
operator_type,
dtype,
False,
activation,
ifm_layout,
ifm_layout,
ofm_layout,
)
func = relay.Function(relay.analysis.free_vars(binary_elementwise), binary_elementwise)
func = run_opt_pass(func, relay.transform.InferType())
mod, _ = lower_to_tir(func)
data = []
def _visit(stmt):
if isinstance(stmt, tvm.tir.Call):
data.append(get_binary_elementwise_args(stmt))
tvm.tir.stmt_functor.post_order_visit(mod["main"].body, _visit)
if ifm_layout == "NHWC":
ifm_stride_c = 1
ifm_stride_w = ifm_shape[3] if ifm_shape[2] != 1 else 1
ifm_stride_h = ifm_shape[2] * ifm_shape[3] if ifm_shape[1] != 1 else 1
ifm2_stride_c = 1
ifm2_stride_w = ifm2_shape[3] if ifm2_shape[2] != 1 else 1
ifm2_stride_h = ifm2_shape[2] * ifm2_shape[3] if ifm2_shape[1] != 1 else 1
ofm_height = ifm_shape[1]
ofm_width = ifm_shape[2]
else:
ifm_stride_w = 16
ifm_stride_c = 16 * ifm_shape[3]
ifm_stride_h = 16 * ifm_shape[2] * ifm_shape[3]
ifm2_stride_w = 16
ifm2_stride_c = 16 * ifm2_shape[3]
ifm2_stride_h = 16 * ifm2_shape[2] * ifm2_shape[3]
ofm_height = ifm_shape[1]
ofm_width = ifm_shape[3]
if ofm_layout == "NHWC":
ofm_stride_c = 1
ofm_stride_w = ifm_channels if ofm_width > 1 else 1
ofm_stride_h = ifm_channels * ofm_width if ofm_height > 1 else 1
else:
ofm_stride_w = 16
ofm_stride_c = 16 * ofm_width
ofm_stride_h = 16 * ofm_width * ((ifm_channels - 1) // 16 + 1)
serial_binary_elementwise = spec.SerialBinaryElementwise(
ifm=spec.SerialFeatureMap(
data_type=dtype,
height=ifm_shape[1],
width=ifm_shape[2] if ifm_layout == "NHWC" else ifm_shape[3],
channels=ifm_channels,
tile_height_0=ifm_shape[1],
tile_height_1=0,
tile_width_0=ifm_shape[2] if ifm_layout == "NHWC" else ifm_shape[3],
tile_address_0=0,
tile_address_1=0,
tile_address_2=0,
tile_address_3=0,
scale=1.0,
zero_point=0,
layout=ifm_layout,
stride_h=ifm_stride_h,
stride_w=ifm_stride_w,
stride_c=ifm_stride_c,
),
ifm2=spec.SerialFeatureMap(
data_type=dtype,
height=ifm2_shape[1],
width=ifm2_shape[2] if ifm_layout == "NHWC" else ifm2_shape[3],
channels=ifm2_channels,
tile_height_0=ifm2_shape[1],
tile_height_1=0,
tile_width_0=ifm2_shape[2] if ifm_layout == "NHWC" else ifm2_shape[3],
tile_address_0=0,
tile_address_1=0,
tile_address_2=0,
tile_address_3=0,
scale=1.0,
zero_point=0,
layout=ifm_layout,
stride_h=ifm2_stride_h,
stride_w=ifm2_stride_w,
stride_c=ifm2_stride_c,
),
ofm=spec.SerialFeatureMap(
data_type=dtype,
height=ofm_height,
width=ofm_width,
channels=ifm_channels,
tile_height_0=ofm_height,
tile_height_1=0,
tile_width_0=ofm_width,
tile_address_0=0,
tile_address_1=0,
tile_address_2=0,
tile_address_3=0,
scale=1.0,
zero_point=0,
layout=ofm_layout,
stride_h=ofm_stride_h,
stride_w=ofm_stride_w,
stride_c=ofm_stride_c,
),
operator_type=operator_type,
reversed_operands=False,
activation=spec.SerialActivation(
op=activation,
clip_min=10 if activation == "CLIP" else 0,
clip_max=100 if activation == "CLIP" else 0,
),
)
assert data[0] == ["ethosu_binary_elementwise"] + list(serial_binary_elementwise)
def test_correct_stride_with_multiple_pooling():
"""Testing a specific case of two pooling operations with NHWC inputs/outputs
but a NHCWB16 intermediate tensor. This lead to elements being accessed in the
wrong order by the NPU, due to incorrect stride values being calculated."""
ifm_shape = (1, 4, 4, 8)
ofm_channels = 8
pooling_type = "MAX"
pool_shape = (1, 1)
strides = (1, 1)
padding = (0, 0, 0, 0)
ifm = relay.var("ifm", shape=ifm_shape, dtype="int8")
op = make_ethosu_pooling(
ifm,
pooling_type,
pool_shape,
ofm_channels,
strides,
padding,
ifm_layout="NHWC",
ofm_layout="NHCWB16",
)
op = make_ethosu_pooling(
op,
pooling_type,
pool_shape,
ofm_channels,
strides,
padding,
ifm_layout="NHCWB16",
ofm_layout="NHWC",
)
func = relay.Function(relay.analysis.free_vars(op), op)
func = run_opt_pass(func, relay.transform.InferType())
mod, _ = lower_to_tir(func)
data = []
def _visit(stmt):
if isinstance(stmt, tvm.tir.Call):
data.append(get_pooling_args(stmt))
tvm.tir.stmt_functor.post_order_visit(mod["main"].body, _visit)
serial_pooling_1 = _create_serial_pooling(
[1, 4, 4, 8],
8,
"NHWC",
"NHCWB16",
pool_shape,
pooling_type,
strides,
padding,
)
serial_pooling_2 = _create_serial_pooling(
[1, 4, 1, 4, 16],
8,
"NHCWB16",
"NHWC",
pool_shape,
pooling_type,
strides,
padding,
)
assert data[0] == ["ethosu_pooling"] + list(serial_pooling_1)
assert data[1] == ["ethosu_pooling"] + list(serial_pooling_2)
def test_mixed_read():
def _planner(te_graph, const_dict, sch):
weight = te_graph.inputs[4]
scale_bias = te_graph.inputs[5]
out = te_graph.outputs[0]
conv_compute = Convolution2DCompute.from_output(out)
co = conv_compute.split(sch, 3, 2)
cache_weight = sch.cache_read(weight, "global", [conv_compute.conv2d])
cache_scale_bias = sch.cache_read(scale_bias, "global",
[conv_compute.conv2d])
sch[cache_weight].compute_at(sch[out], co)
sch[cache_scale_bias].compute_at(sch[out], co)
def _get_func():
ifm = relay.var("ifm", shape=(1, 16, 16, 32), dtype="int8")
conv1 = make_ethosu_conv2d(
ifm,
32,
16,
(1, 1),
(0, 0),
(1, 1),
(1, 1),
)
conv2 = make_ethosu_conv2d(
conv1,
16,
8,
(1, 1),
(0, 0),
(1, 1),
(1, 1),
)
func = relay.Function(relay.analysis.free_vars(conv2), conv2)
func = run_opt_pass(func, relay.transform.InferType())
return func
func = _get_func()
mod, consts = lower_to_tir(func, cascader=_planner)
script = tvm.script.asscript(mod, True)
test_mod = tvm.script.from_source(script)
reference_mod = MixedRead()
tvm.ir.assert_structural_equal(test_mod["main"], reference_mod["main"],
True)
reference_const_sizes = {
1: 592,
2: 160,
4: 80,
5: 32,
6: 80,
7: 32,
8: 80,
9: 32,
10: 80,
11: 32,
}
test_const_sizes = {}
for key, value in consts.items():
test_const_sizes[key] = len(value)
assert reference_const_sizes == test_const_sizes
def test_pooling_single(
ifm_shape,
ofm_channels,
ifm_layout,
ofm_layout,
pooling_type,
activation,
):
pool_shape = (3, 2)
strides = (1, 2)
padding = (1, 1, 1, 0)
ifm = relay.var("ifm", shape=ifm_shape, dtype="int8")
pooling = make_ethosu_pooling(
ifm,
pooling_type,
pool_shape,
ofm_channels,
strides,
padding,
activation,
ifm_layout,
ofm_layout,
)
func = relay.Function(relay.analysis.free_vars(pooling), pooling)
func = run_opt_pass(func, relay.transform.InferType())
mod, _ = lower_to_tir(func)
data = []
def _visit(stmt):
if isinstance(stmt, tvm.tir.Call):
data.append(get_pooling_args(stmt))
tvm.tir.stmt_functor.post_order_visit(mod["main"].body, _visit)
if ifm_layout == "NHWC":
ifm_stride_c = 1
ifm_stride_w = ifm_shape[3]
ifm_stride_h = ifm_shape[2] * ifm_shape[3]
ofm_height = (ifm_shape[1] - pool_shape[0] + padding[0] +
padding[0]) // strides[0] + 1
ofm_width = (ifm_shape[2] - pool_shape[1] + padding[1] +
padding[1]) // strides[1] + 1
else:
ifm_stride_w = 16
ifm_stride_c = 16 * ifm_shape[3]
ifm_stride_h = 16 * ifm_shape[2] * ifm_shape[3]
ofm_height = (ifm_shape[1] - pool_shape[0] + padding[0] +
padding[0]) // strides[0] + 1
ofm_width = (ifm_shape[3] - pool_shape[1] + padding[1] +
padding[1]) // strides[1] + 1
if ofm_layout == "NHWC":
ofm_stride_c = 1
ofm_stride_w = ofm_channels if ofm_width > 1 else 1
ofm_stride_h = ofm_channels * ofm_width if ofm_height > 1 else 1
else:
ofm_stride_w = 16
ofm_stride_c = 16 * ofm_width
ofm_stride_h = 16 * ofm_width * ((ofm_channels - 1) // 16 + 1)
serial_pooling = spec.SerialPooling(
ifm=spec.SerialFeatureMap(
data_type="int8",
height=ifm_shape[1],
width=ifm_shape[2] if ifm_layout == "NHWC" else ifm_shape[3],
channels=ofm_channels,
tile_height_0=ifm_shape[1],
tile_height_1=0,
tile_width_0=ifm_shape[2]
if ifm_layout == "NHWC" else ifm_shape[3],
tile_address_0=0,
tile_address_1=0,
tile_address_2=0,
tile_address_3=0,
scale=1.0,
zero_point=0,
layout=ifm_layout,
stride_h=ifm_stride_h,
stride_w=ifm_stride_w,
stride_c=ifm_stride_c,
),
ofm=spec.SerialFeatureMap(
data_type="int8",
height=ofm_height,
width=ofm_width,
channels=ofm_channels,
tile_height_0=ofm_height,
tile_height_1=0,
tile_width_0=ofm_width,
tile_address_0=0,
tile_address_1=0,
tile_address_2=0,
tile_address_3=0,
scale=1.0,
zero_point=0,
layout=ofm_layout,
stride_h=ofm_stride_h,
stride_w=ofm_stride_w,
stride_c=ofm_stride_c,
),
pooling_type=pooling_type,
pool_shape=spec.SerialKernel(
width=pool_shape[1],
height=pool_shape[0],
stride_w=strides[1],
stride_h=strides[0],
dilation_w=1,
dilation_h=1,
),
padding=spec.SerialPadding(top=padding[0],
left=padding[1],
bottom=padding[2],
right=padding[3]),
activation=spec.SerialActivation(
op=activation,
clip_min=10 if activation == "CLIP" else 0,
clip_max=100 if activation == "CLIP" else 0,
),
upscale="NONE",
)
assert data[0] == ["ethosu_pooling"] + list(serial_pooling)
def test_conv2d_single(trial):
def _get_func(
ifm_shape,
ifm_channels,
ofm_channels,
kernel_shape,
padding,
strides,
dilation,
activation,
ifm_layout,
ofm_layout,
):
ifm = relay.var("ifm", shape=ifm_shape, dtype="int8")
conv = make_ethosu_conv2d(
ifm,
ifm_channels,
ofm_channels,
kernel_shape,
padding,
strides,
dilation,
activation,
ifm_layout,
ofm_layout,
)
func = relay.Function(relay.analysis.free_vars(conv), conv)
func = run_opt_pass(func, relay.transform.InferType())
return func
# TODO(@mbaret) Fix the tests for these known failures
# These are anticipated to actually be correct, just a testing issue to do with
# equivalent convolutions.
known_failures = [
[(1, 3, 12, 9, 16), 182, 67, (2, 3), (1, 3), (2, 2), (1, 1), "CLIP", "NHCWB16", "NHCWB16"],
[(1, 2, 12, 9, 16), 182, 67, (1, 3), (6, 3), (2, 2), (1, 1), "CLIP", "NHCWB16", "NHCWB16"],
]
func = _get_func(*trial)
mod, _ = lower_to_tir(func)
data = []
def _visit(stmt):
if isinstance(stmt, tvm.tir.Call):
data.append(get_convolutional_args(stmt, remove_constants=True))
tvm.tir.stmt_functor.post_order_visit(mod["main"].body, _visit)
(
ifm_shape,
ifm_channels,
ofm_channels,
kernel_shape,
padding,
strides,
dilation,
activation,
ifm_layout,
ofm_layout,
) = trial
dilated_kernel_h = (kernel_shape[0] - 1) * dilation[0] + 1
dilated_kernel_w = (kernel_shape[1] - 1) * dilation[1] + 1
if ifm_layout == "NHWC":
ifm_stride_c = 1
ifm_stride_w = ifm_shape[3]
ifm_stride_h = ifm_shape[2] * ifm_shape[3]
ofm_height = (ifm_shape[1] - dilated_kernel_h + padding[0] + padding[0]) // strides[0] + 1
ofm_width = (ifm_shape[2] - dilated_kernel_w + padding[1] + padding[1]) // strides[1] + 1
else:
ifm_stride_w = 16
ifm_stride_c = 16 * ifm_shape[3]
ifm_stride_h = 16 * ifm_shape[2] * ifm_shape[3]
ofm_height = (ifm_shape[1] - dilated_kernel_h + padding[0] + padding[0]) // strides[0] + 1
ofm_width = (ifm_shape[3] - dilated_kernel_w + padding[1] + padding[1]) // strides[1] + 1
if ofm_layout == "NHWC":
ofm_stride_c = 1
ofm_stride_w = ofm_channels if ofm_width > 1 else 1
ofm_stride_h = ofm_channels * ofm_width if ofm_height > 1 else 1
else:
ofm_stride_w = 16
ofm_stride_c = 16 * ofm_width
ofm_stride_h = 16 * ofm_width * ((ofm_channels - 1) // 16 + 1)
answer = [
"int8",
ifm_shape[1],
ifm_shape[2] if ifm_layout == "NHWC" else ifm_shape[3],
ifm_channels,
ifm_shape[1],
0,
ifm_shape[2] if ifm_layout == "NHWC" else ifm_shape[3],
0,
0,
0,
0,
0.5,
10,
ifm_layout,
ifm_stride_h,
ifm_stride_w,
ifm_stride_c,
"int8",
ofm_height,
ofm_width,
ofm_channels,
ofm_height,
0,
ofm_width,
0,
0,
0,
0,
0.25,
14,
ofm_layout,
ofm_stride_h,
ofm_stride_w,
ofm_stride_c,
kernel_shape[1],
kernel_shape[0],
strides[1],
strides[0],
dilation[1],
dilation[0],
12,
padding[0],
padding[1],
padding[0],
padding[1],
activation,
10 if activation == "CLIP" else 0,
100 if activation == "CLIP" else 0,
"NONE",
]
assert data[0] == answer, data[0]
