def test_relay_reshape_legalize(ifm_shape, new_shape):
ifm = relay.var("ifm", shape=ifm_shape, dtype="int8")
reshape = relay.op.reshape(ifm, new_shape)
func = relay.Function([ifm], reshape)
mod = tvm.IRModule()
mod["main"] = func
mod = relay.transform.InferType()(mod)
reshape_pattern_table = [
(
ethosu.ReshapeParams.composite_name,
ethosu.reshape_pattern(),
lambda pat: ethosu.ReshapeParams(pat).is_valid(),
),
]
mod = partition_ethosu_by_table(mod, reshape_pattern_table)
mod["tvmgen_default_ethos_u_main_0"] = dataflow_pattern.rewrite(
legalize.ReshapeRewriter(), mod["tvmgen_default_ethos_u_main_0"])
mod["tvmgen_default_ethos_u_main_0"] = dataflow_pattern.rewrite(
legalize.NoOpRewriter(), mod["tvmgen_default_ethos_u_main_0"])
mod = relay.transform.InferType()(mod)
ext_func = mod["tvmgen_default_ethos_u_main_0"]
identity = ext_func.body
assert identity.op.name == "contrib.ethosu.identity"
# check that the reshape is still there
reshape = identity.args[0]
assert reshape.op.name == "reshape"
# check that identity's output shape matches reshape's output shape
assert tuple(identity.checked_type.shape) == new_shape
def transform_module(self, mod: tvm.ir.IRModule,
ctx: tvm.ir.transform.PassContext) -> tvm.ir.IRModule:
for global_var, func in mod.functions.items():
func = rewrite(PartitionedSplitRewriter(), func)
func = rewrite(SplitRewriter(), func)
mod.update_func(global_var, func)
return mod
def transform_module(
self, mod: tvm.ir.IRModule, ctx: tvm.ir.transform.PassContext
) -> tvm.ir.IRModule:
for global_var, func in mod.functions.items():
func = rewrite(DepthwiseConv2DRewriter(), func)
mod.update_func(global_var, func)
return mod
def test_tflite_tanh_legalize():
dtype = "int8"
ifm_shape = (1, 241, 132, 7)
def create_tflite_graph():
class Model(tf.Module):
@tf.function
def tanh_func(self, x):
op = tf.math.tanh(x)
return op
model = Model()
concrete_func = model.tanh_func.get_concrete_function(
tf.TensorSpec(ifm_shape, dtype=tf.float32))
# Convert the model
def representative_dataset():
for _ in range(100):
data = np.random.rand(*tuple(ifm_shape))
yield [data.astype(np.float32)]
converter = tf.lite.TFLiteConverter.from_concrete_functions(
[concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_dataset
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS_INT8
]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8
tflite_model = converter.convert()
return tflite_model
tflite_graph = create_tflite_graph()
tflite_model = tflite.Model.Model.GetRootAsModel(tflite_graph, 0)
mod, params = relay.frontend.from_tflite(
tflite_model,
shape_dict={"input": ifm_shape},
dtype_dict={"input": dtype},
)
mod = ethosu.partition_for_ethosu(mod, params)
mod["tvmgen_default_ethos_u_main_0"] = dataflow_pattern.rewrite(
legalize.TanhRewriter(), mod["tvmgen_default_ethos_u_main_0"])
mod = relay.transform.InferType()(mod)
func_body = mod["tvmgen_default_ethos_u_main_0"].body
assert func_body.op.name == "contrib.ethosu.identity"
assert func_body.attrs.activation == "TANH"
assert tuple(func_body.args[0].checked_type.shape) == (ifm_shape)
assert tuple(func_body.args[1].checked_type.shape) == (256, )
def test_relay_strided_slice_legalize(ifm_shape, begin, end):
ifm = relay.var("ifm", shape=ifm_shape, dtype="int8")
strided_slice = relay.op.strided_slice(ifm, begin, end)
func = relay.Function([ifm], strided_slice)
mod = tvm.IRModule()
mod["main"] = func
mod = relay.transform.InferType()(mod)
strided_slice_pattern_table = [
(
ethosu.StridedSliceParams.composite_name,
ethosu.strided_slice_pattern(),
lambda pat: ethosu.StridedSliceParams(pat).is_valid(),
),
]
mod = partition_ethosu_by_table(mod, strided_slice_pattern_table)
mod["tvmgen_default_ethos_u_main_0"] = dataflow_pattern.rewrite(
legalize.StridedSliceRewriter(), mod["tvmgen_default_ethos_u_main_0"]
)
mod["tvmgen_default_ethos_u_main_0"] = dataflow_pattern.rewrite(
legalize.NoOpRewriter(), mod["tvmgen_default_ethos_u_main_0"]
)
mod = relay.transform.InferType()(mod)
ext_func = mod["tvmgen_default_ethos_u_main_0"]
identity = ext_func.body
assert identity.op.name == "contrib.ethosu.identity"
# check that the strided_slice is still there
strided_slice = identity.args[0]
assert strided_slice.op.name == "strided_slice"
# check that identity's output shape matches strided slice's output shape
slice_shape = [a - b for a, b in zip(end, begin)]
assert list(identity.checked_type.shape) == slice_shape
def test_binary_add_from_constant_scalar():
dtype = "uint8"
ifm_shape = (1, 4, 4, 8)
def create_graph():
inp = relay.var("input", shape=ifm_shape, dtype=dtype)
scalar = relay.const(np.ones((1, 1, 1, 1), dtype=dtype), dtype=dtype)
add = relay.qnn.op.add(
inp,
scalar,
relay.const(1.0, dtype="float32"),
relay.const(0, dtype="int32"),
relay.const(1.0, dtype="float32"),
relay.const(0, dtype="int32"),
relay.const(1.0, dtype="float32"),
relay.const(0, dtype="int32"),
)
func = relay.Function(relay.analysis.free_vars(add), add)
return tvm.IRModule.from_expr(func)
def verify(ext_func):
op = ext_func.body
assert list(op.args[0].checked_type.shape) == [1, 4, 4, 8]
assert list(op.args[1].checked_type.shape) == [1, 1, 1, 1]
assert op.args[0].checked_type.dtype == "uint8"
assert list(op.checked_type.shape) == [1, 4, 4, 8]
assert op.checked_type.dtype == "uint8"
assert op.attrs.operator_type == "ADD"
rewriter = legalize.AddRewriter()
pattern_table = [
(
ethosu.AddParams.composite_name,
ethosu.qnn_add_pattern(),
lambda pat: ethosu.AddParams(pat).is_valid(),
),
]
mod = create_graph()
mod = partition_ethosu_by_table(mod, pattern_table)
mod["tvmgen_default_ethos_u_main_0"] = dataflow_pattern.rewrite(
rewriter, mod["tvmgen_default_ethos_u_main_0"]
)
verify(mod["tvmgen_default_ethos_u_main_0"])
def test_ethosu_left_shift_binary_elemwise_legalize(ifm_shape, ifm2_shape, reversed_operands):
dtype = "int32"
operator_type = "SHL"
def create_graph():
input1 = relay.var("x1", shape=ifm_shape, dtype=dtype)
input2 = relay.var("x2", shape=ifm2_shape, dtype=dtype)
c1 = relay.left_shift(input1, input2)
f = relay.Function([input1, input2], c1)
mod = tvm.IRModule()
mod["main"] = f
return mod
def verify(ext_func):
out_shape = ifm2_shape if reversed_operands else ifm_shape
shapes = [ifm_shape, ifm2_shape]
ifm_index, ifm2_index = (1, 0) if reversed_operands else (0, 1)
op = ext_func.body
assert list(op.args[0].checked_type.shape) == shapes[ifm_index]
assert list(op.args[1].checked_type.shape) == shapes[ifm2_index]
assert op.args[0].checked_type.dtype == dtype
assert list(op.checked_type.shape) == out_shape
assert op.checked_type.dtype == dtype
assert op.attrs.operator_type == operator_type
assert op.attrs.reversed_operands == reversed_operands
assert str(op.attrs.activation) == "NONE"
rewriter = legalize.ShlRewriter()
pattern_table = [
(
ethosu.ShlParams.composite_name,
ethosu.shl_pattern(),
lambda pat: ethosu.ShlParams(pat).is_valid(),
),
]
mod = create_graph()
mod = partition_ethosu_by_table(mod, pattern_table)
mod["tvmgen_default_ethos_u_main_0"] = dataflow_pattern.rewrite(
rewriter, mod["tvmgen_default_ethos_u_main_0"]
)
verify(mod["tvmgen_default_ethos_u_main_0"])
def transform_npu_function(self, _,
func: relay.Function) -> relay.Function:
"""This is the method that replaces the operations with hardware/codegen supported
operations.
"""
rewriters = [
PartitionedSplitRewriter(),
SplitRewriter(),
Conv2DRewriter(),
Conv2DTransposeRewriter(),
DepthwiseConv2DRewriter(),
FullyConnectedRewriter(),
MaxPoolingRewriter(),
AvgPoolingRewriter(),
AddRewriter(),
SubRewriter(),
MulRewriter(),
MinRewriter(),
MaxRewriter(),
ShlRewriter(),
AbsRewriter(),
TanhRewriter(),
LeakyReLURewriter(),
MeanRewriter(),
ConcatRewriter(),
SigmoidRewriter(),
RequantizeRewriter(),
Resize2dRewriter(),
ExpandDimsRewriter(),
SqueezeRewriter(),
ReshapeRewriter(),
StridedSliceRewriter(),
NoOpRewriter(),
]
for rewriter in rewriters:
func = rewrite(rewriter, func)
return func
def test_tflite_binary_elemwise_legalize(
operator_type,
ifm_shape,
ifm2_shape,
reversed_operands,
activation_function,
):
dtype = "int8"
def create_tflite_graph():
class Model(tf.Module):
@tf.function
def tf_function(self, x, y):
if operator_type == "ADD":
op = tf.math.add(x, y)
elif operator_type == "SUB":
op = tf.math.subtract(x, y)
elif operator_type == "MUL":
op = tf.math.multiply(x, y)
elif operator_type == "MIN":
op = tf.math.minimum(x, y)
elif operator_type == "MAX":
op = tf.math.maximum(x, y)
if activation_function == "RELU":
op = tf.nn.relu(op)
return op
model = Model()
concrete_func = model.tf_function.get_concrete_function(
tf.TensorSpec(ifm_shape, dtype=tf.float32), tf.TensorSpec(ifm2_shape, dtype=tf.float32)
)
# Convert the model
def representative_dataset():
for _ in range(100):
data = np.random.rand(*tuple(ifm_shape))
data2 = np.random.rand(*tuple(ifm2_shape)) * 2
yield [data.astype(np.float32), data2.astype(np.float32)]
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_dataset
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8
tflite_model = converter.convert()
return tflite_model
def verify(ext_func):
out_shape = ifm2_shape if reversed_operands else ifm_shape
shapes = [ifm_shape, ifm2_shape]
ifm_index, ifm2_index = (1, 0) if reversed_operands else (0, 1)
op = ext_func.body
assert list(op.args[0].checked_type.shape) == shapes[ifm_index]
assert list(op.args[1].checked_type.shape) == shapes[ifm2_index]
assert op.args[0].checked_type.dtype == dtype
assert list(op.checked_type.shape) == out_shape
assert op.checked_type.dtype == dtype
assert op.attrs.operator_type == operator_type
assert op.attrs.reversed_operands == reversed_operands
if activation_function == "RELU":
assert str(op.attrs.activation) == "CLIP"
if operator_type == "ADD":
rewriter = legalize.AddRewriter()
pattern_table = [
(
ethosu.AddParams.composite_name,
ethosu.qnn_add_pattern(),
lambda pat: ethosu.AddParams(pat).is_valid(),
),
]
elif operator_type == "SUB":
rewriter = legalize.SubRewriter()
pattern_table = [
(
ethosu.SubParams.composite_name,
ethosu.qnn_subtract_pattern(),
lambda pat: ethosu.SubParams(pat).is_valid(),
),
]
elif operator_type == "MUL":
rewriter = legalize.MulRewriter()
pattern_table = [
(
ethosu.MulParams.composite_name,
ethosu.qnn_mul_pattern(),
lambda pat: ethosu.MulParams(pat).is_valid(),
),
]
elif operator_type == "MIN":
rewriter = legalize.MinRewriter()
pattern_table = [
(
ethosu.MinParams.composite_name,
ethosu.minimum_pattern(),
lambda pat: ethosu.MinParams(pat).is_valid(),
),
]
elif operator_type == "MAX":
rewriter = legalize.MaxRewriter()
pattern_table = [
(
ethosu.MaxParams.composite_name,
ethosu.maximum_pattern(),
lambda pat: ethosu.MaxParams(pat).is_valid(),
),
]
tflite_graph = create_tflite_graph()
tflite_model = tflite.Model.Model.GetRootAsModel(tflite_graph, 0)
mod, _ = relay.frontend.from_tflite(
tflite_model,
shape_dict={"x": ifm_shape, "y": ifm2_shape},
dtype_dict={"x": dtype, "y": dtype},
)
mod = partition_ethosu_by_table(mod, pattern_table)
mod["tvmgen_default_ethos_u_main_0"] = dataflow_pattern.rewrite(
rewriter, mod["tvmgen_default_ethos_u_main_0"]
)
verify(mod["tvmgen_default_ethos_u_main_0"])
def test_tflite_pool2d_legalize(
ifm_shape, pooling_type, strides, pool_shape, activation_function, padding
):
dtype = "int8"
def create_tflite_graph():
class Model(tf.Module):
@tf.function
def tf_function(self, x):
if pooling_type == "MAX":
op = tf.nn.max_pool(x, pool_shape, strides, padding)
elif pooling_type == "AVG":
op = tf.nn.avg_pool(x, pool_shape, strides, padding)
if activation_function == "RELU":
op = tf.nn.relu(op)
return op
model = Model()
concrete_func = model.tf_function.get_concrete_function(
tf.TensorSpec(ifm_shape, dtype=tf.float32)
)
# Convert the model
def representative_dataset():
for _ in range(100):
data = np.random.rand(*tuple(ifm_shape))
yield [data.astype(np.float32)]
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_dataset
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8
tflite_model = converter.convert()
return tflite_model
def verify(ext_func):
ofm_shape = infra.compute_ofm_shape(ifm_shape, padding, pool_shape, strides)
op = ext_func.body
assert list(op.args[0].checked_type.shape) == ifm_shape
assert op.args[0].checked_type.dtype == dtype
assert list(op.checked_type.shape) == ofm_shape
assert op.checked_type.dtype == dtype
assert op.attrs.pooling_type == pooling_type
assert list(op.attrs.strides) == strides
assert list(op.attrs.padding) == infra.compute_padding_shape(
ifm_shape, ofm_shape, padding, pool_shape, strides
)
assert list(op.attrs.pool_shape) == pool_shape
assert op.attrs.ofm_channels == ifm_shape[3]
if activation_function == "RELU":
assert str(op.attrs.activation) == "CLIP"
if pooling_type == "MAX":
rewriter = legalize.MaxPoolingRewriter()
pattern_table = [
(
ethosu.MaxPool2DParams.composite_name,
ethosu.qnn_maxpool2d_pattern(),
lambda pat: ethosu.MaxPool2DParams(pat).is_valid(),
),
]
elif pooling_type == "AVG":
rewriter = legalize.AvgPoolingRewriter()
pattern_table = [
(
ethosu.AvgPool2DParams.composite_name,
ethosu.qnn_avgpool2d_pattern(),
lambda pat: ethosu.AvgPool2DParams(pat).is_valid(),
),
]
tflite_graph = create_tflite_graph()
tflite_model = tflite.Model.Model.GetRootAsModel(tflite_graph, 0)
mod, _ = relay.frontend.from_tflite(
tflite_model,
shape_dict={"x": ifm_shape},
dtype_dict={"x": dtype},
)
mod = partition_ethosu_by_table(mod, pattern_table)
mod["tvmgen_default_ethos_u_main_0"] = dataflow_pattern.rewrite(
rewriter, mod["tvmgen_default_ethos_u_main_0"]
)
verify(mod["tvmgen_default_ethos_u_main_0"])
def test_tflite_depthwise_conv_2d_legalize(
ifm_shape, kernel_shape, padding, strides, dilation, activation
):
dtype = "int8"
def create_tflite_graph():
class Model(tf.Module):
@tf.function
def depthwise_conv2d(self, x):
weight_shape = [kernel_shape[0], kernel_shape[1], ifm_shape[3], 1]
weight = tf.constant(np.random.uniform(size=weight_shape), dtype=tf.float32)
# The input strides to the TensorFlow API needs to be of shape 1x4
tf_strides = [1, strides[0], strides[1], 1]
op = tf.nn.depthwise_conv2d(
x, weight, strides=tf_strides, padding=padding, dilations=dilation
)
if activation:
op = tf.nn.relu(op)
return op
model = Model()
concrete_func = model.depthwise_conv2d.get_concrete_function(
tf.TensorSpec(ifm_shape, dtype=tf.float32)
)
# Convert the model
def representative_dataset():
for _ in range(100):
data = np.random.rand(*tuple(ifm_shape))
yield [data.astype(np.float32)]
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_dataset
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8
tflite_model = converter.convert()
return tflite_model
def verify(ext_func):
op = ext_func.body
ofm_channels = op.attrs.ofm_channels
# check IFM
ifm = op.args[0].checked_type
assert list(ifm.shape) == list(ifm_shape)
assert str(ifm.dtype) == dtype
assert ifm.shape[3] == ofm_channels
# check OFM
ofm = op.checked_type
expected_ofm_shape = infra.compute_ofm_shape(
ifm_shape, padding, kernel_shape, strides, dilation
)
assert list(ofm.shape) == list(expected_ofm_shape)
assert str(ofm.dtype) == dtype
assert ofm.shape[3] == ofm_channels
# check weights
weights_ohwi = op.args[1].data.asnumpy()
assert str(weights_ohwi.dtype) == dtype
assert weights_ohwi.shape[0] == ofm_channels
assert weights_ohwi.shape[1] == kernel_shape[0]
assert weights_ohwi.shape[2] == kernel_shape[1]
assert weights_ohwi.shape[3] == 1 # only depth multiplier 1 is supported
# Check that scale_bias matches weight tensor
assert list(op.args[2].checked_type.shape)[0] == ofm_channels
expected_padding = infra.compute_padding_shape(
ifm_shape, expected_ofm_shape, padding, kernel_shape, strides, dilation
)
assert list(op.attrs.padding) == list(expected_padding)
assert op.attrs.ofm_channels == ofm_channels
assert list(op.attrs.strides) == list(strides)
assert list(op.attrs.dilation) == list(dilation)
if activation == "RELU":
assert str(op.attrs.activation) == "CLIP"
depthwise_pattern_table = [
(
ethosu.QnnDepthwiseConv2DParams.composite_name,
ethosu.qnn_depthwise_conv2d_pattern(),
lambda pat: ethosu.QnnDepthwiseConv2DParams(pat).is_valid(),
)
]
tflite_graph = create_tflite_graph()
tflite_model = tflite.Model.Model.GetRootAsModel(tflite_graph, 0)
mod, params = relay.frontend.from_tflite(
tflite_model,
shape_dict={"input": ifm_shape},
dtype_dict={"input": dtype},
)
mod["main"] = bind_params_by_name(mod["main"], params)
mod = partition_ethosu_by_table(mod, depthwise_pattern_table)
mod["tvmgen_default_ethos_u_main_0"] = dataflow_pattern.rewrite(
legalize.DepthwiseConv2DRewriter(), mod["tvmgen_default_ethos_u_main_0"]
)
verify(mod["tvmgen_default_ethos_u_main_0"])
def test_tflite_unary_elemwise_legalize(
operator_type,
ifm_shape,
):
dtype = "int8"
def create_tflite_graph():
class Model(tf.Module):
@tf.function
def abs_func(self, x):
if operator_type == "ABS":
op = tf.math.abs(x)
return op
model = Model()
# Save the model
concrete_func = model.abs_func.get_concrete_function(
tf.TensorSpec(ifm_shape, dtype=tf.float32))
# Convert the model
def representative_dataset():
for _ in range(100):
data = np.random.rand(*tuple(ifm_shape))
yield [data.astype(np.float32)]
converter = tf.lite.TFLiteConverter.from_concrete_functions(
[concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_dataset
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS_INT8
]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8
tflite_model = converter.convert()
return tflite_model
def verify(ext_func):
out_shape = ifm_shape
func_body = ext_func.body
# If we legalized the unary elementwise op into 4D
if func_body.op.name == "reshape":
reshape = func_body
unary = func_body.args[0]
reshape2 = unary.args[0]
# Check the input to the reshape
reshape2_in_shape = [
i for i in reshape2.args[0].checked_type.shape
]
assert reshape2_in_shape == ifm_shape
# Check that the unary elementwise operator is 4D after reshape
assert len(unary.checked_type.shape) == 4
assert unary.args[0].checked_type.dtype == dtype
# Check that the output of the graph has the same shape as input
reshape_out_shape = [i for i in reshape.checked_type.shape]
assert reshape_out_shape == ifm_shape
assert unary.attrs.operator_type == operator_type
else:
unary = func_body
# Check the IFM
assert list(unary.args[0].checked_type.shape) == ifm_shape
assert unary.args[0].checked_type.dtype == dtype
# Check the OFM
assert list(unary.checked_type.shape) == out_shape
assert unary.checked_type.dtype == dtype
# operator type check
assert unary.attrs.operator_type == operator_type
if operator_type == "ABS":
rewriter = legalize.AbsRewriter()
pattern_table = [
(
ethosu.AbsParams.composite_name,
ethosu.abs_pattern(),
lambda pat: ethosu.AbsParams(pat).is_valid(),
),
]
tflite_graph = create_tflite_graph()
tflite_model = tflite.Model.Model.GetRootAsModel(tflite_graph, 0)
mod, _ = relay.frontend.from_tflite(
tflite_model,
shape_dict={"input": ifm_shape},
dtype_dict={"input": dtype},
)
mod = partition_ethosu_by_table(mod, pattern_table)
mod["tvmgen_default_ethos_u_main_0"] = dataflow_pattern.rewrite(
rewriter, mod["tvmgen_default_ethos_u_main_0"])
verify(mod["tvmgen_default_ethos_u_main_0"])
def test_mean(ifm_shape, axis, keep_dims, use_same_quantization):
dtype = "int8"
def create_tflite_graph():
class Model(tf.Module):
@tf.function
def tf_function(self, x):
op = tf.math.reduce_mean(x, axis=axis, keepdims=keep_dims)
return op
model = Model()
concrete_func = model.tf_function.get_concrete_function(
tf.TensorSpec(ifm_shape, dtype=tf.float32))
# Convert the model
def representative_dataset():
for _ in range(100):
data = np.random.rand(*tuple(ifm_shape))
yield [data.astype(np.float32)]
converter = tf.lite.TFLiteConverter.from_concrete_functions(
[concrete_func])
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_dataset
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS_INT8
]
converter.inference_input_type = tf.int8
converter.inference_output_type = tf.int8
tflite_model = converter.convert()
tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model, 0)
mod, _ = relay.frontend.from_tflite(
tflite_model,
shape_dict={"input": ifm_shape},
dtype_dict={"input": dtype},
)
return mod
def create_relay_graph_with_same_quantization():
ifm = relay.var("input", shape=ifm_shape, dtype=dtype)
cast = relay.cast(ifm, dtype="int32")
mean = relay.mean(cast, axis=axis, keepdims=keep_dims)
requantize = relay.qnn.op.requantize(
mean,
input_scale=relay.const(1.0, dtype="float32"),
input_zero_point=relay.const(0, dtype="int32"),
output_scale=relay.const(1.0, dtype="float32"),
output_zero_point=relay.const(0, dtype="int32"),
)
func = relay.Function(relay.analysis.free_vars(requantize), requantize)
mod = tvm.IRModule.from_expr(func)
return mod
def verify(ext_func):
out_var = ext_func.body
next_op = out_var
mul_op = None
pooling_op = None
depthwise_op = None
if (isinstance(next_op, relay.expr.Call)
and isinstance(next_op.op, tvm.ir.op.Op)
and next_op.op.name == "reshape"):
next_op = next_op.args[0]
if util.is_named_ethosu_op(next_op, "binary_elementwise"):
mul_op = next_op
next_op = next_op.args[0]
if util.is_named_ethosu_op(next_op, "pooling"):
pooling_op = next_op
next_op = next_op.args[0]
if util.is_named_ethosu_op(next_op, "depthwise_conv2d"):
depthwise_op = next_op
next_op = next_op.args[0]
while (isinstance(next_op, relay.expr.Call)
and isinstance(next_op.op, tvm.ir.op.Op)
and next_op.op.name == "reshape"):
next_op = next_op.args[0]
in_var = next_op
def calculate_expected_output_shape():
for i in range(len(ifm_shape)):
if i in axis:
if keep_dims:
yield 1
else:
yield ifm_shape[i]
out_shape = tuple(calculate_expected_output_shape())
# check IFM
assert tuple(in_var.checked_type.shape) == ifm_shape
assert in_var.checked_type.dtype == dtype
# check OFM
assert tuple(out_var.checked_type.shape) == out_shape
assert out_var.checked_type.dtype == dtype
# check expected legalization case
if axis in [(1, 2), (2, 1), (0, 1),
(1, 0)] and keep_dims and dtype == "int8":
assert depthwise_op and mul_op
assert mul_op.attrs.operator_type == "MUL"
elif pooling_op:
attrs = pooling_op.attrs
assert (attrs.ifm_scale == attrs.ofm_scale
and attrs.ifm_zero_point == attrs.ofm_zero_point)
else:
assert depthwise_op
assert not mul_op
rewriter = legalize.MeanRewriter()
pattern_table = [
(
ethosu.MeanParams.composite_name,
ethosu.mean_pattern(),
lambda pat: ethosu.MeanParams(pat).is_valid(),
),
]
mod = (create_relay_graph_with_same_quantization()
if use_same_quantization else create_tflite_graph())
mod = partition_ethosu_by_table(mod, pattern_table)
mod["tvmgen_default_ethos_u_main_0"] = dataflow_pattern.rewrite(
rewriter, mod["tvmgen_default_ethos_u_main_0"])
verify(mod["tvmgen_default_ethos_u_main_0"])
