comaniac commented on a change in pull request #6355:
URL: https://github.com/apache/incubator-tvm/pull/6355#discussion_r483878188
##########
File path: src/runtime/contrib/ethosn/ethosn_runtime.cc
##########
@@ -120,6 +120,14 @@ Module EthosnModule::LoadFromBinary(void* strm) {
return Module(n);
}
+void EthosnModule::SaveToFile(const std::string& path, const std::string&
format) {
Review comment:
It looks me that this still saves a binary file. Are there specific use
cases?
##########
File path: tests/python/contrib/test_ethosn/test_addition.py
##########
@@ -0,0 +1,96 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Ethos-N integration addition tests"""
+
+import tvm
+from tvm import relay
+from tvm.relay.op.contrib.ethosn import ethosn_available
+from . import infrastructure as tei
+import numpy as np
+
+
+def _get_model(input_shape, lhs_zp, lhs_sc, rhs_zp, rhs_sc, out_zp, out_sc,
dtype):
+ """Return a model and any parameters it may have"""
+
+ a = relay.var("a", shape=input_shape, dtype=dtype)
+ b = relay.var("b", shape=input_shape, dtype=dtype)
+ model = relay.qnn.op.add(lhs=a, rhs=b,
+ lhs_scale=relay.const(lhs_sc, 'float32'),
+ lhs_zero_point=relay.const(lhs_zp, 'int32'),
+ rhs_scale=relay.const(rhs_sc, 'float32'),
+ rhs_zero_point=relay.const(rhs_zp, 'int32'),
+ output_scale=relay.const(out_sc, 'float32'),
+ output_zero_point=relay.const(out_zp, 'int32'))
+ return model
+
+
+def _get_addition_qnn_params(input1_zp, input1_sc, input2_zp, input2_sc):
+ input1_max = input1_sc * (255 - input1_zp)
+ input1_min = - input1_sc * input1_zp
+ input2_max = input2_sc * (255 - input2_zp)
+ input2_min = - input2_sc * input2_zp
+ output_max = input1_max + input2_max
+ output_min = input1_min + input2_min
+ output_sc = (output_max - output_min) / 255
+ output_zp = - int(output_min / output_sc)
+ return output_zp, output_sc
+
+
+def test_addition():
+ if not ethosn_available():
+ return
+
+ num_trials = 5
+ np.random.seed(0)
+ for _ in range(num_trials):
Review comment:
Random shapes could make the test flaky, so we should avoid it as
possible.
##########
File path: tests/python/contrib/test_ethosn/test_networks.py
##########
@@ -0,0 +1,143 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Ethos-N integration end-to-end network tests"""
+
+import pytest
+pytest.importorskip('tflite')
+pytest.importorskip('tensorflow')
+
+from tvm import relay
+from tvm.relay.op.contrib.ethosn import ethosn_available, Available
+from tvm.contrib import download
+import tvm.relay.testing.tf as tf_testing
+import tflite.Model
+from . import infrastructure as tei
+
+
+def _get_tflite_model(tflite_model_path, inputs_dict, dtype):
+ with open(tflite_model_path, 'rb') as f:
+ tflite_model_buffer = f.read()
+
+ try:
+ tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buffer,
0)
+ except AttributeError:
+ tflite_model = tflite.Model.GetRootAsModel(tflite_model_buffer, 0)
+ shape_dict = {}
+ dtype_dict = {}
+ for input in inputs_dict:
+ input_shape = inputs_dict[input]
+ shape_dict[input] = input_shape
+ dtype_dict[input] = dtype
+
+ return relay.frontend.from_tflite(
+ tflite_model,
+ shape_dict=shape_dict,
+ dtype_dict=dtype_dict,
+ )
+
+
+def _test_image_network(model_url, model_sub_path, input_dict, compile_hash,
output_count, run=True, host_ops=0, npu_partitions=1):
+ if not ethosn_available():
+ return
+
+ def get_model():
+ if model_url[-3:] in ("tgz", "zip"):
+ model_path = tf_testing.get_workload_official(
+ model_url,
+ model_sub_path,
+ )
+ else:
+ model_path = download.download_testdata(
+ model_url,
+ model_sub_path,
+ )
+ return _get_tflite_model(model_path, input_dict, 'uint8')
+
+ outputs = []
+ inputs = {}
+ for input_name in input_dict:
+ input_shape = input_dict[input_name]
+ inputs[input_name] = tei.get_real_image(input_shape[1], input_shape[2])
+
+ for npu in [False, True]:
+ mod, params = get_model()
+ graph, lib, params = tei.build(mod, params, npu=npu,
expected_host_ops=host_ops, npu_partitions=npu_partitions)
+ if npu:
+ tei.assert_lib_hash(lib, compile_hash)
+ if run:
+ outputs.append(tei.run(graph, lib, params, inputs, output_count,
npu=npu))
+
+ if run:
+ tei.verify(outputs, 1, verify_saturation=False)
+
+
+def test_mobilenet_v1():
+ hw = ethosn_available()
+ _test_image_network(
+ model_url="https://storage.googleapis.com/download.tensorflow.org/"; \
+
"models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz",
+ model_sub_path="mobilenet_v1_1.0_224_quant.tflite",
+ input_dict={"input": (1, 224, 224, 3)},
+ compile_hash="81637c89339201a07dc96e3b5dbf836a",
Review comment:
How this hash comes from? In case someone in the future changes Ethos-N
codegen, how can this be updated?
##########
File path: python/tvm/relay/op/contrib/ethosn.py
##########
@@ -54,18 +54,107 @@ def qnn_conv_pattern():
pattern, is_constant(), is_constant(), is_constant(),
is_constant())
return pattern
+ def qnn_fc_pattern():
+ pattern = is_op('qnn.dense')(
+ wildcard(), is_constant(), is_constant(), is_constant(),
is_constant(), is_constant())
+ pattern = is_op('nn.bias_add')(pattern, is_constant())
+ pattern = is_op('qnn.requantize')(
+ pattern, is_constant(), is_constant(), is_constant(),
is_constant())
+ return pattern
+
+ def qnn_avg_pool2d_pattern():
+ pattern = is_op('cast')(wildcard())
+ pattern = is_op('nn.avg_pool2d')(pattern)
+ pattern = is_op('cast')(pattern)
+ return pattern
+
+ def qnn_sigmoid_pattern():
+ pattern = is_op('qnn.dequantize')(wildcard(), is_constant(),
is_constant())
+ pattern = is_op('sigmoid')(pattern)
+ pattern = is_op('qnn.quantize')(pattern, is_constant(), is_constant())
+ return pattern
+
def check_conv2d(extract):
"""Check if a conv2d is supported by Ethos-N."""
if not ethosn_available():
return False
return support.conv2d(extract)
+ def check_fc(extract):
+ """Check if a fully connected is supported by Ethos-N."""
+ if not ethosn_available():
+ return False
+
+ return support.fc(extract)
+
+ def check_avg_pool2d(extract):
+ """Check if a avg pool2d is supported by Ethos-N."""
+ if not ethosn_available():
+ return False
+
+ return support.avg_pool2d(extract)
+
+ def check_sigmoid(extract):
+ """Check if a sigmoid is supported by Ethos-N."""
+ if not ethosn_available():
+ return False
+
+ if extract.attrs.out_dtype != 'uint8':
+ return False
+
+ return support.sigmoid(extract)
+
return [
("ethos-n.qnn_conv2d", qnn_conv_pattern(), check_conv2d),
+ ("ethos-n.qnn_avg_pool2d", qnn_avg_pool2d_pattern(), check_avg_pool2d),
+ ("ethos-n.qnn_sigmoid", qnn_sigmoid_pattern(), check_sigmoid),
+ ("ethos-n.qnn_fc", qnn_fc_pattern(), check_fc),
]
+def _is_ethos_composite(node):
Review comment:
Very miner point: should this be `_is_ethosn_composite `?
##########
File path: tests/python/contrib/test_ethosn/test_fullyconnected.py
##########
@@ -0,0 +1,118 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Ethos-N integration fully connected tests"""
+
+import numpy as np
+import tvm
+from tvm import relay
+from tvm.relay.op.contrib.ethosn import ethosn_available
+from . import infrastructure as tei
+
+
+def _get_model(shape, weight_shape,
+ input_zp, input_sc,
+ kernel_zp, kernel_sc,
+ output_zp, output_sc,
+ dtype):
+ """Return a model an any parameters it may have"""
+ a = relay.var('a', shape=shape, dtype=dtype)
+ w = tvm.nd.array(np.ones(weight_shape, dtype))
+ weights = relay.const(w, dtype)
+ fc = relay.qnn.op.dense(
+ a,
+ weights,
+ input_zero_point=relay.const(input_zp, "int32"),
+ kernel_zero_point=relay.const(kernel_zp, "int32"),
+ input_scale=relay.const(input_sc, "float32"),
+ kernel_scale=relay.const(kernel_sc, "float32"),
+ units=weight_shape[0],
+ out_dtype='int32'
+ )
+ b = tvm.nd.array(np.random.randint(0, high=255, size=(shape[0],),
dtype="int32"))
+ biasc = relay.const(b, "int32")
+ bias = relay.nn.bias_add(fc, biasc, axis=0)
+ req = relay.qnn.op.requantize(
+ bias,
+ relay.const(input_sc * kernel_sc, 'float32'), # input zero scale
+ relay.const(input_zp * kernel_zp, 'int32'), # input zero point
+ relay.const(output_sc, 'float32'), # output zero scale
+ relay.const(output_zp, 'int32'), # output zero point
+ out_dtype="uint8"
+ )
+ params = {"w": w,
+ "b": b}
+ return req, params
+
+
+def test_fullyconnected():
+ if not ethosn_available():
+ return
+
+ dtype = "uint8"
+ np.random.seed(0)
+ for shape in [(1, 32*32), (1, 64*64), (1, 128*128)]:
+ inputs = {
+ "a": tvm.nd.array(np.random.randint(0, high=255, size=shape,
dtype=dtype)),
+ }
+ outputs = []
+ input_zp = np.random.randint(0, 255)
+ input_sc = np.random.random() * 2
+ kernel_zp = np.random.randint(0, 255)
+ kernel_sc = np.random.random() * 2
Review comment:
ditto.
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