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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/04/22 10:12:13 UTC
[GitHub] [tvm] lhutton1 commented on a diff in pull request #10939: [CMSIS-NN] Moved TFLite model making to common area
lhutton1 commented on code in PR #10939:
URL: https://github.com/apache/tvm/pull/10939#discussion_r856022562
##########
tests/python/contrib/test_cmsisnn/test_conv2d.py:
##########
@@ -314,25 +312,29 @@ def test_conv2d_int8_tflite(ifm_shape, kernel_shape, strides, dilation, padding,
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
+
+ from tvm.relay.testing.tflite import TFLiteModel
+
+ tfl_model = TFLiteModel(dtype)
+ tfl_model.create_tflite_model(
+ "conv2d_single", ifm_shape, kernel_shape, strides, padding, dilation, activation
)
+ relay_mod, relay_params = tfl_model.convert_to_relay()
- cmsisnn_mod = cmsisnn.partition_for_cmsisnn(relay_mod, params)
+ 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 = generate_ref_data_tflite(tflite_model)
Review Comment:
Is the plan to remove these functions from utils after the rest of the tests have been converted?
##########
tests/python/contrib/test_cmsisnn/test_conv2d.py:
##########
@@ -314,25 +312,29 @@ def test_conv2d_int8_tflite(ifm_shape, kernel_shape, strides, dilation, padding,
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
+
+ from tvm.relay.testing.tflite import TFLiteModel
+
+ tfl_model = TFLiteModel(dtype)
+ tfl_model.create_tflite_model(
+ "conv2d_single", ifm_shape, kernel_shape, strides, padding, dilation, activation
)
+ relay_mod, relay_params = tfl_model.convert_to_relay()
- cmsisnn_mod = cmsisnn.partition_for_cmsisnn(relay_mod, params)
+ 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 = generate_ref_data_tflite(tflite_model)
Review Comment:
Is the plan to remove these functions after the rest of the tests have been converted?
##########
python/tvm/relay/testing/tflite.py:
##########
@@ -0,0 +1,152 @@
+# 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.
+"""Common utilities for creating TFLite models"""
+from distutils.version import LooseVersion
+import numpy as np
+import pytest
+import tvm
+
+pytest.importorskip("tflite")
+pytest.importorskip("tensorflow")
+import tflite.Model # pylint: disable=wrong-import-position
+import tensorflow as tf # pylint: disable=wrong-import-position
+
+
+class TFLiteModel:
+ """Creates TFLite Model and facilitates reference data generation"""
+
+ def __init__(self, dtype):
+ self.serial_model = None # This is what TFLite convert() provides
+ self.dtype = dtype # This is the dtype of graph inputs
+ self.shape_dict = {}
+ self.dtype_dict = {}
+
+ @tf.function
+ def conv2d_single_function(self, ifm_tensor, args):
Review Comment:
I feel like creation of this function should be part of the test itself so its clear what the test stimulus is, rather than hidden away in common testing utilities
##########
tests/python/contrib/test_cmsisnn/test_conv2d.py:
##########
@@ -314,25 +312,29 @@ def test_conv2d_int8_tflite(ifm_shape, kernel_shape, strides, dilation, padding,
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
+
+ from tvm.relay.testing.tflite import TFLiteModel
+
+ tfl_model = TFLiteModel(dtype)
+ tfl_model.create_tflite_model(
+ "conv2d_single", ifm_shape, kernel_shape, strides, padding, dilation, activation
)
+ relay_mod, relay_params = tfl_model.convert_to_relay()
- cmsisnn_mod = cmsisnn.partition_for_cmsisnn(relay_mod, params)
+ 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 = generate_ref_data_tflite(tflite_model)
+ input_map, output_map, output_tolerance = tfl_model.generate_reference_data()
compile_and_run(
Review Comment:
Could we wrap this part in a `compare_cmsisnn_with_reference` function similar to `compare_ethosu_with_reference`?
##########
python/tvm/relay/testing/tflite.py:
##########
@@ -0,0 +1,152 @@
+# 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.
+"""Common utilities for creating TFLite models"""
+from distutils.version import LooseVersion
+import numpy as np
+import pytest
+import tvm
+
+pytest.importorskip("tflite")
+pytest.importorskip("tensorflow")
+import tflite.Model # pylint: disable=wrong-import-position
+import tensorflow as tf # pylint: disable=wrong-import-position
+
+
+class TFLiteModel:
+ """Creates TFLite Model and facilitates reference data generation"""
+
+ def __init__(self, dtype):
+ self.serial_model = None # This is what TFLite convert() provides
+ self.dtype = dtype # This is the dtype of graph inputs
+ self.shape_dict = {}
+ self.dtype_dict = {}
+
+ @tf.function
+ def conv2d_single_function(self, ifm_tensor, args):
Review Comment:
I feel like creation of this function should be part of the test itself so its clear what's being tested, rather than hidden away in common testing utilities
##########
python/tvm/relay/testing/tflite.py:
##########
@@ -0,0 +1,152 @@
+# 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.
+"""Common utilities for creating TFLite models"""
+from distutils.version import LooseVersion
+import numpy as np
+import pytest
+import tvm
+
+pytest.importorskip("tflite")
+pytest.importorskip("tensorflow")
+import tflite.Model # pylint: disable=wrong-import-position
+import tensorflow as tf # pylint: disable=wrong-import-position
+
+
+class TFLiteModel:
+ """Creates TFLite Model and facilitates reference data generation"""
+
+ def __init__(self, dtype):
+ self.serial_model = None # This is what TFLite convert() provides
+ self.dtype = dtype # This is the dtype of graph inputs
+ self.shape_dict = {}
+ self.dtype_dict = {}
+
+ @tf.function
+ def conv2d_single_function(self, ifm_tensor, args):
+ """Returns TFLite Conv2d layer"""
+ assert len(args) == 6, "Conv2D needs (ifm_shape, kernel_shape, strides, padding, dilation)"
+ _, kernel_shape, strides, padding, dilation, activation = args
+ op = tf.nn.conv2d(
+ ifm_tensor,
+ filters=tf.constant(
+ np.random.uniform(size=[kernel_shape[0], kernel_shape[1], 3, 3]),
+ dtype=tf.float32,
+ ),
+ strides=[1, strides[0], strides[1], 1],
+ padding=padding,
+ dilations=dilation,
+ )
+ if activation == "RELU":
+ op = tf.nn.relu(op)
+ elif activation == "NONE":
+ pass
+ else:
+ assert False, "Unsupported activation {}".format(activation)
+ return op
+
+ def create_tflite_model(self, op_type, *args):
+ """Returns TFLite serial graph, Relay module, Relay params based on op_type"""
+ concrete_func = None
+ input_shape = None
+ if op_type == "conv2d_single":
+ input_shape = args[0]
+ ifm_tensor = tf.TensorSpec(input_shape, dtype=tf.float32, name="input")
+ concrete_func = self.conv2d_single_function.get_concrete_function(ifm_tensor, args)
+ else:
+ assert False, "Unsupported op_type {}".format(op_type)
+
+ def representative_dataset():
+ for _ in range(100):
+ data = np.random.rand(*tuple(input_shape))
Review Comment:
It might be worth including negative values here as well as this exposed a couple of issues in the microNPU integration
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