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Add dynamic executor support to TF plugin. (#5686)
* Add dynamic executor support to TF plugin. * Add tests that wouldn't work with legacy executor --------- Signed-off-by: Michal Zientkiewicz <michalz@nvidia.com>
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| # Copyright (c) 2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved. | ||
| # | ||
| # Licensed 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. | ||
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| import tensorflow as tf | ||
| import numpy as np | ||
| import os.path | ||
| from nvidia.dali import pipeline_def | ||
| import nvidia.dali.fn as fn | ||
| import nvidia.dali.types as types | ||
| import nvidia.dali.plugin.tf as dali_tf | ||
| from nose_utils import with_setup | ||
| from test_utils_tensorflow import skip_inputs_for_incompatible_tf | ||
| from test_utils import get_dali_extra_path | ||
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| test_data_root = get_dali_extra_path() | ||
| lmdb_folder = os.path.join(test_data_root, "db", "lmdb") | ||
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| @pipeline_def( | ||
| enable_conditionals=True, | ||
| batch_size=5, | ||
| num_threads=4, | ||
| device_id=0, | ||
| experimental_exec_dynamic=True, | ||
| ) | ||
| def dali_exec2_pipeline(): | ||
| iter_id = fn.external_source(source=lambda x: np.array(x.iteration), batch=False) | ||
| if iter_id & 1 == 0: | ||
| output = types.Constant(np.array(-1), device="gpu") | ||
| else: | ||
| output = types.Constant(np.array(1), device="gpu") | ||
| return output.cpu() | ||
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| @with_setup(skip_inputs_for_incompatible_tf) | ||
| def test_tf_dataset_exec2(): | ||
| """Test that exec_dynamic is propagated to DALI pipeline from dali_tf.DALIDatasetWithInputs""" | ||
| # From Tensorflow's perspective, this is a CPU pipeline | ||
| with tf.device("/cpu:0"): | ||
| dali_dataset = dali_tf.experimental.DALIDatasetWithInputs( | ||
| pipeline=dali_exec2_pipeline(), | ||
| batch_size=5, | ||
| output_shapes=(5,), | ||
| output_dtypes=(tf.int32), | ||
| num_threads=4, | ||
| device_id=0, | ||
| ) | ||
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| @tf.function | ||
| def tf_function_with_conditionals(dali_dataset): | ||
| negative = tf.constant(0) | ||
| positive = tf.constant(0) | ||
| for input in dali_dataset: | ||
| if tf.reduce_sum(input) < 0: | ||
| negative = negative + 1 | ||
| else: | ||
| positive = positive + 1 | ||
| return negative, positive | ||
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| pos, neg = tf_function_with_conditionals(dali_dataset.take(5)) | ||
| assert pos == 3 | ||
| assert neg == 2 | ||
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| @pipeline_def(num_threads=4, experimental_exec_dynamic=True) | ||
| def daliop_pipe(): | ||
| jpegs, labels = fn.readers.caffe(path=lmdb_folder, random_shuffle=False) | ||
| imgs = fn.decoders.image(jpegs, device="mixed") | ||
| imgs = fn.resize(imgs, size=(100, 100)) | ||
| shape = imgs.shape(dtype=types.UINT32) | ||
| return imgs.cpu(), shape | ||
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| def get_batch_dali(batch_size): | ||
| pipe = daliop_pipe(batch_size=batch_size, num_threads=4, device_id=0) | ||
| pipe.build() | ||
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| daliop = dali_tf.DALIIterator() | ||
| images = [] | ||
| labels = [] | ||
| with tf.device("/cpu:0"): | ||
| image, label = daliop( | ||
| pipeline=pipe, | ||
| shapes=[ | ||
| (batch_size, 100, 100, 3), | ||
| ( | ||
| batch_size, | ||
| 3, | ||
| ), | ||
| ], | ||
| dtypes=[tf.uint8, tf.int32], | ||
| device_id=0, | ||
| ) | ||
| images.append(image) | ||
| labels.append(label) | ||
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| return [images, labels] | ||
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| def test_tf_op(): | ||
| """Test that exec_dynamic is propagated to DALI pipeline from dali_tf.DALIIterator""" | ||
| try: | ||
| tf.compat.v1.disable_eager_execution() | ||
| except ModuleNotFoundError: | ||
| pass | ||
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| batch_size = 8 | ||
| iterations = 2 | ||
| test_batch = get_batch_dali(batch_size) | ||
| try: | ||
| from tensorflow.compat.v1 import Session | ||
| except ImportError: | ||
| # Older TF versions don't have compat.v1 layer | ||
| from tensorflow import Session | ||
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| with Session() as sess: | ||
| for i in range(iterations): | ||
| imgs, shapes = sess.run(test_batch) | ||
| for img, shape in zip(imgs, shapes): | ||
| for i in range(batch_size): | ||
| assert tuple(img[i].shape) == tuple(shape[i]) |
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