Merge branch 'develop' into type-string-driver

examples/nlp/README.md

+# Natural Language Processing Inference Examples
+
+- [Python BERT-SQuAD](./python_bert_squad)
\ No newline at end of file

examples/nlp/python_bert_squad/README.md

+# BERT-SQuAD Example with MIGraphX
+Question answering with BERT using MIGraphX optimizations on ROCm platform.
+
+There are two ways to run the example:
+1) Install MIGraphX and Jupyter notebook to your system and then utilize `BERT-Squad.ipynb` notebook file.
+2) Install MIGraphx to your system and follow the steps executing the python script `bert-squad-migraphx.py`.
+
+# Steps
+1) Install MIGraphX to your environment. Please follow the steps to build MIGraphX given at https://github.com/ROCmSoftwarePlatform/AMDMIGraphX
+2) Upgrade your pip3 to latest version
+```
+pip3 install --upgrade pip 
+```
+3) Install the requirements file
+```
+pip3 install -r requirements_bertsquad.txt
+```
+4) Install `unzip` and fetch the uncased file (vocabulary):
+```
+apt-get install unzip
+wget -q https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip
+unzip uncased_L-12_H-768_A-12.zip
+```
+5) Get BERT ONNX model (bertsquad-10.onnx):
+```
+wget https://github.com/onnx/models/raw/main/text/machine_comprehension/bert-squad/model/bertsquad-10.onnx
+```
+6) Run the inference, it will compile and run the model on three questions and small data provided in `inputs.json`:
+```
+python3 bert-squad-migraphx.py
+```
+## References
+This example utilizes the following notebook :notebook: and applies it to MIGraphX:
+https://github.com/onnx/models/blob/master/text/machine_comprehension/bert-squad/BERT-Squad.ipynb

examples/nlp/python_bert_squad/bert-squad-migraphx.py

+import numpy as np
+import json
+import os.path
+import tokenizers
+import collections
+from run_onnx_squad import read_squad_examples, write_predictions, convert_examples_to_features
+import migraphx
+
+RawResult = collections.namedtuple("RawResult",
+                                   ["unique_id", "start_logits", "end_logits"])
+
+#######################################
+input_file = 'inputs_amd.json'
+with open(input_file) as json_file:
+    test_data = json.load(json_file)
+    print(json.dumps(test_data, indent=2))
+
+# preprocess input
+predict_file = 'inputs_amd.json'
+
+# Use read_squad_examples method from run_onnx_squad to read the input file
+eval_examples = read_squad_examples(input_file=predict_file)
+
+max_seq_length = 256
+doc_stride = 128
+max_query_length = 64
+batch_size = 1
+n_best_size = 20
+max_answer_length = 30
+
+vocab_file = os.path.join('uncased_L-12_H-768_A-12', 'vocab.txt')
+tokenizer = tokenizers.BertWordPieceTokenizer(vocab_file)
+
+# Use convert_examples_to_features method from run_onnx_squad to get parameters from the input
+input_ids, input_mask, segment_ids, extra_data = convert_examples_to_features(
+    eval_examples, tokenizer, max_seq_length, doc_stride, max_query_length)
+
+#######################################
+# Compile
+print("INFO: Parsing and compiling the model...")
+model = migraphx.parse_onnx("bertsquad-10.onnx")
+model.compile(migraphx.get_target("gpu"))
+#model.print()
+
+print(model.get_parameter_names())
+print(model.get_parameter_shapes())
+
+n = len(input_ids)
+bs = batch_size
+all_results = []
+
+for idx in range(0, n):
+    item = eval_examples[idx]
+    print(item)
+
+    result = model.run({
+        "unique_ids_raw_output___9:0":
+        np.array([item.qas_id], dtype=np.int64),
+        "input_ids:0":
+        input_ids[idx:idx + bs],
+        "input_mask:0":
+        input_mask[idx:idx + bs],
+        "segment_ids:0":
+        segment_ids[idx:idx + bs]
+    })
+
+    in_batch = result[1].get_shape().lens()[0]
+    start_logits = [float(x) for x in result[1].tolist()]
+    end_logits = [float(x) for x in result[0].tolist()]
+    for i in range(0, in_batch):
+        unique_id = len(all_results)
+        all_results.append(
+            RawResult(unique_id=unique_id,
+                      start_logits=start_logits,
+                      end_logits=end_logits))
+
+output_dir = 'predictions'
+os.makedirs(output_dir, exist_ok=True)
+output_prediction_file = os.path.join(output_dir, "predictions.json")
+output_nbest_file = os.path.join(output_dir, "nbest_predictions.json")
+write_predictions(eval_examples, extra_data, all_results, n_best_size,
+                  max_answer_length, True, output_prediction_file,
+                  output_nbest_file)
+
+with open(output_prediction_file) as json_file:
+    test_data = json.load(json_file)
+    print(json.dumps(test_data, indent=2))

examples/nlp/python_bert_squad/requirements_bertsquad.txt

+tensorflow==2.7.2
+onnxruntime
+tokenizers
\ No newline at end of file

examples/vision/README.md

+# Vision Inference Examples
+
+- [CPP MNIST](./cpp_mnist)
+- [Python Resnet50](./python_resnet50)
+- [Python Super Resolution](./python_super_resolution)
+- [Python NFNet](./python_nfnet)
+- [Python U-Net](./python_unet)
+- [Python 3D-UNet](./python_3dunet)
\ No newline at end of file

examples/vision/cpp_mnist/CMakeLists.txt

+cmake_minimum_required(VERSION 3.5)
+project (CAI)
+
+set (CMAKE_CXX_STANDARD 14)
+set (EXAMPLE mnist_inference)
+
+list (APPEND CMAKE_PREFIX_PATH /opt/rocm/hip /opt/rocm)
+find_package (migraphx)
+
+message("source file: " ${EXAMPLE}.cpp " ---> bin: " ${EXAMPLE})
+add_executable(${EXAMPLE} ${EXAMPLE}.cpp)
+
+target_link_libraries(${EXAMPLE} migraphx::c)