Merge remote-tracking branch 'upstream/main' into git-lfs-files

e2eshark/onnx/models/QuantizedMLP/model.py

@@ -0,0 +1,40 @@
+import numpy, torch, sys
+import onnxruntime
+
+# import from e2eshark/tools to allow running in current dir, for run through
+# run.pl, commutils is symbolically linked to allow any rundir to work
+sys.path.insert(0, "../../../tools/stubs")
+from commonutils import E2ESHARK_CHECK_DEF
+
+# Create an instance of it for this test
+E2ESHARK_CHECK = dict(E2ESHARK_CHECK_DEF)
+
+
+# The generated or checked in onnx file must always be called model.onnx
+# the tools/stubs/onnxmodel.py is appended to model.py
+# to form runmodel.py in the rundirectory which is then taken
+# through flow
+
+
+# start an onnxrt session
+session = onnxruntime.InferenceSession("model.onnx", None)
+
+# Even if model is quantized, the inputs and outputs are
+# not, so apply float32
+model_input_X = numpy.random.rand(1, 16).astype(numpy.float32)
+
+# gets X in inputs[0] and Y in inputs[1]
+inputs = session.get_inputs()
+# gets Z in outputs[0]
+outputs = session.get_outputs()
+
+
+model_output = session.run(
+    [outputs[0].name],
+    {inputs[0].name: model_input_X},
+)[0]
+E2ESHARK_CHECK["input"] = [torch.from_numpy(model_input_X)]
+E2ESHARK_CHECK["output"] = [torch.from_numpy(arr) for arr in model_output]
+
+print("Input:", E2ESHARK_CHECK["input"])
+print("Output:", E2ESHARK_CHECK["output"])

e2eshark/onnx/operators/GatherND/model.py

@@ -0,0 +1,77 @@
+# Copyright 2024 Advanced Micro Devices
+#
+# Licensed under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+# run.py creates runmodel.py by concatenating this file model.py
+# and tools/stubs/onnxmodel.py
+# Description: testing GatherND
+# See https://onnx.ai/onnx/intro/python.html for intro on creating
+# onnx model using python onnx API
+import numpy, torch, sys
+import onnxruntime
+from onnx import numpy_helper, TensorProto, save_model
+from onnx.helper import make_model, make_node, make_graph, make_tensor_value_info
+from onnx.checker import check_model
+
+# import from e2eshark/tools to allow running in current dir, for run through
+# run.pl, commutils is symbolically linked to allow any rundir to work
+sys.path.insert(0, "../../../tools/stubs")
+from commonutils import E2ESHARK_CHECK_DEF
+
+# Create an instance of it for this test
+E2ESHARK_CHECK = dict(E2ESHARK_CHECK_DEF)
+
+# Create an input (ValueInfoProto)
+D = make_tensor_value_info("D", TensorProto.FLOAT, [2, 2, 3])
+I = make_tensor_value_info("I", TensorProto.INT64, [2, 3, 2])
+
+# Create an output
+Z = make_tensor_value_info("Z", TensorProto.FLOAT, [2, 3, 3])
+
+# Create a node (NodeProto)
+gather_nd_node = make_node(
+    "GatherND", ["D", "I"], ["Z"], "gather_nd_node"  # node name  # inputs  # outputs
+)
+
+# Create the graph (GraphProto)
+graph = make_graph(
+    [gather_nd_node],
+    "gather_nd_graph",
+    [D, I],
+    [Z],
+)
+
+# Create the model (ModelProto)
+onnx_model = make_model(graph)
+onnx_model.opset_import[0].version = 13
+
+# Save the model
+# save_model(onnx_model, "model.onnx")
+with open("model.onnx", "wb") as f:
+    f.write(onnx_model.SerializeToString())
+
+
+session = onnxruntime.InferenceSession("model.onnx", None)
+model_input_D = numpy.random.randn(2, 2, 3).astype(numpy.float32)
+model_input_I = numpy.random.randint(2, size=(2, 3, 2)).astype(numpy.int64)
+# gets D in inputs[0] and I in inputs[1]
+inputs = session.get_inputs()
+# gets Z in outputs[0]
+outputs = session.get_outputs()
+
+model_output = session.run(
+    [outputs[0].name],
+    {inputs[0].name: model_input_D, inputs[1].name: model_input_I},
+)
+
+# Moving to torch to handle bfloat16 as numpy does not support bfloat16
+E2ESHARK_CHECK["input"] = [
+    torch.from_numpy(model_input_D),
+    torch.from_numpy(model_input_I),
+]
+E2ESHARK_CHECK["output"] = [torch.from_numpy(arr) for arr in model_output]
+
+print("Input:", E2ESHARK_CHECK["input"])
+print("Output:", E2ESHARK_CHECK["output"])

iree_tests/README.md

@@ -55,7 +55,7 @@ Tests are run using the [pytest](https://docs.pytest.org/en/stable/) framework.
 A [`conftest.py`](conftest.py) file collects test cases from subdirectories,
 wrapping each directory matching the format described above to one test case
 per test configuration. Test configurations are defined in JSON config files
-like [`configs/config_cpu_llvm_sync.json`](./configs/config_cpu_llvm_sync.json).
+like [`configs/config_onnx_cpu_llvm_sync.json`](./configs/config_onnx_cpu_llvm_sync.json).
 
 ### Common venv setup with deps
 
@@ -110,15 +110,15 @@ Run ONNX tests on CPU and print all errors:
 ```bash
 $ pytest iree_tests/onnx -n auto \
     --ignore-xfails \
-    --config-files ./iree_tests/configs/config_cpu_llvm_sync.json
+    --config-files ./iree_tests/configs/config_onnx_cpu_llvm_sync.json
 ```
 
 Run ONNX compilation tests only and print all errors:
 
 ```bash
 $ pytest iree_tests/onnx -n auto \
     --ignore-xfails --skip-all-runs \
-    --config-files ./iree_tests/configs/config_cpu_llvm_sync.json
+    --config-files ./iree_tests/configs/config_onnx_cpu_llvm_sync.json
 ```
 
 ### Advanced pytest usage tips