[Test] add new Brevitas qlinear export test

tests/brevitas/test_brevitas_qlinear.py

+# Copyright (c) 2021, Xilinx
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+#   list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+#   this list of conditions and the following disclaimer in the documentation
+#   and/or other materials provided with the distribution.
+#
+# * Neither the name of FINN nor the names of its
+#   contributors may be used to endorse or promote products derived from
+#   this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import pytest
+import os
+import numpy as np
+import torch
+import brevitas.onnx as bo
+from brevitas.nn import QuantLinear
+from brevitas.core.quant import QuantType
+from finn.core.modelwrapper import ModelWrapper
+from finn.core.datatype import DataType
+import finn.core.onnx_exec as oxe
+from finn.transformation.infer_shapes import InferShapes
+from finn.util.basic import gen_finn_dt_tensor
+
+export_onnx_path = "test_brevitas_qlinear.onnx"
+
+
+@pytest.mark.parametrize("bias", [False, True])
+@pytest.mark.parametrize("out_features", [4])
+@pytest.mark.parametrize("in_features", [3])
+@pytest.mark.parametrize("w_bits", [4])
+@pytest.mark.parametrize("i_dtype", [DataType.UINT4])
+def test_brevitas_qlinear(bias, out_features, in_features, w_bits, i_dtype):
+    i_shape = (1, in_features)
+    w_shape = (out_features, in_features)
+    b_linear = QuantLinear(
+        out_features=out_features,
+        in_features=in_features,
+        bias=bias,
+        bias_quant_type=QuantType.FP,
+        weight_bit_width=w_bits,
+        weight_quant_type=QuantType.INT,
+        weight_scaling_per_output_channel=True,
+    )
+    weight_tensor_fp = np.random.uniform(low=-1.0, high=1.0, size=w_shape).astype(
+        np.float32
+    )
+    b_linear.weight.data = torch.from_numpy(weight_tensor_fp)
+    b_linear.eval()
+    bo.export_finn_onnx(b_linear, i_shape, export_onnx_path)
+    model = ModelWrapper(export_onnx_path)
+    model = model.transform(InferShapes())
+    inp_tensor = gen_finn_dt_tensor(i_dtype, i_shape)
+    idict = {model.graph.input[0].name: inp_tensor}
+    odict = oxe.execute_onnx(model, idict, True)
+    produced = odict[model.graph.output[0].name]
+    inp_tensor = torch.from_numpy(inp_tensor).float()
+    expected = b_linear.forward(inp_tensor).detach().numpy()
+
+    assert np.isclose(produced, expected, atol=1e-3).all()
+    os.remove(export_onnx_path)