Merge pull request #339 from Xilinx/feature/brevitas_bias_fix

Upgrade Brevitas to get bias export fixes

.github/workflows/quicktest-dev-pr.yml

@@ -11,7 +11,7 @@ jobs:
 
   test:
     name: Run quicktest on PR branch
-    runs-on: ubuntu-16.04
+    runs-on: ubuntu-18.04
 
     steps:
       - name: checkout

docker/finn_entrypoint.sh

@@ -14,7 +14,7 @@ gecho () {
 # the repos themselves are cloned in the Dockerfile
 FINN_BASE_COMMIT=ac0b86a63eb937b869bfa453a996a8a8b8506546
 FINN_EXP_COMMIT=e9f97dcdb4db2f889b0f36af079a6a1792b7d4de
-BREVITAS_COMMIT=14abbe1e7ef82485d79415871fcf5766b0a40a00
+BREVITAS_COMMIT=d7ded80fa9557da2998ea310669edee7fb2d9526
 CNPY_COMMIT=4e8810b1a8637695171ed346ce68f6984e585ef4
 HLSLIB_COMMIT=4d74baefa79df48b5a0348d63f39a26df075de51
 PYVERILATOR_COMMIT=e2ff74030de3992dcac54bf1b6aad2915946e8cb

requirements.txt

@@ -6,6 +6,7 @@ future==0.18.2
 gspread==3.6.0
 numpy==1.18.0
 onnx==1.7.0
+onnxoptimizer==0.2.6
 onnxruntime==1.4.0
 pre-commit==2.6.0
 scipy==1.5.2

tests/brevitas/test_brevitas_QConv2d.py

@@ -50,8 +50,6 @@ export_onnx_path = "test_brevitas_conv.onnx"
 @pytest.mark.parametrize("bias", [True, False])
 @pytest.mark.parametrize("in_channels", [32])
 def test_brevitas_QConv2d(dw, bias, in_channels):
-    if bias:
-        pytest.xfail("bias export bug")
     ishape = (1, 32, 111, 111)
     if dw is True:
         groups = in_channels

tests/brevitas/test_brevitas_qlinear.py

@@ -48,8 +48,6 @@ export_onnx_path = "test_brevitas_qlinear.onnx"
 @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):
-    if bias:
-        pytest.xfail("bias export bug")
     i_shape = (1, in_features)
     w_shape = (out_features, in_features)
     b_linear = QuantLinear(

tests/end2end/test_end2end_bnn_pynq.py

@@ -347,6 +347,8 @@ class TestEnd2End:
         assert os.path.isfile(chkpt_preproc_name)
         # join preprocessing and core model
         pre_model = ModelWrapper(chkpt_preproc_name)
+        pre_model = pre_model.transform(InferShapes())
+        pre_model = pre_model.transform(FoldConstants())
         model = model.transform(MergeONNXModels(pre_model))
         # add input quantization annotation: UINT8 for all BNN-PYNQ models
         global_inp_name = model.graph.input[0].name

tests/end2end/test_end2end_mobilenet_v1.py

@@ -101,6 +101,7 @@ def test_end2end_mobilenet_export():
     # set input finn datatype to UINT8
     preproc_model.set_tensor_datatype(preproc_model.graph.input[0].name, DataType.UINT8)
     preproc_model = preproc_model.transform(InferShapes())
+    preproc_model = preproc_model.transform(FoldConstants())
     preproc_model = preproc_model.transform(GiveUniqueNodeNames())
     preproc_model = preproc_model.transform(GiveUniqueParameterTensors())
     preproc_model = preproc_model.transform(GiveReadableTensorNames())