[Test] First draft for vvau cppsim test

tests/fpgadataflow/test_fpgadataflow_vvau.py

+# Copyright (c) 2020, 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
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# 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 numpy as np
+from onnx import TensorProto, helper
+
+import finn.core.onnx_exec as oxe
+from finn.core.datatype import DataType
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
+from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
+from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
+from finn.util.basic import gen_finn_dt_tensor
+
+
+def make_single_vvau_modelwrapper(W, pe, dim, ch, k, wdt, idt, odt, T=None, tdt=None):
+
+    inp = helper.make_tensor_value_info(
+        "inp", TensorProto.FLOAT, [1, dim, dim, k * k * ch]
+    )
+    outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, [1, dim, dim, ch])
+    if T is not None:
+        no_act = 0
+        node_inp_list = ["inp", "weights", "thresh"]
+        if odt == DataType.BIPOLAR:
+            actval = 0
+        else:
+            actval = odt.min()
+    else:
+        # no thresholds
+        node_inp_list = ["inp", "weights"]
+        actval = 0
+        no_act = 1
+
+    VVAU_node = helper.make_node(
+        "Vector_Vector_Activate_Batch",
+        node_inp_list,
+        ["outp"],
+        domain="finn",
+        backend="fpgadataflow",
+        resType="ap_resource_lut()",
+        PE=pe,
+        Dim=dim,
+        Channels=ch,
+        Kernel=k,
+        ActVal=actval,
+        inputDataType=idt.name,
+        weightDataType=wdt.name,
+        outputDataType=odt.name,
+        noActivation=no_act,
+    )
+    graph = helper.make_graph(
+        nodes=[VVAU_node], name="vvau-graph", inputs=[inp], outputs=[outp]
+    )
+
+    model = helper.make_model(graph, producer_name="vvau-model")
+    model = ModelWrapper(model)
+    model.set_tensor_datatype("inp", idt)
+    model.set_tensor_datatype("outp", odt)
+    model.set_tensor_datatype("weights", wdt)
+    model.set_initializer("weights", W)
+
+    if T is not None:
+        model.set_tensor_datatype("thresh", tdt)
+        model.set_initializer("thresh", T)
+    return model
+
+
+# pe
+@pytest.mark.parametrize("pe", [1, 2])  # , 4, 8])
+# input datatype
+@pytest.mark.parametrize("idt", [DataType.INT2, DataType.INT4])
+# weight datatype
+@pytest.mark.parametrize("wdt", [DataType.INT2, DataType.INT4])
+# kernel size
+@pytest.mark.parametrize("k", [2, 4])
+# dimension
+@pytest.mark.parametrize("dim", [4, 6])
+# channels
+@pytest.mark.parametrize("ch", [2])  # 2, 4])
+def test_fpgadataflow_vvau_cppsim(pe, idt, wdt, k, dim, ch):
+    odt = DataType.INT32
+    # generate weights
+    W = gen_finn_dt_tensor(wdt, (ch, 1, k, k))
+    model = make_single_vvau_modelwrapper(W, pe, dim, ch, k, wdt, idt, odt)
+    model = model.transform(SetExecMode("cppsim"))
+    model = model.transform(PrepareCppSim())
+    model = model.transform(CompileCppSim())
+    # generate inputs
+    x = gen_finn_dt_tensor(idt, (1, dim, dim, k * k * ch))
+
+    idict = {"inp": x}
+    y_produced = oxe.execute_onnx(model, idict)["outp"]
+
+    # test
+    W_sparse = np.zeros((ch, ch, k, k))
+    for c in range(ch):
+        W_sparse[c][c] = W[c][0]
+
+    if pe == 2:
+        W_sparse = W_sparse.transpose(0, 2, 3, 1)
+    W_sparse = W_sparse.reshape(ch, k * k * ch)
+    y_expected = np.matmul(x, W_sparse.T)
+
+    assert (y_produced == y_expected).all()