From 33aecc82f87fbe6d43412191e7bf7810c572e8e8 Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <yamanu@xilinx.com>
Date: Thu, 14 Oct 2021 23:31:11 +0200
Subject: [PATCH] [Test] restructure lookup test
---
..._lookup.py => test_fpgadataflow_lookup.py} | 52 ++++++++++---------
1 file changed, 27 insertions(+), 25 deletions(-)
rename tests/fpgadataflow/{test_lookup.py => test_fpgadataflow_lookup.py} (81%)
diff --git a/tests/fpgadataflow/test_lookup.py b/tests/fpgadataflow/test_fpgadataflow_lookup.py
similarity index 81%
rename from tests/fpgadataflow/test_lookup.py
rename to tests/fpgadataflow/test_fpgadataflow_lookup.py
index 11f5a27c9..45678bbdf 100644
--- a/tests/fpgadataflow/test_lookup.py
+++ b/tests/fpgadataflow/test_fpgadataflow_lookup.py
@@ -26,9 +26,9 @@
# 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
-import onnx # noqa
-import os
import torch
from brevitas.export import FINNManager
from torch import nn
@@ -48,8 +48,6 @@ from finn.transformation.infer_datatypes import InferDataTypes
from finn.transformation.infer_shapes import InferShapes
from finn.util.basic import gen_finn_dt_tensor
-tmpdir = os.environ["FINN_BUILD_DIR"]
-
def make_lookup_model(embeddings, ishape, idt, edt):
num_embeddings, embedding_dim = embeddings.shape
@@ -81,18 +79,23 @@ def make_lookup_model(embeddings, ishape, idt, edt):
return model
-def test_lookup_export_convert():
- export_path = tmpdir + "/test_lookup_export.onnx"
+# embedding DataType
+@pytest.mark.parametrize("edt", [DataType["FIXED<8,2>"]])
+# other embedding config
+@pytest.mark.parametrize(
+ "embedding_cfg", [(130, DataType["UINT8"], 25), (5145, DataType["UINT16"], 20)]
+)
+# execution mode
+@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
+@pytest.mark.vivado
+@pytest.mark.slow
+def test_fpgadataflow_lookup(edt, embedding_cfg, exec_mode):
ishape = (1, 10)
- idt = DataType["UINT8"]
- edt = DataType["FIXED<8,2>"]
- num_embeddings = 2 ** idt.bitwidth()
- embedding_dim = 2
+ num_embeddings, idt, embedding_dim = embedding_cfg
eshape = (num_embeddings, embedding_dim)
exp_oshape = tuple(list(ishape) + [embedding_dim])
embeddings = gen_finn_dt_tensor(edt, eshape)
model = make_lookup_model(embeddings, ishape, idt, edt)
- model.save(export_path)
assert len(model.graph.node) == 1
assert model.graph.node[0].op_type == "Gather"
iname = model.graph.input[0].name
@@ -105,6 +108,7 @@ def test_lookup_export_convert():
assert tuple(model.get_tensor_shape(oname)) == exp_oshape
assert (model.get_initializer(ename) == embeddings).all()
itensor = gen_finn_dt_tensor(idt, ishape).astype(np.int64)
+ itensor = np.clip(itensor, 0, num_embeddings - 1)
ret = execute_onnx(model, {iname: itensor})
exp_out = np.take(embeddings, itensor, axis=0)
assert (exp_out == ret[oname]).all()
@@ -114,17 +118,15 @@ def test_lookup_export_convert():
assert model.graph.node[0].input[0] == iname
assert model.graph.node[0].input[1] == ename
assert model.graph.node[0].output[0] == oname
- # prepare and execute cppsim
- model = model.transform(PrepareCppSim())
- model = model.transform(CompileCppSim())
- model = model.transform(SetExecMode("cppsim"))
- ret_cppsim = execute_onnx(model, {iname: itensor})
- assert (exp_out == ret_cppsim[oname]).all()
- # prepare and execute rtlsim
- model = model.transform(GiveUniqueNodeNames())
- model = model.transform(PrepareIP("xc7z020clg400-1", 10))
- model = model.transform(HLSSynthIP())
- model = model.transform(SetExecMode("rtlsim"))
- model = model.transform(PrepareRTLSim())
- ret_rtlsim = execute_onnx(model, {iname: itensor})
- assert (exp_out == ret_rtlsim[oname]).all()
+ if exec_mode == "cppsim":
+ model = model.transform(PrepareCppSim())
+ model = model.transform(CompileCppSim())
+ model = model.transform(SetExecMode("cppsim"))
+ elif exec_mode == "rtlsim":
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(PrepareIP("xc7z020clg400-1", 10))
+ model = model.transform(HLSSynthIP())
+ model = model.transform(SetExecMode("rtlsim"))
+ model = model.transform(PrepareRTLSim())
+ ret_sim = execute_onnx(model, {iname: itensor})
+ assert (exp_out == ret_sim[oname]).all()
--
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