From 825cb51a2435ddcc41c5336acf262e00f99790f4 Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <maltanar@gmail.com>
Date: Mon, 30 Mar 2020 14:58:02 +0100
Subject: [PATCH] [Test] debug changes to ip stitch test
---
.../test_fpgadataflow_ip_stitch.py | 49 +++++++------------
1 file changed, 19 insertions(+), 30 deletions(-)
diff --git a/tests/fpgadataflow/test_fpgadataflow_ip_stitch.py b/tests/fpgadataflow/test_fpgadataflow_ip_stitch.py
index fc385be06..6c79af8bb 100644
--- a/tests/fpgadataflow/test_fpgadataflow_ip_stitch.py
+++ b/tests/fpgadataflow/test_fpgadataflow_ip_stitch.py
@@ -67,7 +67,7 @@ def create_one_fc_model():
# create a model with a StreamingFCLayer instance with no activation
# the wider range of the full accumulator makes debugging a bit easier
wdt = DataType.INT2
- idt = DataType.INT2
+ idt = DataType.INT32
odt = DataType.INT32
m = 4
no_act = 1
@@ -120,16 +120,14 @@ def create_one_fc_model():
def create_two_fc_model():
# create a model with two StreamingFCLayer instances
wdt = DataType.INT2
- idt = DataType.INT2
- odt = DataType.INT2
- act = DataType.INT2
+ idt = DataType.INT32
+ odt = DataType.INT32
m = 4
- tdt = DataType.INT32
- actval = odt.min()
- no_act = 0
+ actval = 0
+ no_act = 1
binary_xnor_mode = 0
- pe = 2
- simd = 2
+ pe = 1
+ simd = 1
inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, [1, m])
mid = helper.make_tensor_value_info("mid", TensorProto.FLOAT, [1, m])
@@ -137,7 +135,7 @@ def create_two_fc_model():
fc0 = helper.make_node(
"StreamingFCLayer_Batch",
- ["inp", "w0", "t0"],
+ ["inp", "w0"],
["mid"],
domain="finn",
backend="fpgadataflow",
@@ -145,25 +143,27 @@ def create_two_fc_model():
MW=m,
MH=m,
SIMD=simd,
- PE=pe,
+ PE=2,
inputDataType=idt.name,
weightDataType=wdt.name,
outputDataType=odt.name,
ActVal=actval,
binaryXnorMode=binary_xnor_mode,
noActivation=no_act,
+ mem_mode="decoupled",
)
fc1 = helper.make_node(
"StreamingFCLayer_Batch",
- ["mid", "w1", "t1"],
+ ["mid", "w1"],
["outp"],
domain="finn",
backend="fpgadataflow",
resType="ap_resource_lut()",
+ mem_mode="decoupled",
MW=m,
MH=m,
- SIMD=simd,
+ SIMD=2,
PE=pe,
inputDataType=idt.name,
weightDataType=wdt.name,
@@ -191,25 +191,11 @@ def create_two_fc_model():
model.set_tensor_datatype("w1", wdt)
# generate weights
- w0 = gen_finn_dt_tensor(wdt, (m, m))
- w1 = gen_finn_dt_tensor(wdt, (m, m))
+ w0 = np.eye(m, dtype=np.float32)
+ w1 = np.eye(m, dtype=np.float32)
model.set_initializer("w0", w0)
model.set_initializer("w1", w1)
- # generate thresholds
- (min, max) = calculate_signed_dot_prod_range(idt, wdt, m)
- n_steps = act.get_num_possible_values() - 1
- t0 = np.random.randint(min, max - 1, (m, n_steps)).astype(np.float32)
- t1 = np.random.randint(min, max - 1, (m, n_steps)).astype(np.float32)
- # provide non-decreasing thresholds
- t0 = np.sort(t0, axis=1)
- t1 = np.sort(t1, axis=1)
-
- model.set_initializer("t0", t0)
- model.set_initializer("t1", t1)
- model.set_tensor_datatype("t0", tdt)
- model.set_tensor_datatype("t1", tdt)
-
model = model.transform(CreateDataflowPartition())
return model
@@ -251,6 +237,7 @@ def test_fpgadataflow_ipstitch_do_stitch():
def test_fpgadataflow_ipstitch_rtlsim():
model = ModelWrapper(ip_stitch_model_dir + "/test_fpgadataflow_ip_stitch.onnx")
+ model.set_metadata_prop("rtlsim_trace", "whole_trace.vcd")
sim = pyverilate_stitched_ip(model)
exp_io = [
"ap_clk_0",
@@ -268,7 +255,9 @@ def test_fpgadataflow_ipstitch_rtlsim():
model.set_metadata_prop("exec_mode", "rtlsim")
idt = model.get_tensor_datatype("inp")
ishape = model.get_tensor_shape("inp")
- x = gen_finn_dt_tensor(idt, ishape)
+ # x = gen_finn_dt_tensor(idt, ishape)
+ # x = np.zeros(ishape, dtype=np.float32)
+ x = np.asarray([[-2, -1, 0, 1]], dtype=np.float32)
rtlsim_res = execute_onnx(model, {"inp": x})["outp"]
assert (rtlsim_res == x).all()
--
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