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Commit 36c13f46 authored by Yaman Umuroglu's avatar Yaman Umuroglu
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[Test] cleaner i/w combos using pytest decorators

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tests/fpgadataflow/test_fpgadataflow_fclayer.py
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import pytest
import numpy as np
from onnx import TensorProto, helper
......@@ -5,11 +7,7 @@ import finn.core.onnx_exec as oxe
import finn.custom_op.xnorpopcount as xp
from finn.core.datatype import DataType
from finn.core.modelwrapper import ModelWrapper
from finn.core.utils import (
gen_finn_dt_tensor,
interleave_matrix_outer_dim_from_partitions
)
from finn.custom_op.multithreshold import multithreshold
from finn.core.utils import gen_finn_dt_tensor
def make_single_fclayer_modelwrapper(W, pe, simd, wdt, idt, odt, T=None, tdt=None):
......@@ -23,10 +21,8 @@ def make_single_fclayer_modelwrapper(W, pe, simd, wdt, idt, odt, T=None, tdt=Non
sf = mw // simd
if T is not None:
tmem = nf
n_thres_steps = T.shape[1]
else:
tmem = 0
n_thres_steps = 0
inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, [1, sf, simd])
outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, [1, nf, pe])
......@@ -53,7 +49,7 @@ def make_single_fclayer_modelwrapper(W, pe, simd, wdt, idt, odt, T=None, tdt=Non
outputDataType=odt.name,
)
graph = helper.make_graph(
nodes=[FCLayer_node], name="fclayer_graph", inputs=[inp], outputs=[outp],
nodes=[FCLayer_node], name="fclayer_graph", inputs=[inp], outputs=[outp]
)
model = helper.make_model(graph, producer_name="fclayer-model")
......@@ -74,10 +70,12 @@ def prepare_inputs(model, input_tensor, idt):
input_tensor = (np.asarray(input_tensor, dtype=np.float32)).reshape(*ishape)
return {"inp": input_tensor}
def create_noactivation_testcases(idt, wdt, odt):
mh = 8
mw = 8
def create_noactivation_testcases(idt, wdt, mh=8, mw=8):
if wdt == DataType.BIPOLAR and idt == DataType.BIPOLAR:
odt = DataType.UINT32
else:
odt = DataType.INT32
# generate weights
W = gen_finn_dt_tensor(wdt, (mw, mh))
# generate input data
......@@ -88,13 +86,13 @@ def create_noactivation_testcases(idt, wdt, odt):
simd_values = [1, int(mw / 2), mw]
for pe in pe_values:
for simd in simd_values:
model = make_single_fclayer_modelwrapper(W, pe, simd, wdt, idt, odt, T, tdt)
model = make_single_fclayer_modelwrapper(W, pe, simd, wdt, idt, odt)
# prepare input data
input_dict = prepare_inputs(model, x, idt)
if wdt == DataType.BIPOLAR and idt == DataType.BIPOLAR:
# convert inputs to binary and use xnorpopcountmatmul
y = xp.xnorpopcountmatmul((x+1)/2, (W+1)/2)
y = xp.xnorpopcountmatmul((x + 1) / 2, (W + 1) / 2)
else:
y = np.matmul(x, W)
oshape = model.get_tensor_shape("outp")
......@@ -104,191 +102,7 @@ def create_noactivation_testcases(idt, wdt, odt):
assert (y_produced.reshape(y_expected.shape) == y_expected).all()
# no activation cases
# no act -all bipolar
def test_fpgadataflow_fclayer_ibp_wbp_noact():
wdt = idt = DataType.BIPOLAR
odt = DataType.UINT32
create_noativation_testcases(idt, wdt, odt)
# no act - all signed
def test_fpgadataflow_fclayer_iint2_wint2_noact():
wdt = idt = DataType.INT2
odt = DataType.INT32
create_noativation_testcases(idt, wdt, odt)
# no act - all ternary
def test_fpgadataflow_fclayer_it_wt_noact():
wdt = idt = DataType.TERNARY
odt = DataType.INT32
create_noativation_testcases(idt, wdt, odt)
# no act - idt: bipolar wdt: signed
def test_fpgadataflow_fclayer_ibp_wint2_noact():
wdt = DataType.INT2
idt = DataType.BIPOLAR
odt = DataType.INT32
create_noativation_testcases(idt, wdt, odt)
# no act - idt: bipolar wdt: ternary
def test_fpgadataflow_fclayer_ibp_wt_noact():
wdt = DataType.INT2
idt = DataType.TERNARY
odt = DataType.INT32
create_noativation_testcases(idt, wdt, odt)
# no act - idt: signed wdt: bipolar
def test_fpgadataflow_fclayer_it_wbp_noact():
wdt = DataType.BIPOLAR
idt = DataType.INT2
odt = DataType.INT32
create_noativation_testcases(idt, wdt, odt)
# no act - idt: signed wdt: ternary
def test_fpgadataflow_fclayer_it_wbp_noact():
wdt = DataType.TERNARY
idt = DataType.INT2
odt = DataType.INT32
create_noativation_testcases(idt, wdt, odt)
# no act - idt: ternary wdt: bipolar
def test_fpgadataflow_fclayer_it_wbp_noact():
wdt = DataType.BIPOLAR
idt = DataType.TERNARY
odt = DataType.INT32
create_noativation_testcases(idt, wdt, odt)
# no act - idt: ternary wdt: signed
def test_fpgadataflow_fclayer_it_wbp_noact():
wdt = DataType.INT2
idt = DataType.TERNARY
odt = DataType.INT32
create_noativation_testcases(idt, wdt, odt)
def test_fpgadataflow_fclayer_all_bipolar():
mh = 8
mw = 8
wdt = idt = odt = DataType.BIPOLAR
tdt = DataType.UINT32
# generate weights
W = gen_finn_dt_tensor(wdt, (mw, mh))
# single global threshold at zero
T = np.zeros((1, 1))
# generate input data
x = gen_finn_dt_tensor(idt, (1, mw))
# set up layers with different pe and simd
pe_values = [1, int(mh / 2), mh]
simd_values = [1, int(mw / 2), mw]
for pe in pe_values:
for simd in simd_values:
model = make_single_fclayer_modelwrapper(W, pe, simd, wdt, idt, odt, T, tdt)
# prepare input data
input_dict = prepare_inputs(model, x, idt)
# execute model
produced = oxe.execute_onnx(model, input_dict)["outp"]
# TODO fix this to resemble the noact tester above
# expected output
# correction of bipolar values to enable xnorpopcountmutmal
Wb = (W + 1) * 0.5
xb = (x + 1) * 0.5
y = xp.xnorpopcountmatmul(Wb, xb.reshape(-1, 1))
expected = multithreshold(y.reshape(1, mh), T)
assert (produced.reshape(expected.shape) == expected).all()
wdt = idt = odt = DataType.BIPOLAR
create_testcases(idt, wdt, odt)
def test_fpgadataflow_fclayer_all_signed():
mh = 8
mw = 8
wdt = idt = odt = DataType.INT2
tdt = DataType.INT32
# generate weights
W = gen_finn_dt_tensor(wdt, [mh, mw])
# single global threshold at zero
T = np.zeros((1, 1))
# generate input data
x = gen_finn_dt_tensor(idt, mw)
# set up layers with different pe and simd
pe_values = [1, int(mh / 2), mh]
simd_values = [1, int(mw / 2), mw]
for pe in pe_values:
for simd in simd_values:
model = make_single_fclayer_modelwrapper(W, pe, simd, wdt, idt, odt, T, tdt)
# prepare input data
input_dict = prepare_inputs(model, x, idt)
# execute model
produced = oxe.execute_onnx(model, input_dict)["outp"]
# expected output
oshape = model.get_tensor_shape("outp")
y = np.dot(W, x).reshape(oshape.shape)
expected = multithreshold(y.reshape(1, mh), T)
assert (produced.reshape(expected.shape) == expected).all()
def test_fpgadataflow_fclayer_all_ternary():
mh = 8
mw = 8
wdt = idt = odt = DataType.TERNARY
tdt = DataType.INT32
# generate weights
W = gen_finn_dt_tensor(wdt, [mh, mw])
# single global threshold at zero
T = np.zeros((1, 1))
# generate input data
x = gen_finn_dt_tensor(idt, mw)
# set up layers with different pe and simd
pe_values = [1, int(mh / 2), mh]
simd_values = [1, int(mw / 2), mw]
for pe in pe_values:
for simd in simd_values:
model = make_single_fclayer_modelwrapper(W, pe, simd, wdt, idt, odt, T, tdt)
# prepare input data
input_dict = prepare_inputs(model, x, idt)
# execute model
produced = oxe.execute_onnx(model, input_dict)["outp"]
# expected output
oshape = model.get_tensor_shape("outp")
y = np.dot(W, x).reshape(oshape.shape)
expected = multithreshold(y.reshape(1, mh), T)
assert (produced.reshape(expected.shape) == expected).all()
@pytest.mark.parametrize("wdt", [DataType.BIPOLAR, DataType.INT2])
@pytest.mark.parametrize("idt", [DataType.BIPOLAR, DataType.INT2])
def test_fpgadataflow_fclayer_noact(wdt, idt):
create_noactivation_testcases(idt, wdt)
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