From 9a99b31141f372417914ed47b73d1ede1b287a02 Mon Sep 17 00:00:00 2001
From: icolbert <Ian.Colbert@amd.com>
Date: Tue, 28 Feb 2023 18:06:52 -0800
Subject: [PATCH] Adding threshold data types for accumulator width unit test
---
.../streamline/test_minimize_bit_width.py | 37 +++++++++++++++----
1 file changed, 29 insertions(+), 8 deletions(-)
diff --git a/tests/transformation/streamline/test_minimize_bit_width.py b/tests/transformation/streamline/test_minimize_bit_width.py
index 221be75da..7cb866c6e 100644
--- a/tests/transformation/streamline/test_minimize_bit_width.py
+++ b/tests/transformation/streamline/test_minimize_bit_width.py
@@ -51,7 +51,7 @@ def make_unit_test_model(wdt: DataType, idt: DataType, tdt: Optional[DataType] =
outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, [1, 32, 32, 64])
layer1 = helper.make_node(
"VectorVectorActivation",
- ["inp", "params0", "thresh"] if tdt is not None else ["inp", "params0"],
+ ["inp", "params0", "thresh0"] if tdt is not None else ["inp", "params0"],
["hid"],
domain="finn.custom_op.fpgadataflow",
backend="fpgadataflow",
@@ -67,7 +67,7 @@ def make_unit_test_model(wdt: DataType, idt: DataType, tdt: Optional[DataType] =
)
layer2 = helper.make_node(
"MatrixVectorActivation",
- ["hid", "params1", "thresh"] if tdt is not None else ["hid", "params1"],
+ ["hid", "params1", "thresh1"] if tdt is not None else ["hid", "params1"],
["outp"],
domain="finn.custom_op.fpgadataflow",
backend="fpgadataflow",
@@ -100,9 +100,21 @@ def make_unit_test_model(wdt: DataType, idt: DataType, tdt: Optional[DataType] =
model.set_initializer("params1",
gen_finn_dt_tensor(wdt, (32, 64))
)
+ # if the threshold data type is specified, then we need to generate
+ # some dummy threshold values
if tdt is not None:
- model.set_tensor_datatype("thresh", tdt)
- # model.set_initializer("thresh", thresholds)
+ model.set_tensor_datatype("thresh0", tdt)
+ model.set_tensor_datatype("thresh1", tdt)
+ # Create threshold tensors
+ n_steps: int = idt.get_num_possible_values() - 1
+ thresholds: Optional[np.ndarray] = np.random.randint(tdt.min(), tdt.max() - 1, \
+ (32, n_steps)).astype(np.float32) # generate thresholds for the activations
+ thresholds = np.sort(thresholds, axis=1) # provide non-decreasing thresholds
+ model.set_initializer("thresh0", thresholds)
+ thresholds: Optional[np.ndarray] = np.random.randint(tdt.min(), tdt.max() - 1, \
+ (64, n_steps)).astype(np.float32) # generate thresholds for the activations
+ thresholds = np.sort(thresholds, axis=1) # provide non-decreasing thresholds
+ model.set_initializer("thresh1", thresholds)
return model
@@ -170,7 +182,7 @@ def calculate_accumulator_bit_width(
inst: Union[MatrixVectorActivation, VectorVectorActivation],
model: ModelWrapper
) -> Union[DataType, IntType]:
- """Calculate the accumulator bit width use the closed-form expressions
+ """Calculate the accumulator bit width using the closed-form expressions
derived in `Quantized Neural Networks for Low-Precision Accumulation
with Guaranteed Overflow Avoidance` (2023) by I.Colbert, A. Pappalardo,
J. Petri-Koenig
@@ -217,21 +229,30 @@ def calculate_accumulator_bit_width(
return DataType[f"INT{int(P)}"]
+thresh_data_types = [
+ None,
+ DataType['INT32'],
+ DataType['INT24'],
+ DataType['INT16'],
+]
+
+
@pytest.mark.parametrize("wdt", weight_data_types)
@pytest.mark.parametrize("idt", input_data_types)
+@pytest.mark.parametrize("tdt", thresh_data_types)
@pytest.mark.parametrize("rww", [True, False])
-def test_minimize_accumulator_width(wdt: DataType, idt:DataType, rww: bool):
+def test_minimize_accumulator_width(wdt: DataType, idt: DataType, tdt: DataType, rww: bool):
"""Testing MinimizeAccumulatorWidth for VVAU and MVAU.
:param wdt: (DataType) The data type that we are testing for the weights
:param idt: (DataType) The data type that we are testing for the activations
+ :param tdt: (DataType) The data type that we are testing for the thresholds
:param rww: (bool) Whether or not to use runtime-writeable weights"""
if not wdt.signed():
pytest.skip("Closed-form accumulator calculation is designed to consider only signed weights")
# Create uniform-precision model
- # TODO: add thresholds (tdt) to unit tests
- model = make_unit_test_model(wdt, idt)
+ model = make_unit_test_model(wdt, idt, tdt)
def_adt = DataType["INT32"]
# If runtime-writeable weights, specify as a node attribute
--
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