Updating matvec accumulator range calculation

src/qonnx/util/basic.py

@@ -181,31 +181,18 @@ def pad_tensor_to_multiple_of(ndarray, pad_to_dims, val=0, distr_pad=False):
     return ret
 
 
-def calculate_matvec_accumulator_range(matrix, vec_dt):
+def calculate_matvec_accumulator_range(matrix: np.ndarray, vec_dt: DataType):
     """Calculate the minimum and maximum possible result (accumulator) values
     for a dot product x * A, given matrix A of dims (MW, MH), and vector (1, MW)
     with datatype vec_dt. Returns (acc_min, acc_max).
     """
-    min_weight = matrix.min()
-    max_weight = matrix.max()
-    perceptive_field_elems = matrix.shape[0]
-    min_input = vec_dt.min()
-    max_input = vec_dt.max()
-    # calculate minimum and maximum values of accumulator
-    # assume inputs span the whole range of the input datatype
-    acc_min = perceptive_field_elems * min(
-        min_weight * max_input,
-        min_weight * min_input,
-        max_weight * max_input,
-        max_weight * min_input,
-    )
-    acc_max = perceptive_field_elems * max(
-        min_weight * max_input,
-        min_weight * min_input,
-        max_weight * max_input,
-        max_weight * min_input,
-    )
-    return (acc_min, acc_max)
+    max_weight = abs(matrix).sum(axis=0).max()
+    max_input = max(abs(vec_dt.min()), abs(vec_dt.max()))
+    max_value = max_input * max_weight
+    # If either the weight and input datatypes are signed, then the minimum
+    # value that their accumulated product can be is -max_value. Else, it's 0.
+    min_value = -max_value if (matrix.min() < 0) or vec_dt.signed() else 0
+    return (min_value, max_value)
 
 
 def gen_finn_dt_tensor(finn_dt, tensor_shape):

tests/util/test_matvec_range.py

@@ -0,0 +1,63 @@
+# Copyright (c) 2022 Xilinx, Inc.
+# 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 Xilinx 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
+
+import qonnx.util.basic as util
+from qonnx.core.datatype import DataType
+
+"""Calculate the minimum and maximum possible result (accumulator) values
+for a dot product x * A, given matrix A of dims (MW, MH), and vector (1, MW)
+with datatype vec_dt. Returns (acc_min, acc_max).
+"""
+
+np.random.seed(0)
+
+datatypes = [
+    DataType["UINT8"],
+    DataType["INT8"],
+    DataType["UINT3"],
+    DataType["INT3"],
+    DataType["BIPOLAR"],
+    DataType["TERNARY"],
+]
+
+
+@pytest.mark.parametrize("wdt", datatypes)
+@pytest.mark.parametrize("idt", datatypes)
+@pytest.mark.parametrize("size", [1, 10, 100])
+def test_calculate_matvec_accumulator_range(wdt: DataType, idt: DataType, size: int):
+    weights = util.gen_finn_dt_tensor(wdt, tensor_shape=(size, 1))  # (MW, MH)
+    acc_min, acc_max = util.calculate_matvec_accumulator_range(weights, idt)
+    for _ in range(1000):
+        inputs = util.gen_finn_dt_tensor(idt, tensor_shape=(1, size))
+        acc_vals = inputs @ weights
+        assert acc_vals.min() >= acc_min
+        assert acc_vals.max() <= acc_max