Moved ExtractBiasFromConv transformation from FINN to finn-base.

docker/Dockerfile.finn

@@ -86,7 +86,7 @@ RUN pip install -e git+https://github.com/fbcotter/dataset_loading.git@0.0.4#egg
 
 # git-based Python repo dependencies
 # these are installed in editable mode for easier co-development
-ARG FINN_BASE_COMMIT="78e4098ad3fc78f72db40b6a3cf29c82c2a567b1"
+ARG FINN_BASE_COMMIT="e97b9fc5c23d9bf95961501fcd94a7463b419c37"
 ARG QONNX_COMMIT="6d55dce220c7f744ef23585686460b9370b672a0"
 ARG FINN_EXP_COMMIT="f82c0d9868bb88ea045dfadb28508d327d287221"
 ARG BREVITAS_COMMIT="efc1217b94a71d616e3b4a37e56bd28a07c55be0"

src/finn/transformation/qonnx/convert_qonnx_to_finn.py

@@ -29,9 +29,9 @@
 from qonnx.transformation.quant_constant_folding import FoldTransposeIntoQuantInit
 
 from finn.transformation.base import Transformation
+from finn.transformation.extract_conv_bias import ExtractBiasFromConv
 from finn.transformation.gemm_to_matmul import GemmToMatMul
 from finn.transformation.infer_datatypes import InferDataTypes
-from finn.transformation.qonnx.extract_conv_bias import ExtractBiasFromConv
 from finn.transformation.qonnx.fold_quant_weights import FoldQuantWeights
 from finn.transformation.qonnx.infer_quant_avg_pool_2d import (
     AvgPoolAndTruncToQuantAvgPool,

src/finn/transformation/qonnx/extract_conv_bias.py

-# Copyright (c) 2021, Xilinx
-# 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 FINN 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 warnings
-from onnx import TensorProto, helper
-
-from finn.transformation.base import Transformation
-
-# ToDo: Move this transformation into finn-base?
-
-
-class ExtractBiasFromConv(Transformation):
-    """
-    Extracts the (optional) Bias from a Conv node and inserts it behind the
-    Conv node as an Add node.
-    """
-
-    def apply(self, model):
-        graph = model.graph
-        node_ind = 0
-        for n in graph.node:
-            node_ind += 1
-            if n.op_type == "Conv":
-                # Check if the node has a bias input
-                if len(n.input) > 2:
-                    # Extract bias
-                    bias = model.get_initializer(n.input[2])
-                    if bias is None:
-                        warnings.warn(
-                            f"Could not extract bias from Conv node {n}, "
-                            f"due to missing static initialization."
-                        )
-                        continue
-
-                    # Insert bias as Add node behind the Conv node
-                    out_shape = model.get_tensor_shape(n.output[0])
-                    # Reshape bias tensor
-                    add_shape = [1] * len(out_shape)
-                    # ToDo: this must change to "add_shape[-1] = bias.shape[0]" when
-                    #  channels last comes around
-                    add_shape[1] = bias.shape[0]
-                    model.set_initializer(n.input[2], bias.reshape(add_shape))
-
-                    act_add_tensor = helper.make_tensor_value_info(
-                        model.make_new_valueinfo_name(),
-                        TensorProto.FLOAT,
-                        out_shape,
-                    )
-                    graph.value_info.append(act_add_tensor)
-
-                    add_node = helper.make_node(
-                        "Add",
-                        [act_add_tensor.name, n.input[2]],
-                        [n.output[0]],
-                    )
-                    graph.node.insert(node_ind, add_node)
-
-                    # Repoint Conv output and remove bias tensor
-                    n.output[0] = act_add_tensor.name
-                    n.input.remove(n.input[2])
-
-                    return model, True
-
-        return model, False