[CnvLower] add a first draft of conv lowering transformation

src/finn/transformation/lower_convs_to_matmul.py

+# Copyright (c) 2020, 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.
+
+from onnx import TensorProto
+from onnx import helper
+
+from finn.transformation import Transformation
+from finn.transformation.infer_shapes import InferShapes
+from finn.util.basic import get_by_name
+
+
+class LowerConvsToMatMul(Transformation):
+    """Replace Conv layers with pairs of Im2Col-MatMul layers, plus Transpose
+    layers to keep the original data layout."""
+
+    def apply(self, model):
+        graph = model.graph
+        node_ind = 0
+        graph_modified = False
+        for n in graph.node:
+            node_ind += 1
+            if n.op_type == "Conv":
+                graph_modified = True
+                cnv_input = n.input[0]
+                cnv_output = n.output[0]
+                # extract conv parameters
+                k = get_by_name(n.attribute, "kernel_shape")[-1]
+                pad = get_by_name(n.attribute, "pads")[-1]
+                stride = get_by_name(n.attribute, "strides")[-1]
+                weight_name = n.input[1]
+                W_conv = model.get_initializer(weight_name)
+                ifm_ch = W_conv.shape[1]
+                ofm_ch = W_conv.shape[0]
+                ifm_dim = model.get_tensor_shape(n.input[0])[-1]  # assume NCHW
+                ofm_dim = model.get_tensor_shape(n.output[0])[-1]  # assume NCHW
+                # reuse conv weights for new matmul weights
+                W_matmul = W_conv.reshape(ofm_ch, ifm_ch * k * k).T
+                model.set_initializer(weight_name, W_matmul)
+                # create new intermediate values
+                inp_trans_out = helper.make_tensor_value_info(
+                    model.make_new_valueinfo_name(),
+                    TensorProto.FLOAT,
+                    (1, ifm_dim, ifm_dim, ifm_ch),  # NHWC
+                )
+                graph.value_info.append(inp_trans_out)
+                inp_trans_out = inp_trans_out.name
+
+                im2col_out = helper.make_tensor_value_info(
+                    model.make_new_valueinfo_name(),
+                    TensorProto.FLOAT,
+                    (1, ofm_dim, ofm_dim, ifm_ch * k * k),
+                )
+                graph.value_info.append(im2col_out)
+                im2col_out = im2col_out.name
+
+                matmul_out = helper.make_tensor_value_info(
+                    model.make_new_valueinfo_name(),
+                    TensorProto.FLOAT,
+                    (1, ofm_dim, ofm_dim, ofm_ch),
+                )
+                graph.value_info.append(matmul_out)
+                matmul_out = matmul_out.name
+
+                # create new nodes
+                # NCHW -> NHWC
+                inp_trans_node = helper.make_node(
+                    "Transpose", [cnv_input], [inp_trans_out], perm=[0, 2, 3, 1]
+                )
+                # lower input tensor
+                im2col_node = helper.make_node(
+                    "Im2Col",
+                    [inp_trans_out],
+                    [im2col_out],
+                    domain="finn",
+                    stride=stride,
+                    kernel_size=k,
+                    pad_amount=pad,
+                    input_shape="(1,{},{},{})".format(ifm_dim, ifm_dim, ifm_ch),
+                )
+                # do matmul
+                matmul_node = helper.make_node(
+                    "MatMul", [im2col_out, weight_name], [matmul_out]
+                )
+                # NHWC -> NCHW
+                out_trans_node = helper.make_node(
+                    "Transpose", [matmul_out], [cnv_output], perm=[0, 3, 1, 2]
+                )
+                # insert nodes where the conv is to preserve topological ordering
+                graph.node.insert(node_ind, inp_trans_node)
+                graph.node.insert(node_ind + 1, im2col_node)
+                graph.node.insert(node_ind + 2, matmul_node)
+                graph.node.insert(node_ind + 3, out_trans_node)
+                # remove old nodes
+                graph.node.remove(n)
+        model = model.transform(InferShapes())
+        return (model, graph_modified)