Merge pull request #170 from Xilinx/feature/move_flatten_past_affine

Feature/move flatten past affine

src/finn/transformation/streamline/reorder.py

@@ -31,7 +31,9 @@ import warnings
 from onnx import helper as oh
 
 from finn.transformation import Transformation
+import finn.core.data_layout as DataLayout
 from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
 from finn.transformation.infer_data_layouts import InferDataLayouts
 from finn.core.datatype import DataType
 from finn.core.onnx_exec import execute_node
@@ -674,7 +676,95 @@ class MoveMaxPoolPastMultiThreshold(Transformation):
         model = model.transform(InferShapes())
         return (model, graph_modified)
 
+class MoveFlattenPastAffine(Transformation):
+    """Moves a node that implements a (1, -1) reshape past a MatMul, Mul or Add node."""
 
+    def apply(self, model):
+        graph = model.graph
+        graph_modified = False
+        node_ind = 0
+        for n in graph.node:
+            node_ind += 1
+            if (
+                n.op_type == "Flatten"
+                and not model.is_fork_node(n)
+                and not model.is_join_node(n)
+            ):
+                consumer = model.find_consumer(n.output[0])
+                if (
+                    consumer is not None
+                    and (
+                        consumer.op_type == "MatMul"
+                        or consumer.op_type == "Mul"
+                        or consumer.op_type == "Add"
+                    )
+                    and not model.is_join_node(consumer)
+                ):
+                    # move flatten past operation and rewire tensors
+                    start_name = n.input[0]
+                    # check if datalyout is set to NHWC and H=W=1
+                    datalayout = model.get_tensor_layout(start_name)
+                    if datalayout == DataLayout.NHWC:
+                        (b, h, w, c) = model.get_tensor_shape(start_name)
+                        if h != 1 or w != 1:
+                            warnings.warn(
+                                """The Transformation can only be performed if
+                            H=W=1."""
+                            )
+                            continue
+                    else:
+                        warnings.warn(
+                            """The Transformation can only be performed on
+                            operations that operate on data layout NHWC."""
+                        )
+                        continue
+                    middle_name = n.output[0]
+                    end_name = consumer.output[0]
+                    op_param_name = consumer.input[1]
+                    A = model.get_initializer(op_param_name)
+                    if A is None:
+                        warnings.warn("Param is not constant, skipping")
+                        continue
+                    op_in_dt = model.get_tensor_datatype(consumer.input[0])
+                    op_out_dt = model.get_tensor_datatype(consumer.output[0])
+                    start_shape = model.get_tensor_shape(start_name)
+                    dummy_in = np.random.uniform(low=0, high=1, size=(start_shape))
+
+                    if consumer.op_type == "MatMul":
+                        dummy_out = np.matmul(dummy_in, A)
+                    elif consumer.op_type == "Mul":
+                        dummy_out = dummy_in * A
+                    elif consumer.op_type == "Add":
+                        dummy_out = dummy_in + A
+
+                    new_op = oh.make_node(
+                        consumer.op_type,
+                        [start_name, op_param_name],
+                        [middle_name],
+                        name=consumer.name,
+                    )
+                    new_flatten = oh.make_node("Flatten", [middle_name], [end_name])
+                    graph.node.insert(node_ind, new_op)
+                    graph.node.insert(node_ind + 1, new_flatten)
+                    model.set_tensor_shape(middle_name, dummy_out.shape)
+                    # because a flatten node doesn't change the datatype we need
+                    # only the datatype of the op node
+                    model.set_tensor_datatype(start_name, op_in_dt)
+                    model.set_tensor_datatype(middle_name, op_out_dt)
+                    model.set_tensor_datatype(end_name, op_out_dt)
+                    # set datalayout
+                    model.set_tensor_layout(start_name, DataLayout.NHWC)
+                    model.set_tensor_layout(middle_name, DataLayout.NHWC)
+                    # remove old nodes
+                    graph.node.remove(n)
+                    graph.node.remove(consumer)
+                    graph_modified = True
+
+        model = model.transform(InferShapes())
+        model = model.transform(InferDataTypes())
+        model = model.transform(InferDataLayouts())                  
+        return (model, graph_modified)
+      
 class MoveTransposePastScalarMul(Transformation):
     """Moves a Transpose node past a scalar Mul node"""
 
@@ -736,3 +826,4 @@ class MoveTransposePastScalarMul(Transformation):
             model = model.transform(InferDataLayouts())
             model = model.transform(InferShapes())
         return (model, graph_modified)
+

tests/transformation/test_move_flatten_past_affine.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.
+import pytest
+
+import numpy as np
+from onnx import TensorProto, helper
+
+from finn.core.modelwrapper import ModelWrapper
+from finn.core.datatype import DataType
+import finn.core.data_layout as DataLayout
+from finn.util.basic import gen_finn_dt_tensor
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.infer_data_layouts import InferDataLayouts
+from finn.transformation.general import GiveUniqueNodeNames, GiveReadableTensorNames
+from finn.transformation.streamline.reorder import MoveFlattenPastAffine
+import finn.core.onnx_exec as oxe
+
+# data layout
+@pytest.mark.parametrize("data_layout", [DataLayout.NHWC, DataLayout.NCHW])
+# batch size
+@pytest.mark.parametrize("batch_size", [1, 2])
+def test_move_flatten_past_affine(data_layout, batch_size):
+    if data_layout == DataLayout.NHWC:
+        ishape = [batch_size, 1, 1, 1024]
+        oshape = [batch_size, 1000]
+    else:
+        ishape = [batch_size, 1024, 1, 1]
+        oshape = [batch_size, 1000]
+
+    inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, ishape)
+    a0 = helper.make_tensor_value_info("a1", TensorProto.FLOAT, [1024, 1000])
+    a1 = helper.make_tensor_value_info("a2", TensorProto.FLOAT, [])
+    a2 = helper.make_tensor_value_info("a3", TensorProto.FLOAT, [1000])
+    outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, oshape)
+
+    flatten_node = helper.make_node("Flatten", ["inp"], ["flatten_out"])
+    matmul_node = helper.make_node("MatMul", ["flatten_out", "a0"], ["matmul_out"])
+    mul_node = helper.make_node("Mul", ["matmul_out", "a1"], ["mul_out"])
+    add_node = helper.make_node("Add", ["mul_out", "a2"], ["outp"])
+
+    graph = helper.make_graph(
+        nodes=[flatten_node, matmul_node, mul_node, add_node],
+        name="move-reshape-graph",
+        inputs=[inp],
+        outputs=[outp],
+        value_info=[a0, a1, a2],
+    )
+
+    model = helper.make_model(graph, producer_name="move_reshape_model")
+    model = ModelWrapper(model)
+
+    # initialize values
+    a0_values = gen_finn_dt_tensor(DataType.TERNARY, [1024, 1000])
+    model.set_initializer("a0", a0_values)
+    a1_values = np.random.uniform(low=0.1, high=0.99, size=(1)).astype(np.float32)
+    model.set_initializer("a1", a1_values)
+    a2_values = np.random.uniform(low=-1, high=1, size=(1000)).astype(np.float32)
+    model.set_initializer("a2", a2_values)
+
+    model.set_tensor_datatype("inp", DataType.INT2)
+    model.set_tensor_layout("inp", data_layout)
+    model = model.transform(InferShapes())
+    model = model.transform(InferDataTypes())
+    model = model.transform(InferDataLayouts())
+    model = model.transform(GiveUniqueNodeNames())
+    model = model.transform(GiveReadableTensorNames())
+
+    # compare execution before and after transformation
+    inp_values = gen_finn_dt_tensor(DataType.INT2, ishape)
+    idict = {model.graph.input[0].name: inp_values}
+    model_transformed = model.transform(MoveFlattenPastAffine())
+    assert oxe.compare_execution(model, model_transformed, idict)
+
+    # depending on data layout check if graph is transformed or not
+    if data_layout == DataLayout.NHWC:
+        # check if nodes have new order in transformed graph
+        assert model.graph != model_transformed.graph
+        assert model_transformed.graph.node[-1].op_type == "Flatten"
+    else:
+        assert model.graph == model_transformed.graph