Merge pull request #12 from eki-project/feature/attention-streamline

Streamlining of Scaled Dot-Product Attention

src/finn/transformation/streamline/__init__.py

@@ -76,8 +76,8 @@ def apply(self, model):
             BatchNormToAffine(),
             ConvertSignToThres(),
             MoveMulPastMaxPool(),
-            MoveScalarLinearPastInvariants(),
             AbsorbSignBiasIntoMultiThreshold(),
+            MoveScalarLinearPastInvariants(),
             MoveAddPastMul(),
             MoveScalarAddPastMatMul(),
             MoveAddPastConv(),

src/finn/transformation/streamline/absorb.py

@@ -30,6 +30,10 @@
 import qonnx.core.data_layout as DataLayout
 import warnings
 from onnx import helper as oh
+# Protobuf onnx graph node type
+from onnx import NodeProto  # noqa
+# QONNX wrapper of ONNX model graphs
+from qonnx.core.modelwrapper import ModelWrapper
 from qonnx.core.datatype import DataType
 
 # QONNX wrapper of ONNX model graphs
@@ -261,7 +265,7 @@ def apply(self, model):
 
 
 class Absorb1BitMulIntoMatMul(Transformation):
-    """Absorb bipolar or binary multiplications into the preciding matrix
+    """Absorb bipolar or binary multiplications into the preceding matrix
     multiply."""
 
     def apply(self, model):
@@ -270,16 +274,28 @@ def apply(self, model):
         graph_modified = False
         for n in graph.node:
             node_ind += 1
-            if n.op_type == "MatMul":
+            # Note: Join-node test is implicitly covered by testing for the
+            # initializer below
+            # Note: This cannot handle fork-nodes, as only the first consumer is
+            # considered below.
+            # TODO: Fork-nodes could be handled if the muls are the same in all
+            #  branches, but this is not checked nor rewired at all right now.
+            if n.op_type == "MatMul" and not model.is_fork_node(n):
                 matmul_weight_name = n.input[1]
                 W = model.get_initializer(matmul_weight_name)
                 Wdt = model.get_tensor_datatype(matmul_weight_name)
-                assert W is not None, "Initializer for matmul weights is not set."
+                # Just skip matmuls with non-existing weight initializers
+                if W is None:
+                    continue
                 consumer = model.find_consumer(n.output[0])
+                # Note: Join-node test is implicitly covered by testing for the
+                # initializer below
                 if consumer is not None and consumer.op_type == "Mul":
                     mul_weight_name = consumer.input[1]
                     A = model.get_initializer(mul_weight_name)
-                    assert A is not None, "Initializer for mul weights is not set."
+                    # Just skip muls with non-existing scale initializers
+                    if A is None:
+                        continue
                     is_1bit = model.get_tensor_datatype(mul_weight_name).bitwidth() == 1
                     if is_1bit:
                         Wnew = A * W
@@ -298,24 +314,36 @@ def apply(self, model):
 
 
 class Absorb1BitMulIntoConv(Transformation):
-    """Absorb bipolar or binary multiplications into the preciding convolution."""
+    """Absorb bipolar or binary multiplications into the preceding convolution."""
 
     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":
+            # Note: Join-node test is implicitly covered by testing for the
+            # initializer below
+            # Note: This cannot handle fork-nodes, as only the first consumer is
+            # considered below.
+            # TODO: Fork-nodes could be handled if the muls are the same in all
+            #  branches, but this is not checked nor rewired at all right now.
+            if n.op_type == "Conv" and not model.is_fork_node(n):
                 conv_weight_name = n.input[1]
                 W = model.get_initializer(conv_weight_name)
                 Wdt = model.get_tensor_datatype(conv_weight_name)
-                assert W is not None, "Initializer for conv weights is not set."
+                # Just skip convs with non-existing weight initializers
+                if W is None:
+                    continue
                 consumer = model.find_consumer(n.output[0])
+                # Note: Join-node test is implicitly covered by testing for the
+                # initializer below
                 if consumer is not None and consumer.op_type == "Mul":
                     mul_weight_name = consumer.input[1]
                     A = model.get_initializer(mul_weight_name)
-                    assert A is not None, "Initializer for mul weights is not set."
+                    # Just skip muls with non-existing scale initializers
+                    if A is None:
+                        continue
                     is_1bit = model.get_tensor_datatype(mul_weight_name).bitwidth() == 1
                     is_scalar = np.prod(A.shape) == 1
                     actual_ndims = len(tuple(filter(lambda x: x > 1, A.shape)))

src/finn/transformation/streamline/reorder.py

@@ -116,58 +116,133 @@ def apply(self, model):
         return model, graph_modified
 
 
+# Tests whether a tensor is a scalar, i.e., whether all dimensions are 1
+def is_scalar(tensor):
+    return tensor is not None and all(x == 1 for x in tensor.shape)
+
+
+# Tests whether a node is a scalar multiplication with a constant scale factor
+def is_const_scalar_mul(node, model):
+    # Only handle existing Mul type nodes
+    if node is not None and node.op_type == "Mul":
+        # The constant must be an initializer
+        #   Note: Assumes the constant parameter to always be the second input
+        scale = model.get_initializer(node.input[1])
+        # Test for existence of a constant scale factor
+        return scale is not None and is_scalar(scale)
+    # Did not match the operator type
+    return False
+
+
+# Refactored version of the MoveScalarMulPastMatMul transform capable of
+# transforming two-input MatMul, like those being part of the attention operator
 class MoveScalarMulPastMatMul(Transformation):
     """Move scalar mul operations past matmul operations. We want to have muls
     next to each other such that they can be collapsed into a single mul."""
 
+    # Applies the transform to a whole model graph
     def apply(self, model):
+        # Get the model graph out of the model wrapper object
         graph = model.graph
-        node_ind = 0
+        # Keep track of whether the graph has been modified
         graph_modified = False
-        for n in graph.node:
-            node_ind += 1
-            if n.op_type == "Mul" 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"
-                    and not model.is_join_node(consumer)
-                ):
-                    mul_weight_name = n.input[1]
-                    matmul_weight_name = consumer.input[1]
-                    A = model.get_initializer(mul_weight_name)
-                    W = model.get_initializer(matmul_weight_name)
-                    if (A is None) or (W is None):
-                        warnings.warn("MatMul or Mul params are not constant, skipping")
+
+        # Iterate all nodes in the graph keeping track of the index
+        for index, node in enumerate(graph.node):
+            # First pattern matching condition: For the transform to be
+            # applicable, the node has to be a MatMul operator
+            if node.op_type == "MatMul":
+                # Note: When touching the following code, remember to treat both
+                # branches equivalently!
+                # TODO: Can this be enforced or at least be made easier by
+                #  extracting common code patterns to a function?
+
+                # Get the left hand side and right hand side inputs
+                #   Note: Assumes the ordering of left to right inputs to match
+                #   indices 0 to 1. However, it does not "hurt" if it is
+                #   reversed as both sides are treated equivalently.
+                lhs = model.find_producer(node.input[0])
+                rhs = model.find_producer(node.input[1])
+
+                # Give precedence to the left hand side input testing for the
+                # presence of a scalar multiplication
+                if is_const_scalar_mul(lhs, model):
+                    # Cannot handle fork nodes: We would have to distribute the
+                    # Mul into all branches
+                    # TODO: Maybe reconsider this at some point, there is
+                    #  probably nothing preventing this in general, it is just
+                    #  more difficult and apparently not necessary right now.
+                    if model.is_fork_node(lhs):
+                        # Softly skip this node
                         continue
-                    start_name = n.input[0]
-                    middle_name = n.output[0]
-                    end_name = consumer.output[0]
-                    mm_out_shape = model.get_tensor_shape(end_name)
-                    if all(x == 1 for x in A.shape):
-                        # if the mul is scalar, we can simply swap the order of ops
-                        # make and insert new nodes
-                        new_matmul = oh.make_node(
-                            "MatMul",
-                            [start_name, matmul_weight_name],
-                            [middle_name],
-                            name=consumer.name,
-                        )
-                        new_mul = oh.make_node(
-                            "Mul",
-                            [middle_name, mul_weight_name],
-                            [end_name],
-                            name=n.name,
-                        )
-                        graph.node.insert(node_ind, new_matmul)
-                        graph.node.insert(node_ind + 1, new_mul)
-                        model.set_tensor_shape(middle_name, mm_out_shape)
-                        # remove old nodes
-                        graph.node.remove(n)
-                        graph.node.remove(consumer)
-                        graph_modified = True
+                    # Unpack the connection pattern of a scalar mul feeding the
+                    # lhs input of the matmul
+                    # Names of the three input tensors to the mul-matmul complex
+                    a, b, c = lhs.input[0], lhs.input[1], node.input[1]
+                    # Names of the intermediate and the global output
+                    m, o = lhs.output[0], node.output[0]  # noqa: Duplicate code
+                    # Rewire the operator connections locally, swapping mul and
+                    # matmul operator order
+                    matmul = oh.make_node("MatMul", [a, c], [m], node.name)
+                    mul = oh.make_node("Mul", [m, b], [o], lhs.name)
+                    # Insert the rewired nodes into the graph
+                    graph.node.insert(index, matmul)
+                    graph.node.insert(index + 1, mul)
+                    # Adapt the shape of the intermediate tensor as it changed
+                    # according to the output shape of the matmul
+                    model.set_tensor_shape(m, model.get_tensor_shape(o))
+                    # Remove the old nodes from the graph
+                    graph.node.remove(lhs)
+                    graph.node.remove(node)
+                    # The graph has been modified, this needs to be reported
+                    # back to the caller
+                    graph_modified = True
+                    # Cannot further modify the node (i.e., the rhs) as the
+                    # index and state of the nodes changed and need to be
+                    # queried again from the graph.node at the start of the next
+                    # iteration.
+                    continue
+
+                # Next try whether the right hand side matches the pattern of a
+                # scalar multiplication
+                if is_const_scalar_mul(rhs, model):
+                    # Cannot handle fork nodes: We would have to distribute the
+                    # Mul into all branches
+                    # TODO: Maybe reconsider this at some point, there is
+                    #  probably nothing preventing this in general, it is just
+                    #  more difficult and apparently not necessary right now.
+                    if model.is_fork_node(rhs):
+                        # Softly skip this node
+                        continue
+                    # Unpack the connection pattern of a scalar mul feeding the
+                    # rhs input of the matmul
+                    # Names of the three input tensors to the mul-matmul complex
+                    a, b, c = node.input[0], rhs.input[0], rhs.input[1]
+                    # Names of the intermediate and the global output
+                    m, o = rhs.output[0], node.output[0]  # noqa: Duplicate code
+                    # Rewire the operator connections locally, swapping mul and
+                    # matmul operator order
+                    matmul = oh.make_node("MatMul", [a, b], [m], node.name)
+                    mul = oh.make_node("Mul", [m, c], [o], rhs.name)
+                    # Insert the rewired nodes into the graph
+                    graph.node.insert(index, matmul)
+                    graph.node.insert(index + 1, mul)
+                    # Adapt the shape of the intermediate tensor as it changed
+                    # according to the output shape of the matmul
+                    model.set_tensor_shape(m, model.get_tensor_shape(o))
+                    # Remove the old nodes from the graph
+                    graph.node.remove(rhs)
+                    graph.node.remove(node)
+                    # The graph has been modified, this needs to be reported
+                    # back to the caller
+                    graph_modified = True
+
+        # Finalize the transformation by inferring shapes again (as these might
+        # have changed)
         model = model.transform(InferShapes())
-        return (model, graph_modified)
+        # Return the transformed model and indicate whether the graph actually
+        # has been transformed
+        return model, graph_modified
 
 
 class MoveScalarAddPastMatMul(Transformation):
@@ -617,6 +692,7 @@ def apply(self, model):
                     graph_modified = True
                 else:
                     continue
+
         # Note: Running shape inference is necessary as shape annotations have
         # been deleted above
         model = model.transform(InferShapes())
@@ -634,6 +710,7 @@ class MoveScalarLinearPastInvariants(Transformation):
     GlobalAveragePool
     """
 
+    # Op-types of currently supported invariants
     # Op-types of currently supported invariants
     SUPPORTED_INVARIANTS = {
         "GlobalAveragePool",

tests/transformation/streamline/test_move_scalar_past_matmul.py

@@ -72,6 +72,43 @@ def test_move_scalar_mul_past_matmul():
     assert new_model.graph.node[0].output[0] == new_model.graph.node[1].input[0]
 
 
+@pytest.mark.streamline
+def test_move_scalar_mul_past_join_matmul():
+    top_in1 = oh.make_tensor_value_info("top_in1", TensorProto.FLOAT, [1, 2])
+    top_in2 = oh.make_tensor_value_info("top_in2", TensorProto.FLOAT, [2, 1])
+    mul1_param = oh.make_tensor_value_info("mul1_param", TensorProto.FLOAT, [1, 1])
+    mul2_param = oh.make_tensor_value_info("mul2_param", TensorProto.FLOAT, [1, 1])
+    top_out = oh.make_tensor_value_info("top_out", TensorProto.FLOAT, [1, 1])
+    modelproto = qonnx_make_model(
+        oh.make_graph(
+            name="test",
+            inputs=[top_in1, top_in2],
+            outputs=[top_out],
+            value_info=[mul1_param, mul2_param],
+            nodes=[
+                oh.make_node("Mul", ["top_in1", "mul1_param"], ["middle1"]),
+                oh.make_node("Mul", ["top_in2", "mul2_param"], ["middle2"]),
+                oh.make_node("MatMul", ["middle1", "middle2"], ["top_out"]),
+            ],
+        )
+    )
+    model = ModelWrapper(modelproto)
+    model = model.transform(InferShapes())
+    model.set_initializer("mul1_param", np.asarray([[3]], dtype=np.float32))
+    model.set_initializer("mul2_param", np.asarray([[3]], dtype=np.float32))
+    new_model = model.transform(MoveScalarMulPastMatMul())
+    inp_dict = {
+        "top_in1": np.asarray([[-1.0, 1.0]], dtype=np.float32),
+        "top_in2": np.asarray([[1.0], [-1.0]], dtype=np.float32),
+    }
+    assert ox.compare_execution(model, new_model, inp_dict)
+    assert new_model.graph.node[0].op_type == "MatMul"
+    assert new_model.graph.node[1].op_type == "Mul"
+    assert new_model.graph.node[2].op_type == "Mul"
+    assert new_model.graph.node[0].output[0] == new_model.graph.node[1].input[0]
+    assert new_model.graph.node[1].output[0] == new_model.graph.node[2].input[0]
+
+
 @pytest.mark.streamline
 def test_move_scalar_add_past_matmul():
     top_in = oh.make_tensor_value_info("top_in", TensorProto.FLOAT, [1, 2])