Use XNN fused RoPE for HF-style RoPE patterns

backends/xnnpack/_passes/__init__.py

@@ -19,6 +19,7 @@
     Conv1dUnsqueezePass,
 )
 from executorch.backends.xnnpack._passes.convert_to_linear import ConvertToLinearPass
+from executorch.backends.xnnpack._passes.convert_to_rope import ConvertToRopePass
 from executorch.backends.xnnpack._passes.convert_to_sdpa import ConvertToSDPAPass
 from executorch.backends.xnnpack._passes.convert_to_upsample_bilinear2d import (
     ConvertToUpsampleBilinear2d,
@@ -75,6 +76,7 @@ def __init__(
                 ConvertToLinearPass,
                 PropagateCustomMetaPass,
                 ConvertToSDPAPass,
+                ConvertToRopePass,
                 ConstPropPass,
                 FuseBatchNormPass,
                 DecomposeBatchNorm,

backends/xnnpack/_passes/convert_to_rope.py

@@ -0,0 +1,235 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import enum
+import logging
+
+import torch
+from executorch.backends.xnnpack._passes.xnnpack_pass import XNNPACKPass
+from executorch.backends.xnnpack.partition.graphs import rope
+from executorch.exir.dialects._ops import ops as exir_ops
+
+from torch.fx.passes.infra.pass_base import PassResult
+from torch.fx.passes.utils.matcher_utils import InternalMatch, SubgraphMatcher
+
+logger = logging.getLogger(__name__)
+
+
+class _Layout(enum.Enum):
+    BSHD = enum.auto()
+    BHSD = enum.auto()
+
+
+class ConvertToRopePass(XNNPACKPass):
+    _BHSD_TO_BSHD_PERM = [0, 2, 1, 3]
+
+    def _build_weights(
+        self,
+        graph_module: torch.fx.GraphModule,
+        cos_node: torch.fx.Node,
+        sin_node: torch.fx.Node,
+        output_node: torch.fx.Node,
+    ) -> torch.fx.Node:
+        """
+        Construct the XNNPACK RoPE weights tensor from cos and sin inputs.
+
+        The most common HF RoPE pattern doubles the frequencies:
+            cos/sin shape: [batch, seq, head_dim] where head_dim = 2 * (dim // 2)
+            The first half and second half are identical.
+
+        XNNPACK expects weights: [tokens, channels] where:
+            weights[:, :C/2] = cos values (unique half)
+            weights[:, C/2:] = sin values (unique half)
+
+        We insert graph nodes to slice the unique halves and concatenate them.
+
+        Note that this assumes that cos and sin come from a cat([x, x]) node for
+        this to be sound. We check this in the pass.
+        """
+        head_dim = cos_node.meta["val"].shape[-1]
+        half_dim = head_dim // 2
+
+        with graph_module.graph.inserting_before(output_node):
+            cos_half = graph_module.graph.call_function(
+                exir_ops.edge.aten.slice_copy.Tensor,
+                args=(cos_node, -1, 0, half_dim),
+            )
+            sin_half = graph_module.graph.call_function(
+                exir_ops.edge.aten.slice_copy.Tensor,
+                args=(sin_node, -1, 0, half_dim),
+            )
+            weights = graph_module.graph.call_function(
+                exir_ops.edge.aten.cat.default,
+                args=([cos_half, sin_half], -1),
+            )
+
+        return weights
+
+    @staticmethod
+    def _trace_through_unsqueezes(node: torch.fx.Node) -> torch.fx.Node:
+        """Walk backwards through consecutive unsqueeze_copy ops to find the source."""
+        current = node
+        while (
+            current.op == "call_function"
+            and current.target == exir_ops.edge.aten.unsqueeze_copy.default
+        ):
+            current = current.args[0]
+        return current
+
+    @staticmethod
+    def _find_trig_source(node: torch.fx.Node) -> torch.fx.Node | None:
+        """Walk backwards through unsqueeze_copy ops to find cos/sin op."""
+        current = node
+        for _ in range(10):
+            if current.op != "call_function":
+                return None
+            if current.target in (
+                exir_ops.edge.aten.cos.default,
+                exir_ops.edge.aten.sin.default,
+            ):
+                return current
+            if current.target == exir_ops.edge.aten.unsqueeze_copy.default:
+                current = current.args[0]
+                continue
+            return None
+        return None
+
+    @classmethod
+    def _is_doubled_cat(cls, trig_node: torch.fx.Node) -> bool:
+        """Check that a cos/sin node's input is cat(x, x) with identical args."""
+        cat_node = trig_node.args[0]
+        if (
+            cat_node.op != "call_function"
+            or cat_node.target != exir_ops.edge.aten.cat.default
+        ):
+            return False
+        tensors = cat_node.args[0]
+        return len(tensors) == 2 and tensors[0] is tensors[1]
+
+    @classmethod
+    def _has_doubled_freqs(
+        cls,
+        cos_unsqueezed: torch.fx.Node,
+        sin_unsqueezed: torch.fx.Node,
+    ) -> bool:
+        """Verify that cos/sin frequencies are doubled (first half == second half).
+
+        Traces back through unsqueeze_copy ops to find the cos/sin producer,
+        then verifies its input is cat(x, x) where both args are the same
+        node — a structural proof that the first and second halves are identical.
+        """
+        cos_trig = cls._find_trig_source(cos_unsqueezed)
+        sin_trig = cls._find_trig_source(sin_unsqueezed)
+
+        if cos_trig is None or sin_trig is None:
+            return False
+
+        return cls._is_doubled_cat(cos_trig) and cls._is_doubled_cat(sin_trig)
+
+    @staticmethod
+    def _trace_through_permute(node: torch.fx.Node) -> torch.fx.Node | None:
+        """If node is a permute_copy that swaps dims 1 and 2, return its input."""
+        if (
+            node.op == "call_function"
+            and node.target == exir_ops.edge.aten.permute_copy.default
+            and list(node.args[1]) == [0, 2, 1, 3]
+        ):
+            return node.args[0]
+        return None
+
+    @staticmethod
+    def _get_layout(cos_unsqueezed: torch.fx.Node) -> _Layout | None:
+        """Determine the tensor layout from the cos unsqueeze dimension."""
+        if not (
+            cos_unsqueezed.op == "call_function"
+            and cos_unsqueezed.target == exir_ops.edge.aten.unsqueeze_copy.default
+        ):
+            return None
+        unsqueeze_dim = cos_unsqueezed.args[1]
+        ndim = len(cos_unsqueezed.meta["val"].shape)
+        normalized = unsqueeze_dim if unsqueeze_dim >= 0 else unsqueeze_dim + ndim
+        if normalized == 2:
+            return _Layout.BSHD
+        if normalized == 1:
+            return _Layout.BHSD
+        return None
+
+    def create_rope(
+        self,
+        graph_module: torch.fx.GraphModule,
+        match: InternalMatch,
+    ):
+        logger.debug(f"Matched RoPE subgraph: {match}")
+
+        # placeholder_nodes are in the order of the pattern's placeholder ops:
+        # [x, cos_unsqueezed, sin_unsqueezed]
+        x_node = match.placeholder_nodes[0]
+        cos_unsqueezed = match.placeholder_nodes[1]
+        sin_unsqueezed = match.placeholder_nodes[2]
+        output_node = match.returning_nodes[0]
+
+        # xnn_define_rope expects NTHC (batch, tokens, heads, channels) input.
+        # BSHD (unsqueeze_dim=2) maps directly to NTHC.
+        # BHSD (unsqueeze_dim=1) requires tracing through the BSHD→BHSD permute
+        # to recover the BSHD input, then re-permuting the output back to BHSD.
+        layout = self._get_layout(cos_unsqueezed)
+        if layout == _Layout.BSHD:
+            rope_input = x_node
+        elif layout == _Layout.BHSD:
+            rope_input = self._trace_through_permute(x_node)
+            if rope_input is None:
+                logger.debug("Skipping RoPE fusion: BHSD but x is not a permute_copy")
+                return
+        else:
+            logger.debug("Skipping RoPE fusion: unrecognized layout")
+            return
+
+        cos_node = self._trace_through_unsqueezes(cos_unsqueezed)
+        sin_node = self._trace_through_unsqueezes(sin_unsqueezed)
+
+        if not self._has_doubled_freqs(cos_unsqueezed, sin_unsqueezed):
+            logger.debug("Skipping RoPE fusion: cannot verify doubled frequencies")
+            return
+
+        weights = self._build_weights(graph_module, cos_node, sin_node, output_node)
+
+        with graph_module.graph.inserting_before(output_node):
+            rope_node = graph_module.graph.create_node(
+                "call_function",
+                torch.ops.xnnpack.rope.default,
+                args=(rope_input, weights),
+            )
+
+            if layout == _Layout.BHSD:
+                permute_node = graph_module.graph.call_function(
+                    exir_ops.edge.aten.permute_copy.default,
+                    args=(rope_node, self._BHSD_TO_BSHD_PERM),
+                )
+                result_node = permute_node
+            else:
+                result_node = rope_node
+
+        output_node.replace_all_uses_with(result_node)
+        graph_module.graph.eliminate_dead_code()
+
+    # override
+    def call(self, graph_module: torch.fx.GraphModule):
+        total_matches = 0
+        total_fused = 0
+        for pattern in rope.get_graphs():
+            sm = SubgraphMatcher(pattern.graph, ignore_literals=True)
+            matches = list(sm.match(graph_module.graph))
+            total_matches += len(matches)
+            for match in matches:
+                try:
+                    self.create_rope(graph_module, match)
+                    total_fused += 1
+                except Exception:
+                    logger.warning("Failed to fuse RoPE pattern", exc_info=True)
+        graph_module.recompile()
+        graph_module = super().call(graph_module).graph_module
+
+        return PassResult(graph_module, True)

backends/xnnpack/operators/__init__.py

@@ -43,6 +43,7 @@
     op_prelu,
     op_quant_dequant,
     op_relu,
+    op_rope,
     op_rsqrt,
     op_sigmoid,
     op_sin,

backends/xnnpack/operators/op_rope.py

@@ -0,0 +1,77 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from typing import Dict
+
+import torch
+from executorch.backends.xnnpack.operators.node_visitor import (
+    NodeVisitor,
+    register_node_visitor,
+)
+from executorch.backends.xnnpack.serialization.xnnpack_graph_schema import (
+    XNNGraph,
+    XNNRope,
+    XNode,
+)
+from executorch.backends.xnnpack.utils.utils import get_input_node
+
+# Register the custom op used by the fusion pass. The fused node targets
+# this op after ConvertToRopePass replaces the decomposed HF RoPE subgraph.
+lib = torch.library.Library("xnnpack", "FRAGMENT")
+lib.define("rope(Tensor input, Tensor weights) -> Tensor")
+
+
+@torch.library.impl(lib, "rope", "CompositeExplicitAutograd")
+def rope_impl(input: torch.Tensor, weights: torch.Tensor) -> torch.Tensor:
+    channels = input.shape[-1]
+    half_c = channels // 2
+    cos = weights[..., :half_c]
+    sin = weights[..., half_c:]
+
+    x_real = input[..., :half_c]
+    x_imag = input[..., half_c:]
+
+    out_real = x_real * cos - x_imag * sin
+    out_imag = x_real * sin + x_imag * cos
+    return torch.cat([out_real, out_imag], dim=-1)
+
+
+@torch.library.impl(lib, "rope", "Meta")
+def rope_meta(input: torch.Tensor, weights: torch.Tensor) -> torch.Tensor:
+    return torch.empty_like(input)
+
+
+@register_node_visitor
+class RopeVisitor(NodeVisitor):
+    target = "rope.default"
+
+    def __init__(self, *args) -> None:
+        super().__init__(*args)
+
+    def define_node(
+        self,
+        node: torch.fx.Node,
+        xnn_graph: XNNGraph,
+        vals_to_ids: Dict[torch.fx.Node, int],
+        debug_handle: int,
+    ) -> None:
+        self.define_nodes_tensor_inputs_outputs(node, xnn_graph, vals_to_ids)
+
+        input_id = vals_to_ids[get_input_node(node, 0)]
+        weights_id = vals_to_ids[get_input_node(node, 1)]
+        output_id = vals_to_ids[node]
+
+        ser_node = XNode(
+            xnode_union=XNNRope(
+                max_tokens=0,
+                input_id=input_id,
+                weights_id=weights_id,
+                output_id=output_id,
+                flags=0,
+            ),
+            debug_handle=debug_handle,
+        )
+        xnn_graph.xnodes.append(ser_node)

backends/xnnpack/operators/op_squeeze.py

@@ -13,6 +13,7 @@
 )
 from executorch.backends.xnnpack.serialization.xnnpack_graph_schema import (
     XNNGraph,
+    XNNStaticExpandDims,
     XNNStaticReshape,
     XNode,
 )
@@ -98,46 +99,21 @@ def define_node(
         vals_to_ids: Dict[torch.fx.Node, int],
         debug_handle: int,
     ) -> None:
-
-        dim = cast(int, node.args[1])
-        check_or_raise(
-            dim == -1 or dim == len(node.args[0].meta["val"].shape),
-            "XNNPACK currently only supports unsqueezing in last dimension",
-        )
-
         self.define_nodes_tensor_inputs_outputs(node, xnn_graph, vals_to_ids)
         input_node = get_input_node(node, 0)
 
-        # input
         input_id = vals_to_ids[input_node]
-
-        # output
         output_id = vals_to_ids[node]
 
-        check_or_raise(
-            "val" in input_node.meta,
-            "Missing val in tensor metadata for input when serializing XNNStaticReshape node",
-        )
-        dynamic_shape = node.meta["val"].shape
-        new_shape = []
-
-        num_dynamic_dims = 0
-        for dim in dynamic_shape:
-            if free_symbols(dim):
-                num_dynamic_dims += 1
-                new_shape.append(0)
-            else:
-                new_shape.append(dim)
-
-        check_or_raise(
-            num_dynamic_dims <= 1,
-            "XNNPACK reshape only supports 1 dynamic dimension. This may occur when ",
-        )
+        dim = cast(int, node.args[1])
+        input_ndim = len(input_node.meta["val"].shape)
+        if dim < 0:
+            dim = input_ndim + 1 + dim
 
         ser_node = XNode(
-            xnode_union=XNNStaticReshape(
-                num_dims=len(new_shape),
-                new_shape=new_shape,
+            xnode_union=XNNStaticExpandDims(
+                num_new_axes=1,
+                new_axes=[dim],
                 input_id=input_id,
                 output_id=output_id,
                 flags=0,