Fix QNN simulator failures after HadamardTransform support

backends/qualcomm/_passes/recompose_hadamard.py

@@ -116,6 +116,7 @@ def _rewrite_channel_dim(self, graph, node, scale):
                 "call_function", self.hadamard_target, (pre, scale)
             )
             hadamard_node.meta = copy_meta(node.meta)
+            hadamard_node.meta["val"] = pre.meta["val"]
             post = graph.create_node(
                 "call_function",
                 torch.ops.aten.permute.default,
@@ -155,9 +156,12 @@ def _is_hadamard_transform(self, graph_module, node):
         return True
 
     def call(self, graph_module: torch.fx.GraphModule):
-        # HadamardTransform is only supported by QNN 2.47+. On older SDKs skip the
-        # rewrite so the op keeps its normal lowering path.
-        if not os.environ.get("QNN_SDK_ROOT") or is_qnn_sdk_version_less_than("2.47"):
+        try:
+            if not os.environ.get("QNN_SDK_ROOT") or is_qnn_sdk_version_less_than(
+                "2.47"
+            ):
+                return PassResult(graph_module, False)
+        except Exception:
             return PassResult(graph_module, False)
 
         graph = graph_module.graph

backends/qualcomm/builders/custom_ops.py

@@ -7,10 +7,6 @@
 import torch
 from torch.library import impl, Library, register_fake
 
-# Dedicated namespace, separate from the "qaisw" context-binary namespace.
-hadamard_op_lib = Library("qnn_custom", "DEF")
-hadamard_op_lib.define("hadamard_transform(Tensor input, float scale) -> Tensor")
-
 
 def _hadamard_matrix(dim: int, device, dtype) -> torch.Tensor:
     # Sylvester construction of the (unnormalized, ±1) Hadamard matrix.
@@ -20,18 +16,26 @@ def _hadamard_matrix(dim: int, device, dtype) -> torch.Tensor:
     return h
 
 
-@impl(hadamard_op_lib, "hadamard_transform", "CompositeExplicitAutograd")
-def hadamard_transform_impl(input: torch.Tensor, scale: float) -> torch.Tensor:
-    # Normalized Walsh-Hadamard transform along the last dim, times scale.
-    # Matches a linear/matmul whose weight is scipy.linalg.hadamard(dim) * s,
-    # where the rewrite pass sets scale = s * sqrt(dim) (scale == 1 when the
-    # weight is the orthonormal H / sqrt(dim)).
-    dim = input.shape[-1]
-    h = _hadamard_matrix(dim, input.device, input.dtype)
-    return torch.matmul(input, h) * (scale / (dim**0.5))
-
-
-@register_fake("qnn_custom::hadamard_transform")
-def hadamard_transform_fake(input: torch.Tensor, scale: float) -> torch.Tensor:
-    # Hadamard weight is square, so the transform preserves shape.
-    return torch.empty_like(input)
+if not hasattr(torch.ops, "qnn_custom") or not hasattr(
+    torch.ops.qnn_custom, "hadamard_transform"
+):
+    hadamard_op_lib = Library("qnn_custom", "DEF")
+    hadamard_op_lib.define("hadamard_transform(Tensor input, float scale) -> Tensor")
+
+    @impl(hadamard_op_lib, "hadamard_transform", "CompositeExplicitAutograd")
+    def hadamard_transform_impl(input: torch.Tensor, scale: float) -> torch.Tensor:
+        # Normalized Walsh-Hadamard transform along the last dim, times scale.
+        # Matches a linear/matmul whose weight is scipy.linalg.hadamard(dim) * s,
+        # where the rewrite pass sets scale = s * sqrt(dim) (scale == 1 when the
+        # weight is the orthonormal H / sqrt(dim)).
+        dim = input.shape[-1]
+        h = _hadamard_matrix(dim, input.device, input.dtype)
+        return torch.matmul(input, h) * (scale / (dim**0.5))
+
+    @register_fake("qnn_custom::hadamard_transform")
+    def hadamard_transform_fake(input: torch.Tensor, scale: float) -> torch.Tensor:
+        # Hadamard weight is square, so the transform preserves shape.
+        return torch.empty_like(input)
+
+else:
+    hadamard_op_lib = Library("qnn_custom", "FRAGMENT")

backends/qualcomm/utils/check_qnn_version.py

@@ -4,6 +4,7 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+import functools
 import os
 import platform
 import re
@@ -25,25 +26,31 @@ def _get_qnn_host_lib_dir_name() -> str:
     return "x86_64-linux-clang"
 
 
-def get_sdk_build_id():
-    qnn_sdk_root = os.environ.get("QNN_SDK_ROOT")
-    if not qnn_sdk_root:
-        raise EnvironmentError(
-            "QNN_SDK_ROOT must be set to query the QNN SDK build id."
-        )
+@functools.cache
+def _get_sdk_build_id(qnn_sdk_root: str):
     htp_library_path = os.path.join(
         qnn_sdk_root,
         "lib",
         _get_qnn_host_lib_dir_name(),
         get_qnn_lib_name("QnnHtp"),
     )
-    # The GetQnnSdkBuildId API can be used without needing to create a backend first, so it works regardless of which backend is used.
-    sdk_build_id = PyQnnManagerAdaptor.GetQnnSdkBuildId(htp_library_path)
-    return sdk_build_id
+    return PyQnnManagerAdaptor.GetQnnSdkBuildId(htp_library_path)
+
+
+def get_sdk_build_id():
+    qnn_sdk_root = os.environ.get("QNN_SDK_ROOT")
+    if not qnn_sdk_root:
+        raise EnvironmentError(
+            "QNN_SDK_ROOT must be set to query the QNN SDK build id."
+        )
+    return _get_sdk_build_id(qnn_sdk_root)
 
 
 def is_qnn_sdk_version_less_than(target_version):
-    current_version = get_sdk_build_id()
+    try:
+        current_version = get_sdk_build_id()
+    except Exception:
+        return True
 
     match = re.search(r"v(\d+)\.(\d+)", current_version)
     if match:
@@ -59,7 +66,10 @@ def is_qnn_sdk_version_less_than(target_version):
 
 
 def is_qnn_sdk_version_greater_than(target_version):
-    current_version = get_sdk_build_id()
+    try:
+        current_version = get_sdk_build_id()
+    except Exception:
+        return False
 
     match = re.search(r"v(\d+)\.(\d+)", current_version)
     if match: