diff --git a/backends/arm/scripts/docgen/generate_vgf_op_support.py b/backends/arm/scripts/docgen/generate_vgf_op_support.py
index 96bb8b32046..dbd3ed9213a 100644
--- a/backends/arm/scripts/docgen/generate_vgf_op_support.py
+++ b/backends/arm/scripts/docgen/generate_vgf_op_support.py
@@ -40,6 +40,8 @@
 from pathlib import Path
 from typing import cast, Iterable, Mapping, Protocol, Sequence
 
+import torch
+
 
 DEFAULT_OUTPUT = Path("docs/source/backends/arm-vgf/VGF_op_support.md")
 TEST_ROOT = Path("backends/arm/test")
@@ -237,6 +239,18 @@
 # higher-level PyTorch APIs. These are what should appear in the published page.
 # Unknown operators fall back to a conservative ``torch.<aten-name>`` spelling.
 PYTORCH_API_ALIASES: dict[str, tuple[str, ...]] = {
+    # Internal / higher-order operators with explicit public API spellings.
+    "torch.ops.aten._assert_scalar.default": ("torch._assert_scalar",),
+    "torch.ops.aten.t_copy.default": (
+        "torch.t",
+        "torch.Tensor.t",
+    ),
+    "torch.ops.aten.transpose_copy.int": (
+        "torch.transpose",
+        "torch.Tensor.transpose",
+    ),
+    "torch.ops.higher_order.cond": ("torch.cond",),
+    "torch.ops.higher_order.while_loop": ("torch.while_loop",),
     # Arithmetic and comparisons.
     "torch.ops.aten.add.Tensor": ("torch.add", "+"),
     "torch.ops.aten.add.Scalar": ("torch.add", "+"),
@@ -777,6 +791,12 @@ def _normalize_pytorch_op_name(  # noqa: C901
         return None
 
     original = text
+
+    # Higher-order operators are not ATen operators. Preserve their namespace
+    # instead of rewriting torch.ops.higher_order.* as torch.ops.aten.*.
+    if text.startswith("torch.ops.higher_order."):
+        return text
+
     if text.startswith(generated_prefix):
         text = _edge_generated_name_to_aten(text.removeprefix(generated_prefix))
 
@@ -847,14 +867,29 @@ def _split_exported_op(exported_op: str) -> tuple[str, str] | None:
     return name, overload
 
 
+def _torch_api_exists(api: str) -> bool:
+    """Return whether a dotted torch API exists and is callable."""
+
+    if not api.startswith("torch."):
+        return False
+
+    obj: object = torch
+    for part in api.split(".")[1:]:
+        if not hasattr(obj, part):
+            return False
+        obj = getattr(obj, part)
+
+    return callable(obj)
+
+
 def _fallback_pytorch_api_aliases(exported_op: str) -> tuple[str, ...]:
     split = _split_exported_op(exported_op)
     if split is None:
-        return (exported_op,)
+        return ()
 
     name, _overload = split
-    public_name = name.removeprefix("_").removesuffix("_")
-    return (f"torch.{public_name}",)
+    candidate = f"torch.{name}"
+    return (candidate,) if _torch_api_exists(candidate) else ()
 
 
 def _pytorch_api_aliases(exported_op: str) -> tuple[str, ...]:
@@ -2033,6 +2068,9 @@ def _public_rows_from_exact_rows(
 ) -> list[PublicCoverage]:
     public_rows: dict[str, PublicCoverage] = {}
     for exact in exact_rows.values():
+        if not exact.pytorch_apis:
+            continue
+
         key = _api_key(exact.pytorch_apis)
         row = public_rows.setdefault(
             key,
diff --git a/backends/arm/scripts/pre-push b/backends/arm/scripts/pre-push
index 784efd81c61..be12e14fccf 100755
--- a/backends/arm/scripts/pre-push
+++ b/backends/arm/scripts/pre-push
@@ -32,6 +32,9 @@ DOCGEN_OUTPUTS=(
     "docs/source/backends/arm-vgf/tutorials/vgf-getting-started.md"
 )
 
+VGF_OP_SUPPORT_SCRIPT="backends/arm/scripts/docgen/generate_vgf_op_support.py"
+VGF_OP_SUPPORT_OUTPUT="docs/source/backends/arm-vgf/VGF_op_support.md"
+
 RUN_DOCGEN=0
 
 is_docgen_trigger_file() {
@@ -70,6 +73,31 @@ run_docgen_check() {
     fi
 }
 
+run_vgf_op_support_checks() {
+    echo -e "${INFO} Generating VGF operator support documentation"
+
+    if ! python "$VGF_OP_SUPPORT_SCRIPT"; then
+        echo -e "${ERROR} Failed to generate VGF operator support documentation"
+        FAILED=1
+    elif ! git diff --quiet HEAD -- "$VGF_OP_SUPPORT_OUTPUT"; then
+        echo -e "${ERROR} VGF operator support documentation is out of date." >&2
+        echo -e "${INFO} Review and commit the regenerated operator support list before pushing."
+        git diff HEAD -- "$VGF_OP_SUPPORT_OUTPUT"
+        FAILED=1
+    else
+        echo -e "${SUCCESS} VGF operator support documentation OK"
+    fi
+
+    echo -e "${INFO} Checking VGF operator support coverage"
+
+    if ! python "$VGF_OP_SUPPORT_SCRIPT" --check; then
+        echo -e "${ERROR} VGF operator support coverage check failed" >&2
+        FAILED=1
+    else
+        echo -e "${SUCCESS} VGF operator support coverage OK"
+    fi
+}
+
 run_public_api_validator() {
     if ! backends/arm/scripts/public_api_manifest/validate_all_public_api_manifests.sh; then
         echo -e "${ERROR} Arm public API manifest validation failed"
@@ -318,6 +346,7 @@ else
     echo -e "${INFO} Skipping Arm docgen (no public API inputs changed)"
 fi
 
+run_vgf_op_support_checks
 run_public_api_validator
 
 if [[ $FAILED ]]; then
diff --git a/docs/source/backends/arm-vgf/VGF_op_support.md b/docs/source/backends/arm-vgf/VGF_op_support.md
index 1242f00bab1..14ee1a37be0 100644
--- a/docs/source/backends/arm-vgf/VGF_op_support.md
+++ b/docs/source/backends/arm-vgf/VGF_op_support.md
@@ -1,150 +1,165 @@
-# EdgeIR Operator support for the VGF backend
-This list contains operators with silicon acceleration support.
-The ExecuTorch portable kernels allow running more operations with a fallback.
-8x8 designates 8-bit activation and 8-bit weight. 16x8 means 16-bit activation and 8-bit weight.
-Per-tensor and per-channel quantization are supported.
+# PyTorch operator support for the VGF backend
 
-| EdgeIR operator | Compute DType                 | Quantization |
-| --------------- | ------------------------------| ------------ |
-| _log_softmax.default | Static integer quantization | 8x8 |
-| _native_batch_norm_legit_no_training.default | Static integer quantization | 8x8 |
-| _softmax.default | Static integer quantization | 8x8 |
-| _to_copy.default | Static integer quantization | 8x8 |
-| abs.default | Static integer quantization | 8x8 |
-| acos.default | Static integer quantization | 8x8 |
-| acosh.default | Static integer quantization | 8x8 |
-| adaptive_avg_pool2d.default | Static integer quantization | 8x8 |
-| add.Tensor | Static integer quantization | 8x8 |
-| addmm.default | Static integer quantization | 8x8 |
-| alias_copy.default | Static integer quantization | 8x8 |
-| amax.default | Static integer quantization | 8x8 |
-| amin.default | Static integer quantization | 8x8 |
-| any.default | Static integer quantization | 8x8 |
-| arange.start_step | Static integer quantization | 8x8 |
-| asin.default | Static integer quantization | 8x8 |
-| asinh.default | Static integer quantization | 8x8 |
-| atan.default | Static integer quantization | 8x8 |
-| atanh.default | Static integer quantization | 8x8 |
-| avg_pool2d.default | Static integer quantization | 8x8 |
-| bitwise_and.Scalar | Static integer quantization | 8x8 |
-| bitwise_and.Tensor | Static integer quantization | 8x8 |
-| bitwise_left_shift.Scalar | Static integer quantization | 8x8 |
-| bitwise_left_shift.Tensor | Static integer quantization | 8x8 |
-| bitwise_not.default | Static integer quantization | 8x8 |
-| bitwise_or.Scalar | Static integer quantization | 8x8 |
-| bitwise_or.Tensor | Static integer quantization | 8x8 |
-| bitwise_right_shift.Scalar | Static integer quantization | 8x8 |
-| bitwise_right_shift.Tensor | Static integer quantization | 8x8 |
-| bitwise_xor.Scalar | Static integer quantization | 8x8 |
-| bitwise_xor.Tensor | Static integer quantization | 8x8 |
-| bmm.default | Static integer quantization | 8x8 |
-| cat.default | Static integer quantization | 8x8 |
-| ceil.default | Static integer quantization | 8x8 |
-| clamp.default | Static integer quantization | 8x8 |
-| clone.default | Static integer quantization | 8x8 |
-| constant_pad_nd.default | Static integer quantization | 8x8 |
-| convolution.default | Static integer quantization | 8x8 |
-| copy.default | Static integer quantization | 8x8 |
-| cos.default | Static integer quantization | 8x8 |
-| cosh.default | Static integer quantization | 8x8 |
-| cumsum.default | Static integer quantization | 8x8 |
-| div.Tensor | Static integer quantization | 8x8 |
-| div.Tensor_mode | Static integer quantization | 8x8 |
-| elu.default | Static integer quantization | 8x8 |
-| embedding.default | Static integer quantization | 8x8 |
-| eq.Scalar | Static integer quantization | 8x8 |
-| erf.default | Static integer quantization | 8x8 |
-| exp.default | Static integer quantization | 8x8 |
-| expand_copy.default | Static integer quantization | 8x8 |
-| expm1.default | Static integer quantization | 8x8 |
-| eye.default | Static integer quantization | 8x8 |
-| fill.Scalar | Static integer quantization | 8x8 |
-| floor.default | Static integer quantization | 8x8 |
-| floor_divide.default | Static integer quantization | 8x8 |
-| full.default | Static integer quantization | 8x8 |
-| gather.default | Static integer quantization | 8x8 |
-| ge.Scalar | Static integer quantization | 8x8 |
-| ge.Tensor | Static integer quantization | 8x8 |
-| gelu.default | Static integer quantization | 8x8 |
-| glu.default | Static integer quantization | 8x8 |
-| gt.Scalar | Static integer quantization | 8x8 |
-| gt.Tensor | Static integer quantization | 8x8 |
-| hardsigmoid.default | Static integer quantization | 8x8 |
-| hardswish.default | Static integer quantization | 8x8 |
-| hardtanh.default | Static integer quantization | 8x8 |
-| index_select.default | Static integer quantization | 8x8 |
-| le.Scalar | Static integer quantization | 8x8 |
-| le.Tensor | Static integer quantization | 8x8 |
-| leaky_relu.default | Static integer quantization | 8x8 |
-| linear.default | Static integer quantization | 8x8 |
-| linspace.default | Static integer quantization | 8x8 |
-| log.default | Static integer quantization | 8x8 |
-| log1p.default | Static integer quantization | 8x8 |
-| logical_and.default | Static integer quantization | 8x8 |
-| logical_not.default | Static integer quantization | 8x8 |
-| logical_or.default | Static integer quantization | 8x8 |
-| logical_xor.default | Static integer quantization | 8x8 |
-| logit.default | Static integer quantization | 8x8 |
-| lt.Scalar | Static integer quantization | 8x8 |
-| lt.Tensor | Static integer quantization | 8x8 |
-| masked_fill.Scalar | Static integer quantization | 8x8 |
-| max.dim | Static integer quantization | 8x8 |
-| maximum.default | Static integer quantization | 8x8 |
-| mean.dim | Static integer quantization | 8x8 |
-| min.dim | Static integer quantization | 8x8 |
-| minimum.default | Static integer quantization | 8x8 |
-| mm.default | Static integer quantization | 8x8 |
-| mul.Tensor | Static integer quantization | 8x8 |
-| multihead_attention.default | Static integer quantization | 8x8 |
-| native_group_norm.default | Static integer quantization | 8x8 |
-| native_layer_norm.default | Static integer quantization | 8x8 |
-| ne.Scalar | Static integer quantization | 8x8 |
-| ne.Tensor | Static integer quantization | 8x8 |
-| neg.default | Static integer quantization | 8x8 |
-| ones.default | Static integer quantization | 8x8 |
-| permute_copy.default | Static integer quantization | 8x8 |
-| pixel_shuffle.default | Static integer quantization | 8x8 |
-| pixel_unshuffle.default | Static integer quantization | 8x8 |
-| pow.Tensor_Scalar | Static integer quantization | 8x8 |
-| reciprocal.default | Static integer quantization | 8x8 |
-| relu.default | Static integer quantization | 8x8 |
-| remainder.Scalar | Static integer quantization | 8x8 |
-| remainder.Tensor | Static integer quantization | 8x8 |
-| repeat.default | Static integer quantization | 8x8 |
-| round.default | Static integer quantization | 8x8 |
-| rsqrt.default | Static integer quantization | 8x8 |
-| rsub.Scalar | Static integer quantization | 8x8 |
-| scalar_tensor.default | Static integer quantization | 8x8 |
-| sdpa.default | Static integer quantization | 8x8 |
-| select_copy.int | Static integer quantization | 8x8 |
-| select_scatter.default | Static integer quantization | 8x8 |
-| sigmoid.default | Static integer quantization | 8x8 |
-| sign.default | Static integer quantization | 8x8 |
-| silu.default | Static integer quantization | 8x8 |
-| sin.default | Static integer quantization | 8x8 |
-| sinh.default | Static integer quantization | 8x8 |
-| slice_copy.Tensor | Static integer quantization | 8x8 |
-| slice_scatter.default | Static integer quantization | 8x8 |
-| split_copy.Tensor | Static integer quantization | 8x8 |
-| split_with_sizes_copy.default | Static integer quantization | 8x8 |
-| sqrt.default | Static integer quantization | 8x8 |
-| squeeze_copy.dim | Static integer quantization | 8x8 |
-| squeeze_copy.dims | Static integer quantization | 8x8 |
-| stack.default | Static integer quantization | 8x8 |
-| sub.Tensor | Static integer quantization | 8x8 |
-| sum.dim_IntList | Static integer quantization | 8x8 |
-| t_copy.default | Static integer quantization | 8x8 |
-| tan.default | Static integer quantization | 8x8 |
-| tanh.default | Static integer quantization | 8x8 |
-| transpose_copy.int | Static integer quantization | 8x8 |
-| tril.default | Static integer quantization | 8x8 |
-| unbind_copy.int | Static integer quantization | 8x8 |
-| unflatten.int | Static integer quantization | 8x8 |
-| unsqueeze_copy.default | Static integer quantization | 8x8 |
-| upsample_bilinear2d.vec | Static integer quantization | 8x8 |
-| upsample_nearest2d.vec | Static integer quantization | 8x8 |
-| var.dim | Static integer quantization | 8x8 |
-| vector_norm.default | Static integer quantization | 8x8 |
-| view_copy.default | Static integer quantization | 8x8 |
-| where.self | Static integer quantization | 8x8 |
-| zeros.default | Static integer quantization | 8x8 |
+<!-- DO NOT EDIT: generated by `python backends/arm/scripts/docgen/generate_vgf_op_support.py`. -->
+
+This page lists VGF-supported PyTorch APIs and the dtype and quantization modes covered by the VGF backend test pipeline.
+
+`8x8` means 8-bit activations and 8-bit weights. `16x8` means 16-bit activations and 8-bit weights. `8x4` means 8-bit activations and 4-bit weights.
+
+Total supported PyTorch APIs: **153**.
+
+| PyTorch API | Support profile | DType | Quantization mode |
+| --- | --- | --- | --- |
+| `torch._assert_scalar` | FP | `FP32` | - |
+| `torch.abs` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8` | 8x8 |
+| `torch.acos` | FP, INT | `FP32`, `INT8`, `BOOL` | 8x8 |
+| `torch.acosh` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.add` / `+` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8`, `INT16` | 8x8, 16x8 |
+| `torch.addmm` | FP | `FP32` | - |
+| `torch.alias_copy` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8` | 8x8 |
+| `torch.amax` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8`, `INT16` | 8x8, 16x8 |
+| `torch.amin` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8`, `INT16` | 8x8, 16x8 |
+| `torch.any` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.arange` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.argmax` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8` | 8x8 |
+| `torch.as_strided_copy` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.asin` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.asinh` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.atan` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.atanh` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.batch_norm` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.bitwise_and` / `&` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.bitwise_left_shift` / `<<` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.bitwise_not` / `~` | FP, INT | `FP32`, `INT8`, `BOOL` | 8x8 |
+| `torch.bitwise_or` / `|` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.bitwise_right_shift` / `>>` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.bitwise_xor` / `^` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.bmm` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.cat` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8` | 8x8 |
+| `torch.ceil` | FP, INT | `FP16`, `BF16`, `INT8` | 8x8 |
+| `torch.clamp` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8`, `INT16` | 8x8, 16x8 |
+| `torch.clone` / `torch.Tensor.clone` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.cond` | INT | `INT8` | 8x8 |
+| `torch.conv1d` | FP, INT | `FP32`, `INT8`, `INT4` | 8x8, 8x4 |
+| `torch.conv2d` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8`, `INT16`, `INT4` | 8x8, 8x4, 16x8 |
+| `torch.conv3d` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8`, `INT16`, `INT4` | 8x8, 8x4, 16x8 |
+| `torch.conv_transpose2d` | FP, INT | `FP16`, `BF16`, `INT8`, `INT16`, `INT4` | 8x8, 8x4, 16x8 |
+| `torch.cos` | FP, INT | `FP16`, `BF16`, `INT8` | 8x8 |
+| `torch.cosh` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.cumsum` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.div` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.div` / `/` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.eq` / `==` | FP, INT | `FP32`, `INT8`, `INT16` | 8x8, 16x8 |
+| `torch.erf` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8` | 8x8 |
+| `torch.erfinv` | FP, INT | `FP32`, `FP16`, `INT8` | 8x8 |
+| `torch.exp` | FP, INT | `FP16`, `BF16`, `INT8` | 8x8 |
+| `torch.expm1` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.eye` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.fill_` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.flip` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.floor` | FP, INT | `FP16`, `BF16`, `INT8` | 8x8 |
+| `torch.floor_divide` / `//` | FP | `FP32` | - |
+| `torch.full` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.full_like` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.gather` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8`, `BOOL` | 8x8 |
+| `torch.ge` / `>=` | FP, INT | `FP32`, `INT8`, `INT16` | 8x8, 16x8 |
+| `torch.grid_sampler` | FP | `FP32` | - |
+| `torch.grid_sampler_2d` | FP | `FP32` | - |
+| `torch.group_norm` | FP | `FP32` | - |
+| `torch.gt` / `>` | FP, INT | `FP32`, `INT8`, `INT16` | 8x8, 16x8 |
+| `torch.index_put_` | INT | `INT8` | 8x8 |
+| `torch.index_select` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8`, `BOOL` | 8x8 |
+| `torch.layer_norm` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.le` / `<=` | FP, INT | `FP32`, `INT8`, `INT16` | 8x8, 16x8 |
+| `torch.linspace` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.log` | FP, INT | `FP16`, `BF16`, `INT8` | 8x8 |
+| `torch.log10` | INT | `INT8` | 8x8 |
+| `torch.log1p` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.log_softmax` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.logical_and` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.logical_not` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.logical_or` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.logical_xor` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.logit` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.lt` / `<` | FP, INT | `FP32`, `INT8`, `INT16` | 8x8, 16x8 |
+| `torch.masked_fill` / `torch.Tensor.masked_fill` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.matmul` / `@` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.max` | FP | `FP32`, `FP16`, `BF16` | - |
+| `torch.max_pool1d` | FP | `FP32` | - |
+| `torch.max_pool2d` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8`, `INT16` | 8x8, 16x8 |
+| `torch.maximum` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8`, `INT16` | 8x8, 16x8 |
+| `torch.mean` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.min` | FP | `FP32`, `FP16`, `BF16` | - |
+| `torch.minimum` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8`, `INT16` | 8x8, 16x8 |
+| `torch.mm` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.mul` / `*` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8` | 8x8 |
+| `torch.ne` / `!=` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.neg` / `unary -` | FP, INT | `FP32`, `INT8`, `INT16` | 8x8, 16x8 |
+| `torch.nn.AdaptiveAvgPool2d` / `torch.nn.functional.adaptive_avg_pool2d` | FP | `FP32` | - |
+| `torch.nn.AvgPool2d` / `torch.nn.functional.avg_pool2d` | FP, INT | `FP32`, `INT8`, `INT16` | 8x8, 16x8 |
+| `torch.nn.BatchNorm2d` / `torch.nn.functional.batch_norm` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.nn.CELU` / `torch.nn.functional.celu` | FP | `FP32` | - |
+| `torch.nn.Conv2d` / `torch.nn.functional.conv2d` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8`, `INT16`, `INT4` | 8x8, 8x4, 16x8 |
+| `torch.nn.ELU` / `torch.nn.functional.elu` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.nn.Embedding` / `torch.nn.functional.embedding` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.nn.functional.pad` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8`, `INT16` | 8x8, 16x8 |
+| `torch.nn.GELU` / `torch.nn.functional.gelu` | FP, INT | `FP32`, `BF16`, `INT8` | 8x8 |
+| `torch.nn.GroupNorm` / `torch.nn.functional.group_norm` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.nn.Hardsigmoid` / `torch.nn.functional.hardsigmoid` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.nn.Hardswish` / `torch.nn.functional.hardswish` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.nn.Hardtanh` / `torch.nn.functional.hardtanh` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.nn.LeakyReLU` / `torch.nn.functional.leaky_relu` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.nn.Linear` / `torch.nn.functional.linear` | FP, INT | `FP32`, `INT8`, `INT4` | 8x8, 8x4 |
+| `torch.nn.LogSoftmax` / `torch.nn.functional.log_softmax` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.nn.MaxPool2d` / `torch.nn.functional.max_pool2d` | FP | `FP32` | - |
+| `torch.nn.SELU` / `torch.nn.functional.selu` | FP | `FP32` | - |
+| `torch.nn.SiLU` / `torch.nn.functional.silu` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.nn.Softmax` / `torch.nn.functional.softmax` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.ones` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.permute` / `torch.Tensor.permute` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8` | 8x8 |
+| `torch.pixel_shuffle` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.pixel_unshuffle` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.pow` / `**` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8` | 8x8 |
+| `torch.reciprocal` | FP, INT | `FP16`, `BF16`, `INT8` | 8x8 |
+| `torch.relu` / `torch.nn.ReLU` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.remainder` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.repeat_interleave` | FP, INT | `FP16`, `BF16`, `INT8`, `INT16`, `BOOL` | 8x8, 16x8 |
+| `torch.round` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8` | 8x8 |
+| `torch.rsqrt` | FP, INT | `FP32`, `INT8`, `INT16` | 8x8, 16x8 |
+| `torch.rsub` | FP | `FP32` | - |
+| `torch.scalar_tensor` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.select` / `torch.Tensor.select` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.select_scatter` | FP | `FP32` | - |
+| `torch.sigmoid` / `torch.nn.Sigmoid` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8` | 8x8 |
+| `torch.sign` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.sin` | FP, INT | `FP16`, `BF16`, `INT8` | 8x8 |
+| `torch.sinh` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.slice_scatter` | FP | `FP32` | - |
+| `torch.softmax` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.split` / `torch.Tensor.split` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.sqrt` | FP | `FP32` | - |
+| `torch.squeeze` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.squeeze` / `torch.Tensor.squeeze` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.stack` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.sub` / `-` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8`, `INT16` | 8x8, 16x8 |
+| `torch.sum` | FP, INT | `FP16`, `BF16`, `INT8` | 8x8 |
+| `torch.t` / `torch.Tensor.t` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.tan` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.tanh` / `torch.nn.Tanh` | FP, INT | `FP16`, `BF16`, `INT8` | 8x8 |
+| `torch.Tensor.__getitem__` / `tensor indexing` | FP, INT | `FP16`, `BF16`, `INT8` | 8x8 |
+| `torch.Tensor.__getitem__` / `tensor slicing` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8` | 8x8 |
+| `torch.Tensor.__setitem__` / `tensor indexing assignment` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.Tensor.copy_` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.Tensor.expand` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.Tensor.repeat` | FP, INT | `FP16`, `BF16`, `INT8`, `INT16`, `BOOL` | 8x8, 16x8 |
+| `torch.Tensor.unfold` | FP, INT | `FP32`, `FP16`, `BF16`, `INT8`, `BOOL` | 8x8 |
+| `torch.Tensor.view` | FP, INT | `FP16`, `INT8` | 8x8 |
+| `torch.transpose` / `torch.Tensor.transpose` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.tril` | FP | `FP32` | - |
+| `torch.unbind` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.unflatten` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.unsqueeze` / `torch.Tensor.unsqueeze` | FP, INT | `FP32`, `INT8` | 8x8 |
+| `torch.var` | FP | `FP32` | - |
+| `torch.where` | FP, INT | `FP32`, `INT8`, `INT16` | 8x8, 16x8 |
+| `torch.while_loop` | INT | `INT8` | 8x8 |
+| `torch.zeros` | FP, INT | `FP32`, `INT8` | 8x8 |