[ExecuTorch] Add fast_hadamard_transform and fast_hadamard_transform_28N kernels

.lintrunner.toml

@@ -74,6 +74,8 @@ exclude_patterns = [
     # NB: Objective-C is not supported
     'examples/apple/**',
     'examples/demo-apps/apple_ios/**',
+    # File contains @generated
+    'extension/llm/custom_ops/spinquant/fast_hadamard_transform_special.h',
 ]
 command = [
     'python',
@@ -177,6 +179,8 @@ exclude_patterns = [
     '**/*.bat',
     '**/*.jpg',
     '**/*.jar',
+    # File contains @generated
+    'extension/llm/custom_ops/spinquant/fast_hadamard_transform_special.h',
 ]
 command = [
     'python',

extension/llm/custom_ops/spinquant/README.md

@@ -0,0 +1,16 @@
+# SpinQuant
+
+This is an implementation of the [Fast Hadamard
+Transform](https://en.wikipedia.org/wiki/Fast_Walsh–Hadamard_transform)
+as used in [SpinQuant](https://arxiv.org/abs/2405.16406) (for the R3
+and R4 matrices), [QuaRot](https://arxiv.org/abs/2404.00456), and
+[Quip#](https://arxiv.org/pdf/2402.04396). We follow those papers'
+method (as implemented in
+https://github.com/Dao-AILab/fast-hadamard-transform/) for extending
+the transform to non-power-of-two input sizes. CUDA is not considered
+because https://github.com/Dao-AILab/fast-hadamard-transform/ is
+already available.
+
+The intended long-term destination for this code is pytorch/ao; it is
+in ExecuTorch temporarily until we get C++ dependency from ExecuTorch
+on torchao figured out.

extension/llm/custom_ops/spinquant/TARGETS

@@ -0,0 +1,5 @@
+load(":targets.bzl", "define_common_targets")
+
+oncall("executorch")
+
+define_common_targets()

extension/llm/custom_ops/spinquant/fast_hadamard_transform.h

@@ -0,0 +1,93 @@
+/*
+ * 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.
+ */
+
+// (c) Meta Platforms, Inc. and affiliates.
+#pragma once
+
+#include <cassert>
+#include <cmath>
+#include <cstdint>
+
+#include "fast_hadamard_transform_special.h"
+
+namespace executorch {
+namespace internal {
+
+// Square root of 1 << log2_n.
+template <typename T>
+T fast_sqrt_of_power_of_2(int log2_n) {
+  // The square root of 2**N is, by definition, 2**(N/2), which is
+  // trivial to compute for even N using a left shift.
+  //
+  // For odd N, 2**(N/2) = 2**(floor(N/2) + 1/2)
+  //                     = 2**(floor(N/2)) * (2 ** (1/2))
+  //                     = 2**(floor(N/2)) * sqrt(2)
+  // which is again fast to compute.
+  return T(1 << (log2_n / 2)) * ((log2_n % 2) ? T(std::sqrt(2)) : T(1));
+}
+
+template <typename T>
+void normalize_after_fht(T* out, int log2_vec_size) {
+  const T inv_sqrt = T(1) / fast_sqrt_of_power_of_2<T>(log2_vec_size);
+  const int vec_size = 1 << log2_vec_size;
+  for (int ii = 0; ii < vec_size; ++ii) {
+    out[ii] *= inv_sqrt;
+  }
+}
+
+template <typename T>
+void fast_hadamard_transform_simple_impl(T* vec, int log2_vec_size) {
+  if (log2_vec_size == 0) {
+    return;
+  }
+
+  int step = 1;
+  const auto vec_size = 1 << log2_vec_size;
+  while (step < vec_size) {
+    for (int ii = 0; ii < vec_size; ii += step * 2) {
+      for (int jj = ii; jj < ii + step; ++jj) {
+        auto x = vec[jj];
+        auto y = vec[jj + step];
+        vec[jj] = x + y;
+        vec[jj + step] = x - y;
+      }
+    }
+    step *= 2;
+  }
+
+  normalize_after_fht(vec, log2_vec_size);
+}
+
+} // namespace internal
+
+// Compute the fast Walsh-Hadamard transform
+// (https://en.wikipedia.org/wiki/Fast_Walsh%E2%80%93Hadamard_transform)
+// of vec, which must be of length (1 << log2_vec_size).
+template <typename T>
+void fast_hadamard_transform(T* vec, int log2_vec_size) {
+  internal::fast_hadamard_transform_simple_impl(vec, log2_vec_size);
+}
+
+// Like fast_hadamard_transform, but vec must be of length 28 * (1 <<
+// log2_vec_size) and the transform is computed by interpreting vec as
+// a (28, 1 << log2_vec_size) matrix and performing 28 FHTs, followed
+// by (1 << log2_vec_size) multiplications by a particular Hadamard
+// matrix of size 28x28 (see special_hadamard_code_gen.py for the
+// exact matrix).
+template <typename T>
+void fast_hadamard_transform_28N(T* vec, int log2_vec_size) {
+  const int vec_size = (1 << log2_vec_size);
+  for (int ii = 0; ii < 28; ++ii) {
+    fast_hadamard_transform(&vec[ii * vec_size], log2_vec_size);
+  }
+  for (int ii = 0; ii < vec_size; ++ii) {
+    hadamard_mult_28_strided(&vec[ii], vec_size);
+  }
+}
+
+} // namespace executorch