[Common] Support scaled & clamped swiglu, srelu for BF16

tests/cpp/operator/CMakeLists.txt

@@ -27,6 +27,7 @@ add_executable(test_operator
                test_cast_transpose_dbias_dgelu.cu
                test_cast_transpose_dgeglu.cu
                test_act.cu
+               test_scaled_activation.cu
                test_normalization.cu
                test_normalization_mxfp8.cu
                test_memset.cu

tests/cpp/operator/test_scaled_activation.cu

@@ -0,0 +1,291 @@
+/*************************************************************************
+ * Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ *
+ * See LICENSE for license information.
+ ************************************************************************/
+
+#include <algorithm>
+#include <cmath>
+#include <memory>
+#include <string>
+#include <tuple>
+
+#include <cuda_runtime.h>
+#include <gtest/gtest.h>
+
+#include <transformer_engine/activation.h>
+
+#include "../test_common.h"
+
+using namespace transformer_engine;
+
+namespace {
+
+enum class ScaledActivationCase {
+  kSwiGLU,
+  kClampedSwiGLU,
+  kSReLU,
+};
+
+constexpr float kClampedLimit = 0.5f;
+constexpr float kClampedAlpha = 1.702f;
+constexpr float kClampedLinearOffset = 0.5f;
+
+const char *activation_name(ScaledActivationCase activation) {
+  switch (activation) {
+    case ScaledActivationCase::kSwiGLU:
+      return "scaled_swiglu";
+    case ScaledActivationCase::kClampedSwiGLU:
+      return "scaled_clamped_swiglu";
+    case ScaledActivationCase::kSReLU:
+      return "scaled_srelu";
+  }
+  return "unknown";
+}
+
+inline void glu_indices(const size_t row, const size_t col, const size_t hidden,
+                        const int64_t interleave, size_t *act_idx, size_t *linear_idx) {
+  if (interleave > 0) {
+    const size_t block = col / static_cast<size_t>(interleave);
+    const size_t lane = col % static_cast<size_t>(interleave);
+    const size_t base = row * hidden * 2 + block * static_cast<size_t>(interleave) * 2 + lane;
+    *act_idx = base;
+    *linear_idx = base + static_cast<size_t>(interleave);
+  } else {
+    const size_t base = row * hidden * 2;
+    *act_idx = base + col;
+    *linear_idx = base + hidden + col;
+  }
+}
+
+inline void gated_grads(const ScaledActivationCase activation, const float act_in,
+                        const float linear_in, float *dact, float *dlinear, float *unscaled) {
+  switch (activation) {
+    case ScaledActivationCase::kSwiGLU: {
+      const float act = test::silu(act_in);
+      *unscaled = act * linear_in;
+      *dact = test::dsilu(act_in) * linear_in;
+      *dlinear = act;
+      return;
+    }
+    case ScaledActivationCase::kClampedSwiGLU: {
+      const bool dlinear_mask = linear_in <= kClampedLimit && linear_in >= -kClampedLimit;
+      const float act = test::qgelu(fminf(kClampedLimit, act_in));
+      const float dact_base =
+          act_in <= kClampedLimit ? test::dqgelu(fminf(kClampedLimit, act_in)) : 0.0f;
+      const float linear =
+          fminf(fmaxf(-kClampedLimit, linear_in), kClampedLimit) + kClampedLinearOffset;
+      *unscaled = act * linear;
+      *dact = dact_base * linear;
+      *dlinear = dlinear_mask ? act : 0.0f;
+      return;
+    }
+    case ScaledActivationCase::kSReLU:
+      *unscaled = test::srelu(act_in);
+      *dact = test::dsrelu(act_in);
+      *dlinear = 0.0f;
+      return;
+  }
+}
+
+template <typename DataT, typename ScaleT>
+void compute_reference(ScaledActivationCase activation, const DataT *input, const ScaleT *scales,
+                       const DataT *grad_output, DataT *output, DataT *grad_input,
+                       DataT *grad_scales, const size_t rows, const size_t hidden,
+                       const int64_t interleave, const bool compute_grad_scales) {
+  const bool is_gated = activation != ScaledActivationCase::kSReLU;
+  const size_t input_cols = is_gated ? hidden * 2 : hidden;
+  std::fill(grad_input, grad_input + rows * input_cols, static_cast<DataT>(0.0f));
+
+  for (size_t row = 0; row < rows; ++row) {
+    const float scale = static_cast<float>(scales[row]);
+    float scale_grad = 0.0f;
+    for (size_t col = 0; col < hidden; ++col) {
+      const size_t out_idx = row * hidden + col;
+      float unscaled = 0.0f;
+      float dact = 0.0f;
+      float dlinear = 0.0f;
+      if (is_gated) {
+        size_t act_idx = 0;
+        size_t linear_idx = 0;
+        glu_indices(row, col, hidden, interleave, &act_idx, &linear_idx);
+        const float act_in = static_cast<float>(input[act_idx]);
+        const float linear_in = static_cast<float>(input[linear_idx]);
+        gated_grads(activation, act_in, linear_in, &dact, &dlinear, &unscaled);
+
+        const float scaled_grad = static_cast<float>(grad_output[out_idx]) * scale;
+        grad_input[act_idx] = static_cast<DataT>(scaled_grad * dact);
+        grad_input[linear_idx] = static_cast<DataT>(scaled_grad * dlinear);
+      } else {
+        const float x = static_cast<float>(input[out_idx]);
+        unscaled = test::srelu(x);
+        const float scaled_grad = static_cast<float>(grad_output[out_idx]) * scale;
+        grad_input[out_idx] = static_cast<DataT>(scaled_grad * test::dsrelu(x));
+      }
+
+      output[out_idx] = static_cast<DataT>(unscaled * scale);
+      scale_grad += static_cast<float>(grad_output[out_idx]) * unscaled;
+    }
+    if (compute_grad_scales) {
+      grad_scales[row] = static_cast<DataT>(scale_grad);
+    }
+  }
+}
+
+template <typename DataT, typename ScaleT>
+void run_scaled_activation_test(ScaledActivationCase activation, const size_t rows,
+                                const size_t hidden, const int64_t interleave,
+                                const bool compute_grad_scales) {
+  using namespace test;
+  const DType data_type = TypeInfo<DataT>::dtype;
+  const DType scale_type = TypeInfo<ScaleT>::dtype;
+  const bool is_gated = activation != ScaledActivationCase::kSReLU;
+  const size_t input_cols = is_gated ? hidden * 2 : hidden;
+
+  Tensor input("input", std::vector<size_t>{rows, input_cols}, data_type);
+  Tensor scales("act_scales", std::vector<size_t>{rows}, scale_type);
+  Tensor output("output", std::vector<size_t>{rows, hidden}, data_type);
+  Tensor grad_output("grad_output", std::vector<size_t>{rows, hidden}, data_type);
+  Tensor grad_input("grad_input", std::vector<size_t>{rows, input_cols}, data_type);
+  Tensor grad_scales("grad_scales", std::vector<size_t>{rows}, data_type);
+
+  fillUniform(&input);
+  fillUniform(&scales);
+  fillUniform(&grad_output);
+
+  std::unique_ptr<DataT[]> ref_output = std::make_unique<DataT[]>(rows * hidden);
+  std::unique_ptr<DataT[]> ref_grad_input = std::make_unique<DataT[]>(rows * input_cols);
+  std::unique_ptr<DataT[]> ref_grad_scales = std::make_unique<DataT[]>(rows);
+
+  compute_reference(activation, input.rowwise_cpu_dptr<DataT>(), scales.rowwise_cpu_dptr<ScaleT>(),
+                    grad_output.rowwise_cpu_dptr<DataT>(), ref_output.get(),
+                    ref_grad_input.get(), ref_grad_scales.get(), rows, hidden, interleave,
+                    compute_grad_scales);
+
+  switch (activation) {
+    case ScaledActivationCase::kSwiGLU:
+      nvte_scaled_swiglu(input.data(), scales.data(), output.data(), interleave, 0);
+      nvte_scaled_dswiglu(grad_output.data(), input.data(), scales.data(), grad_input.data(),
+                          compute_grad_scales ? grad_scales.data() : nullptr, interleave, 0);
+      break;
+    case ScaledActivationCase::kClampedSwiGLU:
+      nvte_scaled_clamped_swiglu(input.data(), scales.data(), output.data(), kClampedLimit,
+                                 kClampedAlpha, kClampedLinearOffset, interleave, 0);
+      nvte_scaled_clamped_dswiglu(
+          grad_output.data(), input.data(), scales.data(), grad_input.data(),
+          compute_grad_scales ? grad_scales.data() : nullptr, kClampedLimit, kClampedAlpha,
+          kClampedLinearOffset, interleave, 0);
+      break;
+    case ScaledActivationCase::kSReLU:
+      nvte_scaled_srelu(input.data(), scales.data(), output.data(), 0);
+      nvte_scaled_dsrelu(grad_output.data(), input.data(), scales.data(), grad_input.data(),
+                         compute_grad_scales ? grad_scales.data() : nullptr, 0);
+      break;
+  }
+
+  NVTE_CHECK_CUDA(cudaDeviceSynchronize());
+  auto err = cudaGetLastError();
+  ASSERT_EQ(err, cudaSuccess) << cudaGetErrorString(err);
+
+  auto [atol, rtol] = getTolerances(data_type);
+  if (data_type == DType::kFloat32) {
+    atol = 5e-5;
+    rtol = 5e-5;
+  }
+  compareResults("scaled_activation_output", output, ref_output.get(), true, atol, rtol);
+  compareResults("scaled_activation_grad_input", grad_input, ref_grad_input.get(), true, atol,
+                 rtol);
+  if (compute_grad_scales) {
+    compareResults("scaled_activation_grad_scales", grad_scales, ref_grad_scales.get(), true, atol,
+                   rtol);
+  }
+}
+
+class ScaledActivationTest
+    : public ::testing::TestWithParam<
+          std::tuple<ScaledActivationCase, DType, DType, std::pair<size_t, size_t>, int64_t,
+                     bool>> {
+};
+
+std::string test_name_generator(
+    const testing::TestParamInfo<ScaledActivationTest::ParamType> &info) {
+  const auto activation = std::get<0>(info.param);
+  const auto data_type = std::get<1>(info.param);
+  const auto scale_type = std::get<2>(info.param);
+  const auto shape = std::get<3>(info.param);
+  const auto interleave = std::get<4>(info.param);
+  const auto compute_grad_scales = std::get<5>(info.param);
+  return std::string(activation_name(activation)) + "_data_" + test::typeName(data_type) +
+         "_scale_" + test::typeName(scale_type) + "_m_" + std::to_string(shape.first) + "_h_" +
+         std::to_string(shape.second) + "_interleave_" + std::to_string(interleave) +
+         (compute_grad_scales ? "_with_scale_grad" : "_no_scale_grad");
+}
+
+}  // namespace
+
+TEST_P(ScaledActivationTest, ForwardBackward) {
+  const auto activation = std::get<0>(GetParam());
+  const auto data_type = std::get<1>(GetParam());
+  const auto scale_type = std::get<2>(GetParam());
+  const auto shape = std::get<3>(GetParam());
+  const auto interleave = std::get<4>(GetParam());
+  const auto compute_grad_scales = std::get<5>(GetParam());
+
+  if (activation == ScaledActivationCase::kSReLU && interleave != 0) {
+    GTEST_SKIP() << "Interleave has no meaning for SReLU.";
+  }
+  if (activation != ScaledActivationCase::kSReLU && interleave > 0 &&
+      shape.second % static_cast<size_t>(interleave) != 0) {
+    GTEST_SKIP() << "Hidden size must be divisible by GLU interleave.";
+  }
+
+  using namespace test;
+  TRANSFORMER_ENGINE_TYPE_SWITCH_ALL(data_type, DataT, {
+    TRANSFORMER_ENGINE_TYPE_SWITCH_ALL(scale_type, ScaleT, {
+      run_scaled_activation_test<DataT, ScaleT>(activation, shape.first, shape.second, interleave,
+                                                compute_grad_scales);
+    });
+  });
+}
+
+// Test axes (the six tuple elements consumed by ScaledActivationTest):
+//   1. Activation         : SwiGLU and ClampedSwiGLU are gated (input is [M, 2H]);
+//                           SReLU is unary (input is [M, H], no gate split).
+//   2. Data dtype         : dtype of the activation input/output tensors.
+//   3. Scale dtype        : dtype of act_scales / grad_act_scales.
+//   4. Shape {rows, hidden}: rows = M (tokens), hidden = H (output width; gated input is 2H).
+//   5. GLU interleave      : 0 = contiguous [a | b]; 32 = interleaved a/b blocks. Only valid
+//                            for gated activations with hidden % 32 == 0; SReLU skips != 0.
+//   6. compute_grad_scales : whether the backward also reduces grad_act_scales.
+
+// Interleave is swept over {0, 32}; invalid combinations -- SReLU with any nonzero interleave, or
+// a gated activation whose hidden is not divisible by the interleave -- are skipped at runtime by
+// the GTEST_SKIP guards in the test body.
+INSTANTIATE_TEST_SUITE_P(
+    OperatorTest_ScaledActivation, ScaledActivationTest,
+    ::testing::Combine(
+        ::testing::Values(ScaledActivationCase::kSwiGLU, ScaledActivationCase::kClampedSwiGLU,
+                          ScaledActivationCase::kSReLU),
+        ::testing::Values(DType::kFloat32, DType::kBFloat16),   // data dtype
+        ::testing::Values(DType::kFloat32, DType::kBFloat16),   // scale dtype
+        ::testing::Values(std::pair<size_t, size_t>{17, 64},      // aligned + interleaved
+                          std::pair<size_t, size_t>{13, 100},     // scalar fallback
+                          std::pair<size_t, size_t>{1024, 2048}),  // large FFN-ish width
+        ::testing::Values(0, 32),                                // contiguous + interleaved
+        ::testing::Values(false, true)),                         // grad_act_scales off / on
+    test_name_generator);
+
+// Keep FP16 coverage focused on representative aligned and scalar-fallback shapes instead of
+// multiplying it across the full shape matrix above.
+INSTANTIATE_TEST_SUITE_P(
+    OperatorTest_ScaledActivation_FP16, ScaledActivationTest,
+    ::testing::Combine(
+        ::testing::Values(ScaledActivationCase::kSwiGLU, ScaledActivationCase::kClampedSwiGLU,
+                          ScaledActivationCase::kSReLU),
+        ::testing::Values(DType::kFloat16),                       // data dtype
+        ::testing::Values(DType::kFloat32, DType::kFloat16),      // scale dtype
+        ::testing::Values(std::pair<size_t, size_t>{17, 64},      // aligned/interleaved
+                          std::pair<size_t, size_t>{13, 100}),    // scalar fallback
+        ::testing::Values(0, 32),                                 // contiguous + interleaved
+        ::testing::Values(false, true)),                          // grad_act_scales off / on
+    test_name_generator);

transformer_engine/common/CMakeLists.txt

@@ -252,6 +252,9 @@ list(APPEND transformer_engine_cuda_arch_specific_sources
      activation/relu_dbias.cu
      activation/relu_grouped.cu
      activation/relu_grouped_dbias.cu
+     activation/scaled_activation.cu
+     activation/scaled_srelu.cu
+     activation/scaled_swiglu.cu
      activation/swiglu.cu
      activation/swiglu_dbias.cu
      activation/swiglu_grouped.cu
@@ -583,6 +586,9 @@ if (NVTE_BUILD_ACTIVATION_WITH_FAST_MATH)
                                           activation/relu_dbias.cu
                                           activation/relu_grouped.cu
                                           activation/relu_grouped_dbias.cu
+                                          activation/scaled_activation.cu
+                                          activation/scaled_srelu.cu
+                                          activation/scaled_swiglu.cu
                                           activation/swiglu.cu
                                           activation/swiglu_dbias.cu
                                           activation/swiglu_grouped.cu