[Executorch] parallelize op_choose_qparams

Pull Request resolved: #15607

When doing prefill for quantized kv cache, with large prefill length, parallelizing this op helps.

Differential Revision: [D84962234](https://our.internmc.facebook.com/intern/diff/D84962234/)

**NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D84962234/)!
ghstack-source-id: 321455123

Author: kimishpatel

kernels/quantized/cpu/op_choose_qparams.cpp

@@ -8,6 +8,7 @@
 
 #include <executorch/kernels/portable/cpu/vec_ops.h>
 #include <executorch/runtime/kernel/kernel_includes.h>
+#include <executorch/runtime/kernel/thread_parallel_interface.h>
 #include <algorithm>
 #include <cinttypes>
 #include <cmath>
@@ -202,17 +203,42 @@ void choose_qparams_per_token(
     num_tokens *= input.size(i);
   }
   auto token_dim_size = input.size(input.dim() - 1);
-  for (auto i = 0; i < num_tokens; i++) {
-    // vec_minf uses std::min_element. Check if it actually
-    // gets vectorized.
-    float min = torch::executor::vec_minf(x_fp32, token_dim_size);
-    float max = torch::executor::vec_maxf(x_fp32, token_dim_size);
-    double scale;
-    int32_t zero_point;
-    calculate_scale_and_zero_point(min, max, qmin, qmax, scale, zero_point);
-    scale_out.mutable_data_ptr<double>()[i] = scale;
-    zero_point_out.mutable_data_ptr<int64_t>()[i] = zero_point;
-    x_fp32 += token_dim_size;
+
+  const int64_t total_elements = num_tokens * token_dim_size;
+  constexpr int64_t MIN_ELEMENTS_FOR_PARALLEL = 512;
+  const bool use_parallel = total_elements >= MIN_ELEMENTS_FOR_PARALLEL;
+
+  if (use_parallel) {
+    auto* scale_data = scale_out.mutable_data_ptr<double>();
+    auto* zero_point_data = zero_point_out.mutable_data_ptr<int64_t>();
+
+    ::executorch::extension::parallel_for(
+        0, num_tokens, 1, [&](const int64_t begin, const int64_t end) {
+          for (int64_t i = begin; i < end; i++) {
+            const float* token_data = x_fp32 + i * token_dim_size;
+            float min = torch::executor::vec_minf(token_data, token_dim_size);
+            float max = torch::executor::vec_maxf(token_data, token_dim_size);
+            double scale;
+            int32_t zero_point;
+            calculate_scale_and_zero_point(
+                min, max, qmin, qmax, scale, zero_point);
+            scale_data[i] = scale;
+            zero_point_data[i] = zero_point;
+          }
+        });
+  } else {
+    for (auto i = 0; i < num_tokens; i++) {
+      // vec_minf uses std::min_element. Check if it actually
+      // gets vectorized.
+      float min = torch::executor::vec_minf(x_fp32, token_dim_size);
+      float max = torch::executor::vec_maxf(x_fp32, token_dim_size);
+      double scale;
+      int32_t zero_point;
+      calculate_scale_and_zero_point(min, max, qmin, qmax, scale, zero_point);
+      scale_out.mutable_data_ptr<double>()[i] = scale;
+      zero_point_out.mutable_data_ptr<int64_t>()[i] = zero_point;
+      x_fp32 += token_dim_size;
+    }
   }
 }
 } // namespace

kernels/quantized/cpu/targets.bzl

@@ -9,6 +9,7 @@ _QUANT_OPS = (
         name = "op_choose_qparams",
         deps = [
             "//executorch/kernels/portable/cpu:vec_ops",
+            "//executorch/extension/threadpool:threadpool",
         ],
     ),
     op_target(

kernels/quantized/test/op_choose_qparams_test.cpp

@@ -15,6 +15,7 @@
 #include <executorch/test/utils/DeathTest.h>
 
 #include <gtest/gtest.h>
+#include <cmath>
 #include <limits>
 
 using namespace ::testing;
@@ -163,3 +164,97 @@ TEST(OpChooseQparamsPerTokenAsymmetricTensorOutTest, DynamicShapeFloat) {
   EXPECT_TENSOR_CLOSE_WITH_TOL(scale_out, new_expected_scale, 1e-4, 1e-4);
   EXPECT_TENSOR_EQ(zero_point_out, new_expected_zero_point);
 }
+
+TEST(
+    OpChooseQparamsPerTokenAsymmetricTensorOutTest,
+    LargeInputParallelization) {
+  et_pal_init();
+  TensorFactory<ScalarType::Float> tf_float;
+  TensorFactory<ScalarType::Double> tf_double;
+  TensorFactory<ScalarType::Long> tf_long;
+
+  // Create input with 8 tokens x 128 elements per token = 1024 total elements
+  // This exceeds the MIN_ELEMENTS_FOR_PARALLEL threshold of 512
+  const int num_tokens = 8;
+  const int token_size = 128;
+  std::vector<float> input_data(num_tokens * token_size);
+
+  // Generate test data with known min/max per token for easier verification
+  std::vector<float> expected_min(num_tokens);
+  std::vector<float> expected_max(num_tokens);
+
+  for (int i = 0; i < num_tokens; i++) {
+    float token_min = -1.0f * (i + 1);
+    float token_max = 2.0f * (i + 1);
+    expected_min[i] = token_min;
+    expected_max[i] = token_max;
+
+    for (int j = 0; j < token_size; j++) {
+      // Linearly interpolate between min and max
+      float t = j / static_cast<float>(token_size - 1);
+      input_data[i * token_size + j] = token_min + t * (token_max - token_min);
+    }
+  }
+
+  Tensor input = tf_float.make({num_tokens, token_size}, input_data);
+  Tensor scale_out = tf_double.zeros({num_tokens, 1});
+  Tensor zero_point_out = tf_long.zeros({num_tokens, 1});
+
+  choose_qparams_per_token_asymmetric_out(
+      input, ScalarType::Float, scale_out, zero_point_out);
+
+  // Manually calculate expected scale and zero_point using the same algorithm
+  // as calculate_scale_and_zero_point function
+  const int32_t qmin = -128;
+  const int32_t qmax = 127;
+  const float SMALL_SCALE_THRESHOLD = 6.1e-5f;
+
+  for (int i = 0; i < num_tokens; i++) {
+    float min = std::min(expected_min[i], 0.0f);
+    float max = std::max(expected_max[i], 0.0f);
+
+    // Calculate scale
+    double scale = (static_cast<double>(max) - min) / (qmax - qmin);
+    if (float(scale) == 0.0f || std::isinf(1.0f / float(scale))) {
+      scale = 0.1;
+    }
+
+    // Cut off small scale
+    if (scale < SMALL_SCALE_THRESHOLD) {
+      scale = SMALL_SCALE_THRESHOLD;
+      if (min == 0.0f) {
+        max = SMALL_SCALE_THRESHOLD * (qmax - qmin);
+      } else if (max == 0.0f) {
+        min = -SMALL_SCALE_THRESHOLD * (qmax - qmin);
+      } else {
+        float amplifier = SMALL_SCALE_THRESHOLD / scale;
+        min *= amplifier;
+        max *= amplifier;
+      }
+    }
+
+    // Calculate zero_point
+    double zero_point_from_min = qmin - min / scale;
+    double zero_point_from_max = qmax - max / scale;
+    double zero_point_from_min_error = std::abs(qmin) - std::abs(min / scale);
+    double zero_point_from_max_error = std::abs(qmax) - std::abs(max / scale);
+    double initial_zero_point =
+        zero_point_from_min_error < zero_point_from_max_error
+        ? zero_point_from_min
+        : zero_point_from_max;
+
+    int32_t nudged_zero_point = 0;
+    if (initial_zero_point < qmin) {
+      nudged_zero_point = qmin;
+    } else if (initial_zero_point > qmax) {
+      nudged_zero_point = qmax;
+    } else {
+      nudged_zero_point =
+          std::nearbyint(static_cast<float>(initial_zero_point));
+    }
+
+    // Verify computed values match expected
+    EXPECT_NEAR(scale_out.const_data_ptr<double>()[i], scale, 1e-6);
+    EXPECT_EQ(zero_point_out.const_data_ptr<int64_t>()[i], nudged_zero_point);
+  }
+}