Add support rowwise_adagrad_wtith_counter on CPU (#5146)

Summary:
X-link: facebookresearch/FBGEMM#2145


Initial support has been added in D81998586.

Differential Revision: D87104079

Author: gchalump

fbgemm_gpu/codegen/genscript/optimizers.py

@@ -600,23 +600,132 @@ def rowwise_adagrad_with_counter() -> Dict[str, Any]:
     exp_reg_correction = SHFL_SYNC(exp_reg_correction, 0);
     """
     split_weight_update_cpu = """
+        auto offset_idx = momentum1_offsets_data[feature_begin] + idx;
+
+        // Counter update logic with halflife decay
+        at::acc_type<grad_t, true> freq = 1.0;
+        at::acc_type<grad_t, true> tail_id_threshold_val = tail_id_threshold;
+        if (max_counter != 0.0) {
+            if (is_tail_id_thresh_ratio == 1) {
+                tail_id_threshold_val = std::floor(tail_id_threshold * max_counter);
+            }
+
+            if (counter_halflife > 0) {
+                // Decay based on counter_halflife
+                const auto iter_delta = prev_iter_host[offset_idx] == 0 ? 1.0 : iter * 1.0 - prev_iter_host[offset_idx];
+                const auto counter_log_rho = std::log(2.0) / counter_halflife;
+                row_counter_host[offset_idx] = 1.0 + std::exp(-iter_delta * counter_log_rho) * row_counter_host[offset_idx];
+            } else if (counter_halflife == 0) {
+                // Count only 1 (appear or not)
+                row_counter_host[offset_idx] = 1.0;
+            } else {
+                // Count raw appearance without decaying
+                row_counter_host[offset_idx] += 1.0;
+            }
+        }
+        freq = counter_halflife / row_counter_host[offset_idx];
+
+        // Compute gradient statistics
         at::acc_type<grad_t, true> g_local_sum_square = 0.0;
+        at::acc_type<grad_t, true> w_local_sum_square = 0.0;
+
         for (int64_t d = 0; d < D; ++d) {
-            g_local_sum_square += grad_buffer[d] * grad_buffer[d];
+            auto grad = grad_buffer[d];
+            // For L2 regularization (weight_decay_mode=1), add weight_decay to gradient before other computation
+            if (weight_decay_mode == 1) {
+                grad += weight_decay * host_weights_data[embedding_begin + d];
+            }
+            g_local_sum_square += grad * grad;
+
+            // COW-clip (regularization_mode=4) requires weight norm
+            if (regularization_mode == 4) {
+                const auto weight = host_weights_data[embedding_begin + d];
+                w_local_sum_square += weight * weight;
+            }
         }
-        auto g_avg_square = g_local_sum_square / D;
-        auto offset_idx = momentum1_offsets_data[feature_begin] + idx;
+
+        const auto g_sum_square = g_local_sum_square;
+        const auto g_avg_square = g_sum_square / D;
+        const auto w_sum_square = w_local_sum_square;
+
+        // Update momentum
         at::acc_type<grad_t, true> new_sum_square_grads = momentum1_host[offset_idx] + g_avg_square;
         momentum1_host[offset_idx] = new_sum_square_grads;
-        at::acc_type<grad_t, true> multiplier;
-        multiplier = learning_rate / (sqrtf(new_sum_square_grads) + eps);
-        const auto iter_delta = iter * 1.0 - prev_iter_host[offset_idx];
+        const auto multiplier = learning_rate / (std::sqrt(new_sum_square_grads) + eps);
+        const auto adjustment_enabled = adjustment_iter <= 0 || (adjustment_iter > 0 && iter > adjustment_iter);
+
+        // Compute adjusted multiplier and regularization correction
+        at::acc_type<grad_t, true> adjusted_multiplier = 0.0;
+        at::acc_type<grad_t, true> exp_reg_correction = 0.0;
+
+        if (regularization_mode == 3) {
+            // Counter-based regularization (regularization_mode=3)
+            adjusted_multiplier = multiplier;
+            if (learning_rate_mode >= 0 && adjustment_enabled) {
+                if (row_counter_host[offset_idx] > tail_id_threshold_val) {
+                    if (learning_rate_mode == 0) {
+                        adjusted_multiplier = multiplier * std::max(std::min(std::pow(max_counter / (row_counter_host[offset_idx] + 1.0), adjustment_ub), 10.0), 1.0);
+                    } else if (learning_rate_mode == 1) {
+                        adjusted_multiplier = multiplier * std::min(std::max(std::pow((row_counter_host[offset_idx] + 1.0) / max_counter, adjustment_ub), 0.1), 1.0);
+                    } else if (learning_rate_mode == 2) {
+                        adjusted_multiplier = learning_rate / (std::sqrt(adjustment_ub * row_counter_host[offset_idx]) + eps);
+                    }
+                }
+            }
+        } else if (regularization_mode == 4) {
+            // COW-clip (regularization_mode=4)
+            const auto clip_thresh = row_counter_host[offset_idx] * std::max(weight_norm_coefficient * std::sqrt(w_sum_square), lower_bound);
+            adjusted_multiplier = std::min(1.0f, static_cast<float>(clip_thresh / std::sqrt(g_sum_square))) * multiplier;
+        } else {
+            // Default: no special regularization
+            adjusted_multiplier = multiplier;
+        }
+
+        // Compute regularization correction
+        exp_reg_correction = 1.0;
+        if (regularization_mode == 3) {
+            // Counter-based regularization (regularization_mode=3)
+            if (adjustment_enabled) {
+                if (weight_decay_mode == 3) {
+                    // AdagradW (weight_decay_mode=3)
+                    if (counter_halflife == -1) {
+                        adjusted_multiplier = multiplier * std::sqrt(row_counter_host[offset_idx] * 1.0);
+                    } else if (counter_halflife == -2) {
+                        adjusted_multiplier = std::min(static_cast<float>(learning_rate * std::pow(row_counter_host[offset_idx] * 1.0, 1.0)), adjustment_ub) / (std::sqrt(new_sum_square_grads) + eps);
+                    }
+                    exp_reg_correction = 1.0 - weight_decay * learning_rate;
+                    const auto lazy_delta = prev_iter_host[offset_idx] == 0 ? 1.0 : iter * 1.0 - prev_iter_host[offset_idx];
+                    const auto lazy_multiplier = std::pow(exp_reg_correction, std::min(lazy_delta, iter * 1.0 - adjustment_iter) - 1.0);
+                    adjusted_multiplier *= lazy_multiplier;
+                    exp_reg_correction *= lazy_multiplier;
+                } else if (weight_decay_mode == 2) {
+                    // Decoupled weight decay (weight_decay_mode=2)
+                    exp_reg_correction = 1.0 - freq * weight_decay * learning_rate;
+                } else if (weight_decay_mode == 1) {
+                    // L2 regularization (coupled wd)
+                    exp_reg_correction = 1.0 - freq * weight_decay * multiplier;
+                }
+            }
+        } else if (regularization_mode == 4) {
+            // COW-clip (regularization_mode=4)
+            if (weight_decay_mode == 2) {
+                // Decoupled weight decay (weight_decay_mode=2)
+                exp_reg_correction = 1.0 - weight_decay * learning_rate;
+            } else if (weight_decay_mode == 1) {
+                // L2 regularization (coupled wd)
+                exp_reg_correction = 1.0 - weight_decay * adjusted_multiplier;
+            }
+        } else {
+            // Default regularization
+            exp_reg_correction = 1.0;
+        }
+
+        // Update prev_iter
         prev_iter_host[offset_idx] = iter * 1.0;
-        const auto exp_reg = 1.0 / (weight_decay * multiplier + 1.0);
-        const auto exp_reg_correction = powf(exp_reg, iter_delta);
+
+        // Apply weight updates
         for (int64_t d = 0; d < D; ++d) {
-            const auto weight = host_weights_data[embedding_begin + d];
-            host_weights_data[embedding_begin + d] = exp_reg_correction * weight - exp_reg * multiplier * grad_buffer[d];
+            host_weights_data[embedding_begin + d] = exp_reg_correction * host_weights_data[embedding_begin + d] - adjusted_multiplier * grad_buffer[d];
         }
     """