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core/framework/model/model_args.h

@@ -0,0 +1,95 @@
+/* Minimal ModelArgs for project_6 layerwise split.
+   Migrated from xllm/core/framework/model/model_args.h — only the fields
+   and functions used by kv_cache_estimation.cpp's layerwise_split code path.
+
+   Full ModelArgs in upstream is 861 lines; we keep only the interface that
+   the layerwise split scheduling layer needs. */
+
+#pragma once
+
+#include <algorithm>
+#include <cstdint>
+#include <optional>
+#include <string>
+#include <string_view>
+#include <vector>
+
+#include "common/macros.h"
+
+namespace xllm {
+
+class ModelArgs {
+  PROPERTY(std::string, model_type);
+  PROPERTY(int64_t, n_layers) = 0;
+  PROPERTY(int64_t, head_dim) = 0;
+  PROPERTY(int64_t, n_heads) = 0;
+  PROPERTY(std::optional<int64_t>, n_kv_heads);
+  PROPERTY(int64_t, hidden_size) = 0;
+
+  // Linear attention (GDN/DeltaNet) fields — Qwen3.5
+  PROPERTY(int64_t, full_attention_interval) = 1;
+  PROPERTY(std::vector<std::string>, layer_types);
+  PROPERTY(int64_t, linear_num_value_heads) = 0;
+  PROPERTY(int64_t, linear_key_head_dim) = 0;
+  PROPERTY(int64_t, linear_value_head_dim) = 0;
+  PROPERTY(int64_t, linear_conv_kernel_dim) = 0;
+  PROPERTY(std::string, mamba_ssm_dtype);
+
+  // MLA (DeepSeek)
+  PROPERTY(bool, enable_mla) = false;
+  PROPERTY(int64_t, kv_lora_rank) = 0;
+  PROPERTY(int64_t, qk_rope_head_dim) = 0;
+
+  // DSA indexer
+  PROPERTY(int64_t, index_n_heads) = 0;
+  PROPERTY(int64_t, index_head_dim) = 0;
+
+  // Sliding window
+  PROPERTY(int64_t, window_size) = 0;
+  PROPERTY(int64_t, max_seq_len) = 0;
+
+  // DeepSeek V4 compress ratios
+  PROPERTY(std::vector<int32_t>, compress_ratios);
+
+  // Speculative
+  PROPERTY(int64_t, num_nextn_predict_layers) = 0;
+};
+
+// Qwen hybrid models may describe full-attention layers explicitly via
+// layer_types or implicitly via full_attention_interval.
+inline bool is_full_attention_layer(const ModelArgs& args, int64_t layer_id) {
+  const auto& hybrid_layer_types = args.layer_types();
+  if (layer_id >= 0 &&
+      layer_id < static_cast<int64_t>(hybrid_layer_types.size())) {
+    const auto& layer_type = hybrid_layer_types[layer_id];
+    return layer_type == "full_attention" || layer_type == "attention";
+  }
+
+  int32_t attention_interval = args.full_attention_interval();
+  if (attention_interval <= 1) {
+    return true;
+  }
+  return (layer_id + 1) % attention_interval == 0;
+}
+
+inline bool has_linear_attention_layers(const ModelArgs& args) {
+  const auto& hybrid_layer_types = args.layer_types();
+  if (!hybrid_layer_types.empty()) {
+    return std::any_of(hybrid_layer_types.begin(),
+                       hybrid_layer_types.end(),
+                       [](const std::string& layer_type) {
+                         return layer_type != "full_attention" &&
+                                layer_type != "attention";
+                       });
+  }
+  return args.full_attention_interval() > 1;
+}
+
+// Closed set by design: a new target variant must be enumerated here rather
+// than matched by a "qwen3_5_" prefix.
+inline bool is_qwen3_5_target_model_type(std::string_view model_type) {
+  return model_type == "qwen3_5" || model_type == "qwen3_5_moe" ||
+         model_type == "qwen3_5_text" || model_type == "qwen3_5_moe_text";
+}
+
+}  // namespace xllm