init v0.23.0

Signed-off-by: Sun Ruoxi <sunruoxi@4paradigm.com>

vllm_ascend/patch/platform/patch_speculative_config.py

@@ -0,0 +1,137 @@
+from typing import TYPE_CHECKING, Any
+
+from vllm.config.speculative import SpeculativeConfig
+from vllm.utils.import_utils import LazyLoader
+
+if TYPE_CHECKING:
+    import vllm.model_executor.layers.quantization as me_quant
+    from transformers import PretrainedConfig
+else:
+    PretrainedConfig = Any
+
+    me_quant = LazyLoader("model_executor", globals(), "vllm.model_executor.layers.quantization")
+
+
+def hf_config_override(hf_config: PretrainedConfig) -> PretrainedConfig:
+    initial_architecture = hf_config.architectures[0]
+    if hf_config.model_type in ("deepseek_v3", "deepseek_v32", "deepseek_v4", "glm_moe_dsa"):
+        target_model_type = hf_config.model_type
+        hf_config.model_type = "deepseek_mtp"
+    if hf_config.model_type == "deepseek_mtp":
+        if target_model_type == "deepseek_v4":
+            hf_config.update({"architectures": ["DeepSeekV4MTPModel"]})
+        else:
+            n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
+            hf_config.update({"n_predict": n_predict, "architectures": ["DeepSeekMTPModel"]})
+    if hf_config.model_type in ("pangu_ultra_moe"):
+        hf_config.model_type = "pangu_ultra_moe_mtp"
+    if hf_config.model_type == "pangu_ultra_moe_mtp":
+        n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
+        hf_config.update({"n_predict": n_predict, "architectures": ["OpenPanguMTPModel"]})
+
+    if hf_config.architectures[0] == "MiMoForCausalLM":
+        hf_config.model_type = "mimo_mtp"
+        n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
+        hf_config.update(
+            {
+                "num_hidden_layers": 0,
+                "n_predict": n_predict,
+                "architectures": ["MiMoMTPModel"],
+            }
+        )
+
+    if hf_config.architectures[0] == "Glm4MoeForCausalLM":
+        hf_config.model_type = "glm4_moe_mtp"
+        n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
+        hf_config.update(
+            {
+                "n_predict": n_predict,
+                "architectures": ["Glm4MoeMTPModel"],
+            }
+        )
+
+    if hf_config.architectures[0] == "Glm4MoeLiteForCausalLM":
+        hf_config.model_type = "glm4_moe_lite_mtp"
+        n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
+        hf_config.update(
+            {
+                "num_hidden_layers": 0,
+                "n_predict": n_predict,
+                "architectures": ["Glm4MoeLiteMTPModel"],
+            }
+        )
+
+    if hf_config.architectures[0] == "GlmOcrForConditionalGeneration":
+        hf_config.model_type = "glm_ocr_mtp"
+        n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
+        hf_config.update(
+            {
+                "num_hidden_layers": 0,
+                "n_predict": n_predict,
+                "architectures": ["GlmOcrMTPModel"],
+            }
+        )
+
+    if hf_config.model_type == "ernie4_5_moe":
+        hf_config.model_type = "ernie_mtp"
+    if hf_config.model_type == "ernie_mtp":
+        n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
+        hf_config.update({"n_predict": n_predict, "architectures": ["ErnieMTPModel"]})
+
+    if (
+        hf_config.model_type == "nemotron_h"
+        and hasattr(hf_config, "num_nextn_predict_layers")
+        and hf_config.num_nextn_predict_layers > 0
+    ):
+        # Check if this is an MTP variant
+        hf_config.model_type = "nemotron_h_mtp"
+    if hf_config.model_type == "nemotron_h_mtp":
+        n_predict = getattr(hf_config, "num_nextn_predict_layers", 1)
+        hf_config.update({"n_predict": n_predict, "architectures": ["NemotronHMTPModel"]})
+
+    if hf_config.model_type == "qwen3_next":
+        hf_config.model_type = "qwen3_next_mtp"
+    if hf_config.model_type == "qwen3_next_mtp":
+        n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
+        hf_config.update({"n_predict": n_predict, "architectures": ["Qwen3NextMTP"]})
+
+    if hf_config.model_type == "exaone_moe":
+        hf_config.model_type = "exaone_moe_mtp"
+    if hf_config.model_type == "exaone_moe_mtp":
+        n_predict = getattr(hf_config, "num_nextn_predict_layers", None)
+        hf_config.update({"n_predict": n_predict, "architectures": ["ExaoneMoeMTP"]})
+
+    if hf_config.model_type in ("qwen3_5", "qwen3_5_moe"):
+        is_moe = hf_config.model_type == "qwen3_5_moe"
+        hf_config.model_type = "qwen3_5_mtp"
+        n_predict = getattr(hf_config, "mtp_num_hidden_layers", None)
+        hf_config.update(
+            {
+                "n_predict": n_predict,
+                "architectures": ["Qwen3_5MoeMTP" if is_moe else "Qwen3_5MTP"],
+            }
+        )
+    if hf_config.model_type == "longcat_flash":
+        hf_config.model_type = "longcat_flash_mtp"
+        n_predict = getattr(hf_config, "num_nextn_predict_layers", 1)
+        hf_config.update({"n_predict": n_predict, "architectures": ["LongCatFlashMTPModel"]})
+
+    if hf_config.model_type in ("step3p5", "step3p7") or hf_config.architectures[0] in (
+        "Step3p5ForCausalLM",
+        "Step3p7ForConditionalGeneration",
+    ):
+        quantization_config = getattr(hf_config, "quantization_config", None)
+        hf_config = getattr(hf_config, "text_config", hf_config)
+        if quantization_config is not None and getattr(hf_config, "quantization_config", None) is None:
+            hf_config.update({"quantization_config": quantization_config})
+        hf_config.model_type = "step3p5_mtp"
+        n_predict = getattr(hf_config, "num_nextn_predict_layers", 1)
+        hf_config.update({"n_predict": n_predict, "architectures": ["Step3p5MTP"]})
+
+    if initial_architecture == "MistralLarge3ForCausalLM":
+        hf_config.update({"architectures": ["EagleMistralLarge3ForCausalLM"]})
+
+    return hf_config
+
+
+SpeculativeConfig.hf_config_override = hf_config_override