[Feature] Enable inference support for Deepseekr1-w8a8-MTP (#1584)

### What this PR does / why we need it?

1. Support the inference of the Deepseekr1-w8a8-mtp model with
statically-quantized shared_head in MTP layers.

Signed-off-by: curryliu <120010041@link.cuhk.edu.cn>

---------

Signed-off-by: curryliu <120010041@link.cuhk.edu.cn>

Author: Irving11-BKN

vllm_ascend/models/deepseek_mtp.py

@@ -28,8 +28,8 @@
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.sampler import get_sampler
-from vllm.model_executor.layers.vocab_parallel_embedding import \
-    VocabParallelEmbedding
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead, VocabParallelEmbedding)
 from vllm.model_executor.models.deepseek_mtp import (
     DeepSeekMTP, DeepSeekMultiTokenPredictor, DeepSeekMultiTokenPredictorLayer,
     SharedHead)
@@ -40,6 +40,20 @@
 from .deepseek_v2 import CustomDeepseekV2DecoderLayer
 
 
+class CustomDeepSeekShareHead(SharedHead):
+
+    def __init__(self,
+                 config: PretrainedConfig,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = "") -> None:
+        nn.Module.__init__(self)
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.head = ParallelLMHead(config.vocab_size,
+                                   config.hidden_size,
+                                   quant_config=quant_config,
+                                   prefix=maybe_prefix(prefix, "head"))
+
+
 class CustomDeepSeekMultiTokenPredictorLayer(DeepSeekMultiTokenPredictorLayer):
 
     def __init__(
@@ -61,7 +75,10 @@ def __init__(
         self.eh_proj = nn.Linear(config.hidden_size * 2,
                                  config.hidden_size,
                                  bias=False)
-        self.shared_head = SharedHead(config=config, quant_config=quant_config)
+        self.shared_head = CustomDeepSeekShareHead(config=config,
+                                                   quant_config=quant_config,
+                                                   prefix=maybe_prefix(
+                                                       prefix, "shared_head"))
         self.mtp_block = CustomDeepseekV2DecoderLayer(config, prefix,
                                                       model_config,
                                                       cache_config,

vllm_ascend/models/deepseek_v2.py

@@ -738,7 +738,9 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         if get_pp_group().is_last_rank:
             self.lm_head = ParallelLMHead(config.vocab_size,
                                           config.hidden_size,
-                                          quant_config=quant_config)
+                                          quant_config=quant_config,
+                                          prefix=maybe_prefix(
+                                              prefix, "lm_head"))
         else:
             self.lm_head = PPMissingLayer()
         self.logits_processor = LogitsProcessor(config.vocab_size)

vllm_ascend/quantization/quant_config.py

@@ -34,6 +34,8 @@
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    UnquantizedEmbeddingMethod, VocabParallelEmbedding)
 from vllm.model_executor.parameter import PerTensorScaleParameter
 from vllm.model_executor.utils import set_weight_attrs
 
@@ -104,6 +106,12 @@ def get_quant_method(self, layer: torch.nn.Module,
                 return AscendUnquantizedFusedMoEMethod()
             return AscendFusedMoEMethod(self, prefix,
                                         self.packed_modules_mapping)
+        elif isinstance(layer, VocabParallelEmbedding):
+            if self.is_layer_skipped_ascend(prefix,
+                                            self.packed_modules_mapping):
+                return UnquantizedEmbeddingMethod()
+            return AscendEmbeddingMethod(self, prefix,
+                                         self.packed_modules_mapping)
         return None
 
     def is_layer_skipped_ascend(
@@ -352,3 +360,20 @@ def apply(
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         if hasattr(self.quant_method, "process_weights_after_loading"):
             self.quant_method.process_weights_after_loading(layer)
+
+
+class AscendEmbeddingMethod(AscendLinearMethod):
+    """Embedding method for Ascend quantization.
+
+      This class calls AscendQuantizer to search a specific quantization
+      implementations supported on ascend hardware for Embedding methods.
+
+      Args:
+          quant_config: The Ascend quantization config.
+    """
+
+    def __init__(self, quant_config: AscendQuantConfig, prefix: str,
+                 packed_modules_mapping: Dict[str, Any]) -> None:
+        self.quantizer = AscendQuantizer.get_quantizer(
+            quant_config.quant_description, prefix, packed_modules_mapping)
+        self.quant_method = self.quantizer.build_linear_method()