[perf] add VLLM_USE_FUSED_FILL_RMS_CAT to use lightop for dpsk mtp fill + rms*2 + cat

vllm/envs.py

@@ -288,6 +288,7 @@ if TYPE_CHECKING:
     VLLM_USE_OPT_RESHAPE_AND_CACHE: bool = False
     VLLM_USE_TOPK_RENORM: bool = False
     VLLM_USE_FUSED_RMS_ROPE: bool = False
+    VLLM_USE_FUSED_FILL_RMS_CAT: bool = False
     VLLM_W8A8_BACKEND: int = 3
 
 
@@ -1819,6 +1820,10 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_USE_FUSED_RMS_ROPE":
         lambda: (os.environ.get("VLLM_USE_FUSED_RMS_ROPE", "True").lower() in
                  ("true", "1")),
+    # vLLM will use lightop for dpsk mtp fill + rms*2 + cat
+    "VLLM_USE_FUSED_FILL_RMS_CAT":
+        lambda: (os.environ.get("VLLM_USE_FUSED_FILL_RMS_CAT", "False").lower() in
+                 ("true", "1")),
     # W8A8 GEMM backend selection for vLLM quantized models.
     # lightop/triton: 1
     # cutlass: 2 (will remove in the future)

vllm/model_executor/model_loader/utils.py

@@ -192,6 +192,8 @@ def _get_model_architecture(model_config: ModelConfig) -> tuple[type[nn.Module],
                     os.environ['VLLM_USE_LIGHTOP'] = '1'
                 if not envs.is_set("VLLM_USE_OPT_CAT"):
                     os.environ['VLLM_USE_OPT_CAT'] = '1'
+                if not envs.is_set("VLLM_USE_FUSED_FILL_RMS_CAT"):
+                    os.environ['VLLM_USE_FUSED_FILL_RMS_CAT'] = '1'
                 # if model_config.quantization in {"slimquant_w4a8", "slimquant_w4a8_marlin", "slimquant_compressed_tensors_marlin", "compressed-tensors"}:
                 #     if not envs.is_set("USE_FUSED_RMS_QUANT"):
                 #         os.environ['USE_FUSED_RMS_QUANT'] = '1'
@@ -224,6 +226,8 @@ def _get_model_architecture(model_config: ModelConfig) -> tuple[type[nn.Module],
                     os.environ['VLLM_USE_LIGHTOP'] = '1'
                 if not envs.is_set("VLLM_USE_OPT_CAT"):
                     os.environ['VLLM_USE_OPT_CAT'] = '1'
+                if not envs.is_set("VLLM_USE_FUSED_FILL_RMS_CAT"):
+                    os.environ['VLLM_USE_FUSED_FILL_RMS_CAT'] = '1'
                 # if model_config.quantization in {"slimquant_w4a8", "slimquant_w4a8_marlin", "slimquant_compressed_tensors_marlin", "compressed-tensors"}:
                 #     if not envs.is_set("USE_FUSED_RMS_QUANT"):
                 #         os.environ['USE_FUSED_RMS_QUANT'] = '1'

vllm/model_executor/models/deepseek_mtp.py

@@ -39,6 +39,7 @@ from .deepseek_v2 import (
 from .utils import maybe_prefix
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.blockwise_int8 import BlockInt8Config
+import vllm.envs as envs
 
 logger = init_logger(__name__)
 
@@ -109,6 +110,11 @@ class DeepSeekMultiTokenPredictorLayer(nn.Module):
     ) -> torch.Tensor:
         assert inputs_embeds is not None
         # masking inputs at position 0, as not needed by MTP
+        if envs.VLLM_USE_FUSED_FILL_RMS_CAT:
+            hidden_states_fuse = torch.empty(inputs_embeds.shape[0], inputs_embeds.shape[1]*2, device=inputs_embeds.device, dtype=inputs_embeds.dtype)
+            torch.ops.vllm.fuse_fill_rms_x2_concat(hidden_states_fuse, positions, inputs_embeds, previous_hidden_states, self.enorm.weight, self.hnorm.weight, self.enorm.variance_epsilon)
+            hidden_states = self.eh_proj(hidden_states_fuse)
+        else:
             inputs_embeds = torch.where(positions.unsqueeze(-1) == 0, 0, inputs_embeds)
             inputs_embeds = self.enorm(inputs_embeds)
             previous_hidden_states = self.hnorm(previous_hidden_states)