Switch default w8a8 gemm impl to blaslt.

vllm/envs.py

@@ -207,6 +207,7 @@ if TYPE_CHECKING:
     VLLM_USE_FUSED_QA_KVA_GEMM: bool = False
     VLLM_V1_FAST_TOKEN_ID_COPY: bool = False
     VLLM_DISABLE_SHARED_EXPERTS_STREAM:bool = True
+    VLLM_W8A8_BACKEND: int = 3
 
 def get_default_cache_root():
     return os.getenv(
@@ -1335,6 +1336,13 @@ environment_variables: dict[str, Callable[[], Any]] = {
         int(os.getenv("VLLM_DISABLE_SHARED_EXPERTS_STREAM", "1"))
     ),
 
+    # W8A8 GEMM backend selection for vLLM quantized models.
+    # lightop/triton: 1
+    # cutlass: 2 (will remove in the future)
+    # blaslt: 3 (default)
+    # rocblas: others
+    "VLLM_W8A8_BACKEND": lambda: int(os.getenv("VLLM_W8A8_BACKEND", "3")),
+
 }
 
 # --8<-- [end:env-vars-definition]
@@ -1399,6 +1407,7 @@ def compute_hash() -> str:
         "VLLM_DP_SIZE",
         "VLLM_USE_STANDALONE_COMPILE",
         "VLLM_FUSED_MOE_CHUNK_SIZE",
+        "VLLM_W8A8_BACKEND",
     ]
     for key in environment_variables_to_hash:
         if key in environment_variables:

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py

@@ -20,6 +20,7 @@ from vllm.model_executor.parameter import (BasevLLMParameter,
                                            PerTensorScaleParameter)
 from vllm.utils import W8a8GetCacheJSON
 from vllm import _custom_ops as ops
+import vllm.envs as envs
 
 logger = init_logger(__name__)
 
@@ -31,8 +32,8 @@ class CompressedTensorsW8A8Int8(CompressedTensorsScheme):
                  input_symmetric: bool):
         self.strategy = strategy
         self.is_static_input_scheme = is_static_input_scheme
-        self.tritonsingleton= W8a8GetCacheJSON()
-        self.w8a8_strategy=int(os.getenv('W8A8_SUPPORT_METHODS', '1'))
+        self.tritonsingleton = W8a8GetCacheJSON()
+        self.w8a8_strategy = envs.VLLM_W8A8_BACKEND
         self.input_symmetric = input_symmetric
 
     @classmethod

vllm/model_executor/layers/quantization/slimquant_w4a8.py

@@ -92,8 +92,8 @@ class SlimQuantW4A8Int8LinearMethod(LinearMethodBase):
 
     def __init__(self, quantization_config: SlimQuantW4A8Int8Config):
         self.quantization_config = quantization_config
-        self.tritonsingleton= W8a8GetCacheJSON()
-        self.w8a8_strategy=int(os.getenv('W8A8_SUPPORT_METHODS', '1'))
+        self.tritonsingleton = W8a8GetCacheJSON()
+        self.w8a8_strategy = envs.VLLM_W8A8_BACKEND
         
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         n=layer.weight.shape[0]

vllm/model_executor/models/falcon.py

@@ -58,6 +58,7 @@ from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
 
 from vllm import _custom_ops as ops
 from vllm.model_executor.utils import pad_weight, gemm_bank_conf
+import vllm.envs as envs
 
 FalconConfig = Union[HF_FalconConfig, RWConfig]
 
@@ -393,7 +394,7 @@ class FalconModel(nn.Module):
         self.use_gemm_pad = os.environ.get('GEMM_PAD') == '1'
         self.use_fa_pad = os.environ.get('FA_PAD') == '1'
         self.use_awq_pad = os.environ.get('AWQ_PAD') == '1'
-        self.w8a8_strategy=int(os.getenv('W8A8_SUPPORT_METHODS', '1'))
+        self.w8a8_strategy = envs.VLLM_W8A8_BACKEND
 
     def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.word_embeddings(input_ids)

vllm/model_executor/models/glm4.py

@@ -31,6 +31,7 @@ from typing import Optional, Union
 import torch
 from torch import nn
 from transformers import Glm4Config
+import vllm.envs as envs
 
 
 class MultiModalConfigProxy:
@@ -332,7 +333,7 @@ class Glm4ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         self.use_gemm_pad = os.environ.get('GEMM_PAD') == '1'
         self.use_fa_pad = os.environ.get('FA_PAD') == '1'
         self.use_awq_pad = os.environ.get('AWQ_PAD') == '1'
-        self.w8a8_strategy=int(os.getenv('W8A8_SUPPORT_METHODS', '1'))
+        self.w8a8_strategy = envs.VLLM_W8A8_BACKEND
 
     def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.model.get_input_embeddings(input_ids)

vllm/model_executor/models/qwen3_moe.py

@@ -453,7 +453,7 @@ class Qwen3MoeModel(nn.Module):
         self.use_gemm_pad = os.environ.get('GEMM_PAD') == '1'
         self.use_fa_pad = os.environ.get('FA_PAD') == '1'
         self.use_awq_pad = os.environ.get('AWQ_PAD') == '1'
-        self.w8a8_strategy=int(os.getenv('W8A8_SUPPORT_METHODS', '1'))
+        self.w8a8_strategy = envs.VLLM_W8A8_BACKEND
 
     def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
         return self.embed_tokens(input_ids)

vllm/model_executor/models/telechat2.py

@@ -37,6 +37,7 @@ from .utils import (AutoWeightsLoader, PPMissingLayer, WeightsMapper,
 from vllm.utils import W8a8GetCacheJSON
 from vllm import _custom_ops as ops
 from vllm.model_executor.utils import pad_weight, gemm_bank_conf
+import vllm.envs as envs
 
 
 class TeleChat2Model(LlamaModel):
@@ -66,8 +67,7 @@ class TeleChat2Model(LlamaModel):
         self.use_gemm_pad = os.environ.get('GEMM_PAD') == '1'
         self.use_fa_pad = os.environ.get('FA_PAD') == '1'
         self.use_awq_pad = os.environ.get('AWQ_PAD') == '1'
-        self.w8a8_strategy=int(os.getenv('W8A8_SUPPORT_METHODS', '1'))
-
+        self.w8a8_strategy = envs.VLLM_W8A8_BACKEND
     def load_weights(self, weights: Iterable[tuple[str,
                                                    torch.Tensor]]) -> set[str]:
         stacked_params_mapping = [