[AMD] [Quantization] Add override flag for attention dtype instead of using kv_cache_dtype trigger

vllm/attention/backends/rocm_flash_attn.py

@@ -16,6 +16,7 @@
                                            CommonMetadataBuilder)
 from vllm.attention.ops.paged_attn import (PagedAttention,
                                            PagedAttentionMetadata)
+from vllm.config import get_current_vllm_config
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
 from vllm.platforms.rocm import use_rocm_custom_paged_attention
@@ -580,6 +581,7 @@ def __init__(
                 logger.debug("Using naive (SDPA) attention in ROCmBackend")
 
         self.aiter_kv_scales_initialized = False
+        self.vllm_config = get_current_vllm_config()
 
     def repeat_kv(self, x: torch.Tensor, n_rep: int) -> torch.Tensor:
         """torch.repeat_interleave(x, dim=1, repeats=n_rep)"""
@@ -766,9 +768,16 @@ def forward(
                             query.dtype,
                             seq_lens,
                             make_attn_mask=causal_mask)  # type: ignore
+
+                    vllm_config_use_fp8_scales = (
+                        True if self.vllm_config is None
+                        or self.vllm_config.model_config is None else
+                        self.vllm_config.model_config.override_attention_dtype
+                        == "fp8")
                     use_fp8_scales = (layer._q_scale and layer._k_scale
                                       and layer._v_scale and layer._prob_scale
-                                      and self.kv_cache_dtype == "fp8")
+                                      and vllm_config_use_fp8_scales)
+
                     full_scales = (
                         layer._q_scale, layer._k_scale, layer._v_scale,
                         layer._prob_scale) if use_fp8_scales else None

vllm/config.py

@@ -407,6 +407,8 @@ class ModelConfig:
     available.\n
     - "vllm" will use the vLLM model implementation.\n
     - "transformers" will use the Transformers model implementation."""
+    override_attention_dtype: str = "fp8"
+    """Override dtype for attention"""
 
     def compute_hash(self) -> str:
         """

vllm/engine/arg_utils.py

@@ -410,6 +410,7 @@ class EngineArgs:
     override_generation_config: dict[str, Any] = \
         get_field(ModelConfig, "override_generation_config")
     model_impl: str = ModelConfig.model_impl
+    override_attention_dtype: str = ModelConfig.override_attention_dtype
 
     calculate_kv_scales: bool = CacheConfig.calculate_kv_scales
 
@@ -526,6 +527,8 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
         model_group.add_argument("--model-impl",
                                  choices=[f.value for f in ModelImpl],
                                  **model_kwargs["model_impl"])
+        model_group.add_argument("--override-attention-dtype",
+                                 **model_kwargs["override_attention_dtype"])
 
         # Model loading arguments
         load_kwargs = get_kwargs(LoadConfig)
@@ -909,6 +912,7 @@ def create_model_config(self) -> ModelConfig:
             override_generation_config=self.override_generation_config,
             enable_sleep_mode=self.enable_sleep_mode,
             model_impl=self.model_impl,
+            override_attention_dtype=self.override_attention_dtype,
         )
 
     def create_load_config(self) -> LoadConfig: