fix auto conversion

server/text_generation_server/layers/fp8.py

@@ -67,8 +67,9 @@ def fp8_quantize(weight, scale_upper_bound=None, qdtype=torch.float8_e4m3fn):
 class HybridFP8UnquantLoader(WeightsLoader):
     """Weight loader that loads FP8 and unquantized Torch tensors."""
 
-    def __init__(self, activation_scale_ub: Optional[float]):
+    def __init__(self, activation_scale_ub: Optional[float], to_fp8: bool):
         self.activation_scale_ub = activation_scale_ub
+        self.to_fp8 = to_fp8
 
     def get_weights_col_packed(
         self,
@@ -91,6 +92,8 @@ class HybridFP8UnquantLoader(WeightsLoader):
                 activation_scale_ub=self.activation_scale_ub,
                 dtype=weights.dtype,
             )
+        if self.to_fp8:
+            return Fp8Weight(weight=w, dtype=weights.dtype)
 
         return UnquantizedWeight(w)
 
@@ -111,6 +114,8 @@ class HybridFP8UnquantLoader(WeightsLoader):
                 activation_scale_ub=self.activation_scale_ub,
                 dtype=weights.dtype,
             )
+        if self.to_fp8:
+            return Fp8Weight(weight=w, dtype=weights.dtype)
 
         return UnquantizedWeight(w)
 
@@ -125,6 +130,8 @@ class HybridFP8UnquantLoader(WeightsLoader):
                 activation_scale_ub=self.activation_scale_ub,
                 dtype=weights.dtype,
             )
+        if self.to_fp8:
+            return Fp8Weight(weight=w, dtype=weights.dtype)
 
         return UnquantizedWeight(w)
 
@@ -186,8 +193,6 @@ class Fp8Linear(torch.nn.Module):
 
     def forward(self, input: torch.Tensor) -> torch.Tensor:
         if FBGEMM_MM_AVAILABLE:
-            log_once(logger.info, "Using FBGEMM fp8 kernels")
-
             qinput, scale = fp8_quantize(
                 input, scale_upper_bound=self.scale_upper_bound
             )

server/text_generation_server/models/custom_modeling/flash_llama_modeling.py

@@ -33,7 +33,6 @@ from text_generation_server.layers.attention import (
     attention,
     reshape_and_cache,
 )
-from text_generation_server.models.globals import FLASH_DECODING
 from text_generation_server.layers import (
     TensorParallelRowLinear,
     TensorParallelColumnLinear,
@@ -42,16 +41,15 @@ from text_generation_server.layers import (
     TensorParallelMultiAdapterLinear,
     TensorParallelAdapterRowLinear,
 )
-from text_generation_server.layers.fp8 import Fp8Weight
 from text_generation_server.layers.rotary import PositionRotaryEmbedding
 from text_generation_server.layers.layernorm import (
     FastRMSNorm,
 )
 from text_generation_server.utils.weights import (
-    DefaultWeightsLoader,
     UnquantizedWeight,
     Weights,
 )
+from text_generation_server.layers.fp8 import HybridFP8UnquantLoader
 
 if SYSTEM == "rocm":
     try:
@@ -113,12 +111,12 @@ def load_attention(config, prefix: str, weights, layer_id):
 
 @contextmanager
 def no_fp8(weights: Weights):
+    """De-activate fp8 auto conversion for the duration of this context manager"""
     weights_loader = weights.weights_loader
-    if (
-        isinstance(weights_loader, DefaultWeightsLoader)
-        and weights_loader.weight_class is Fp8Weight
-    ):
-        weights_loader = DefaultWeightsLoader(UnquantizedWeight)
+    if isinstance(weights_loader, HybridFP8UnquantLoader) and weights_loader.to_fp8:
+        weights_loader = HybridFP8UnquantLoader(
+            weights_loader.activation_scale_ub, to_fp8=False
+        )
 
     with weights.use_loader(weights_loader):
         yield

server/text_generation_server/utils/quantization.py

@@ -172,6 +172,6 @@ def get_loader(
         if isinstance(quantizer_config, _FP8QuantizerConfig):
             activation_scale_ub = quantizer_config.activation_scale_ub
 
-        return HybridFP8UnquantLoader(activation_scale_ub)
+        return HybridFP8UnquantLoader(activation_scale_ub, to_fp8=quantize == "fp8")
     else:
         raise ValueError(f"Unknown quantization method: {quantize}")