[Misc] Support FP8 kv cache scales from compressed-tensors

tests/quantization/test_compressed_tensors.py

@@ -150,3 +150,10 @@ def test_compressed_tensors_fp8(vllm_runner):
 
         output = llm.generate_greedy("Hello my name is", max_tokens=20)
         assert output
+
+
+def test_compressed_tensors_kv_cache(vllm_runner):
+    model_path = "nm-testing/TinyLlama-1.1B-compressed-tensors-kv-cache-scheme"
+    with vllm_runner(model_path, kv_cache_dtype="fp8") as llm:
+        output = llm.generate_greedy("Hello world!", max_tokens=20)
+        assert output

vllm/attention/layer.py

@@ -9,7 +9,7 @@
 from vllm.config import CacheConfig
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
-from vllm.model_executor.layers.quantization.fp8 import Fp8KVCacheMethod
+from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
 
 
 class Attention(nn.Module):
@@ -59,19 +59,18 @@ def __init__(
         quant_method = quant_config.get_quant_method(
             self, prefix=prefix) if quant_config else None
         if quant_method is not None:
-            assert isinstance(quant_method, Fp8KVCacheMethod)
+            assert isinstance(quant_method, BaseKVCacheMethod)
             # TODO (mgoin): kv cache dtype should be specified in the FP8
             # checkpoint config and become the "auto" behavior
-            if "fp8" in self.kv_cache_dtype:
-                if self.kv_cache_dtype == "fp8_e5m2":
-                    raise ValueError("fp8_e5m2 kv-cache is not supported with "
-                                     "fp8 checkpoints.")
-                # When FP8 quantization is enabled, we make a parameter
-                # "kv_scale" so that it can be loaded from FP8 checkpoint.
-                # The k/v_scale will then be converted back to
-                # self._kv_scale in a native float32 value after weight loading
-                self.quant_method = quant_method
-                self.quant_method.create_weights(self)
+            if self.kv_cache_dtype == "fp8_e5m2":
+                raise ValueError("fp8_e5m2 kv-cache is not supported with "
+                                 "fp8 checkpoints.")
+            # If quantization is enabled, we make "k_scale" and "v_scale"
+            # parameters so that it can be loaded from the model checkpoint.
+            # The k/v_scale will then be converted back to native float32
+            # values after weight loading.
+            self.quant_method = quant_method
+            self.quant_method.create_weights(self)
 
         # During model initialization, the default dtype is set as the model
         # weight and activation dtype.

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

@@ -5,7 +5,7 @@
 
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
 from vllm.model_executor.layers.quantization.base_config import (  # noqa: E501
-    QuantizationConfig)
+    QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     W4A16SPARSE24_SUPPORTED_BITS, WNA16_SUPPORTED_BITS,
     CompressedTensorsScheme, CompressedTensorsUnquantized,
@@ -15,18 +15,23 @@
     CompressionFormat, QuantizationArgs, QuantizationStrategy,
     QuantizationType, find_matched_target, is_activation_quantization_format,
     should_ignore_layer)
+from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
 from vllm.platforms import current_platform
 
 
 class CompressedTensorsConfig(QuantizationConfig):
 
-    def __init__(self, target_scheme_map: Dict[str, Any], ignore: List[str],
-                 quant_format: str):
+    def __init__(self,
+                 target_scheme_map: Dict[str, Any],
+                 ignore: List[str],
+                 quant_format: str,
+                 kv_cache_scheme: Optional[Dict[str, Any]] = None):
 
         self.ignore = ignore
         self.quant_format = quant_format
         # Map from [target -> scheme]
         self.target_scheme_map = target_scheme_map
+        self.kv_cache_scheme = kv_cache_scheme
 
     def get_linear_method(self) -> "CompressedTensorsLinearMethod":
         return CompressedTensorsLinearMethod(self)
@@ -50,9 +55,12 @@ def get_quant_method(
         self,
         layer: torch.nn.Module,
         prefix: str,
-    ) -> Optional["CompressedTensorsLinearMethod"]:
+    ) -> Optional["QuantizeMethodBase"]:
+        from vllm.attention.layer import Attention  # Avoid circular import
         if isinstance(layer, LinearBase):
             return CompressedTensorsLinearMethod(self)
+        if isinstance(layer, Attention):
+            return CompressedTensorsKVCacheMethod(self)
         return None
 
     @classmethod
@@ -85,7 +93,8 @@ def from_config(cls, config: Dict[str, Any]) -> "CompressedTensorsConfig":
 
         return cls(target_scheme_map=target_scheme_map,
                    ignore=ignore,
-                   quant_format=quant_format)
+                   quant_format=quant_format,
+                   kv_cache_scheme=config.get("kv_cache_scheme"))
 
     @classmethod
     def get_config_filenames(cls) -> List[str]:
@@ -309,3 +318,47 @@ def apply(self,
         if scheme is None:
             raise ValueError("A scheme must be defined for each layer")
         return scheme.apply_weights(layer, x, bias=bias)
+
+
+class CompressedTensorsKVCacheMethod(BaseKVCacheMethod):
+    """
+    Supports loading kv-cache scaling factors from compressed-tensors
+    checkpoints.
+    """
+
+    def __init__(self, quant_config: CompressedTensorsConfig):
+        self.validate_kv_cache_scheme(quant_config.kv_cache_scheme)
+        super().__init__(quant_config)
+
+    @staticmethod
+    def validate_kv_cache_scheme(kv_cache_scheme: Optional[Dict[str, Any]]):
+        """
+        Validator for the kv cache scheme. Useful for controlling the
+        kv cache quantization schemes, that are being supported in vLLM
+        :param kv_cache_scheme: the compressed-tensors kv cache scheme
+        """
+        if kv_cache_scheme is None:
+            return
+
+        type_ = kv_cache_scheme.get("type")
+        num_bits = kv_cache_scheme.get("num_bits")
+
+        if type_ != "float" and num_bits != 8:
+            raise NotImplementedError(
+                "Currently supported kv cache quantization is "
+                "num_bits=8, type=float, however "
+                f"received num_bits={num_bits}, type={type_}")
+
+        strategy = kv_cache_scheme.get("strategy")
+        if strategy != "tensor":
+            raise NotImplementedError(
+                "Only support per-tensor scaling factor "
+                "for compressed-tensors KV cache. "
+                f"Expected strategy: tensor, found strategy: {strategy}")
+
+        is_symmetric = kv_cache_scheme.get("symmetric")
+        if not is_symmetric:
+            raise NotImplementedError(
+                "Only support symmetric scaling factor "
+                "for compressed-tensors KV cache. "
+                f"However found symmetric: {is_symmetric}")

vllm/model_executor/layers/quantization/compressed_tensors/utils.py

@@ -209,6 +209,23 @@ def _find_first_match(value: str,
     return None
 
 
+def get_compressed_tensors_cache_scale(name: str) -> Optional[str]:
+    """
+    Check whether the param name matches the format for k/v cache scales
+    in compressed-tensors. If this is the case, return its equivalent
+    param name expected by vLLM
+
+    :param name: param name
+    :return: matching param name for KV cache scale in vLLM
+    """
+    if name.endswith(".output_scale") and ".k_proj" in name:
+        return name.replace(".k_proj.output_scale", ".attn.k_scale")
+    if name.endswith(".output_scale") and ".v_proj" in name:
+        return name.replace(".v_proj.output_scale", ".attn.v_scale")
+    # If no matches, return None
+    return None
+
+
 def _is_equal_or_regex_match(value: str,
                              target: str,
                              check_contains: bool = False) -> bool:

vllm/model_executor/layers/quantization/fp8.py

@@ -11,6 +11,7 @@
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
 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.quantization.utils.marlin_utils_fp8 import (
     apply_fp8_marlin_linear, prepare_fp8_layer_for_marlin)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
@@ -400,64 +401,10 @@ def apply(self,
                          topk_group=topk_group)
 
 
-class Fp8KVCacheMethod(QuantizeMethodBase):
-    """Supports loading kv-cache scaling factors from FP8 checkpoints.
+class Fp8KVCacheMethod(BaseKVCacheMethod):
+    """
+    Supports loading kv-cache scaling factors from FP8 checkpoints.
     """
 
     def __init__(self, quant_config: Fp8Config):
-        self.quant_config = quant_config
-
-    def create_weights(self, layer: torch.nn.Module):
-        """Create "weight" (aka k_scale and v_scale) for an attention layer.
-
-        Args:
-            layer: The layer that is using the QuantizeMethodBase factory.
-        """
-        # Initialize the KV cache scales to -1.0, which is an invalid value.
-        # If the k/v_scale appears in the checkpoint, it will be
-        # overwritten when loading weights.
-        layer.k_scale = Parameter(torch.tensor(-1.0), requires_grad=False)
-        layer.v_scale = Parameter(torch.tensor(-1.0), requires_grad=False)
-
-    def apply(self, layer: torch.nn.Module) -> torch.Tensor:
-        raise RuntimeError("Fp8KVCacheMethod.apply should not be called.")
-
-    def process_weights_after_loading(self, layer: Module) -> None:
-        # If the kv-cache dtype is auto, we enforce the k/v_scale to be 1.0
-        # regardless whether the kv-scale is available in the checkpoint.
-        if layer.kv_cache_dtype != "auto":
-            if layer.k_scale > 0.0 and layer.v_scale > 0.0:
-                # We prefer to use separate k_scale and v_scale if present
-                k_scale = layer.k_scale.to("cpu").tolist()
-                v_scale = layer.v_scale.to("cpu").tolist()
-            elif layer.k_scale < 0.0 and layer.v_scale < 0.0:
-                # If no scales were loaded (both scales are invalid negative
-                # values), use the default value of 1.0
-                k_scale = Parameter(torch.tensor(1.0), requires_grad=False)
-                v_scale = Parameter(torch.tensor(1.0), requires_grad=False)
-            else:
-                # If we find a single kv_scale in the checkpoint, we remap
-                # kv_scale to k_scale during weight loading, and duplicate
-                # k_scale to v_scale here
-                assert layer.k_scale > 0.0
-                scale_to_duplicate = max(layer.k_scale, layer.v_scale)
-                k_scale = scale_to_duplicate.to("cpu").tolist()
-                v_scale = scale_to_duplicate.to("cpu").tolist()
-
-            if not isinstance(k_scale, float) or not isinstance(
-                    v_scale, float):
-                raise ValueError("Only support per-tensor scaling factor "
-                                 "for fp8 KV cache")
-
-            # These are used in the final Attention.forward()
-            layer._k_scale = k_scale
-            layer._v_scale = v_scale
-            if (layer._k_scale == 1.0 and layer._v_scale == 1.0
-                    and "e5m2" not in layer.kv_cache_dtype):
-                print_warning_once(
-                    "Using KV cache scaling factor 1.0 for fp8_e4m3. This "
-                    "may cause accuracy issues. Please make sure k/v_scale "
-                    "scaling factors are available in the fp8 checkpoint.")
-
-        del layer.k_scale
-        del layer.v_scale
+        super().__init__(quant_config)

vllm/model_executor/layers/quantization/kv_cache.py

@@ -0,0 +1,78 @@
+import torch
+
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.utils import print_warning_once
+
+
+class BaseKVCacheMethod(QuantizeMethodBase):
+    """
+    Quant method that adds `_k_scale` and `_v_scale` attributes to the
+    Attention layer to support loading those scaling factors from checkpoints. 
+    The k/v_scale will be used to:
+        - quantize k/v_cache entries before saving them to the cache
+        - dequantize k/v_cache entries before fetching them from the cache
+
+    :param quant_config: the appropriate QuantizationConfig 
+    """
+
+    def __init__(self, quant_config: QuantizationConfig):
+        self.quant_config = quant_config
+
+    def create_weights(self, layer: torch.nn.Module):
+        """
+        Create "weight" (aka k_scale and v_scale) for an attention layer.
+        """
+        # Initialize the KV cache scales to -1.0, which is an invalid value.
+        # If the k/v_scale appears in the checkpoint, it will be
+        # overwritten when loading weights.
+        layer.k_scale = torch.nn.Parameter(torch.tensor(-1.0),
+                                           requires_grad=False)
+        layer.v_scale = torch.nn.Parameter(torch.tensor(-1.0),
+                                           requires_grad=False)
+
+    def apply(self, layer: torch.nn.Module) -> torch.Tensor:
+        raise RuntimeError(
+            f"{self.__class__.__name__}.apply should not be called.")
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        # If the kv-cache dtype is auto, we enforce the k/v_scale to be 1.0
+        # regardless whether the kv-scale is available in the checkpoint.
+        if layer.kv_cache_dtype != "auto":
+            if layer.k_scale > 0.0 and layer.v_scale > 0.0:
+                # We prefer to use separate k_scale and v_scale if present
+                k_scale = layer.k_scale.to("cpu").tolist()
+                v_scale = layer.v_scale.to("cpu").tolist()
+            elif layer.k_scale < 0.0 and layer.v_scale < 0.0:
+                # If no scales were loaded (both scales are invalid negative
+                # values), use the default value of 1.0
+                k_scale = torch.nn.Parameter(torch.tensor(1.0),
+                                             requires_grad=False)
+                v_scale = torch.nn.Parameter(torch.tensor(1.0),
+                                             requires_grad=False)
+            else:
+                # If we find a single kv_scale in the checkpoint, we remap
+                # kv_scale to k_scale during weight loading, and duplicate
+                # k_scale to v_scale here
+                assert layer.k_scale > 0.0
+                scale_to_duplicate = max(layer.k_scale, layer.v_scale)
+                k_scale = scale_to_duplicate.to("cpu").tolist()
+                v_scale = scale_to_duplicate.to("cpu").tolist()
+
+            if not isinstance(k_scale, float) or not isinstance(
+                    v_scale, float):
+                raise ValueError("Only support per-tensor scaling factor "
+                                 "for fp8 KV cache")
+
+            # These are used in the final Attention.forward()
+            layer._k_scale = k_scale
+            layer._v_scale = v_scale
+            if (layer._k_scale == 1.0 and layer._v_scale == 1.0
+                    and "e5m2" not in layer.kv_cache_dtype):
+                print_warning_once(
+                    "Using KV cache scaling factor 1.0 for fp8_e4m3. This "
+                    "may cause accuracy issues. Please make sure k/v_scale "
+                    "scaling factors are available in the fp8 checkpoint.")
+
+        del layer.k_scale
+        del layer.v_scale

vllm/model_executor/models/llama.py

@@ -39,6 +39,8 @@
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
 from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig)
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    get_compressed_tensors_cache_scale)
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.sampler import Sampler
 from vllm.model_executor.layers.vocab_parallel_embedding import (
@@ -467,6 +469,14 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
                 # Models trained using ColossalAI may include these tensors in
                 # the checkpoint. Skip them.
                 continue
+            if scale_name := get_compressed_tensors_cache_scale(name):
+                # Loading kv cache scales for compressed-tensors quantization
+                param = params_dict[scale_name]
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                loaded_weight = loaded_weight[0]
+                weight_loader(param, loaded_weight)
+                continue
             for (param_name, weight_name, shard_id) in stacked_params_mapping:
                 if weight_name not in name:
                     continue