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[Misc] Support FP8 kv cache scales from compressed-tensors (#6528)

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This commit is contained in:
Michael Goin
2024-07-23 00:11:50 -04:00
committed by GitHub
parent e519ae097a
commit 9e0b558a09
7 changed files with 186 additions and 75 deletions
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63
vllm/model_executor/layers/quantization/fp8.py
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@@ -11,6 +11,7 @@ from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase,
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 @@ class Fp8MoEMethod(FusedMoEMethodBase):
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)