Naive Scaling

neural_compressor/torch/algorithms/fp8_quant/_core/fp_utils.py

@@ -17,6 +17,9 @@
 from neural_compressor.torch.utils.auto_accelerator import auto_detect_accelerator, INCAcceleratorType
 cur_accelerator = auto_detect_accelerator()
 
+from neural_compressor.torch.utils import environ
+from neural_compressor.common.utils import logger
+
 descale_fcn = lambda x, scale: torch.mul(x, scale)
 scale_fcn = lambda x, scale: torch.div(x, scale)
 cast_fcn = lambda x, dtype: x.to(dtype=dtype)
@@ -106,6 +109,9 @@ def get_fp8_hw_alligned_scales(dtype, device):
 }
 
 def calc_maxabs_scale(xmaxabs, fullscale, backoff=1):
+    if environ.INC_FORCE_NAIVE_SCALING:
+        backoff = 1.0
+        logger.warning_once(f"Enabled naive scaling, backoff is set to {backoff}")
     scale = xmaxabs / (fullscale * backoff)
     return scale
 

neural_compressor/torch/algorithms/fp8_quant/_core/scale_methods/scale_method_factory.py

@@ -16,7 +16,8 @@
 from .round_scales_function import *
 from ..common import get_device_type_for_scales
 from .scales_method import *
-
+from neural_compressor.torch.utils import environ
+from neural_compressor.common.utils import logger
 
 class QuantTensorName(Enum):
     INPUT = auto()
@@ -40,6 +41,9 @@ class ScaleValueType(Enum):
 
 def parse_rounding_method(config, device_for_scales):
     round_method = ScaleIdentity()
+    if environ.INC_FORCE_NAIVE_SCALING:
+        logger.warning_once("Enabled naive scaling")
+        return round_method
     if "single" in config and "hw" in config:
         round_method = ScaleHwAlignedFixed(device_for_scales)
     elif "unit" in config:

neural_compressor/torch/utils/environ.py

@@ -32,6 +32,10 @@
     world_size = int(os.getenv("WORLD_SIZE", "-1"))
 
 
+
+INC_FORCE_NAIVE_SCALING = os.getenv("INC_FORCE_NAIVE_SCALING", "0").lower() in ["1", "true"]
+
+
 ################ Check imported sys.module first to decide behavior #################
 def is_ipex_imported() -> bool:
     """Check whether intel_extension_for_pytorch is imported."""