cleanup, logging

Signed-off-by: Kyle Sayers <[email protected]>

Author: kylesayrs

src/compressed_tensors/quantization/lifecycle/initialize.py

@@ -104,7 +104,7 @@ def initialize_module_for_quantization(
         if scheme.input_activations is not None:
             base_name = "input"
             args = scheme.input_activations
-            observed_shape = (1, weight.size(-1))
+            observed_shape = (1, weight.shape[-1])
             observed_dtype = weight.dtype
 
         if scheme.weights is not None:
@@ -185,7 +185,7 @@ def _initialize_scale_zero_point(
         if len(observed_shape) < 1:
             raise ValueError("Channel quant requires at least 1 observed dimension")
 
-        expected_shape = (observed_shape[-1], 1)
+        expected_shape = (observed_shape[-2], 1)
 
     elif strategy in (QuantizationStrategy.GROUP, QuantizationStrategy.TENSOR_GROUP):
         assert quantization_args.group_size is not None

src/compressed_tensors/quantization/quant_scheme.py

@@ -24,6 +24,7 @@
     QuantizationType,
 )
 from pydantic import BaseModel, ConfigDict, model_validator
+from loguru import logger
 
 
 __all__ = [
@@ -60,15 +61,19 @@ def validate_model_after(model: "QuantizationScheme") -> "QuantizationScheme":
         format = model.format
 
         if inputs is not None:
-            if inputs.strategy not in (
-                QuantizationStrategy.TOKEN,
-                QuantizationStrategy.TENSOR,
-                QuantizationStrategy.GROUP,
-                QuantizationStrategy.TENSOR_GROUP,
-            ):
-                raise NotImplementedError(
-                    f"Using {inputs.strategy} strategy is not supported for "
-                    "activation quantization"
+            if inputs.strategy == QuantizationStrategy.CHANNEL:
+                raise ValueError(
+                    "Channel-wise activation quantization is equivalent to "
+                    "tensor/token-wise activation quantization, please use one of "
+                    "those. If you mean to quantize each activation value "
+                    "individually, please use group quantization with `group_size = 1`"
+                )
+
+            if inputs.strategy == QuantizationStrategy.BLOCK:
+                raise ValueError(
+                    "Block-wise activation quantization is not supported. If you mean "
+                    "to quantize each activation value individually, please use group "
+                    "quantization with `group_size = 1`"
                 )
 
             if inputs.actorder is not None: