[ExecuTorch] XNNPACK: prefer qc over qb when gs == k for non-int4

Pull Request resolved: #14173

* Prefer chanelwise over groupwise when possible for perf and for int8 which doesn't have groupwise support
* Fix bug / improve behavior for affine q/dq with gs == k for per_channel
* refactor is_per_channel_group state variable
* add QuantParams.__str__()

TODO - improve affine quant primitives - T237476295
ghstack-source-id: 309177704
@exported-using-ghexport

Differential Revision: [D82060758](https://our.internmc.facebook.com/intern/diff/D82060758/)

**NOTE FOR REVIEWERS**: This PR has internal Meta-specific changes or comments, please review them on [Phabricator](https://our.internmc.facebook.com/intern/diff/D82060758/)!

Author: digantdesai

backends/xnnpack/operators/node_visitor.py

@@ -232,7 +232,7 @@ def get_per_channel_dtype(
             if quant_params.dtype == torch.int32:
                 return XNNDatatype.xnn_datatype_qcint32
             elif quant_params.dtype == torch.int8:
-                if quant_params.is_per_channel_group:
+                if quant_params.per_channel_group:
                     # 4-bit per channel group quantized weights
                     # No 8-bit support yet
                     assert (
@@ -282,7 +282,7 @@ def get_quant_params(
             buffer_idx = len(xnn_graph.constant_data)
             num_scales = scale.numel()
 
-            if quant_params.is_per_channel_group:
+            if quant_params.per_channel_group:
                 scale = scale.to(torch.bfloat16)
 
             num_bytes = scale.untyped_storage().nbytes()
@@ -300,7 +300,7 @@ def get_quant_params(
                 scale_name, bytes(scale_array), CONSTANT_TENSOR_ALIGNMENT
             )
 
-            if quant_params.is_per_channel_group:
+            if quant_params.per_channel_group:
                 return PerChannelGroupQuant(
                     scale=[],
                     channel_dim=quant_params.axis,
@@ -335,7 +335,7 @@ def _check_per_channel_group_params(
     ) -> None:
         # Make sure things are lining up for per_channel_group quantization case
         # Has to be done this late because we don't have clean access to the actual tensor
-        assert quant_params.is_per_channel_group, "Not per_channel_group quantization"
+        assert quant_params.per_channel_group, "Not per_channel_group quantization"
         # linear weights will be in [oc, ic]. And per_channel quantization must be on axis 0
         num_groups = cast(torch.Tensor, quant_params.scale).shape[1]
         assert (

backends/xnnpack/operators/quant_params.py

@@ -89,19 +89,39 @@ def __init__(
         # Groupwise quantization for weight
         self.per_channel_group = False
         self.group_size = group_size
+
+        tensor = q_input.meta["val"]
+
         if self.group_size > 0:
             assert (
                 self.per_channel is True
             ), "Only per channel quantization supports groupwise quantization"
             assert (
                 cast(torch.Tensor, scale).ndim == 2
             ), "Scale must be 2D for per channel groupwise quant"
-            self.per_channel_group = True
-            assert group_size > 0, "Group size must be greater than 0"
-        self.is_per_channel_group = self.per_channel and self.group_size > 0
+            # Assumed scale shape - [out_channels, in_channels/group_size]
+            input_channels = cast(torch.Tensor, scale).shape[1] * self.group_size
+            # 2d weight tensor shape - [out_channels, in_channels]
+            assert (
+                tensor.shape[1] == input_channels
+            ), "Invalid input channels for groupwise quant"
+            # Prefer per_channel over per_channel_group when group_size == input_channels for non int4 cases only
+            # int4 case need more fixes to map qb4w to qc4w. Incorrect scales being passed down to xnnpack.
+            self.per_channel_group = (
+                self.group_size <= input_channels
+                if self.is_qc4w
+                else self.group_size < input_channels
+            )
+
+            if not self.per_channel_group:
+                if cast(torch.Tensor, scale).ndim == 2:
+                    # TODO: don't reshape scale for per_channel cases
+                    assert (
+                        cast(torch.Tensor, scale).shape[1] == 1
+                    ), "Invalid scale shape for per channel quantization"
+                    scale = cast(torch.Tensor, scale).squeeze(1)
 
-        if per_channel and not self.is_per_channel_group:
-            tensor = q_input.meta["val"]
+        if per_channel and not self.per_channel_group:
             assert (
                 tensor.shape[self.axis] == cast(torch.Tensor, self.scale).shape[0]
             ), f"Invalid size of per channel quantization scales, axis: {self.axis}, scale size: {self.scale.shape}, tensor shape: {tensor.shape}"
@@ -110,6 +130,39 @@ def __init__(
                 tensor.shape[self.axis] == cast(torch.Tensor, self.zp).shape[0]
             ), f"Invalid size of per channel quantization zero-points, axis: {self.axis}, zp size: {self.zp.shape}, tensor shape: {tensor.shape}"
 
+    def __str__(self) -> str:
+        """String representation of QuantParams for debugging and logging."""
+        assert isinstance(self.scale, float) or isinstance(self.scale, torch.Tensor)
+        scale_str = (
+            f"{self.scale}"
+            if isinstance(self.scale, float)
+            else f"tensor{tuple(self.scale.shape)}"
+        )
+        assert isinstance(self.zp, float) or isinstance(self.zp, torch.Tensor)
+        zp_str = (
+            f"{self.zp}"
+            if isinstance(self.zp, float)
+            else f"tensor{tuple(self.zp.shape)}"
+        )
+
+        return (
+            f"QuantParams("
+            f"per_channel={self.per_channel}, "
+            f"per_channel_group={self.per_channel_group}, "
+            f"scale={scale_str}, "
+            f"zp={zp_str}, "
+            f"axis={self.axis}, "
+            f"dtype={self.dtype}, "
+            f"qmin={self.qmin}, "
+            f"qmax={self.qmax}, "
+            f"is_dynamic={self.is_dynamic}, "
+            f"is_input={self.is_input}, "
+            f"is_output={self.is_output}, "
+            f"group_size={self.group_size}, "
+            f"is_qc4w={self.is_qc4w}"
+            f")"
+        )
+
     def quantize_tensor(self, tensor: torch.Tensor) -> torch.Tensor:
         # Do nothing if already quantized by the Quantizer
         if tensor.dtype == self.dtype: