[Bugfix] Update expected shape for per token strategy

src/compressed_tensors/quantization/lifecycle/initialize.py

@@ -174,7 +174,10 @@ def _initialize_scale_zero_point(
         device = get_execution_device(module)
 
     # infer expected scale/zero point shape
-    expected_shape = 1  # per tensor
+    if quantization_args.strategy == QuantizationStrategy.TOKEN:
+        expected_shape = (1, 1)
+    else:
+        expected_shape = 1
 
     if base_name == "weight" and weight_shape is not None:
         if quantization_args.strategy == QuantizationStrategy.CHANNEL:

tests/conftest.py

@@ -44,8 +44,6 @@ def update_scale_zp(module: torch.nn.Module, base_name: str, value: torch.Tensor
         min_val = torch.amin(value, dim=dim, keepdims=True)
         max_val = torch.amax(value, dim=dim, keepdims=True)
         scale, zp = calculate_qparams(min_val, max_val, args)
-        scale = scale.reshape((1, 1))
-        zp = zp.reshape((1, 1))
         update_parameter_data(module, scale, f"{base_name}_scale")
         update_parameter_data(module, zp, f"{base_name}_zero_point")
 

tests/test_quantization/lifecycle/test_initialize.py

@@ -14,15 +14,25 @@
 
 
 import pytest
+from compressed_tensors.quantization import (
+    ActivationOrdering,
+    QuantizationArgs,
+    QuantizationScheme,
+    QuantizationStatus,
+    QuantizationStrategy,
+)
 from compressed_tensors.quantization.lifecycle.initialize import (
     initialize_module_for_quantization,
 )
-from compressed_tensors.quantization.quant_args import QuantizationArgs
-from compressed_tensors.quantization.quant_config import QuantizationStatus
 from torch.nn import Linear
 
 
 NUM_BITS = 8
+Q_PARAM_NAMES = {
+    "input_activations": "input",
+    "weights": "weight",
+    "output_activations": "output",
+}
 
 
 @pytest.mark.parametrize(
@@ -77,3 +87,78 @@ def test_initialize_module_for_quantization(
     assert hasattr(layer, "quantization_status")
 
     assert layer.quantization_status == QuantizationStatus.INITIALIZED
+
+
+@pytest.mark.parametrize(
+    "weights,input_activations",
+    [
+        (
+            QuantizationArgs(strategy="tensor"),
+            QuantizationArgs(strategy="tensor"),
+        ),
+        (
+            QuantizationArgs(strategy="channel"),
+            None,
+        ),
+        (
+            QuantizationArgs(strategy="group", group_size=2),
+            None,
+        ),
+        (
+            QuantizationArgs(strategy="group", group_size=2, actorder="group"),
+            None,
+        ),
+        (
+            QuantizationArgs(strategy="group", group_size=2, actorder="weight"),
+            None,
+        ),
+        (
+            QuantizationArgs(strategy="block"),
+            QuantizationArgs(strategy="block"),
+        ),
+        (
+            QuantizationArgs(strategy="token"),
+            QuantizationArgs(strategy="token"),
+        ),
+    ],
+)
+def test_initialize_quantization_parameters(weights, input_activations):
+    quantization_scheme = QuantizationScheme(
+        targets=["*"],
+        weights=weights,
+        input_activations=input_activations,
+    )
+    layer = Linear(7, 8)
+    initialize_module_for_quantization(layer, quantization_scheme)
+
+    for q_type in ("input_activations", "weights"):
+        args = getattr(quantization_scheme, q_type)
+        if args is None:
+            continue
+        q_param_name = Q_PARAM_NAMES[q_type]
+
+        # scale and zero point
+        if args.strategy == QuantizationStrategy.TENSOR:
+            expected_shape = (1,)
+
+        elif args.strategy == QuantizationStrategy.CHANNEL:  # only weight
+            expected_shape = (layer.weight.shape[0], 1)
+
+        elif args.strategy == QuantizationStrategy.GROUP:  # only weight
+            num_groups = layer.weight.shape[1] // args.group_size
+            expected_shape = (layer.weight.shape[0], max(num_groups, 1))
+
+        elif args.strategy == QuantizationStrategy.BLOCK:
+            expected_shape = (1,)
+
+        elif args.strategy == QuantizationStrategy.TOKEN:
+            expected_shape = (1, 1)
+
+        assert getattr(layer, f"{q_param_name}_scale").shape == expected_shape
+        assert getattr(layer, f"{q_param_name}_zero_point").shape == expected_shape
+
+        # g_idx
+        if args.actorder == ActivationOrdering.GROUP:
+            assert getattr(layer, f"{q_param_name}_g_idx").shape == (
+                layer.weight.shape[1],
+            )

tests/test_quantization/test_configs/test_strategies.py

@@ -67,8 +67,8 @@ def test_channelwise(
     if input_symmetry is not None:
         mock_per_channel_calibration(model, base_name="input", value=inputs)
 
-    assert list(model.weight_scale.shape) == [model_shape[1], 1]
-    assert list(model.weight_zero_point.shape) == [model_shape[1], 1]
+    assert model.weight_scale.shape == (model_shape[1], 1)
+    assert model.weight_zero_point.shape == (model_shape[1], 1)
 
 
 @torch.no_grad
@@ -97,14 +97,14 @@ def test_group(
             model, base_name="input", value=inputs, group_size=group_size
         )
 
-    assert list(model.weight_scale.shape) == [
+    assert model.weight_scale.shape == (
         model_shape[1],
         int(model_shape[0] / group_size),
-    ]
-    assert list(model.weight_zero_point.shape) == [
+    )
+    assert model.weight_zero_point.shape == (
         model_shape[1],
         int(model_shape[0] / group_size),
-    ]
+    )
 
 
 @torch.no_grad
@@ -131,8 +131,8 @@ def test_token(
     mock_per_channel_calibration(model, base_name="weight", value=model.weight)
     mock_per_token_calibration(model, base_name="input", value=inputs)
 
-    assert list(model.input_scale.shape) == [1, 1]
-    assert list(model.input_zero_point.shape) == [1, 1]
+    assert model.input_scale.shape == (1, 1)
+    assert model.input_zero_point.shape == (1, 1)
 
-    assert list(model.weight_scale.shape) == [256, 1]
-    assert list(model.weight_zero_point.shape) == [256, 1]
+    assert model.weight_scale.shape == (256, 1)
+    assert model.weight_zero_point.shape == (256, 1)

tests/test_quantization/test_utils/test_helpers.py

@@ -0,0 +1,58 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pytest
+import torch
+from compressed_tensors.quantization import QuantizationArgs, QuantizationStrategy
+from compressed_tensors.quantization.utils import calculate_qparams
+
+
+@pytest.mark.parametrize(
+    "keepdims,strategy,exp_shape",
+    [
+        (
+            False,
+            QuantizationStrategy.TENSOR,
+            torch.Size(
+                [
+                    1,
+                ]
+            ),
+        ),
+        (True, QuantizationStrategy.CHANNEL, torch.Size([1, 1])),
+        (True, QuantizationStrategy.GROUP, torch.Size([1, 1])),
+        (
+            False,
+            QuantizationStrategy.BLOCK,
+            torch.Size(
+                [
+                    1,
+                ]
+            ),
+        ),
+        (True, QuantizationStrategy.TOKEN, torch.Size([1, 1])),
+    ],
+)
+def test_calculate_qparams(keepdims, strategy, exp_shape):
+    value = torch.randn(14, 5)
+    min_val = torch.amin(value, dim=tuple(), keepdims=keepdims)
+    max_val = torch.amax(value, dim=tuple(), keepdims=keepdims)
+
+    if strategy == QuantizationStrategy.GROUP:
+        args = QuantizationArgs(strategy=strategy, group_size=2)
+    else:
+        args = QuantizationArgs(strategy=strategy)
+        scale, zp = calculate_qparams(min_val, max_val, args)
+        assert scale.shape == exp_shape
+        assert zp.shape == exp_shape