[PTQ][Experimental stats] MAD and Percentile aggregators accept dynamic tensors

nncf/common/tensor_statistics/collectors.py

@@ -326,6 +326,42 @@ def mean_per_channel(x: NNCFTensor, axis: int) -> NNCFTensor:
         :return: Reduced NNCFTensor.
         """
 
+    @staticmethod
+    @abstractmethod
+    def transpose(x: NNCFTensor, axes: Tuple[int, ...]) -> NNCFTensor:
+        """
+        Returns an array with axes transposed.
+
+        :param x: The input tensor.
+        :param axes: Tuple which contains a permutation of [0,1,…,N-1] where N is the number of axes of a.
+            The ith axis of the returned array will correspond to the axis numbered axes[i] of the input.
+        :return: x with its axes permuted.
+        """
+
+    @staticmethod
+    @abstractmethod
+    def reshape(x: NNCFTensor, shape: Tuple[int, ...]) -> NNCFTensor:
+        """
+        Gives a new shape to an array without changing its data.
+
+        :param x: The input tensor.
+        :param shape: New shape for the input tensor. The new shape should be compatible with the original shape.
+            One shape dimension can be -1. In this case, the value is inferred
+            from the length of the array and remaining dimensions.
+        :return: Reshaped x.
+        """
+
+    @staticmethod
+    @abstractmethod
+    def cat(x: List[NNCFTensor], axis: int) -> NNCFTensor:
+        """
+        Join a sequence of arrays along an existing axis.
+
+        :param x: The input tensor.
+        :param axis: The axis along which the arrays will be joined.
+        :return: The concatenated array.
+        """
+
     @staticmethod
     def logical_or(input_: NNCFTensor, other: NNCFTensor) -> NNCFTensor:
         """

nncf/experimental/common/tensor_statistics/collectors.py

@@ -739,20 +739,23 @@ def _register_reduced_input_impl(self, x: TensorType) -> None:
         return self._container.append(x)
 
     def _aggregate_impl(self) -> Dict[str, NNCFTensor]:
-        stacked_val = self._tensor_processor.stack(self._container)
+        stacked_val, shape_after_aggregation = _moveaxes_flatten_cat(
+            self._container, [x - 1 for x in self._aggregation_axes if x > 0], self._tensor_processor
+        )
 
         mask = self._tensor_processor.zero_elements(stacked_val)
-        median_per_ch = self._tensor_processor.masked_median(
-            stacked_val, mask=mask, axis=self._aggregation_axes, keepdims=True
-        )
+        median_per_ch = self._tensor_processor.masked_median(stacked_val, mask=mask, axis=0, keepdims=True)
 
         mad_values = self._tensor_processor.median(
             self._tensor_processor.abs(self._tensor_processor.sub(stacked_val, median_per_ch)),
-            axis=self._aggregation_axes,
-            keepdims=self._keepdims,
+            axis=0,
+            keepdims=False,
         )
-        if not self._keepdims:
-            median_per_ch = self._tensor_processor.squeeze(median_per_ch, self._aggregation_axes)
+        if self._keepdims:
+            median_per_ch = self._tensor_processor.reshape(median_per_ch, shape_after_aggregation)
+            mad_values = self._tensor_processor.reshape(mad_values, shape_after_aggregation)
+        else:
+            median_per_ch = self._tensor_processor.squeeze(median_per_ch, 0)
         return {
             MedianMADTensorStatistic.MEDIAN_VALUES_STAT: median_per_ch.tensor,
             MedianMADTensorStatistic.MAD_VALUES_STAT: mad_values.tensor,
@@ -777,17 +780,60 @@ def _register_reduced_input_impl(self, x: TensorType) -> None:
         return self._container.append(x)
 
     def _aggregate_impl(self) -> Dict[float, NNCFTensor]:
-        stacked_val = self._tensor_processor.stack(self._container)
+        stacked_val, shape_after_aggregation = _moveaxes_flatten_cat(
+            self._container, [x - 1 for x in self._aggregation_axes if x > 0], self._tensor_processor
+        )
 
         percentiles = self._tensor_processor.percentile(
-            stacked_val, self._percentiles_to_collect, axis=self._aggregation_axes, keepdims=self._keepdims
+            stacked_val, self._percentiles_to_collect, axis=0, keepdims=False
         )
         retval = {}
         for idx, percentile in enumerate(self._percentiles_to_collect):
-            retval[percentile] = percentiles[idx].tensor
+            value = percentiles[idx]
+            if self._keepdims:
+                value = self._tensor_processor.reshape(value, shape_after_aggregation)
+            retval[percentile] = value.tensor
         return retval
 
 
+def _moveaxes_flatten_cat(
+    tensor_list: List[NNCFTensor], aggregation_axes: Tuple[int, ...], tensor_processor: NNCFCollectorTensorProcessor
+) -> Tuple[NNCFTensor, Tuple[int, ...]]:
+    """
+    Moves aggregation axes to the begining of the tensor shape for each tensor from the list, flattens
+    and concatenates them in 0 dimension. Computes target shape for the processed tensor
+    after an aggregation function is applied to it. Target shape preserves original order
+    of dimensions and replaces aggregated dimensions by 1.
+
+    :param tensor_list: NNCFTensor list to process.
+    :param aggregation_axes: Aggregation axes to move, flatten and concatinate.
+    :param tensor_processor: Backed-specific tensor processor instance.
+    :return: Tuple of the processed tensor and
+        target shape for the processed tensor after an aggregation function is applied to it.
+    """
+    tensor_shape = list(tensor_list[0].shape)
+
+    # Transpose dims to move aggregation axes forward
+    transpose_dims = list(range(len(tensor_shape)))
+    for idx, axis in enumerate(aggregation_axes):
+        transpose_dims[axis], transpose_dims[idx] = transpose_dims[idx], transpose_dims[axis]
+
+    # Shape to flatten aggregation axes
+    reshape_shape = [
+        -1,
+    ] + [
+        tensor_shape[dim] for dim in transpose_dims
+    ][len(aggregation_axes) :]
+
+    reshaped_tensors = []
+    for tensor in tensor_list:
+        transposed_t = tensor_processor.transpose(tensor, transpose_dims)
+        reshaped_tensors.append(tensor_processor.reshape(transposed_t, reshape_shape))
+
+    shape_after_aggregation = tuple(1 if idx in aggregation_axes else dim for idx, dim in enumerate(tensor_shape))
+    return tensor_processor.cat(reshaped_tensors, axis=0), shape_after_aggregation
+
+
 AGGREGATORS_MAP = {
     AggregatorType.MIN: MinAggregator,
     AggregatorType.MAX: MaxAggregator,

nncf/onnx/statistics/collectors.py

@@ -139,6 +139,19 @@ def mean_per_channel(x: NNCFTensor, axis: int) -> NNCFTensor:
         t = x.reshape(x.shape[0], x.shape[1], -1)
         return ONNXNNCFTensor(np.mean(t, axis=(0, 2)))
 
+    @staticmethod
+    def transpose(x: NNCFTensor, axes: Tuple[int, ...]) -> NNCFTensor:
+        return ONNXNNCFTensor(np.transpose(x.tensor, axes))
+
+    @staticmethod
+    def reshape(x: NNCFTensor, shape: Tuple[int, ...]) -> NNCFTensor:
+        return ONNXNNCFTensor(np.reshape(x.tensor, shape))
+
+    @staticmethod
+    def cat(x: List[NNCFTensor], axis: int) -> NNCFTensor:
+        x = [t.tensor for t in x]
+        return ONNXNNCFTensor(np.concatenate(x, axis))
+
     @staticmethod
     def batch_mean(x: NNCFTensor) -> NNCFTensor:
         return ONNXNNCFTensor(np.mean(x.tensor, axis=0, keepdims=True))

nncf/openvino/statistics/collectors.py

@@ -117,6 +117,19 @@ def mean_per_channel(x: NNCFTensor, axis: int) -> NNCFTensor:
         t = x.reshape(x.shape[0], x.shape[1], -1)
         return OVNNCFTensor(np.mean(t, axis=(0, 2)))
 
+    @staticmethod
+    def transpose(x: NNCFTensor, axes: Tuple[int, ...]) -> NNCFTensor:
+        return OVNNCFTensor(np.transpose(x.tensor, axes))
+
+    @staticmethod
+    def reshape(x: NNCFTensor, shape: Tuple[int, ...]) -> NNCFTensor:
+        return OVNNCFTensor(np.reshape(x.tensor, shape))
+
+    @staticmethod
+    def cat(x: List[NNCFTensor], axis: int) -> NNCFTensor:
+        x = [t.tensor for t in x]
+        return OVNNCFTensor(np.concatenate(x, axis))
+
     @staticmethod
     def batch_mean(x: NNCFTensor) -> NNCFTensor:
         return OVNNCFTensor(np.mean(x.tensor, axis=0, keepdims=True))

nncf/tensorflow/tensor_statistics/collectors.py

@@ -128,6 +128,18 @@ def percentile(
     def mean_per_channel(x: NNCFTensor, axis: int) -> NNCFTensor:
         raise NotImplementedError()
 
+    @staticmethod
+    def transpose(x: NNCFTensor, axes: Tuple[int, ...]) -> NNCFTensor:
+        raise NotImplementedError()
+
+    @staticmethod
+    def reshape(x: NNCFTensor, shape: Tuple[int, ...]) -> NNCFTensor:
+        raise NotImplementedError()
+
+    @staticmethod
+    def cat(x: List[NNCFTensor], axis: int) -> NNCFTensor:
+        raise NotImplementedError()
+
     @staticmethod
     def sub(a: NNCFTensor, b: NNCFTensor) -> NNCFTensor:
         raise NotImplementedError()

nncf/torch/tensor_statistics/collectors.py

@@ -82,7 +82,7 @@ def median(x: NNCFTensor, axis: Union[int, Tuple[int, ...], List[int]], keepdims
             device = x.tensor.device
             result = torch.tensor(np.median(x.tensor.detach().cpu().numpy(), axis=axis, keepdims=keepdims))
             return PTNNCFTensor(result.type(x.tensor.dtype).to(device))
-        return PTNNCFTensor(torch.quantile(x.tensor, q=0.5, dim=axis, keepdim=keepdims).values)
+        return PTNNCFTensor(torch.quantile(x.tensor, q=0.5, dim=axis, keepdim=keepdims))
 
     @classmethod
     def masked_mean(
@@ -123,6 +123,19 @@ def mean_per_channel(x: NNCFTensor, axis: int) -> NNCFTensor:
     def batch_mean(x: NNCFTensor) -> NNCFTensor:
         return PTNNCFTensor(torch.mean(x.tensor, axis=0, keepdims=True))
 
+    @staticmethod
+    def transpose(x: NNCFTensor, axes: Tuple[int, ...]) -> NNCFTensor:
+        return PTNNCFTensor(torch.permute(x.tensor, axes))
+
+    @staticmethod
+    def reshape(x: NNCFTensor, shape: Tuple[int, ...]) -> NNCFTensor:
+        return PTNNCFTensor(torch.reshape(x.tensor, shape))
+
+    @staticmethod
+    def cat(x: List[NNCFTensor], axis: int) -> NNCFTensor:
+        x = [t.tensor for t in x]
+        return PTNNCFTensor(torch.cat(x, axis))
+
     @staticmethod
     def logical_or(input_: NNCFTensor, other: NNCFTensor) -> NNCFTensor:
         return PTNNCFTensor(torch.logical_or(input_.tensor, other.tensor))

tests/common/experimental/test_reducers_and_aggregators.py

@@ -326,6 +326,19 @@ def test_mean_median_agggregators(self, aggregator_cls, refs, tensor_processor,
 
         assert self.all_close(ret_val, self.cast_tensor(refs, Dtype.FLOAT))
 
+    @pytest.fixture(
+        name="aggregator_cls",
+        params=[
+            MedianAbsoluteDeviationAggregator,
+            partial(
+                PercentileAggregator,
+                percentiles_to_collect=[5, 10, 90, 95],
+            ),
+        ],
+    )
+    def aggregator_cls_fixture(self, request):
+        return request.param
+
     REF_MAD_PERCENTILE_REF_VALUES = {
         MedianAbsoluteDeviationAggregator: {
             None: {
@@ -363,20 +376,10 @@ def test_mean_median_agggregators(self, aggregator_cls, refs, tensor_processor,
         },
     }
 
-    @pytest.mark.parametrize(
-        "aggregator_cls",
-        [
-            MedianAbsoluteDeviationAggregator,
-            partial(
-                PercentileAggregator,
-                percentiles_to_collect=[5, 10, 90, 95],
-            ),
-        ],
-    )
     @pytest.mark.parametrize("aggregation_axes", [None, (0,), (0, 1)])
     def test_mad_percentile_aggregators(self, aggregator_cls, tensor_processor, aggregation_axes):
         aggregator = aggregator_cls(tensor_processor=tensor_processor, aggregation_axes=aggregation_axes)
-        input_ = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=np.float32)
+        input_ = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])
         for i in range(9):
             aggregator.register_reduced_input(self.get_nncf_tensor(input_ * i, Dtype.FLOAT))
 
@@ -386,6 +389,32 @@ def test_mad_percentile_aggregators(self, aggregator_cls, tensor_processor, aggr
         for k, v in ref_values.items():
             assert self.all_close(ret_val[k], self.cast_tensor(v, Dtype.FLOAT))
 
+    REF_MAD_PERCENTILE_REF_VALUES_DYNAMIC_TENSORS = {
+        MedianAbsoluteDeviationAggregator: {
+            "median_values": np.array([28.5, 35.5, 43.5]),
+            "mad_values": np.array([24.0, 24.0, 24.0]),
+        },
+        PercentileAggregator: {
+            5: np.array([0.95, 5.95, 9.95]),
+            10: np.array([1.9, 7.9, 15.5]),
+            90: np.array([75.1, 83.1, 91.1]),
+            95: np.array([77.05, 85.05, 93.05]),
+        },
+    }
+
+    def test_mad_percentile_aggregators_different_sizes(self, aggregator_cls, tensor_processor):
+        aggregator = aggregator_cls(tensor_processor=tensor_processor, aggregation_axes=(0, 1, 3))
+        for shape in ((2, 3, 4), (4, 3, 8)):
+            aggregator.register_reduced_input(
+                self.get_nncf_tensor(np.arange(np.prod(shape)).reshape(shape), Dtype.FLOAT)
+            )
+        ret_val = aggregator.aggregate()
+
+        ref_values = self.REF_MAD_PERCENTILE_REF_VALUES_DYNAMIC_TENSORS[aggregator.__class__]
+        assert len(ret_val) == len(ref_values)
+        for k, v in ref_values.items():
+            assert self.all_close(ret_val[k], self.cast_tensor(v, Dtype.FLOAT))
+
     @pytest.mark.parametrize(
         "reducer_name",
         ["noop", "min", "max", "abs_max", "mean", "quantile", "abs_quantile", "batch_mean", "mean_per_ch"],

tests/openvino/native/quantization/test_reducers_and_aggregators.py

@@ -35,6 +35,10 @@ def tensor_processor(self):
         return OVNNCFCollectorTensorProcessor
 
     def get_nncf_tensor(self, x: np.array, dtype: Optional[Dtype] = None):
+        if dtype is Dtype.INTEGER:
+            x = x.astype(np.int64)
+        if dtype is Dtype.FLOAT:
+            x = x.astype(np.float32)
         return OVNNCFTensor(x)
 
     @pytest.fixture(scope="module")