Enable transpose_a support for LoRA Correction

src/nncf/quantization/algorithms/weight_compression/activation_stats.py

@@ -17,16 +17,23 @@
 from nncf.tensor import functions as fns
 
 
-def process_stats(stats: WCTensorStatistic, subset_size: int, act_ch_axis: int = -1) -> tuple[Tensor, Tensor]:
+def process_stats(
+    stats: WCTensorStatistic,
+    subset_size: int,
+    act_ch_axis: int = -1,
+    transpose_a: bool = False,
+) -> tuple[Tensor, Tensor]:
     """
     A function for processing activations. Shared between AWQ, Scale Estimation and LoRA Correction algorithms.
 
     :param stats: An object containing statistics for the layer.
     :param subset_size: The number of samples for AWQ. If subset_size <= 0, all samples are used.
     :param act_ch_axis: The activation channel axis.
+    :param transpose_a: When True, returns X in [SampleSize, HiddenDim] layout instead of the default
+        [HiddenDim, SampleSize]. Used by LoRA Correction which requires samples as rows.
     :return: tuple of the following tensors:
-        s - maximum channel magnitude across samples [HiddenDim]
-        X - average channel magnitude across tokens in the sequence [HiddenDim, min(SampleSize, ~subset_size)]
+        s - maximum channel magnitude across samples, shape [HiddenDim]
+        X - activation matrix, shape [HiddenDim, SampleSize] normally or [SampleSize, HiddenDim] if transpose_a=True
     """
     X = fns.stack(
         stats.mean_values
@@ -37,8 +44,13 @@ def process_stats(stats: WCTensorStatistic, subset_size: int, act_ch_axis: int =
     axes = list(range(1, len(X.shape))) + [0]
     X_full = fns.transpose(X, axes=axes)
 
-    # The sample dimension is always the last axis after transpose
-    sample_axis = -1
+    if transpose_a:
+        axes = list(range(len(X_full.shape)))
+        axes[-1], axes[-2] = axes[-2], axes[-1]
+        X_full = fns.transpose(X_full, axes=axes)
+
+    # The sample dimension is axis -1 by default, but moves to -2 if transpose_a is True
+    sample_axis = -2 if transpose_a else -1
 
     # Prevent high memory and time consumption by sampling
     if X_full.shape[sample_axis] > subset_size and subset_size > 0:
@@ -47,11 +59,13 @@ def process_stats(stats: WCTensorStatistic, subset_size: int, act_ch_axis: int =
         ]
         step = X_full.shape[sample_axis] // subset_size
         idxs = [i[0] for i in sorted(enumerate(lens), key=lambda x: -x[1])][::step]
-        X = X_full[..., idxs]
+        if transpose_a:
+            X = X_full[..., idxs, :]
+        else:
+            X = X_full[..., idxs]
     else:
         X = X_full
 
-    # Compute max magnitude along the sample axis (last axis)
-    # Result: [HiddenDim] or [No. of Experts, HiddenDim]
+    # Compute max magnitude along the sample axis
     s = fns.max(fns.abs(X_full), axis=sample_axis)
     return s, X

src/nncf/quantization/algorithms/weight_compression/algorithm.py

@@ -1181,11 +1181,6 @@ def apply_with_parameters(
                 )
 
             if self._lora_correction:
-                for wc_params in all_weight_params:
-                    if self._backend_entity.matmul_has_transposed_activations(wc_params.node_with_weight, graph):
-                        msg = "Transposed activations are not supported yet for the LoRa correction algorithm"
-                        raise nncf.UnsupportedModelError(msg)
-
                 lora_correction_params = self._advanced_parameters.lora_correction_params
                 lora_correction_algo = LoraCorrectionAlgorithm(statistics, lora_correction_params)
                 description += " with correction of low-rank adapters"
@@ -1399,7 +1394,7 @@ def _get_statistics_for_weights_compression(
         # Where mean_value is a 1D tensor representing an activation reduced over batch and sequence length dimensions,
         # shape is an original shape of an activation before reduction, n is the size of the dataset (or subset_size).
         statistics = {}
-        for (act_node, output_port_id, _), matmul_nodes in matmul_input_to_output_nodes_map.items():
+        for (act_node, output_port_id, _act_channel_axis), matmul_nodes in matmul_input_to_output_nodes_map.items():
             tensor_collectors = list(
                 statistic_points.get_algo_statistics_for_node(
                     act_node.node_name,

src/nncf/quantization/algorithms/weight_compression/lora_correction.py

@@ -106,14 +106,19 @@ def is_applicable(self, wc_params: WeightCompressionParameters):
         return wc_params.compression_config.num_bits == 4
 
     def calculate_adapters(
-        self, weight: Tensor, compressed_weight: CompressedWeight, wc_params: WeightCompressionParameters
+        self,
+        weight: Tensor,
+        compressed_weight: CompressedWeight,
+        wc_params: WeightCompressionParameters,
+        act_ch_axis: int,
     ) -> tuple[Tensor, Tensor, list[float]]:
         """
         Calculates low rank matrices for a given original and compressed weights.
 
         :param weight: original floating-point weight matrix.
         :param compressed_weight: compressed weight matrix.
         :param wc_params: parameters of weight compression.
+        :param act_ch_axis: axis number of the activation tensor which correspond to it channel.
         :return: two low rank matrices in the order of execution of corresponding linear layers.
         """
         layer_name = wc_params.node_with_weight.node_name
@@ -126,6 +131,7 @@ def calculate_adapters(
             wc_params.reduction_axes,
             self._lora_correction_params,
             layer_statistics,
+            act_ch_axis,
             is_debug,
         )
         if is_debug:
@@ -140,6 +146,7 @@ def calculate_low_rank_matrices(
         reduction_axes: tuple[int, ...],
         lora_correction_params: AdvancedLoraCorrectionParameters,
         layer_statistics: WCTensorStatistic,
+        act_ch_axis: int,
         is_debug: bool | None = False,
     ):
         """
@@ -155,6 +162,7 @@ def calculate_low_rank_matrices(
         :param reduction_axes: axes along which different statistics reduced.
         :param lora_correction_params: parameters to configure the algorithm.
         :param layer_statistics: an object containing statistics for the layer.
+        :param act_ch_axis: axis number of the activation tensor which correspond to it channel.
         :param is_debug: whether to collect debug information, defaults to False.
         :return: two low rank matrices in the order of execution of corresponding linear layers and list of mean noises.
             Noises are collected from each step of the algorithm if debug was enabled.
@@ -168,7 +176,12 @@ def calculate_low_rank_matrices(
         )
         mode = compression_config.mode
         assert len(reduction_axes) == 1, "Assumed a single reduction axis"
-        reduction_axis = reduction_axes[0] if compression_config.group_size != -1 else -1
+
+        if compression_config.group_size != -1:
+            reduction_axis = reduction_axes[0]
+        else:
+            reduction_axis = -1
+
         if mode in (CompressWeightsMode.INT4_SYM, CompressWeightsMode.INT4_ASYM):
             fq_weights = do_integer_dequantization(
                 compressed_weight,
@@ -190,8 +203,8 @@ def calculate_low_rank_matrices(
             svd_residual = fns.transpose(svd_residual)
         residual = svd_residual.clone()  # [H, O]
 
-        s, X = process_stats(layer_statistics, subset_size)  # [H], [H, SS]
-        X = fns.transpose(X)  # [SS, H]
+        # Pass it to process_stats with transpose_a=True to get [SS, H] layout
+        s, X = process_stats(layer_statistics, subset_size, act_ch_axis, transpose_a=True)
         if compression_config.group_size > 0:
             # Multiply residual of weights by maximum channel magnitude of activations normalized per quantization
             # group. As a consequence, weights corresponding to a "noisy" activations has a higher error to correct.

src/nncf/quantization/algorithms/weight_compression/openvino_backend.py

@@ -204,7 +204,8 @@ def insert_adapters(
             A_W = opset.constant(lora_A.data)
             B_W = opset.constant(lora_B.data)
 
-        A_MM = opset.matmul(input_node, A_W, transpose_a=False, transpose_b=True)
+        transpose_a = wc_params.node_with_weight.layer_attributes.input_attributes["transpose"]
+        A_MM = opset.matmul(input_node, A_W, transpose_a=transpose_a, transpose_b=True)
         B_MM = opset.matmul(A_MM, B_W, transpose_a=False, transpose_b=True)
 
         node_output_port = mm_node.output(0)
@@ -361,7 +362,15 @@ def transform_model(
                 compressed_weight.tensor = compressed_weight.tensor.as_numpy_tensor()
                 if compressed_weight.zero_point is not None:
                     compressed_weight.zero_point = compressed_weight.zero_point.as_numpy_tensor()
-                adapters = lora_correction_algo.calculate_adapters(weight, compressed_weight, wc_params)
+
+                activation_port_id = self.get_activation_port_id(wc_params.node_with_weight, graph)
+                activation_edge = graph.get_input_edge_by_port_id(wc_params.node_with_weight, activation_port_id)
+                activation_shape = activation_edge.tensor_shape
+                act_ch_axis = self.get_activation_channel_axis(
+                    wc_params.node_with_weight, activation_port_id, activation_shape
+                )
+
+                adapters = lora_correction_algo.calculate_adapters(weight, compressed_weight, wc_params, act_ch_axis)
                 self.insert_adapters(wc_params, *adapters, int8_lora=lora_correction_algo.use_int8_adapters)
         self.name_to_node_mapping = None