Add initial Sample-Layer Attention for GPTQ (PyTorch)

model_compression_toolkit/core/common/hessian/__init__.py

@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-from model_compression_toolkit.core.common.hessian.hessian_scores_request import HessianScoresRequest, HessianMode, HessianScoresGranularity
+from model_compression_toolkit.core.common.hessian.hessian_scores_request import (
+    HessianScoresRequest, HessianMode, HessianScoresGranularity, HessianEstimationDistribution
+)
 from model_compression_toolkit.core.common.hessian.hessian_info_service import HessianInfoService
 import model_compression_toolkit.core.common.hessian.hessian_info_utils as hessian_utils

model_compression_toolkit/core/common/hessian/hessian_info_service.py

@@ -12,16 +12,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
+import hashlib
 
 import numpy as np
 from functools import partial
 from tqdm import tqdm
-from typing import Callable, List, Dict, Any, Tuple
+from typing import Callable, List, Dict, Any, Tuple, TYPE_CHECKING
 
 from model_compression_toolkit.constants import HESSIAN_NUM_ITERATIONS
 from model_compression_toolkit.core.common.hessian.hessian_scores_request import HessianScoresRequest, \
     HessianScoresGranularity, HessianMode
 from model_compression_toolkit.logger import Logger
+if TYPE_CHECKING:
+    from model_compression_toolkit.core.common import BaseNode
 
 
 class HessianInfoService:
@@ -228,6 +231,51 @@ def compute(self,
         return next_iter_remain_samples if next_iter_remain_samples is not None and len(next_iter_remain_samples) > 0 \
         and len(next_iter_remain_samples[0]) > 0 else None
 
+    def compute_trackable_per_sample_hessian(self,
+                                             hessian_scores_request: HessianScoresRequest,
+                                             inputs_batch: List[np.ndarray]) -> Dict[str, Dict['BaseNode', np.ndarray]]:
+        """
+        Compute hessian score per image hash. We compute the score directly for images rather than via data generator,
+        as data generator might yield different images each time, depending on how it was defined,
+
+        Args:
+            hessian_scores_request: hessian scores request
+            inputs_batch: a list containing a batch of inputs.
+
+        Returns:
+            A dict of Hessian scores per image hash per layer {image hash: {layer: score}}
+        """
+        topo_sorted_nodes_names = [x.name for x in self.graph.get_topo_sorted_nodes()]
+        hessian_scores_request.target_nodes.sort(key=lambda x: topo_sorted_nodes_names.index(x.name))
+
+        hessian_score_by_image_hash = {}
+
+        if not isinstance(inputs_batch, list):
+            raise TypeError('Expected a list of inputs')
+        if len(inputs_batch) > 1:
+            raise NotImplementedError('Per-sample hessian computation is not supported for networks with multiple inputs')
+
+        # Get the framework-specific calculator Hessian-approximation scores
+        fw_hessian_calculator = self.fw_impl.get_hessian_scores_calculator(graph=self.graph,
+                                                                           input_images=inputs_batch,
+                                                                           hessian_scores_request=hessian_scores_request,
+                                                                           num_iterations_for_approximation=self.num_iterations_for_approximation)
+        hessian_scores = fw_hessian_calculator.compute()
+        for b in range(inputs_batch[0].shape[0]):
+            img_hash = self.calc_image_hash(inputs_batch[0][b])
+            hessian_score_by_image_hash[img_hash] = {
+                node: score[b] for node, score in zip(hessian_scores_request.target_nodes, hessian_scores)
+            }
+
+        return hessian_score_by_image_hash
+
+    @staticmethod
+    def calc_image_hash(image):
+        if len(image.shape) != 3:
+            raise ValueError(f'Expected 3d image (without batch) for image hash calculation, got {len(image.shape)}')
+        image_bytes = image.astype(np.float32).tobytes()
+        return hashlib.md5(image_bytes).hexdigest()
+
     def fetch_hessian(self,
                       hessian_scores_request: HessianScoresRequest,
                       required_size: int,

model_compression_toolkit/core/common/hessian/hessian_scores_request.py

@@ -40,6 +40,14 @@ class HessianScoresGranularity(Enum):
     PER_TENSOR = 2
 
 
+class HessianEstimationDistribution(str, Enum):
+    """
+    Distribution for Hutchinson estimator random vector
+    """
+    GAUSSIAN = 'gaussian'
+    RADEMACHER = 'rademacher'
+
+
 class HessianScoresRequest:
     """
     Request configuration for the Hessian-approximation scores.
@@ -53,7 +61,8 @@ class HessianScoresRequest:
     def __init__(self,
                  mode: HessianMode,
                  granularity: HessianScoresGranularity,
-                 target_nodes: List):
+                 target_nodes: List,
+                 distribution: HessianEstimationDistribution = HessianEstimationDistribution.GAUSSIAN):
         """
         Attributes:
             mode (HessianMode): Mode of Hessian-approximation score (w.r.t weights or activations).
@@ -64,16 +73,18 @@ def __init__(self,
         self.mode = mode  # w.r.t activations or weights
         self.granularity = granularity  # per element, per layer, per channel
         self.target_nodes = target_nodes
+        self.distribution = distribution
 
     def __eq__(self, other):
         # Checks if the other object is an instance of HessianScoresRequest
         # and then checks if all attributes are equal.
         return isinstance(other, HessianScoresRequest) and \
                self.mode == other.mode and \
                self.granularity == other.granularity and \
-               self.target_nodes == other.target_nodes
+               self.target_nodes == other.target_nodes and \
+               self.distribution == other.distribution
 
     def __hash__(self):
         # Computes the hash based on the attributes.
         # The use of a tuple here ensures that the hash is influenced by all the attributes.
-        return hash((self.mode, self.granularity, tuple(self.target_nodes)))
+        return hash((self.mode, self.granularity, tuple(self.target_nodes), self.distribution))