[OBCQ Recipe UX Change] Remove target_ids

src/sparseml/modifiers/obcq/base.py

@@ -50,7 +50,6 @@ class SparseGPTModifier(Modifier):
         shape. Defaults to 0:0 which represents an unstructured mask.
     :param targets: list of layer names to compress during OBCQ, or '__ALL__'
         to compress every layer in the model
-    :param target_ids: list of keys in model output to cache
     """
 
     sparsity: Union[float, List[float]]
@@ -62,7 +61,6 @@ class SparseGPTModifier(Modifier):
     prunen_: Optional[int] = None
     prunem_: Optional[int] = None
     targets: Union[str, List[str], None] = ALL_TOKEN
-    target_ids: Optional[List[str]] = None
     layer_prefix: Optional[str] = None
     compressible_layers_: List = None
     quantization_modifier_: Any = None

src/sparseml/modifiers/obcq/pytorch.py

@@ -108,7 +108,6 @@ def apply_obcq(
         # decoder layer. Also return attention_mask as part of kwargs
         extras = self.compress_bottom(
             dev=self.device_,
-            target_ids=self.target_ids,
             layer_prefix=self.layer_prefix_,
             **accum_kwargs,
         )
@@ -166,7 +165,6 @@ def compress_bottom(
         dataloader: List = None,
         nsamples: int = None,
         dev: str = "cuda:0",
-        target_ids: List[str] = None,
         layer_prefix: Optional[str] = None,
     ) -> Dict:
         """
@@ -182,7 +180,11 @@ def compress_bottom(
         """
         layer_prefix = layer_prefix or self.layer_prefix_
         cached_inputs = cache_attention_inputs(
-            self.model, dataloader, dev, nsamples, target_ids, layer_prefix
+            model=self.model,
+            dataloader=dataloader,
+            device=dev,
+            nsamples=nsamples,
+            layer_prefix=layer_prefix,
         )
 
         outputs = cached_inputs.pop("inputs")

src/sparseml/modifiers/obcq/utils/helpers.py

@@ -13,31 +13,46 @@
 # limitations under the License.
 
 import logging
+from collections import defaultdict
 from math import ceil
 
 import torch
 
 
 _LOGGER = logging.getLogger(__name__)
+_DEFAULT_TARGET_IDS = [
+    "attention_mask",
+    "position_ids",
+]
 
 
 class Catcher(torch.nn.Module):
-    def __init__(self, module, target_keys):
+    def __init__(self, module):
         super().__init__()
         self.module = module
-        self.cache = {key: [] for key in target_keys}
-        self.target_keys = target_keys
+        self.target_keys = None
+        self.cache = defaultdict(list)
         self.cache["inputs"] = []
 
     def forward(self, *args, **kwargs):
         self.cache["inputs"].append(args)
+        if self.target_keys is None:
+            self.target_keys = self._get_target_keys(kwargs.keys())
+
         for key in self.target_keys:
             self.cache[key].append(kwargs[key])
         raise ValueError
 
     def get_cache(self):
         return self.cache
 
+    def _get_target_keys(self, input_keys):
+        target_keys = []
+        for key in _DEFAULT_TARGET_IDS:
+            if key in input_keys:
+                target_keys.append(key)
+        return target_keys
+
 
 def replace_module(model, old_module, new_module):
     for module_name, module in model.named_modules():
@@ -51,8 +66,8 @@ def replace_module(model, old_module, new_module):
     setattr(current_module, module_name[-1], new_module)
 
 
-def catch(model, attention_layer, target_keys, data_loader, nsamples):
-    catcher_module = Catcher(attention_layer, target_keys)
+def catch(model, attention_layer, data_loader, nsamples):
+    catcher_module = Catcher(attention_layer)
     replace_module(model, attention_layer, catcher_module)
     device = next(attention_layer.parameters()).device
     for input_id, inp in enumerate(data_loader):
@@ -108,9 +123,7 @@ def execute_offloaded_module(
         return new_buffer
 
 
-def cache_attention_inputs(
-    model, dataloader, device, nsamples, target_ids, layer_prefix
-):
+def cache_attention_inputs(model, dataloader, device, nsamples, layer_prefix):
     if layer_prefix:
         embed_tokens = getattr(model.model, layer_prefix).embed_tokens
         first_layer = getattr(model.model, layer_prefix).layers[0]
@@ -122,7 +135,6 @@ def cache_attention_inputs(
     cached_inputs = catch(
         model,
         first_layer,
-        target_ids,  # ["attention_mask"],
         dataloader,
         nsamples,
     )

src/sparseml/transformers/sparsification/obcq/example.yaml

@@ -55,4 +55,3 @@ test_stage:
         "model.decoder.layers.22",
         "model.decoder.layers.23"
       ]
-      target_ids: ["attention_mask"]

src/sparseml/transformers/sparsification/obcq/example_llama.yaml

@@ -90,5 +90,4 @@ test_stage:
         "model.layers.29",
         "model.layers.30",
         "model.layers.31",
-      ]
-      target_ids: ["attention_mask", "position_ids"]  
+      ]

src/sparseml/transformers/sparsification/obcq/utils/helpers.py

@@ -38,7 +38,7 @@ def opt_forward(model: Module, data_loader: List, device: str, nsamples: int = N
     :return: logits output of the model
     """
     cached_inputs = cache_attention_inputs(
-        model, data_loader, device, nsamples, ["attention_mask"], "decoder"
+        model, data_loader, device, nsamples, "decoder"
     )
     buffer = [b[0] for b in cached_inputs.pop("inputs")]
     for layer in model.model.decoder.layers:
@@ -86,9 +86,7 @@ def llama_forward(model: Module, data_loader: List, device: str, nsamples: int =
 
     :return: logits output of the model
     """
-    cached_inputs = cache_attention_inputs(
-        model, data_loader, device, nsamples, ["attention_mask", "position_ids"], None
-    )
+    cached_inputs = cache_attention_inputs(model, data_loader, device, nsamples, None)
     buffer = [b[0] for b in cached_inputs.pop("inputs")]
     for layer in model.model.layers:
         buffer = execute_offloaded_module(

tests/sparseml/transformers/obcq/test_tiny.yaml

@@ -39,5 +39,4 @@ test_stage:
         "model.layers.3",
         "model.layers.4",
         "model.layers.5"
-      ]
-      target_ids: ["attention_mask", "position_ids"]  
+      ]