[DO NOT MERGE]

src/compressed_tensors/compressors/model_compressors/model_compressor.py

@@ -17,8 +17,9 @@
 import operator
 import os
 import re
+from contextlib import contextmanager
 from copy import deepcopy
-from typing import TYPE_CHECKING, Any, Dict, Optional, TypeVar, Union
+from typing import TYPE_CHECKING, Any, Dict, Optional, Set, TypeVar, Union
 
 import compressed_tensors
 import torch
@@ -38,6 +39,7 @@
     apply_quantization_config,
     load_pretrained_quantization,
 )
+from compressed_tensors.quantization.lifecycle import expand_sparse_target_names
 from compressed_tensors.quantization.quant_args import QuantizationArgs
 from compressed_tensors.quantization.utils import (
     is_module_quantized,
@@ -104,7 +106,6 @@ def from_pretrained(
         """
         config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
         compression_config = getattr(config, QUANTIZATION_CONFIG_NAME, None)
-
         return cls.from_compression_config(compression_config)
 
     @classmethod
@@ -282,8 +283,14 @@ def compress(
                 )
 
         if self.sparsity_compressor is not None:
+            sparse_compression_targets: Set[str] = expand_sparse_target_names(
+                model=model,
+                targets=self.sparsity_config.targets,
+                ignore=self.sparsity_config.ignore,
+            )
             compressed_state_dict = self.sparsity_compressor.compress(
-                compressed_state_dict
+                compressed_state_dict,
+                compression_targets=sparse_compression_targets,
             )
 
         # HACK: Override the dtype_byte_size function in transformers to
@@ -301,23 +308,41 @@ def decompress(self, model_path: str, model: Module):
         :param model: pytorch model to load decompressed weights into
         """
         model_path = get_safetensors_folder(model_path)
+        sparse_decompressed = False
+
         if self.sparsity_compressor is not None:
+            # Sparse decompression is applied on the model_path
             dense_gen = self.sparsity_compressor.decompress(model_path)
             self._replace_weights(dense_gen, model)
             setattr(model, SPARSITY_CONFIG_NAME, self.sparsity_compressor.config)
+            sparse_decompressed = True
 
         if self.quantization_compressor is not None:
-            names_to_scheme = apply_quantization_config(model, self.quantization_config)
-            load_pretrained_quantization(model, model_path)
+            # Temporarily set quantization status to FROZEN to prevent
+            # quantization during apply_quantization_config. This ensures
+            # that the dtypes of the weights are not unintentionally updated.
+            # The status is restored after quantization params are loaded.
+            with override_quantization_status(
+                self.quantization_config, QuantizationStatus.FROZEN
+            ):
+                names_to_scheme = apply_quantization_config(
+                    model, self.quantization_config
+                )
+                load_pretrained_quantization(model, model_path)
+
+            model_path_or_state_dict = (
+                model.state_dict() if sparse_decompressed else model_path
+            )
+
             dense_gen = self.quantization_compressor.decompress(
-                model_path, names_to_scheme=names_to_scheme
+                model_path_or_state_dict, names_to_scheme=names_to_scheme
             )
             self._replace_weights(dense_gen, model)
 
-            def update_status(module):
+            def freeze_quantization_status(module):
                 module.quantization_status = QuantizationStatus.FROZEN
 
-            model.apply(update_status)
+            model.apply(freeze_quantization_status)
             setattr(model, QUANTIZATION_CONFIG_NAME, self.quantization_config)
 
     def update_config(self, save_directory: str):
@@ -402,3 +427,23 @@ def new_dtype_byte_size(dtype):
         raise ValueError(f"`dtype` is not a valid dtype: {dtype}.")
     bit_size = int(bit_search.groups()[0])
     return bit_size // 8
+
+
+@contextmanager
+def override_quantization_status(
+    config: QuantizationConfig, status: QuantizationStatus
+):
+    """
+    Within this context, the quantization status will be set to the
+    supplied status. After the context exits, the original status
+    will be restored.
+
+    :param config: the quantization config to override
+    :param status: the status to temporarily set
+    """
+    original_status = config.quantization_status
+    config.quantization_status = status
+    try:
+        yield
+    finally:
+        config.quantization_status = original_status

src/compressed_tensors/compressors/quantized_compressors/base.py

@@ -13,12 +13,17 @@
 # limitations under the License.
 
 import logging
-from typing import Dict, Generator, Tuple
+from pathlib import Path
+from typing import Any, Dict, Generator, Tuple, Union
 
 import torch
 from compressed_tensors.compressors.base import BaseCompressor
 from compressed_tensors.quantization import QuantizationArgs
-from compressed_tensors.utils import get_nested_weight_mappings, merge_names
+from compressed_tensors.utils import (
+    get_nested_mappings_from_state_dict,
+    get_nested_weight_mappings,
+    merge_names,
+)
 from safetensors import safe_open
 from torch import Tensor
 from tqdm import tqdm
@@ -113,30 +118,55 @@ def compress(
 
     def decompress(
         self,
-        path_to_model_or_tensors: str,
+        path_to_model_or_tensors: Union[str, Path, Dict[str, Any]],
         names_to_scheme: Dict[str, QuantizationArgs],
         device: str = "cpu",
     ) -> Generator[Tuple[str, Tensor], None, None]:
         """
         Reads a compressed state dict located at path_to_model_or_tensors
         and returns a generator for sequentially decompressing back to a
         dense state dict
-
         :param path_to_model_or_tensors: path to compressed safetensors model (directory
             with one or more safetensors files) or compressed tensors file
         :param names_to_scheme: quantization args for each quantized weight
         :param device: optional device to load intermediate weights into
         :return: compressed state dict
         """
+        if isinstance(path_to_model_or_tensors, (str, Path)):
+            yield from self._decompress_from_path(
+                path_to_model_or_tensors, names_to_scheme, device
+            )
+
+        else:
+            yield from self._decompress_from_state_dict(
+                path_to_model_or_tensors, names_to_scheme
+            )
+
+    def _decompress_from_path(self, path_to_model, names_to_scheme, device):
         weight_mappings = get_nested_weight_mappings(
-            path_to_model_or_tensors, self.COMPRESSION_PARAM_NAMES
+            path_to_model, self.COMPRESSION_PARAM_NAMES
         )
         for weight_name in weight_mappings.keys():
             weight_data = {}
             for param_name, safe_path in weight_mappings[weight_name].items():
                 full_name = merge_names(weight_name, param_name)
                 with safe_open(safe_path, framework="pt", device=device) as f:
                     weight_data[param_name] = f.get_tensor(full_name)
+            if "weight_scale" in weight_data:
+                quant_args = names_to_scheme[weight_name]
+                decompressed = self.decompress_weight(
+                    compressed_data=weight_data, quantization_args=quant_args
+                )
+                yield merge_names(weight_name, "weight"), decompressed
+
+    def _decompress_from_state_dict(self, state_dict, names_to_scheme):
+        weight_mappings = get_nested_mappings_from_state_dict(
+            state_dict, self.COMPRESSION_PARAM_NAMES
+        )
+        for weight_name in weight_mappings.keys():
+            weight_data = {}
+            for param_name, param_value in weight_mappings[weight_name].items():
+                weight_data[param_name] = param_value
 
             if "weight_scale" in weight_data:
                 quant_args = names_to_scheme[weight_name]

src/compressed_tensors/compressors/quantized_compressors/naive_quantized.py

@@ -68,9 +68,9 @@ def compress_weight(
         self,
         weight: Tensor,
         scale: Tensor,
+        quantization_args: QuantizationArgs,
         zero_point: Optional[Tensor] = None,
         g_idx: Optional[torch.Tensor] = None,
-        quantization_args: Optional[QuantizationArgs] = None,
         device: Optional[torch.device] = None,
     ) -> Dict[str, torch.Tensor]:
         """
@@ -79,8 +79,8 @@ def compress_weight(
         :param weight: uncompressed weight tensor
         :param scale: quantization scale for weight
         :param zero_point: quantization zero point for weight
-        :param g_idx: optional mapping from column index to group index
         :param quantization_args: quantization parameters for weight
+        :param g_idx: optional mapping from column index to group index
         :param device: optional device to move compressed output to
         :return: dictionary of compressed weight data
         """

src/compressed_tensors/compressors/quantized_compressors/pack_quantized.py

@@ -68,9 +68,9 @@ def compress_weight(
         self,
         weight: Tensor,
         scale: Tensor,
+        quantization_args: QuantizationArgs,
         zero_point: Optional[Tensor] = None,
         g_idx: Optional[torch.Tensor] = None,
-        quantization_args: Optional[QuantizationArgs] = None,
         device: Optional[torch.device] = None,
     ) -> Dict[str, torch.Tensor]:
         """

src/compressed_tensors/compressors/sparse_compressors/base.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 import logging
-from typing import Dict, Generator, Tuple
+from typing import Dict, Generator, Optional, Set, Tuple
 
 from compressed_tensors.compressors.base import BaseCompressor
 from compressed_tensors.utils import get_nested_weight_mappings, merge_names
@@ -30,7 +30,8 @@
 class BaseSparseCompressor(BaseCompressor):
     """
     Base class representing a sparse compression algorithm. Each child class should
-    implement compression_param_info, compress_weight and decompress_weight.
+    implement compression_param_info, compress_weight and decompress_weight; child
+    classes should also define COMPRESSION_PARAM_NAMES.
 
     Compressors support compressing/decompressing a full module state dict or a single
     quantized PyTorch leaf module.
@@ -59,19 +60,33 @@ class BaseSparseCompressor(BaseCompressor):
     :param config: config specifying compression parameters
     """
 
-    def compress(self, model_state: Dict[str, Tensor]) -> Dict[str, Tensor]:
+    def compress(
+        self,
+        model_state: Dict[str, Tensor],
+        compression_targets: Optional[Set[str]] = None,
+    ) -> Dict[str, Tensor]:
         """
         Compresses a dense state dict using bitmask compression
 
         :param model_state: state dict of uncompressed model
+        :param compression_targets: optional set of layer prefixes to compress, if None
+            compress all layers (for backwards compatibility)
         :return: compressed state dict
         """
         compressed_dict = {}
         _LOGGER.debug(
             f"Compressing model with {len(model_state)} parameterized layers..."
         )
         for name, value in tqdm(model_state.items(), desc="Compressing model"):
-            compression_data = self.compress_weight(name, value)
+            ignored = not self.should_compress(name, compression_targets)
+            if ignored:
+                compressed_dict[name] = value
+                continue
+            prefix = name
+            if prefix.endswith(".weight"):
+                prefix = prefix[: -(len(".weight"))]
+
+            compression_data = self.compress_weight(prefix, value)
             for key in compression_data.keys():
                 if key in compressed_dict:
                     _LOGGER.warn(
@@ -97,8 +112,10 @@ def decompress(
         :param device: device to load decompressed weights onto
         :return: iterator for generating decompressed weights
         """
-        weight_mappings = get_nested_weight_mappings(
-            path_to_model_or_tensors, self.COMPRESSION_PARAM_NAMES
+        weight_mappings, ignored_params = get_nested_weight_mappings(
+            path_to_model_or_tensors,
+            self.COMPRESSION_PARAM_NAMES,
+            return_unmatched_params=True,
         )
         for weight_name in weight_mappings.keys():
             weight_data = {}
@@ -107,4 +124,26 @@ def decompress(
                 with safe_open(safe_path, framework="pt", device=device) as f:
                     weight_data[param_name] = f.get_tensor(full_name)
             decompressed = self.decompress_weight(weight_data)
-            yield weight_name, decompressed
+            yield merge_names(weight_name, "weight"), decompressed
+
+        for ignored_param_name, safe_path in ignored_params.items():
+            with safe_open(safe_path, framework="pt", device=device) as f:
+                value = f.get_tensor(ignored_param_name)
+            yield ignored_param_name, value
+
+    @staticmethod
+    def should_compress(name: str, expanded_targets: Optional[Set[str]] = None) -> bool:
+        """
+        Check if a parameter should be compressed.
+        Currently, this only returns True for weight parameters.
+
+        :param name: name of the parameter
+        :param expanded_targets: set of layer prefixes to compress
+        :return: whether or not the parameter should be compressed
+        """
+        if expanded_targets is None:
+            return name.endswith(".weight")
+
+        return (
+            name.endswith(".weight") and name[: -(len(".weight"))] in expanded_targets
+        )

src/compressed_tensors/compressors/sparse_compressors/sparse_bitmask.py

@@ -19,6 +19,7 @@
 from compressed_tensors.compressors.base import BaseCompressor
 from compressed_tensors.compressors.sparse_compressors.base import BaseSparseCompressor
 from compressed_tensors.config import CompressionFormat
+from compressed_tensors.quantization import FP8_DTYPE
 from compressed_tensors.utils import merge_names
 from torch import Tensor
 
@@ -134,9 +135,14 @@ def bitmask_compress(tensor: Tensor) -> Tuple[Tensor, Tensor, Tensor]:
     bytemasks = tensor != 0
     row_counts = bytemasks.sum(dim=-1)
     row_offsets = torch.cumsum(row_counts, 0) - row_counts
-    values = tensor[bytemasks]
+    if tensor.dtype == FP8_DTYPE:
+        # acces raw bytes of the tensor
+        tensor_view = tensor.view(torch.int8)
+        values = tensor_view[bytemasks]
+        values = values.view(FP8_DTYPE)
+    else:
+        values = tensor[bytemasks]
     bitmasks_packed = pack_bitmasks(bytemasks)
-
     return values, bitmasks_packed, row_offsets