Move validate safetensors function

Add tests for the same

src/compressed_tensors/utils/converters/converters.py

@@ -23,9 +23,11 @@
 import torch
 from compressed_tensors.registry.registry import RegistryMixin
 from compressed_tensors.utils.converters.transformations import (
+    remove_unused_tensors,
     transform_autogptq_weights_and_reshape_tensors,
     transform_exllama_names,
 )
+from compressed_tensors.utils.safetensors_load import validate_safetensors_file_path
 from safetensors import safe_open
 from safetensors.torch import save_file
 from tqdm import tqdm
@@ -38,7 +40,7 @@
 
 
 class ConverterNames(str, Enum):
-    EXLLAMA_TO_COMPRESSED_TENSOR = "exllama_to_compressed_tensor"
+    AutoGPTQConverter: str = "exllama_to_compressed_tensor"
 
 
 class BaseConverter(ABC, RegistryMixin):
@@ -71,7 +73,7 @@ def convert_from_safetensors(
         :param save_dir: The directory to save the converted state_dict to
         :return: The directory where the converted state_dict was saved
         """
-        _validate_safetensors_file_path(filepath)
+        validate_safetensors_file_path(filepath)
 
         filepath_: Path = Path(filepath)
         if not save_dir:
@@ -84,30 +86,42 @@ def convert_from_safetensors(
         # transform and save the state_dict
         if filepath_.is_dir():
             tqdm.write(f"Converting directory: {filepath}")
-            tqdm.write(f"Found: {len(list(filepath_.glob('*.safetensors')))} .safetensors files")
+            tqdm.write(
+                f"Found: {len(list(filepath_.glob('*.safetensors')))} "
+                ".safetensors files"
+            )
             for file in filepath_.glob("*.safetensors"):
                 tqdm.write(f"Converting file: {file.name}")
                 new_state_dict = {}
                 state_dict: Iterable[StateDictType] = load_safetensors_state_dict(
                     file, by_layers=True
                 )
-                layer_progress_bar = tqdm(state_dict, total=layer_count(file), desc="Converting layers")
+                layer_progress_bar = tqdm(
+                    state_dict, total=layer_count(file), desc="Converting layers"
+                )
                 for layer_state_dict in layer_progress_bar:
-                    layer_name = list(layer_state_dict.keys())[0][:len("model.layers.0")]
+                    layer_name = list(layer_state_dict.keys())[0][
+                        : len("model.layers.0")
+                    ]
                     layer_progress_bar.set_description(f"Converting layer {layer_name}")
                     layer_progress_bar.update()
                     new_state_dict.update(
                         cls.translate(state_dict=layer_state_dict, **kwargs)
                     )
 
                 if new_state_dict:
+                    # compress before saving
+                    # compressor = Compressor.load_from_registry(
+                    # name=CompressionFormat.pack_quantized.value
+                    # )
+                    # new_state_dict = compressor.compress(new_state_dict)
                     save_file(
                         new_state_dict,
                         filename=save_dir_ / file.name,
                         metadata=metadata,
                     )
             _copy_non_safetensor_files_(filepath_, save_dir_)
-            _update_quantization_config(filepath_, save_dir_)
+            # _update_quantization_config(filepath_, save_dir_)
 
         elif filepath_.is_file():
             new_state_dict = {}
@@ -134,39 +148,28 @@ def transformations(cls) -> Iterable[TransformationType]:
         raise NotImplementedError()
 
 
-@BaseConverter.register(name=ConverterNames.EXLLAMA_TO_COMPRESSED_TENSOR)
-class ExllamaToCompressedTensorConverter(BaseConverter):
+@BaseConverter.register(name=ConverterNames.AutoGPTQConverter)
+class AutoGPTQConverter(BaseConverter):
     """
     A converter that applies transformations to the state_dict of a autogptq
-    quantized model to convert it to a compressed tensor model, which can be
-    loaded by the SparseAutoModel classes
-    """
-
-    @classmethod
-    def transformations(cls):
-        return (transform_autogptq_weights_and_reshape_tensors, transform_exllama_names)
+    quantized model to convert it to a compressed tensor model
 
+    Transformations made:
 
-def _validate_safetensors_file_path(filepath: str):
-    """
-    Given a file path, it is valid if:
-        - The file exists
-        - The file is either a single .safetensors file or a
-            directory containing .safetensors files
-
-    :param filepath: A string file path to validate
+    -> Unpack autogptq 4 bit weight packing
+    -> Translate exllama names to compressed tensor names
+    -> Pack 4 bit weights with compressed tensor format
+    -> Remove unused tensors
+    -> Update quantization config in config.json file
     """
 
-    filepath_: Path = Path(filepath)
-
-    if not filepath_.exists():
-        raise FileNotFoundError(f"File not found: {filepath}")
-
-    if filepath_.is_dir() and not any(filepath_.glob("*.safetensors")):
-        raise FileNotFoundError(f"No .safetensors files found in directory: {filepath}")
-
-    if filepath_.is_file() and not filepath_.suffix == ".safetensors":
-        raise ValueError(f"File must be a .safetensors file: {filepath}")
+    @classmethod
+    def transformations(cls):
+        return (
+            transform_autogptq_weights_and_reshape_tensors,
+            transform_exllama_names,
+            remove_unused_tensors,
+        )
 
 
 def _copy_non_safetensor_files_(source_dir: Path, dest_dir: Path):
@@ -178,7 +181,7 @@ def _copy_non_safetensor_files_(source_dir: Path, dest_dir: Path):
     :param dest_dir: The directory to copy files to
     """
     for file in source_dir.glob("*"):
-        if file.suffix != ".safetensors":
+        if file.suffix != ".safetensors" and file.name != "config.json":
             _LOGGER.info(f"Copying file: {file} to {dest_dir}")
             shutil.copy(file, dest_dir / file.name)
 
@@ -198,7 +201,9 @@ def _update_quantization_config(source_dir: Path, dest_dir: Path):
     if hasattr(config, "quantization_config"):
         _LOGGER.info("Updating quantization config...")
         quantization_config = config.quantization_config
-        config.quantization_config = _convert_to_compressed_tensors_config(quantization_config)
+        config.quantization_config = _convert_to_compressed_tensors_config(
+            quantization_config
+        )
     config.save_pretrained(dest_dir)
 
 
@@ -207,12 +212,14 @@ def _convert_to_compressed_tensors_config(quantization_config):
     Converts the quantization_config attribute from a config.json file
     to a dictionary
 
-    :param quantization_config: The quantization_config attribute from a config.json file
+    :param quantization_config: The quantization_config
+        attribute from a config.json file
     :return: The quantization_config as a dictionary
     """
     compressed_tensor_config = ...
     return compressed_tensor_config
 
+
 def layer_count(file_path: str) -> int:
     """
     Count the number of layers in a safetensors file
@@ -222,16 +229,15 @@ def layer_count(file_path: str) -> int:
     """
     with safe_open(file_path, framework="pt", device="cpu") as f:
         keys = sorted(f.keys())
-        
+
         last_layer_name = None
         layer_count = 0
         for key in keys:
-            layer_name = key[:len("model.layers.0")]
+            layer_name = key[: len("model.layers.0")]
             if layer_name != last_layer_name:
                 last_layer_name = layer_name
                 layer_count += 1
         return layer_count
-
 
 
 def load_safetensors_state_dict(
@@ -251,7 +257,7 @@ def load_safetensors_state_dict(
             current_layer = None
             layer_data = {}
             for key in sorted(f.keys()):
-                layer_name = key[:len("model.layers.0")]
+                layer_name = key[: len("model.layers.0")]
                 if current_layer is None:
                     current_layer = layer_name
                 elif layer_name != current_layer:

src/compressed_tensors/utils/converters/main.py

@@ -15,11 +15,12 @@
 
 from compressed_tensors.utils.converters.converters import BaseConverter, ConverterNames
 
+
 __all__ = ["convert_autogptq_checkpoint"]
 
 
 def convert_autogptq_checkpoint(
-    old_checkpoint_path, new_checkpoint_path ,**kwargs
+    old_checkpoint_path, new_checkpoint_path, **kwargs
 ) -> str:
     """
     Convert an autogptq checkpoint to a compressed tensor checkpoint
@@ -31,7 +32,7 @@ def convert_autogptq_checkpoint(
     :return: the path to the new checkpoint
     """
     converter: BaseConverter = BaseConverter.load_from_registry(
-        ConverterNames.EXLLAMA_TO_COMPRESSED_TENSOR
+        ConverterNames.AutoGPTQConverter
     )
     checkpoint_path = converter.convert_from_safetensors(
         old_checkpoint_path, new_checkpoint_path, **kwargs

src/compressed_tensors/utils/converters/transformations.py

@@ -69,7 +69,7 @@ def transform_exllama_names(state_dict: Dict[str, Tensor]) -> Dict[str, Tensor]:
     name_map: Dict[str, str] = {
         ".scales": ".weight_scale",
         ".qzeros": ".weight_zero_point",
-        ".qweight": ".weight",
+        ".qweight": ".weight_packed",
     }
 
     updated_state_dict = {}
@@ -91,7 +91,7 @@ def transform_autogptq_weights_and_reshape_tensors(
     to CompressedTensors conversion
 
     The transformations include:
-        - Unpack ad dequantize the weight tensor using the scales, zeros, and g_idx tensors
+        - Unpack and dequantize the weight tensor using the scales, zeros, and g_idx tensors
         - Squeeze the scales tensor to [x] from [1, x]
 
     :pre-condition: The state_dict should be for a quantized model
@@ -117,13 +117,15 @@ def transform_autogptq_weights_and_reshape_tensors(
             g_idx = state_dict[key.replace("qweight", "g_idx")]
 
             zeros = unpack_zeros(qzeros)
-            qweight = unpack_int32_into_fp32(
-                qweight=tensor,
-                scales=scales,
-                zeros=zeros,
-                g_idx=g_idx,
-            )
-            transformed_weights_dict[key] = qweight
+            # qweight = unpack_int32_into_fp32(
+            #     qweight=tensor,
+            #     scales=scales,
+            #     zeros=zeros,
+            #     g_idx=g_idx,
+            # )
+            new_shape = torch.tensor([tensor.shape[0] * 8, tensor.shape[1]])
+            transformed_weights_dict[key] = tensor
+            transformed_weights_dict[key.replace("qweight", "weight_shape")] = new_shape
 
     # transform scales
     for key, tensor in state_dict.items():
@@ -222,3 +224,17 @@ def unpack_int32_into_fp32(
     weight = torch.cat(weight, dim=1)
 
     return weight
+
+
+def remove_unused_tensors(state_dict: Dict[str, Tensor]) -> Dict[str, Tensor]:
+    """
+    Remove unused tensors from the state_dict
+
+    :param state_dict: The state_dict to be cleaned
+    :return: The cleaned state_dict
+    """
+    return {
+        key: tensor
+        for key, tensor in state_dict.items()
+        if is_gptq_quantization_target(key) and not key.endswith(".g_idx")
+    }

src/compressed_tensors/utils/safetensors_load.py

@@ -16,6 +16,7 @@
 import os
 import re
 import struct
+from pathlib import Path
 from typing import Dict, List, Optional
 
 from safetensors import safe_open
@@ -32,6 +33,7 @@
     "get_nested_weight_mappings",
     "get_quantization_state_dict",
     "is_quantization_param",
+    "validate_safetensors_file_path",
 ]
 
 
@@ -236,3 +238,25 @@ def is_quantization_param(name: str) -> bool:
         return True
 
     return False
+
+
+def validate_safetensors_file_path(filepath: str):
+    """
+    Given a file path, it is valid if:
+        - The file exists
+        - The file is either a single .safetensors file or a
+            directory containing .safetensors files
+
+    :param filepath: A string file path to validate
+    """
+
+    filepath_: Path = Path(filepath)
+
+    if not filepath_.exists():
+        raise FileNotFoundError(f"File not found: {filepath}")
+
+    if filepath_.is_dir() and not any(filepath_.glob("*.safetensors")):
+        raise FileNotFoundError(f"No .safetensors files found in directory: {filepath}")
+
+    if filepath_.is_file() and not filepath_.suffix == ".safetensors":
+        raise ValueError(f"File must be a .safetensors file: {filepath}")

tests/test_utils/test_safetensors_load.py

@@ -0,0 +1,63 @@
+# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import pytest
+from compressed_tensors.utils.safetensors_load import validate_safetensors_file_path
+
+
+@pytest.fixture
+def temp_dir(tmp_path):
+    return tmp_path / "subdirectory"
+
+
+@pytest.fixture
+def safetensors_file(temp_dir):
+    temp_dir.mkdir(exists_ok=True)
+    safetensors_filepath = temp_dir / "test.safetensors"
+    safetensors_filepath.write_text("content")
+    return safetensors_filepath
+
+
+@pytest.fixture
+def non_safetensors_file(temp_dir):
+    temp_dir.mkdir(exists_ok=True)
+    non_safetensors_filepath = temp_dir / "test.txt"
+    non_safetensors_filepath.write_text("content")
+    return non_safetensors_filepath
+
+
+def test_validate_safetensors_file_path_file_not_found():
+    with pytest.raises(FileNotFoundError):
+        validate_safetensors_file_path("nonexistent_file.safetensors")
+
+
+def test_validate_safetensors_file_path_no_safetensors_files_in_directory(temp_dir):
+    temp_dir.mkdir()
+    with pytest.raises(FileNotFoundError):
+        validate_safetensors_file_path(str(temp_dir))
+
+
+def test_validate_safetensors_file_path_file_is_not_safetensors(non_safetensors_file):
+    with pytest.raises(ValueError):
+        validate_safetensors_file_path(str(non_safetensors_file))
+
+
+def test_validate_safetensors_file_path_valid_safetensors_file(safetensors_file):
+    validate_safetensors_file_path(str(safetensors_file))
+
+
+def test_validate_safetensors_file_path_valid_directory_with_safetensors_files(
+    temp_dir, safetensors_file
+):
+    validate_safetensors_file_path(str(temp_dir))