[Misc] Revert compressed-tensors code reuse (#7521)

requirements-common.txt

@@ -24,4 +24,3 @@ librosa # Required for audio processing
 soundfile # Required for audio processing
 gguf == 0.9.1
 importlib_metadata
-compressed-tensors == 0.5.0

requirements-test.txt

@@ -17,7 +17,7 @@ peft
 requests
 ray
 sentence-transformers # required for embedding
-compressed-tensors==0.5.0 # required for compressed-tensors
+compressed-tensors==0.4.0 # required for compressed-tensors
 timm # required for internvl test
 
 # TODO: Add this after fully implementing llava(mantis)

tests/quantization/test_compressed_tensors.py

@@ -5,12 +5,13 @@ Run `pytest tests/quantization/test_compressed_tensors.py`.
 
 import pytest
 import torch
-from compressed_tensors.quantization import QuantizationType
 
 from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors import (  # noqa: E501
     CompressedTensorsLinearMethod, CompressedTensorsW4A16Sparse24,
     CompressedTensorsW8A8Fp8, CompressedTensorsW8A8Int8,
     CompressedTensorsW8A16Fp8, CompressedTensorsWNA16)
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    QuantizationType)
 
 
 @pytest.mark.parametrize("model_args", [

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py

@@ -1,10 +1,6 @@
 from typing import Any, Dict, List, Optional
 
 import torch
-from compressed_tensors.config import CompressionFormat
-from compressed_tensors.quantization import (QuantizationArgs,
-                                             QuantizationStrategy,
-                                             QuantizationType)
 from pydantic import BaseModel
 
 from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase
@@ -17,7 +13,8 @@ from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsW8A8Int8, CompressedTensorsW8A16Fp8,
     CompressedTensorsWNA16)
 from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
-    find_matched_target, is_activation_quantization_format,
+    CompressionFormat, QuantizationArgs, QuantizationStrategy,
+    QuantizationType, find_matched_target, is_activation_quantization_format,
     should_ignore_layer)
 from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
 from vllm.platforms import current_platform

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a16_fp8.py

@@ -1,10 +1,11 @@
 from typing import Callable, List, Optional
 
 import torch
-from compressed_tensors.quantization import QuantizationStrategy
 
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsScheme)
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    QuantizationStrategy)
 from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
     apply_fp8_marlin_linear, prepare_fp8_layer_for_marlin)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py

@@ -1,11 +1,12 @@
 from typing import Callable, List, Optional
 
 import torch
-from compressed_tensors.quantization import QuantizationStrategy
 from torch.nn import Parameter
 
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsScheme)
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    QuantizationStrategy)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     apply_fp8_linear, cutlass_fp8_supported, requantize_with_max_scale)
 from vllm.model_executor.parameter import (ChannelQuantScaleParameter,

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py

@@ -1,11 +1,12 @@
 from typing import Callable, List, Optional
 
 import torch
-from compressed_tensors.quantization import QuantizationStrategy
 from torch.nn import Parameter
 
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsScheme)
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    QuantizationStrategy)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     apply_int8_linear, convert_to_channelwise)
 from vllm.model_executor.parameter import (BasevLLMParameter,

vllm/model_executor/layers/quantization/compressed_tensors/utils.py

@@ -1,13 +1,85 @@
 import re
-from typing import Iterable, Optional
+from enum import Enum
+from typing import Any, Dict, Iterable, Optional
 
-from compressed_tensors import CompressionFormat
+from pydantic import BaseModel, Field
 from torch.nn import Module
 
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     FUSED_LAYER_NAME_MAPPING)
 
 
+class CompressionFormat(Enum):
+    dense = "dense"
+    sparse_bitmask = "sparse-bitmask"
+    naive_quantized = "naive-quantized"
+    float_quantized = "float-quantized"
+    int_quantized = "int-quantized"
+    pack_quantized = "pack-quantized"
+    marlin_24 = "marlin-24"
+
+
+class QuantizationType(str, Enum):
+    """
+    Enum storing quantization type options
+    """
+
+    INT = "int"
+    FLOAT = "float"
+
+
+class QuantizationStrategy(str, Enum):
+    """
+    Enum storing quantization strategy options
+    """
+
+    TENSOR = "tensor"
+    CHANNEL = "channel"
+    GROUP = "group"
+    BLOCK = "block"
+    TOKEN = "token"
+
+
+class QuantizationArgs(BaseModel):
+    """
+    User facing arguments used to define a quantization config 
+    for weights or activations
+
+    :param num_bits: quantization bit depth
+    :param type: dtype to quantized to, either int or float
+    :param symmetric: whether or not quantization scale is symmetric
+    :param strategy: string determining the scope of scale/zero-point to apply
+    :param group_size: group length to use for the group strategy
+    :param block_structure: 2d block structure to use for the block 
+    strategy, must be of the format "2x4", "8x16", etc.
+    :param dynamic: set True to perform dynamic quantization -
+        values will not be calibrated during calibration phase, 
+        instead during inference new quantization ranges will be 
+        observed with every sample. Defaults to False for static
+        quantization. Note that enabling dynamic quantization 
+        will change the default observer to a memoryless one
+    """
+
+    num_bits: int = 8
+    type: QuantizationType = QuantizationType.INT
+    symmetric: bool = True
+    group_size: Optional[int] = None
+    strategy: Optional[QuantizationStrategy] = None
+    block_structure: Optional[str] = None
+    dynamic: bool = False
+    observer: str = Field(
+        default="minmax",
+        description=("The class to use to compute the quantization param - "
+                     "scale and zero-point'"),
+    )
+    observer_kwargs: Dict[str, Any] = Field(
+        default_factory=dict,
+        description=
+        ("optional dict of kwargs to be passed directly to torch quantization "
+         "Observers constructor excluding quantization range or symmetry"),
+    )
+
+
 def is_activation_quantization_format(format: str) -> bool:
     _ACTIVATION_QUANTIZATION_FORMATS = [
         CompressionFormat.naive_quantized.value,