[Misc] GPTQ Activation Ordering

tests/quantization/test_compressed_tensors.py

@@ -160,4 +160,4 @@ def test_compressed_tensors_kv_cache(vllm_runner):
     model_path = "nm-testing/TinyLlama-1.1B-compressed-tensors-kv-cache-scheme"
     with vllm_runner(model_path, kv_cache_dtype="fp8") as llm:
         output = llm.generate_greedy("Hello world!", max_tokens=20)
-        assert output
+        assert output

tests/weight_loading/models.txt

@@ -15,6 +15,7 @@ compressed-tensors, nm-testing/Phi-3-mini-128k-instruct-FP8, main
 compressed-tensors, neuralmagic/Phi-3-medium-128k-instruct-quantized.w4a16, main
 compressed-tensors, nm-testing/Mixtral-8x7B-Instruct-v0.1-W4A16-quantized, main
 compressed-tensors, nm-testing/Mixtral-8x7B-Instruct-v0.1-W4A16-channel-quantized, main
+compressed-tensors, nm-testing/TinyLlama-1.1B-Chat-v1.0-actorder-group, main
 awq, casperhansen/mixtral-instruct-awq, main
 awq_marlin, casperhansen/mixtral-instruct-awq, main
 fp8, neuralmagic/Meta-Llama-3-8B-Instruct-FP8-KV, main

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

@@ -232,7 +232,8 @@ def _get_scheme_from_parts(
                 return CompressedTensorsWNA16(
                     num_bits=weight_quant.num_bits,
                     strategy=weight_quant.strategy,
-                    group_size=weight_quant.group_size)
+                    group_size=weight_quant.group_size,
+                    actorder=weight_quant.actorder)
 
         # Detect If Activation Quantization.
         # TODO @dsikka: clean-up conditions

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py

@@ -5,10 +5,12 @@
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsScheme)
+from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
+    ActivationOrdering)
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (
     apply_gptq_marlin_linear, marlin_make_empty_g_idx, marlin_make_workspace,
-    marlin_permute_scales, replace_tensor, verify_marlin_supported,
-    verify_marlin_supports_shape)
+    marlin_permute_scales, marlin_sort_g_idx, replace_tensor,
+    verify_marlin_supported, verify_marlin_supports_shape)
 from vllm.model_executor.parameter import (BasevLLMParameter,
                                            ChannelQuantScaleParameter,
                                            GroupQuantScaleParameter,
@@ -28,11 +30,13 @@ class CompressedTensorsWNA16(CompressedTensorsScheme):
     def __init__(self,
                  strategy: str,
                  num_bits: int,
-                 group_size: Optional[int] = None):
+                 group_size: Optional[int] = None,
+                 actorder: Optional[ActivationOrdering] = None):
 
         self.pack_factor = 32 // num_bits
         self.strategy = strategy
         self.group_size = -1 if group_size is None else group_size
+        self.has_g_idx = actorder == ActivationOrdering.GROUP
 
         if self.group_size == -1 and self.strategy != "channel":
             raise ValueError("Marlin kernels require group quantization or "
@@ -123,6 +127,13 @@ def create_weights(self, layer: torch.nn.Module, input_size: int,
         layer.register_parameter("weight_scale", weight_scale)
         layer.register_parameter("weight_shape", weight_shape)
 
+        # group index (for activation reordering)
+        if self.has_g_idx:
+            weight_g_idx = BasevLLMParameter(data=torch.full(
+                (input_size_per_partition, ), -1, dtype=torch.int32),
+                                             weight_loader=weight_loader)
+            layer.register_parameter("weight_g_idx", weight_g_idx)
+
         layer.input_size_per_partition = input_size_per_partition
         layer.output_size_per_partition = output_size_per_partition
         layer.input_size = input_size
@@ -137,9 +148,14 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         layer.workspace = marlin_make_workspace(
             layer.output_size_per_partition, device)
 
-        # Act-order not supported in compressed-tensors yet, so set to empty.
-        layer.g_idx = marlin_make_empty_g_idx(device)
-        layer.g_idx_sort_indices = marlin_make_empty_g_idx(device)
+        # Handle sorting for activation reordering if needed.
+        if self.has_g_idx:
+            g_idx, g_idx_sort_indices = marlin_sort_g_idx(layer.weight_g_idx)
+            layer.g_idx_sort_indices = g_idx_sort_indices
+            replace_tensor(layer, "weight_g_idx", g_idx)
+        else:
+            layer.weight_g_idx = marlin_make_empty_g_idx(device)
+            layer.g_idx_sort_indices = marlin_make_empty_g_idx(device)
 
         # No zero-point
         layer.weight_zp = marlin_make_empty_g_idx(device)
@@ -161,7 +177,8 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         # Permute scales from compressed-tensors format to marlin format.
         marlin_scales = marlin_permute_scales(
             layer.weight_scale,
-            size_k=layer.input_size_per_partition,
+            size_k=(layer.input_size
+                    if self.has_g_idx else layer.input_size_per_partition),
             size_n=layer.output_size_per_partition,
             group_size=layer.group_size)
         replace_tensor(layer, "weight_scale", marlin_scales)
@@ -174,7 +191,7 @@ def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor,
             weight=layer.weight_packed,
             weight_scale=layer.weight_scale,
             weight_zp=layer.weight_zp,
-            g_idx=layer.g_idx,
+            g_idx=layer.weight_g_idx,
             g_idx_sort_indices=layer.g_idx_sort_indices,
             workspace=layer.workspace,
             wtype=self.quant_type,

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

@@ -1,8 +1,8 @@
 import re
 from enum import Enum
-from typing import Any, Dict, Iterable, Optional
+from typing import Any, Dict, Iterable, Optional, Union
 
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, field_validator
 from torch.nn import Module
 
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
@@ -40,6 +40,19 @@ class QuantizationStrategy(str, Enum):
     TOKEN = "token"
 
 
+class ActivationOrdering(str, Enum):
+    """
+    Enum storing strategies for activation ordering
+
+    Group: reorder groups and weight\n
+    Weight: only reorder weight, not groups. Slightly lower latency and
+    accuracy compared to group actorder\n
+    """
+
+    GROUP = "group"
+    WEIGHT = "weight"
+
+
 class QuantizationArgs(BaseModel):
     """
     User facing arguments used to define a quantization config 
@@ -58,6 +71,8 @@ class QuantizationArgs(BaseModel):
         observed with every sample. Defaults to False for static
         quantization. Note that enabling dynamic quantization 
         will change the default observer to a memoryless one
+    :param actorder: whether to apply group quantization in decreasing order of
+        activation. Defaults to None for arbitrary ordering
     """
 
     num_bits: int = 8
@@ -67,6 +82,7 @@ class QuantizationArgs(BaseModel):
     strategy: Optional[QuantizationStrategy] = None
     block_structure: Optional[str] = None
     dynamic: bool = False
+    actorder: Union[ActivationOrdering, bool, None] = None
     observer: str = Field(
         default="minmax",
         description=("The class to use to compute the quantization param - "
@@ -79,6 +95,16 @@ class QuantizationArgs(BaseModel):
          "Observers constructor excluding quantization range or symmetry"),
     )
 
+    @field_validator("actorder", mode="before")
+    def validate_actorder(cls, value) -> Optional[ActivationOrdering]:
+        if isinstance(value, bool):
+            return ActivationOrdering.GROUP if value else None
+
+        if isinstance(value, str):
+            return ActivationOrdering(value.lower())
+
+        return value
+
 
 def is_activation_quantization_format(format: str) -> bool:
     _ACTIVATION_QUANTIZATION_FORMATS = [