[model] added support for w8a8int8 used by neuralmagic/Qwen2-0.5B-Ins… (#9642)

python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors.py

@@ -30,6 +30,7 @@ from sglang.srt.layers.quantization.compressed_tensors.compressed_tensors_moe im
 from sglang.srt.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsScheme,
     CompressedTensorsW8A8Fp8,
+    CompressedTensorsW8A8Int8,
     CompressedTensorsW8A16Fp8,
 )
 from sglang.srt.layers.quantization.compressed_tensors.utils import (

python/sglang/srt/layers/quantization/compressed_tensors/schemes/__init__.py

@@ -2,10 +2,12 @@
 
 from .compressed_tensors_scheme import CompressedTensorsScheme
 from .compressed_tensors_w8a8_fp8 import CompressedTensorsW8A8Fp8
+from .compressed_tensors_w8a8_int8 import CompressedTensorsW8A8Int8
 from .compressed_tensors_w8a16_fp8 import CompressedTensorsW8A16Fp8
 
 __all__ = [
     "CompressedTensorsScheme",
     "CompressedTensorsW8A8Fp8",
     "CompressedTensorsW8A16Fp8",
+    "CompressedTensorsW8A8Int8",
 ]

python/sglang/srt/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py

@@ -0,0 +1,173 @@
+# Adapted from https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/layers/quantization/compressed_tensors
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Callable, Optional
+
+import torch
+from compressed_tensors.quantization import QuantizationStrategy
+from torch.nn import Parameter
+
+from sglang.srt.layers.parameter import (
+    ChannelQuantScaleParameter,
+    ModelWeightParameter,
+    PerTensorScaleParameter,
+)
+from sglang.srt.layers.quantization.compressed_tensors.schemes import (
+    CompressedTensorsScheme,
+)
+from sglang.srt.layers.quantization.int8_kernel import per_token_quant_int8
+from sglang.srt.layers.quantization.utils import requantize_with_max_scale
+from sglang.srt.utils import is_cuda
+
+_is_cuda = is_cuda()
+if _is_cuda:
+    from sgl_kernel import int8_scaled_mm
+
+
+class CompressedTensorsW8A8Int8(CompressedTensorsScheme):
+
+    def __init__(
+        self, strategy: str, is_static_input_scheme: bool, input_symmetric: bool
+    ):
+        self.strategy = strategy
+        self.is_static_input_scheme = is_static_input_scheme
+        self.input_symmetric = input_symmetric
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # lovelace and up
+        return 89
+
+    def process_weights_after_loading(self, layer) -> None:
+        # If per tensor, when we have a fused module (e.g. QKV) with per
+        # tensor scales (thus N scales being passed to the kernel),
+        # requantize so we can always run per channel
+        if self.strategy == QuantizationStrategy.TENSOR:
+            max_w_scale, weight = requantize_with_max_scale(
+                weight=layer.weight,
+                weight_scale=layer.weight_scale,
+                logical_widths=layer.logical_widths,
+            )
+
+            layer.weight = Parameter(weight.t(), requires_grad=False)
+            layer.weight_scale = Parameter(max_w_scale, requires_grad=False)
+
+        # If channelwise, scales are already lined up, so just transpose.
+        elif self.strategy == QuantizationStrategy.CHANNEL:
+            weight = layer.weight
+            weight_scale = layer.weight_scale.data
+
+            layer.weight = Parameter(weight.t(), requires_grad=False)
+            # required by torch.compile to be torch.nn.Parameter
+            layer.weight_scale = Parameter(weight_scale, requires_grad=False)
+
+        else:
+            raise ValueError(f"Unknown quantization strategy {self.strategy}")
+
+        # INPUT SCALE
+        if self.is_static_input_scheme and hasattr(layer, "input_scale"):
+            if self.input_symmetric:
+                layer.input_scale = Parameter(
+                    layer.input_scale.max(), requires_grad=False
+                )
+            else:
+                input_scale = layer.input_scale
+                input_zero_point = layer.input_zero_point
+
+                # reconstruct the ranges
+                int8_traits = torch.iinfo(torch.int8)
+                azps = input_zero_point.to(dtype=torch.int32)
+                range_max = (input_scale * (int8_traits.max - azps)).max()
+                range_min = (input_scale * (int8_traits.min - azps)).min()
+
+                scale = (range_max - range_min) / (int8_traits.max - int8_traits.min)
+
+                # AZP loaded as int8 but used as int32
+                azp = (int8_traits.min - range_min / scale).to(dtype=torch.int32)
+
+                layer.input_scale = Parameter(scale, requires_grad=False)
+                layer.input_zero_point = Parameter(azp, requires_grad=False)
+        else:
+            layer.input_scale = None
+            layer.input_zero_point = None
+
+        # azp_adj is the AZP adjustment term, used to account for weights.
+        # It does not depend on scales or azp, so it is the same for
+        # static and dynamic quantization.
+        # For more details, see csrc/quantization/cutlass_w8a8/Epilogues.md
+        # https://github.com/vllm-project/vllm/blob/8d59dbb00044a588cab96bcdc028006ed922eb06/csrc/quantization/cutlass_w8a8/Epilogues.md
+        if not self.input_symmetric:
+            weight = layer.weight
+            azp_adj = weight.sum(dim=0, keepdim=True, dtype=torch.int32)
+            if self.is_static_input_scheme:
+                # cutlass_w8a8 requires azp to be folded into azp_adj
+                # in the per-tensor case
+                azp_adj = layer.input_zero_point * azp_adj
+            layer.azp_adj = Parameter(azp_adj, requires_grad=False)
+        else:
+            layer.azp_adj = None
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        output_partition_sizes: list[int],
+        input_size_per_partition: int,
+        params_dtype: torch.dtype,
+        weight_loader: Callable,
+        **kwargs,
+    ):
+        output_size_per_partition = sum(output_partition_sizes)
+        layer.logical_widths = output_partition_sizes
+
+        # WEIGHT
+        weight = ModelWeightParameter(
+            data=torch.empty(
+                output_size_per_partition, input_size_per_partition, dtype=torch.int8
+            ),
+            input_dim=1,
+            output_dim=0,
+            weight_loader=weight_loader,
+        )
+
+        layer.register_parameter("weight", weight)
+
+        # WEIGHT SCALE
+        if self.strategy == QuantizationStrategy.CHANNEL:
+            weight_scale = ChannelQuantScaleParameter(
+                data=torch.empty((sum(output_partition_sizes), 1), dtype=torch.float32),
+                output_dim=0,
+                weight_loader=weight_loader,
+            )
+        else:
+            assert self.strategy == QuantizationStrategy.TENSOR
+            weight_scale = PerTensorScaleParameter(
+                data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
+                weight_loader=weight_loader,
+            )
+        layer.register_parameter("weight_scale", weight_scale)
+
+        # INPUT SCALE
+        if self.is_static_input_scheme:
+            input_scale = PerTensorScaleParameter(
+                data=torch.empty(1, dtype=torch.float32), weight_loader=weight_loader
+            )
+            layer.register_parameter("input_scale", input_scale)
+
+            if not self.input_symmetric:
+                # Note: compressed-tensors stores the zp using the same dtype
+                # as the weights
+                # AZP loaded as int8 but used as int32
+                input_zero_point = PerTensorScaleParameter(
+                    data=torch.empty(1, dtype=torch.int8), weight_loader=weight_loader
+                )
+                layer.register_parameter("input_zero_point", input_zero_point)
+
+    def apply_weights(
+        self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor]
+    ) -> torch.Tensor:
+        # TODO: add cutlass_scaled_mm_azp support
+        x_q, x_scale = per_token_quant_int8(x)
+
+        return int8_scaled_mm(
+            x_q, layer.weight, x_scale, layer.weight_scale, out_dtype=x.dtype, bias=bias
+        )