support non-symmetric int4 qat

docker/Dockerfile

@@ -108,9 +108,11 @@ fi
 
 # ====================================== Install main package ============================================
 
-# TODO may improve
 ARG SLIME_COMMIT=main
 RUN git clone https://github.com/THUDM/slime.git /root/slime && \
     cd /root/slime && \
     git checkout ${SLIME_COMMIT} && \
     pip install -e . --no-deps
+
+RUN cd /root/slime/slime/backends/megatron_utils/kernels/int4_qat && \
+    pip install . --no-build-isolation

docker/patch/latest/sglang.patch

@@ -654,6 +654,19 @@ index a1885fade..14d692365 100644
                      moe_sum_reduce_torch_compile(
                          intermediate_cache3.view(*intermediate_cache3.shape),
                          out_hidden_states[begin_chunk_idx:end_chunk_idx],
+diff --git a/python/sglang/srt/layers/moe/fused_moe_triton/layer.py b/python/sglang/srt/layers/moe/fused_moe_triton/layer.py
+index 839463518..7948779aa 100644
+--- a/python/sglang/srt/layers/moe/fused_moe_triton/layer.py
++++ b/python/sglang/srt/layers/moe/fused_moe_triton/layer.py
+@@ -647,7 +647,7 @@ class FusedMoE(torch.nn.Module):
+                     "CompressedTensorsWNA16MarlinMoEMethod",
+                     "CompressedTensorsWNA16MoEMethod",
+                 ]
+-            )
++            ) and "zero" not in weight_name
+             else loaded_weight
+         )
+
 diff --git a/python/sglang/srt/layers/moe/routed_experts_capturer.py b/python/sglang/srt/layers/moe/routed_experts_capturer.py
 index 00bd68755..5a3ca8a67 100644
 --- a/python/sglang/srt/layers/moe/routed_experts_capturer.py
@@ -725,11 +738,87 @@ index 00bd68755..5a3ca8a67 100644
          self.device_cache.capture_fwd_routed_experts(layer_id, topk_ids)
 
      def get_routed_experts(
+diff --git a/python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors.py b/python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors.py
+index b4bdc41b3..3b895ff6a 100644
+--- a/python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors.py
++++ b/python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors.py
+@@ -442,7 +442,7 @@ class CompressedTensorsConfig(QuantizationConfig):
+         )
+         is_static = not weight_quant.dynamic
+
+-        return is_channel_group and input_quant_none and is_symmetric and is_static
++        return is_channel_group and input_quant_none and is_static
+
+     def _get_scheme_from_parts(
+         self, weight_quant: BaseModel, input_quant: BaseModel
 diff --git a/python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors_moe.py b/python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors_moe.py
-index c5e5a11fc..dd321fa13 100644
+index c5e5a11fc..c46526ecc 100644
 --- a/python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors_moe.py
 +++ b/python/sglang/srt/layers/quantization/compressed_tensors/compressed_tensors_moe.py
-@@ -1016,13 +1016,37 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
+@@ -30,7 +30,10 @@ from sglang.srt.layers.quantization.fp8_utils import (
+     normalize_e4m3fn_to_e4m3fnuz,
+ )
+ from sglang.srt.layers.quantization.gptq import gptq_marlin_moe_repack
+-from sglang.srt.layers.quantization.marlin_utils import marlin_moe_permute_scales
++from sglang.srt.layers.quantization.marlin_utils import (
++    marlin_moe_permute_scales,
++    moe_awq_to_marlin_zero_points
++)
+ from sglang.srt.layers.quantization.utils import (
+     all_close_1d,
+     per_tensor_dequantize,
+@@ -865,7 +868,7 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
+         self.strategy = config.strategy
+         self.group_size = config.group_size
+         self.actorder = config.actorder
+-        assert config.symmetric, "Only symmetric quantization is supported for MoE"
++        self.sym = config.symmetric
+
+         if not (
+             self.quant_config.quant_format == CompressionFormat.pack_quantized.value
+@@ -920,7 +923,7 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
+
+         # In the case where we have actorder/g_idx,
+         # we do not partition the w2 scales
+-        load_full_w2 = self.actorder and self.group_size != -1
++        load_full_w2 = (self.actorder != 'static') and self.group_size != -1
+
+         if load_full_w2:
+             w2_scales_size = intermediate_size_per_partition * layer.moe_tp_size
+@@ -968,6 +971,32 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
+         layer.register_parameter("w13_weight_shape", w13_weight_shape)
+         set_weight_attrs(w13_weight_shape, extra_weight_attrs)
+
++        # add zero param
++        if not self.sym:
++            w13_qzeros = torch.nn.Parameter(
++                torch.empty(
++                    num_experts,
++                    num_groups_w13,
++                    2 * intermediate_size_per_partition // self.packed_factor,
++                    dtype=torch.int32,
++                ),
++                requires_grad=False,
++            )
++            layer.register_parameter("w13_weight_zero_point", w13_qzeros)
++            set_weight_attrs(w13_qzeros, extra_weight_attrs)
++
++            w2_qzeros = torch.nn.Parameter(
++                torch.empty(
++                    num_experts,
++                    num_groups_w2,
++                    hidden_size // self.packed_factor,
++                    dtype=torch.int32,
++                ),
++                requires_grad=False,
++            )
++            layer.register_parameter("w2_weight_zero_point", w2_qzeros)
++            set_weight_attrs(w2_qzeros, extra_weight_attrs)
++
+         w13_g_idx = torch.nn.Parameter(
+             torch.empty(
+                 num_experts,
+@@ -1016,13 +1045,40 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
          layer.a2_scale = None
          layer.marlin_state = GPTQMarlinState.REPACK
 
@@ -738,11 +827,14 @@ index c5e5a11fc..dd321fa13 100644
 +
 +        # Force record: these are the target GPTQ shapes for rollback.
 +        layer._original_shapes["w13_weight_packed"] = tuple(w13_weight.shape)
-+        layer._original_shapes["w2_weight_packed"] = tuple(w2_weight.shape)
++        layer._original_shapes["w13_weight_scale"] = tuple(w13_scale.shape)
++        if not self.sym:
++            layer._original_shapes["w13_weight_zero_point"] = w13_qzeros.shape
 +
-+        # Also record the shapes of the scales.
++        layer._original_shapes["w2_weight_packed"] = tuple(w2_weight.shape)
 +        layer._original_shapes["w2_weight_scale"] = tuple(w2_scale.shape)
-+        layer._original_shapes["w13_weight_scale"] = tuple(w13_scale.shape)
++        if not self.sym:
++            layer._original_shapes["w2_weight_zero_point"] = tuple(w2_qzeros.shape)
 +
      def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
 +        # Skip if the layer is already converted to Marlin format to prevent double-packing.
@@ -769,7 +861,7 @@ index c5e5a11fc..dd321fa13 100644
              del new_t
 
          num_experts = layer.w13_weight_g_idx.shape[0]
-@@ -1078,7 +1102,7 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
+@@ -1078,7 +1134,7 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
              layer.w13_weight_packed.shape[2],
              self.num_bits,
          )
@@ -778,7 +870,7 @@ index c5e5a11fc..dd321fa13 100644
          marlin_w2_qweight = gptq_marlin_moe_repack(
              layer.w2_weight_packed,
              layer.w2_g_idx_sort_indices,
-@@ -1086,7 +1110,7 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
+@@ -1086,7 +1142,7 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
              layer.w2_weight_packed.shape[2],
              self.num_bits,
          )
@@ -787,7 +879,7 @@ index c5e5a11fc..dd321fa13 100644
          # Repack scales
          marlin_w13_scales = marlin_moe_permute_scales(
              layer.w13_weight_scale,
-@@ -1094,7 +1118,7 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
+@@ -1094,7 +1150,7 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
              layer.w13_weight_scale.shape[2],
              self.group_size,
          )
@@ -796,13 +888,31 @@ index c5e5a11fc..dd321fa13 100644
 
          marlin_w2_scales = marlin_moe_permute_scales(
              layer.w2_weight_scale,
-@@ -1103,7 +1127,22 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
+@@ -1103,7 +1159,40 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
              layer.w2_weight_scale.shape[2],
              self.group_size,
          )
 -        replace_parameter(layer, "w2_weight_scale", marlin_w2_scales)
 +        replace_tensor("w2_weight_scale", marlin_w2_scales)
 +
++        # Repack zero
++        if not self.sym:
++            marlin_w13_zp = moe_awq_to_marlin_zero_points(
++                layer.w13_weight_zero_point,
++                size_k=layer.w13_weight_zero_point.shape[1],
++                size_n=layer.w13_weight_zero_point.shape[2] * self.packed_factor,
++                num_bits=self.num_bits,
++            )
++            replace_tensor("w13_weight_zero_point", marlin_w13_zp)
++
++            marlin_w2_zp = moe_awq_to_marlin_zero_points(
++                layer.w2_weight_zero_point,
++                size_k=layer.w2_weight_zero_point.shape[1],
++                size_n=layer.w2_weight_zero_point.shape[2] * self.packed_factor,
++                num_bits=self.num_bits,
++            )
++            replace_tensor("w2_weight_zero_point", marlin_w2_zp)
++
 +        layer.is_marlin_converted = True
 +
 +    def restore_weights_before_loading(self, layer: torch.nn.Module):
@@ -820,6 +930,15 @@ index c5e5a11fc..dd321fa13 100644
 
      def create_moe_runner(
          self, layer: torch.nn.Module, moe_runner_config: MoeRunnerConfig
+@@ -1154,6 +1243,8 @@ class CompressedTensorsWNA16MoEMethod(CompressedTensorsMoEMethod):
+             g_idx2=layer.w2_weight_g_idx,
+             sort_indices1=layer.w13_g_idx_sort_indices,
+             sort_indices2=layer.w2_g_idx_sort_indices,
++            w1_zeros=layer.w13_weight_zero_point if not self.sym else None,
++            w2_zeros=layer.w2_weight_zero_point if not self.sym else None,
+             num_bits=self.num_bits,
+             is_k_full=self.is_k_full,
+             routed_scaling_factor=self.moe_runner_config.routed_scaling_factor,
 diff --git a/python/sglang/srt/layers/rotary_embedding.py b/python/sglang/srt/layers/rotary_embedding.py
 index 480579e01..dd8ca7d4f 100644
 --- a/python/sglang/srt/layers/rotary_embedding.py

docker/version.txt

@@ -1 +1 @@
-nightly-dev-20260119a
+nightly-dev-20260120a