Clean up imports (#5467)

python/sglang/srt/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py

@@ -17,7 +17,6 @@ from sglang.srt.layers.quantization.compressed_tensors.schemes import (
 )
 from sglang.srt.layers.quantization.fp8_utils import (
     Fp8LinearOp,
-    maybe_create_device_identity,
     normalize_e4m3fn_to_e4m3fnuz,
 )
 from sglang.srt.layers.quantization.utils import is_fp8_fnuz, requantize_with_max_scale
@@ -99,8 +98,6 @@ class CompressedTensorsW8A8Fp8(CompressedTensorsScheme):
         weight_loader: Callable,
         **kwargs,
     ):
-        maybe_create_device_identity()
-
         output_size_per_partition = sum(output_partition_sizes)
         layer.logical_widths = output_partition_sizes
 

python/sglang/srt/layers/quantization/fp8.py

@@ -8,15 +8,6 @@ import torch.nn.functional as F
 from torch.nn import Module
 from torch.nn.parameter import Parameter
 
-from sglang.srt.layers.quantization.kv_cache import BaseKVCacheMethod
-from sglang.srt.layers.quantization.utils import (
-    all_close_1d,
-    convert_to_channelwise,
-    is_layer_skipped,
-    per_tensor_dequantize,
-    requantize_with_max_scale,
-)
-
 try:
     from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
         apply_fp8_marlin_linear,
@@ -27,11 +18,12 @@ try:
 except ImportError:
     MARLIN_FP8_AVAILABLE = False
 
-    def apply_fp8_marlin_linear(*args, **kwargs):
-        raise ImportError("vllm is not installed")
+    def dummy_func(*args, **kwargs):
+        raise ImportError(
+            "marlin FP8 requires some operators from vllm. Please install vllm."
+        )
 
-    def prepare_fp8_layer_for_marlin(*args, **kwargs):
-        raise ImportError("vllm is not installed")
+    apply_fp8_marlin_linear = prepare_fp8_layer_for_marlin = dummy_func
 
 
 from sglang.srt.distributed import get_tensor_model_parallel_world_size
@@ -49,7 +41,10 @@ from sglang.srt.layers.quantization.base_config import (
     QuantizationConfig,
     QuantizeMethodBase,
 )
-from sglang.srt.layers.quantization.fp8_kernel import per_token_group_quant_fp8
+from sglang.srt.layers.quantization.fp8_kernel import (
+    per_token_group_quant_fp8,
+    scaled_fp8_quant,
+)
 from sglang.srt.layers.quantization.fp8_utils import (
     apply_fp8_linear,
     apply_w8a8_block_fp8_linear,
@@ -57,30 +52,35 @@ from sglang.srt.layers.quantization.fp8_utils import (
     input_to_float8,
     normalize_e4m3fn_to_e4m3fnuz,
 )
+from sglang.srt.layers.quantization.kv_cache import BaseKVCacheMethod
+from sglang.srt.layers.quantization.utils import (
+    all_close_1d,
+    convert_to_channelwise,
+    is_layer_skipped,
+    per_tensor_dequantize,
+    requantize_with_max_scale,
+)
 from sglang.srt.utils import (
     get_bool_env_var,
     is_cuda,
     is_hip,
-    permute_weight,
     print_warning_once,
     set_weight_attrs,
 )
 
-ACTIVATION_SCHEMES = ["static", "dynamic"]
-
 _is_hip = is_hip()
+_is_cuda = is_cuda()
 
 if _is_hip:
     from aiter import ActivationType
     from aiter.fused_moe_bf16_asm import asm_moe, ck_moe_2stages, ck_moe_2stages_win4
     from aiter.ops.shuffle import shuffle_weight
 
-_is_cuda = is_cuda()
+if not _is_cuda:
+    from vllm._custom_ops import scaled_fp8_quant
+
 
-if _is_cuda:
-    from sglang.srt.custom_op import scaled_fp8_quant as sgl_scaled_fp8_quant
-else:
-    from vllm import _custom_ops as vllm_ops
+ACTIVATION_SCHEMES = ["static", "dynamic"]
 
 logger = logging.getLogger(__name__)
 
@@ -243,7 +243,6 @@ class Fp8LinearMethod(LinearMethodBase):
                         )
 
         layer.logical_widths = output_partition_sizes
-
         layer.input_size_per_partition = input_size_per_partition
         layer.output_size_per_partition = output_size_per_partition
         layer.orig_dtype = params_dtype
@@ -327,7 +326,9 @@ class Fp8LinearMethod(LinearMethodBase):
                     layer.weight_scale_inv.data, requires_grad=False
                 )
             return
+
         layer.weight = torch.nn.Parameter(layer.weight.data, requires_grad=False)
+
         # If checkpoint not serialized fp8, quantize the weights.
         if not self.quant_config.is_checkpoint_fp8_serialized:
             if self.cutlass_fp8_supported or self.use_marlin:
@@ -391,12 +392,9 @@ class Fp8LinearMethod(LinearMethodBase):
                 )
 
         if self.use_marlin:
-            try:
             prepare_fp8_layer_for_marlin(layer)
             # Activations not quantized for marlin.
             del layer.input_scale
-            except ImportError:
-                self.use_marlin = False
 
     def apply(
         self,
@@ -406,7 +404,6 @@ class Fp8LinearMethod(LinearMethodBase):
     ) -> torch.Tensor:
 
         if self.use_marlin:
-            try:
             return apply_fp8_marlin_linear(
                 input=x,
                 weight=layer.weight,
@@ -416,8 +413,6 @@ class Fp8LinearMethod(LinearMethodBase):
                 size_k=layer.input_size_per_partition,
                 bias=bias,
             )
-            except ImportError:
-                self.use_marlin = False
 
         if self.block_quant:
             return apply_w8a8_block_fp8_linear(
@@ -516,7 +511,7 @@ class Fp8MoEMethod:
                     )
 
         # WEIGHTS
-        if get_bool_env_var("USE_INT4_WEIGHT"):
+        if _is_hip and get_bool_env_var("USE_INT4_WEIGHT"):
             # INT4 MoE weight - INT32 packed
             w13_weight = torch.nn.Parameter(
                 torch.empty(
@@ -617,7 +612,7 @@ class Fp8MoEMethod:
             set_weight_attrs(w13_weight_scale, extra_weight_attrs)
             set_weight_attrs(w2_weight_scale, extra_weight_attrs)
 
-            if get_bool_env_var("USE_INT4_WEIGHT"):
+            if _is_hip and get_bool_env_var("USE_INT4_WEIGHT"):
                 extra_weight_attrs.update(
                     {"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value}
                 )
@@ -649,7 +644,7 @@ class Fp8MoEMethod:
             layer.w2_input_scale = None
 
     def process_weights_after_loading(self, layer: Module) -> None:
-        if get_bool_env_var("USE_INT4_WEIGHT"):
+        if _is_hip and get_bool_env_var("USE_INT4_WEIGHT"):
             self.process_weights_hip_int4(layer)
             return
 
@@ -706,19 +701,11 @@ class Fp8MoEMethod:
                 requires_grad=False,
             )
             for expert in range(layer.num_experts):
-                if _is_cuda:
                 w13_weight[expert, :, :], layer.w13_weight_scale[expert] = (
-                        sgl_scaled_fp8_quant(layer.w13_weight.data[expert, :, :])
+                    scaled_fp8_quant(layer.w13_weight.data[expert, :, :])
                 )
                 w2_weight[expert, :, :], layer.w2_weight_scale[expert] = (
-                        sgl_scaled_fp8_quant(layer.w2_weight.data[expert, :, :])
-                    )
-                else:
-                    w13_weight[expert, :, :], layer.w13_weight_scale[expert] = (
-                        vllm_ops.scaled_fp8_quant(layer.w13_weight.data[expert, :, :])
-                    )
-                    w2_weight[expert, :, :], layer.w2_weight_scale[expert] = (
-                        vllm_ops.scaled_fp8_quant(layer.w2_weight.data[expert, :, :])
+                    scaled_fp8_quant(layer.w2_weight.data[expert, :, :])
                 )
             layer.w13_weight = torch.nn.Parameter(w13_weight, requires_grad=False)
             layer.w2_weight = torch.nn.Parameter(w2_weight, requires_grad=False)
@@ -796,18 +783,10 @@ class Fp8MoEMethod:
                         layer.w13_weight[expert_id][start : start + shard_size, :],
                         layer.w13_weight_scale[expert_id][shard_id],
                     )
-                    if _is_cuda:
-                        (
-                            layer.w13_weight[expert_id][start : start + shard_size, :],
-                            _,
-                        ) = sgl_scaled_fp8_quant(dq_weight, max_w13_scales[expert_id])
-                    else:
                     (
                         layer.w13_weight[expert_id][start : start + shard_size, :],
                         _,
-                        ) = vllm_ops.scaled_fp8_quant(
-                            dq_weight, max_w13_scales[expert_id]
-                        )
+                    ) = scaled_fp8_quant(dq_weight, max_w13_scales[expert_id])
                     start += shard_size
 
             layer.w13_weight_scale = torch.nn.Parameter(
@@ -930,7 +909,8 @@ class Fp8MoEMethod:
             correction_bias=correction_bias,
         )
 
-        if _is_hip and get_bool_env_var("USE_INT4_WEIGHT"):
+        if _is_hip:
+            if get_bool_env_var("USE_INT4_WEIGHT"):
                 # TODO: add triton kernel and add check get_bool_env_var("CK_MOE")
                 assert not no_combine, f"{no_combine=} is not supported."
                 return ck_moe_2stages_win4(
@@ -942,10 +922,13 @@ class Fp8MoEMethod:
                     layer.w13_weight_scale1,
                     layer.w2_weight_scale1,
                     activation=(
-                    ActivationType.Silu if activation == "silu" else ActivationType.Gelu
+                        ActivationType.Silu
+                        if activation == "silu"
+                        else ActivationType.Gelu
                     ),
                 )
-        if _is_hip and get_bool_env_var("CK_MOE"):
+
+            if get_bool_env_var("CK_MOE"):
                 assert not no_combine, f"{no_combine=} is not supported."
                 if self.block_quant:
                     # TODO(CK_MOE): FP8 block_quant only supports 'silu' for the time-being.
@@ -978,7 +961,7 @@ class Fp8MoEMethod:
                             else ActivationType.Gelu
                         ),
                     )
-        else:
+
         # Expert fusion with FP8 quantization
         return fused_experts(
             x,
@@ -996,9 +979,7 @@ class Fp8MoEMethod:
                 else layer.w13_weight_scale
             ),
             w2_scale=(
-                    layer.w2_weight_scale_inv
-                    if self.block_quant
-                    else layer.w2_weight_scale
+                layer.w2_weight_scale_inv if self.block_quant else layer.w2_weight_scale
             ),
             a1_scale=layer.w13_input_scale,
             a2_scale=layer.w2_input_scale,

python/sglang/srt/layers/quantization/fp8_kernel.py

@@ -34,15 +34,23 @@ from sglang.srt.utils import (
     supports_custom_op,
 )
 
-_enable_jit_deepgemm = False
-
 _is_hip = is_hip()
-fp8_type_ = torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn
-
 _is_cuda = is_cuda()
+_fp8_type = torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn
+if _is_hip:
+    fp8_max = 224.0
+else:
+    fp8_max = torch.finfo(_fp8_type).max
+fp8_min = -fp8_max
+
+_enable_jit_deepgemm = False
 if _is_cuda:
     import deep_gemm
-    from sgl_kernel import sgl_per_token_group_quant_fp8, sgl_per_token_quant_fp8
+    from sgl_kernel import (
+        sgl_per_tensor_quant_fp8,
+        sgl_per_token_group_quant_fp8,
+        sgl_per_token_quant_fp8,
+    )
 
     sm_version = get_device_sm()
     if sm_version == 90 and get_bool_env_var(
@@ -53,6 +61,7 @@ if _is_cuda:
 
 logger = logging.getLogger(__name__)
 
+
 if supports_custom_op():
 
     def deep_gemm_fp8_fp8_bf16_nt(
@@ -179,7 +188,6 @@ def per_token_group_quant_fp8(
     x: torch.Tensor,
     group_size: int,
     eps: float = 1e-10,
-    dtype: torch.dtype = fp8_type_,
     column_major_scales: bool = False,
     scale_tma_aligned: bool = False,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
@@ -192,7 +200,6 @@ def per_token_group_quant_fp8(
         x: The input tenosr with ndim >= 2.
         group_size: The group size used for quantization.
         eps: The minimum to avoid dividing zero.
-        dtype: The dype of output tensor.
 
     Returns:
         Tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the scaling factor for quantization.
@@ -202,15 +209,7 @@ def per_token_group_quant_fp8(
     ), "the last dimension of `x` cannot be divisible by `group_size`"
     assert x.is_contiguous(), "`x` is not contiguous"
 
-    finfo = torch.finfo(dtype)
-    fp8_max = finfo.max
-
-    if _is_hip:
-        fp8_max = 224.0
-
-    fp8_min = -fp8_max
-
-    x_q = torch.empty_like(x, device=x.device, dtype=dtype)
+    x_q = torch.empty_like(x, device=x.device, dtype=_fp8_type)
     M = x.numel() // group_size
     N = group_size
     if column_major_scales:
@@ -276,27 +275,18 @@ def sglang_per_token_group_quant_fp8(
     x: torch.Tensor,
     group_size: int,
     eps: float = 1e-10,
-    dtype: torch.dtype = fp8_type_,
 ):
     assert (
         x.shape[-1] % group_size == 0
     ), "the last dimension of `x` cannot be divisible by `group_size`"
     assert x.is_contiguous(), "`x` is not contiguous"
 
-    finfo = torch.finfo(dtype)
-    fp8_max = finfo.max
-
-    fp8_min = -fp8_max
-
-    x_q = torch.empty_like(x, device=x.device, dtype=dtype)
-    M = x.numel() // group_size
-    N = group_size
+    x_q = torch.empty_like(x, device=x.device, dtype=_fp8_type)
     x_s = torch.empty(
         x.shape[:-1] + (x.shape[-1] // group_size,),
         device=x.device,
         dtype=torch.float32,
     )
-
     sgl_per_token_group_quant_fp8(x, x_q, x_s, group_size, eps, fp8_min, fp8_max)
 
     return x_q, x_s
@@ -304,7 +294,7 @@ def sglang_per_token_group_quant_fp8(
 
 def sglang_per_token_quant_fp8(
     x: torch.Tensor,
-    dtype: torch.dtype = fp8_type_,
+    dtype: torch.dtype = _fp8_type,
 ):
     assert x.is_contiguous(), "`x` is not contiguous"
 
@@ -368,7 +358,6 @@ def static_quant_fp8(
     x: torch.Tensor,
     x_s: torch.Tensor,
     repeat_scale: bool = False,
-    dtype: torch.dtype = fp8_type_,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
     """Function to perform static quantization using the given scale on an input tensor `x`.
 
@@ -386,15 +375,8 @@ def static_quant_fp8(
     """
     assert x.is_contiguous(), "`x` is not contiguous"
     assert x_s.numel() == 1, "only supports per-tensor scale"
-    finfo = torch.finfo(dtype)
-    fp8_max = finfo.max
 
-    if _is_hip:
-        fp8_max = 224.0
-
-    fp8_min = -fp8_max
-
-    x_q = torch.empty_like(x, device=x.device, dtype=dtype)
+    x_q = torch.empty_like(x, device=x.device, dtype=_fp8_type)
     M = x.numel() // x.shape[-1]
     N = x.shape[-1]
     if repeat_scale:
@@ -896,7 +878,7 @@ def _per_tensor_quant_mla_fp8_stage2(
 
 
 def per_tensor_quant_mla_fp8(
-    x: torch.Tensor, eps: float = 1e-12, dtype: torch.dtype = torch.float8_e4m3fn
+    x: torch.Tensor, eps: float = 1e-12
 ) -> Tuple[torch.Tensor, torch.Tensor]:
     """
     This function quantizes input values to float8 values with tensor-wise quantization
@@ -904,13 +886,7 @@ def per_tensor_quant_mla_fp8(
     """
     assert x.dim() == 3, "`x` is not a 3d-tensor"
 
-    finfo = torch.finfo(dtype)
-    fp8_max = finfo.max
-    if _is_hip:
-        dtype = torch.float8_e4m3fnuz
-        fp8_max = 224.0
-
-    x_q = x.new_empty(x.size(), dtype=dtype)
+    x_q = x.new_empty(x.size(), dtype=_fp8_type)
     x_s = torch.zeros((1,), dtype=torch.float32, device=x.device)
 
     num_head, num_seq, head_size = x.shape
@@ -935,9 +911,64 @@ def per_tensor_quant_mla_fp8(
         head_size,
         x.stride(0),
         x.stride(1),
-        -fp8_max,
+        fp8_min,
         fp8_max,
         BLOCK_SIZE,
     )
 
     return x_q, x_s
+
+
+def scaled_fp8_quant(
+    input: torch.Tensor,
+    scale: Optional[torch.Tensor] = None,
+    num_token_padding: Optional[int] = None,
+    use_per_token_if_dynamic: bool = False,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Quantize input tensor to FP8 (8-bit floating point) format.
+
+    Args:
+        input (torch.Tensor): Input tensor to be quantized
+        scale (Optional[torch.Tensor]): Pre-computed scaling factor for static quantization.
+            If None, scales will be computed dynamically.
+        num_token_padding (Optional[int]): If specified, pad the first dimension
+            of the output to at least this value.
+        use_per_token_if_dynamic (bool): When using dynamic scaling (scale=None),
+            determines the quantization granularity:
+            - True: compute scale per token
+            - False: compute single scale per tensor
+
+    Returns:
+        Tuple[torch.Tensor, torch.Tensor]: A tuple containing:
+            - quantized_tensor: The FP8 quantized version of input
+            - scale_tensor: The scaling factors used for quantization
+
+    Raises:
+        AssertionError: If input is not 2D or if static scale's numel != 1
+    """
+    assert input.ndim == 2, f"Expected 2D input tensor, got {input.ndim}D"
+    shape = input.shape
+    out_dtype = torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn
+    if num_token_padding:
+        shape = (max(num_token_padding, input.shape[0]), shape[1])
+    output = torch.empty(shape, device=input.device, dtype=out_dtype)
+
+    if scale is None:
+        # Dynamic scaling
+        if use_per_token_if_dynamic:
+            scale = torch.empty((shape[0], 1), device=input.device, dtype=torch.float32)
+            sgl_per_token_quant_fp8(input, output, scale)
+        else:
+            scale = torch.zeros(1, device=input.device, dtype=torch.float32)
+            sgl_per_tensor_quant_fp8(
+                input, output, scale, is_static=False
+            )  # False for dynamic
+    else:
+        # Static scaling
+        assert scale.numel() == 1, f"Expected scalar scale, got numel={scale.numel()}"
+        sgl_per_tensor_quant_fp8(
+            input, output, scale, is_static=True
+        )  # True for static
+
+    return output, scale

python/sglang/srt/layers/quantization/fp8_utils.py

-import os
 from typing import List, Optional, Tuple
 
 import torch
 
+try:
+    from vllm import _custom_ops as vllm_ops
+
+    VLLM_AVAILABLE = True
+except ImportError:
+    VLLM_AVAILABLE = False
+
 from sglang.srt.layers.quantization.fp8_kernel import (
     _enable_jit_deepgemm,
     per_token_group_quant_fp8,
+    scaled_fp8_quant,
+    sglang_per_token_quant_fp8,
     static_quant_fp8,
     w8a8_block_fp8_matmul,
 )
@@ -17,30 +25,20 @@ from sglang.srt.utils import (
     is_hip,
 )
 
-try:
-    import vllm
-    from vllm import _custom_ops as ops
-
-    VLLM_AVAILABLE = True
-except ImportError:
-    VLLM_AVAILABLE = False
-
-use_vllm_cutlass_w8a8_fp8_kernel = get_bool_env_var("USE_VLLM_CUTLASS_W8A8_FP8_KERNEL")
-
 _is_hip = is_hip()
+_is_cuda = is_cuda()
+
 if _is_hip and get_bool_env_var("CK_MOE"):
     from aiter import gemm_a8w8_blockscale
 
-_is_cuda = is_cuda()
 if _is_cuda:
     from sgl_kernel import fp8_blockwise_scaled_mm, fp8_scaled_mm
 
-    from sglang.srt.custom_op import scaled_fp8_quant as sgl_scaled_fp8_quant
-    from sglang.srt.layers.quantization.fp8_kernel import sglang_per_token_quant_fp8
+use_vllm_cutlass_w8a8_fp8_kernel = get_bool_env_var("USE_VLLM_CUTLASS_W8A8_FP8_KERNEL")
 
 # Input scaling factors are no longer optional in _scaled_mm starting
 # from pytorch 2.5. Allocating a dummy tensor to pass as input_scale
-TORCH_DEVICE_IDENTITY = torch.ones(1, dtype=torch.float32)
+TORCH_DEVICE_IDENTITY = None
 
 _TORCH_VERSION = torch.__version__.split("+")[0]
 try:
@@ -214,7 +212,7 @@ def block_quant_to_tensor_quant(
             x_dq_block_tiles[j][i][:, :] = x_dq_block_tiles[j][i] * x_s[j][i]
 
     x_q_tensor, scale = (
-        sgl_scaled_fp8_quant(x_dq_block)
+        scaled_fp8_quant(x_dq_block)
         if _is_cuda
         else input_to_float8(x_dq_block, dtype=x_q_block.dtype)
     )
@@ -227,7 +225,7 @@ def channel_quant_to_tensor_quant(
 ) -> Tuple[torch.Tensor, torch.Tensor]:
     x_dq_channel = x_q_channel.to(torch.float32) * x_s
     x_q_tensor, scale = (
-        sgl_scaled_fp8_quant(x_dq_channel)
+        scaled_fp8_quant(x_dq_channel)
         if _is_cuda
         else input_to_float8(x_dq_channel, dtype=x_q_channel.dtype)
     )
@@ -264,7 +262,7 @@ def apply_fp8_linear(
             # final solution should be: 1. add support to per-tensor activation scaling.
             # 2. solve the torch.compile error from weight_scale.numel() == 1 and x_scale.numel() > 1 (below line#308)
             if _is_hip and weight_scale.numel() == 1:
-                qinput, x_scale = ops.scaled_fp8_quant(
+                qinput, x_scale = vllm_ops.scaled_fp8_quant(
                     input_2d,
                     input_scale,
                     use_per_token_if_dynamic=use_per_token_if_dynamic,
@@ -275,10 +273,9 @@ def apply_fp8_linear(
                 )
 
     if cutlass_fp8_supported:
-        try:
         if VLLM_AVAILABLE and use_vllm_cutlass_w8a8_fp8_kernel:
             # Fall back to vllm cutlass w8a8 fp8 kernel
-                output = ops.cutlass_scaled_mm(
+            output = vllm_ops.cutlass_scaled_mm(
                 qinput,
                 weight,
                 out_dtype=input.dtype,
@@ -299,8 +296,6 @@ def apply_fp8_linear(
                 bias=bias,
             )
         return output.view(*output_shape)
-        except (ImportError, NameError, AttributeError):
-            pass
 
     # torch.scaled_mm supports per tensor weights + activations only
     # so fallback to naive if per channel or per token
@@ -343,8 +338,10 @@ def apply_fp8_linear(
 
             # Making sure the dummy tensor is on the same device as the weight
             global TORCH_DEVICE_IDENTITY
-            if TORCH_DEVICE_IDENTITY.device != weight.device:
-                TORCH_DEVICE_IDENTITY = TORCH_DEVICE_IDENTITY.to(weight.device)
+            if TORCH_DEVICE_IDENTITY is None:
+                TORCH_DEVICE_IDENTITY = torch.ones(
+                    1, dtype=torch.float32, device=weight.device
+                )
 
             # GEMM
             # This computes C = (X * W).
@@ -372,13 +369,6 @@ def apply_fp8_linear(
             return output.to(dtype=input.dtype).view(*output_shape)
 
 
-def maybe_create_device_identity():
-    # Allocate dummy ones tensor for torch._scaled_mm
-    global TORCH_DEVICE_IDENTITY
-    if TORCH_DEVICE_IDENTITY is None:
-        TORCH_DEVICE_IDENTITY = torch.ones(1, dtype=torch.float32)
-
-
 # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/utils/w8a8_utils.py
 # TODO(luka): follow similar pattern for marlin and block-fp8-linear
 #  https://github.com/vllm-project/vllm/issues/14397
@@ -405,9 +395,7 @@ class Fp8LinearOp:
         # We also don't pad when using torch.compile,
         # as it breaks with dynamic shapes.
         if pad_output is None:
-            enable_torch_compile = os.environ.get(
-                "SGLANG_ENABLE_TORCH_COMPILE", "0"
-            ).lower() in ("1", "true", "yes")
+            enable_torch_compile = get_bool_env_var("SGLANG_ENABLE_TORCH_COMPILE")
             pad_output = not enable_torch_compile
         self.output_padding = 17 if pad_output else None
 
@@ -439,13 +427,13 @@ class Fp8LinearOp:
         # for sgl-kernel fp8_scaled_mm, it support per channel W now
         if self.cutlass_fp8_supported and weight_scale.numel() == weight.shape[1]:
             if _is_cuda:
-                qinput, x_scale = sgl_scaled_fp8_quant(
+                qinput, x_scale = scaled_fp8_quant(
                     input_2d,
                     input_scale,
                     use_per_token_if_dynamic=use_per_token_if_dynamic,
                 )
             else:
-                qinput, x_scale = ops.scaled_fp8_quant(
+                qinput, x_scale = vllm_ops.scaled_fp8_quant(
                     input_2d,
                     input_scale,
                     scale_ub=input_scale_ub,
@@ -455,7 +443,7 @@ class Fp8LinearOp:
             # Fused GEMM_DQ
             if VLLM_AVAILABLE and use_vllm_cutlass_w8a8_fp8_kernel:
                 # Fall back to vllm cutlass w8a8 fp8 kernel
-                output = ops.cutlass_scaled_mm(
+                output = vllm_ops.cutlass_scaled_mm(
                     qinput,
                     weight,
                     out_dtype=input.dtype,
@@ -482,14 +470,14 @@ class Fp8LinearOp:
         else:
             # Maybe apply padding to output, see comment in __init__
             if _is_cuda:
-                qinput, x_scale = sgl_scaled_fp8_quant(
+                qinput, x_scale = scaled_fp8_quant(
                     input_2d,
                     input_scale,
                     num_token_padding=self.output_padding,
                     use_per_token_if_dynamic=use_per_token_if_dynamic,
                 )
             else:
-                qinput, x_scale = ops.scaled_fp8_quant(
+                qinput, x_scale = vllm_ops.scaled_fp8_quant(
                     input_2d,
                     input_scale,
                     num_token_padding=self.output_padding,
@@ -562,9 +550,12 @@ class Fp8LinearOp:
                 # This computes C = (X * W).
                 # Output in fp32 to allow subsequent ops to happen in-place
 
+                # Making sure the dummy tensor is on the same device as the weight
                 global TORCH_DEVICE_IDENTITY
-                if TORCH_DEVICE_IDENTITY.device != weight.device:
-                    TORCH_DEVICE_IDENTITY = TORCH_DEVICE_IDENTITY.to(weight.device)
+                if TORCH_DEVICE_IDENTITY is None:
+                    TORCH_DEVICE_IDENTITY = torch.ones(
+                        1, dtype=torch.float32, device=weight.device
+                    )
 
                 output = torch._scaled_mm(
                     qinput,

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

 # Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/utils/quant_utils.py
 
 from types import MappingProxyType
-from typing import List, Mapping, Optional, Tuple, Union
+from typing import List, Mapping, Tuple, Union
 
 import torch
 
+from sglang.srt.layers.quantization.fp8_kernel import scaled_fp8_quant
 from sglang.srt.utils import is_cuda
 
 _is_cuda = is_cuda()
 
-if _is_cuda:
-    from sglang.srt.custom_op import scaled_fp8_quant as sgl_scaled_fp8_quant
-else:
-    from vllm import _custom_ops as vllm_ops
+if not _is_cuda:
+    from vllm._custom_ops import scaled_fp8_quant
 
 
 def is_fp8_fnuz() -> bool:
@@ -116,12 +115,7 @@ def requantize_with_max_scale(
         for idx, logical_width in enumerate(logical_widths):
             end = start + logical_width
             weight_dq = per_tensor_dequantize(weight[start:end, :], weight_scale[idx])
-            if _is_cuda:
-                weight[start:end, :], _ = sgl_scaled_fp8_quant(weight_dq, max_w_scale)
-            else:
-                weight[start:end, :], _ = vllm_ops.scaled_fp8_quant(
-                    weight_dq, max_w_scale
-                )
+            weight[start:end, :], _ = scaled_fp8_quant(weight_dq, max_w_scale)
             start = end
 
     return max_w_scale, weight

python/sglang/srt/layers/quantization/w8a8_int8.py

 from typing import Any, Callable, Dict, List, Optional
 
 import torch
-
-from sglang.srt.utils import is_cuda_available, set_weight_attrs
-
-is_cuda = is_cuda_available()
-if is_cuda:
-    from sgl_kernel import int8_scaled_mm
-
 from torch.nn.parameter import Parameter
 
 from sglang.srt.distributed import get_tensor_model_parallel_world_size
@@ -18,6 +11,11 @@ from sglang.srt.layers.quantization.base_config import (
     QuantizeMethodBase,
 )
 from sglang.srt.layers.quantization.int8_kernel import per_token_quant_int8
+from sglang.srt.utils import is_cuda_available, set_weight_attrs
+
+is_cuda = is_cuda_available()
+if is_cuda:
+    from sgl_kernel import int8_scaled_mm
 
 
 class W8A8Int8Config(QuantizationConfig):

python/sglang/srt/layers/rotary_embedding.py

@@ -11,10 +11,11 @@ from sglang.srt.custom_op import CustomOp
 from sglang.srt.utils import is_cuda_available
 
 _is_cuda_available = is_cuda_available()
+
 if _is_cuda_available:
     from sgl_kernel import apply_rope_with_cos_sin_cache_inplace
 else:
-    from vllm import _custom_ops as ops
+    from vllm._custom_ops import rotary_embedding as vllm_rotary_embedding
 
 
 def _rotate_neox(x: torch.Tensor) -> torch.Tensor:
@@ -159,7 +160,7 @@ class RotaryEmbedding(CustomOp):
             )
         else:
             self.cos_sin_cache = self.cos_sin_cache.to(query.device, dtype=query.dtype)
-            ops.rotary_embedding(
+            vllm_rotary_embedding(
                 positions,
                 query,
                 key,

python/sglang/srt/lora/backend/__init__.py

-from sglang.srt.lora.backend.base_backend import BaseLoRABackend
-
-
-def get_backend_from_name(name: str) -> BaseLoRABackend:
-    """
-    Get corresponding backend class from backend's name
-    """
-    if name == "triton":
-        from sglang.srt.lora.backend.triton_backend import TritonLoRABackend
-
-        return TritonLoRABackend
-    elif name == "flashinfer":
-        from sglang.srt.lora.backend.flashinfer_backend import FlashInferLoRABackend
-
-        return FlashInferLoRABackend
-    else:
-        raise ValueError(f"Invalid backend: {name}")
-
-
-__all__ = [
-    "BaseLoRABackend",
-    "FlashInferLoRABackend",
-    "TritonLoRABackend",
-    "get_backend_from_name",
-]

python/sglang/srt/lora/backend/base_backend.py

@@ -75,7 +75,7 @@ class BaseLoRABackend:
         qkv_lora_a: torch.Tensor,
         qkv_lora_b: Union[torch.Tensor, Tuple[torch.Tensor]],
         *args,
-        **kwargs
+        **kwargs,
     ) -> torch.Tensor:
         """Run the lora pass for QKV Layer.
 
@@ -98,7 +98,7 @@ class BaseLoRABackend:
         gate_up_lora_a: torch.Tensor,
         gate_up_lora_b: Union[torch.Tensor, Tuple[torch.Tensor]],
         *args,
-        **kwargs
+        **kwargs,
     ) -> torch.Tensor:
         """Run the lora pass for gate_up_proj, usually attached to MergedColumnParallelLayer.
 
@@ -115,3 +115,19 @@ class BaseLoRABackend:
 
     def set_batch_info(self, batch_info: LoRABatchInfo):
         self.batch_info = batch_info
+
+
+def get_backend_from_name(name: str) -> BaseLoRABackend:
+    """
+    Get corresponding backend class from backend's name
+    """
+    if name == "triton":
+        from sglang.srt.lora.backend.triton_backend import TritonLoRABackend
+
+        return TritonLoRABackend
+    elif name == "flashinfer":
+        from sglang.srt.lora.backend.flashinfer_backend import FlashInferLoRABackend
+
+        return FlashInferLoRABackend
+    else:
+        raise ValueError(f"Invalid backend: {name}")

python/sglang/srt/lora/backend/flashinfer_backend.py

@@ -2,7 +2,7 @@ from typing import Tuple
 
 import torch
 
-from sglang.srt.lora.backend import BaseLoRABackend
+from sglang.srt.lora.backend.base_backend import BaseLoRABackend
 from sglang.srt.lora.utils import LoRABatchInfo
 from sglang.srt.utils import is_flashinfer_available
 

python/sglang/srt/lora/backend/triton_backend.py

 import torch
 
-from sglang.srt.lora.backend import BaseLoRABackend
+from sglang.srt.lora.backend.base_backend import BaseLoRABackend
 from sglang.srt.lora.triton_ops import (
     gate_up_lora_b_fwd,
     qkv_lora_b_fwd,

python/sglang/srt/lora/layers.py

@@ -16,7 +16,7 @@ from sglang.srt.layers.linear import (
     RowParallelLinear,
 )
 from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
-from sglang.srt.lora.backend import BaseLoRABackend
+from sglang.srt.lora.backend.base_backend import BaseLoRABackend
 
 
 class BaseLayerWithLoRA(nn.Module):

python/sglang/srt/lora/lora.py

@@ -27,7 +27,7 @@ from torch import nn
 
 from sglang.srt.configs.load_config import LoadConfig
 from sglang.srt.hf_transformers_utils import AutoConfig
-from sglang.srt.lora.backend import BaseLoRABackend
+from sglang.srt.lora.backend.base_backend import BaseLoRABackend
 from sglang.srt.lora.lora_config import LoRAConfig
 from sglang.srt.model_loader.loader import DefaultModelLoader
 

python/sglang/srt/lora/lora_manager.py

@@ -22,7 +22,7 @@ import torch
 
 from sglang.srt.configs.load_config import LoadConfig
 from sglang.srt.hf_transformers_utils import AutoConfig
-from sglang.srt.lora.backend import BaseLoRABackend, get_backend_from_name
+from sglang.srt.lora.backend.base_backend import BaseLoRABackend, get_backend_from_name
 from sglang.srt.lora.layers import BaseLayerWithLoRA, get_lora_layer
 from sglang.srt.lora.lora import LoRAAdapter
 from sglang.srt.lora.lora_config import LoRAConfig

python/sglang/srt/managers/detokenizer_manager.py

@@ -14,7 +14,6 @@
 """DetokenizerManager is a process that detokenizes the token ids."""
 
 import dataclasses
-import json
 import logging
 import os
 import signal

python/sglang/srt/managers/mm_utils.py

 """
-    Multi-modality utils
+Multi-modality utils
 """
 
+import logging
 from abc import abstractmethod
 from typing import Callable, List, Optional, Tuple
 
@@ -12,11 +13,11 @@ from sglang.srt.managers.schedule_batch import (
     MultimodalDataItem,
     MultimodalInputs,
     global_server_args_dict,
-    logger,
 )
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch
 from sglang.srt.utils import print_warning_once
-from sglang.utils import logger
+
+logger = logging.getLogger(__name__)
 
 
 class MultiModalityDataPaddingPattern:

python/sglang/srt/managers/multimodal_processor.py

@@ -5,8 +5,6 @@ import logging
 import pkgutil
 from functools import lru_cache
 
-from transformers import PROCESSOR_MAPPING
-
 from sglang.srt.managers.multimodal_processors.base_processor import (
     BaseMultimodalProcessor,
 )

python/sglang/srt/managers/multimodal_processors/base_processor.py

@@ -8,8 +8,6 @@ from typing import List, Optional
 
 import numpy as np
 import PIL
-from decord import VideoReader, cpu
-from PIL import Image
 from transformers import BaseImageProcessorFast
 
 from sglang.srt.managers.schedule_batch import Modality
@@ -102,6 +100,9 @@ class BaseMultimodalProcessor(ABC):
         """
         estimate the total frame count from all visual input
         """
+        # Lazy import because decord is not available on some arm platforms.
+        from decord import VideoReader, cpu
+
         # Before processing inputs
         estimated_frames_list = []
         for image in image_data:

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -37,11 +37,11 @@ from sglang.srt.model_executor.forward_batch_info import (
 from sglang.srt.patch_torch import monkey_patch_torch_compile
 from sglang.srt.utils import get_available_gpu_memory, is_hip
 
-_is_hip = is_hip()
-
 if TYPE_CHECKING:
     from sglang.srt.model_executor.model_runner import ModelRunner
 
+_is_hip = is_hip()
+
 
 def _to_torch(model: torch.nn.Module, reverse: bool, num_tokens: int):
     for sub in model._modules.values():

python/sglang/srt/model_executor/model_runner.py

@@ -320,7 +320,6 @@ class ModelRunner:
             logger.info(f"DeepEP is turned on. DeepEP mode: {server_args.deepep_mode}")
 
         if not self.use_mla_backend:
-            logger.info("Disable chunked prefix cache for non-MLA backend.")
             server_args.disable_chunked_prefix_cache = True
         elif self.page_size > 1:
             logger.info("Disable chunked prefix cache when page size > 1.")