[Quantization] Add compressed-tensors NVFP4 MoE Support (#19990)

Signed-off-by: Dipika Sikka <dipikasikka1@gmail.com>
Signed-off-by: Dipika <dipikasikka1@gmail.com>

tests/quantization/test_compressed_tensors.py

@@ -17,7 +17,7 @@ from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tenso
     CompressedTensorsW4A4Fp4, CompressedTensorsW4A16Fp4,
     CompressedTensorsW4A16Sparse24, CompressedTensorsW8A8Fp8,
     CompressedTensorsW8A8Int8, CompressedTensorsW8A16Fp8,
-    CompressedTensorsWNA16)
+    CompressedTensorsWNA16, cutlass_fp4_supported)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     sparse_cutlass_supported)
 from vllm.platforms import current_platform
@@ -668,8 +668,8 @@ def test_compressed_tensors_nvfp4(vllm_runner, args):
             assert isinstance(qkv_proj.quant_method,
                               CompressedTensorsLinearMethod)
             if isinstance(qkv_proj.scheme, scheme) or isinstance(
-                    qkv_proj.scheme, CompressedTensorsW4A16Fp4
-            ) and not CompressedTensorsW4A4Fp4.cutlass_fp4_supported():
+                    qkv_proj.scheme,
+                    CompressedTensorsW4A16Fp4) and not cutlass_fp4_supported():
                 assert True
             else:
                 raise AssertionError("FP4 Scheme Mismatch")

vllm/model_executor/layers/fused_moe/layer.py

@@ -1246,6 +1246,7 @@ class FusedMoE(torch.nn.Module):
             param.materialize(final_shape, dtype=loaded_weight.dtype)
 
         expert_data = param.data if full_load else param.data[expert_id]
+
         # Case input scale: input_scale loading is only supported for fp8
         if "input_scale" in weight_name:
             # this is needed for compressed-tensors only
@@ -1273,6 +1274,7 @@ class FusedMoE(torch.nn.Module):
                              tp_rank=self.tp_rank)
             return True if return_success else None
 
+        # TODO @dsikka: ModelOpt should follow the proper MoE loading pattern
         if "ModelOpt" in quant_method_name:
             if ('weight_scale_2' in weight_name
                     or 'input_scale' in weight_name):
@@ -1289,7 +1291,7 @@ class FusedMoE(torch.nn.Module):
                     tp_rank=self.tp_rank)
             return True if return_success else None
 
-        # Case weight scales, zero_points and offset
+        # Case weight scales, zero_points and offset, weight/input global scales
         if ("scale" in weight_name or "zero" in weight_name
                 or "offset" in weight_name):
             # load the weight scales and zp based on the quantization scheme

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

@@ -33,6 +33,8 @@ from vllm.model_executor.layers.quantization.compressed_tensors.utils import (
     find_matched_target, is_activation_quantization_format,
     should_ignore_layer)
 from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
+from vllm.model_executor.layers.quantization.utils.nvfp4_emulation_utils import (  # noqa: E501
+    cutlass_fp4_supported)
 from vllm.platforms import current_platform
 
 logger = init_logger(__name__)
@@ -375,7 +377,7 @@ class CompressedTensorsConfig(QuantizationConfig):
 
         if is_activation_quantization_format(self.quant_format):
             if self._is_fp4a4_nvfp4(weight_quant, input_quant):
-                if CompressedTensorsW4A4Fp4.cutlass_fp4_supported(
+                if cutlass_fp4_supported(
                 ) or envs.VLLM_USE_NVFP4_CT_EMULATIONS:
                     return CompressedTensorsW4A4Fp4()
                 else:

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

@@ -21,8 +21,12 @@ from vllm.model_executor.layers.quantization.utils import replace_parameter
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (
     check_moe_marlin_supports_layer, marlin_make_workspace_new,
     marlin_moe_permute_scales)
+from vllm.model_executor.layers.quantization.utils.marlin_utils_fp4 import (
+    prepare_moe_fp4_layer_for_marlin)
 from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
     prepare_moe_fp8_layer_for_marlin)
+from vllm.model_executor.layers.quantization.utils.nvfp4_emulation_utils import (  # noqa: E501
+    cutlass_fp4_supported)
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     all_close_1d, normalize_e4m3fn_to_e4m3fnuz, per_tensor_dequantize)
 from vllm.model_executor.utils import set_weight_attrs
@@ -46,12 +50,11 @@ class GPTQMarlinState(Enum):
 
 
 __all__ = [
-    "CompressedTensorsMoEMethod",
-    "CompressedTensorsW8A8Fp8MoEMethod",
+    "CompressedTensorsMoEMethod", "CompressedTensorsW8A8Fp8MoEMethod",
     "CompressedTensorsW8A8Fp8MoECutlassMethod",
     "CompressedTensorsW8A8Int8MoEMethod",
-    "CompressedTensorsWNA16MarlinMoEMethod",
-    "CompressedTensorsWNA16MoEMethod",
+    "CompressedTensorsWNA16MarlinMoEMethod", "CompressedTensorsWNA16MoEMethod",
+    "CompressedTensorsW4A4MoeMethod"
 ]
 
 
@@ -84,6 +87,8 @@ class CompressedTensorsMoEMethod(FusedMoEMethodBase):
             else:
                 logger.info_once("Using CompressedTensorsWNA16MarlinMoEMethod")
                 return CompressedTensorsWNA16MarlinMoEMethod(quant_config)
+        elif quant_config._is_fp4a4_nvfp4(weight_quant, input_quant):
+            return CompressedTensorsW4A4MoeMethod()
         elif quant_config._is_fp8_w8a8_sm90(weight_quant, input_quant):
             return CompressedTensorsW8A8Fp8MoECutlassMethod(quant_config)
         elif quant_config._is_fp8_w8a8(weight_quant, input_quant):
@@ -95,6 +100,268 @@ class CompressedTensorsMoEMethod(FusedMoEMethodBase):
                 f"Unsupported FusedMoe scheme: {weight_quant}, {input_quant}")
 
 
+class CompressedTensorsW4A4MoeMethod(CompressedTensorsMoEMethod):
+
+    def __init__(self):
+        self.use_marlin = not cutlass_fp4_supported()
+        self.group_size = 16
+
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size_per_partition: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+
+        layer.num_experts = num_experts
+        layer.params_dtype = params_dtype
+
+        w13_weight = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                # 2 fp4 items are packed in the input dimension
+                hidden_size // 2,
+                requires_grad=False,
+                dtype=torch.uint8),
+            requires_grad=False)
+        layer.register_parameter("w13_weight_packed", w13_weight)
+        set_weight_attrs(w13_weight, extra_weight_attrs)
+
+        w2_weight = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                hidden_size,
+                # 2 fp4 items are packed in the input dimension
+                intermediate_size_per_partition // 2,
+                dtype=torch.uint8),
+            requires_grad=False)
+        layer.register_parameter("w2_weight_packed", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        # Weight Scales
+        w13_weight_scale = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                # 2 fp4 items are packed in the input dimension
+                hidden_size // self.group_size,
+                dtype=torch.float8_e4m3fn),
+            requires_grad=False)
+        layer.register_parameter("w13_weight_scale", w13_weight_scale)
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.GROUP.value})
+        set_weight_attrs(w13_weight_scale, extra_weight_attrs)
+
+        w2_weight_scale = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                hidden_size,
+                # 2 fp4 items are packed in the input dimension
+                intermediate_size_per_partition // self.group_size,
+                dtype=torch.float8_e4m3fn),
+            requires_grad=False)
+        layer.register_parameter("w2_weight_scale", w2_weight_scale)
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.GROUP.value})
+        set_weight_attrs(w2_weight_scale, extra_weight_attrs)
+
+        # Weight Global Scales
+        w13_weight_scale_2 = torch.nn.Parameter(torch.empty(
+            num_experts, 2, dtype=torch.float32),
+                                                requires_grad=False)
+        layer.register_parameter("w13_weight_global_scale", w13_weight_scale_2)
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value})
+        set_weight_attrs(w13_weight_scale_2, extra_weight_attrs)
+
+        w2_weight_scale_2 = torch.nn.Parameter(torch.empty(
+            num_experts, dtype=torch.float32),
+                                               requires_grad=False)
+        layer.register_parameter("w2_weight_global_scale", w2_weight_scale_2)
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value})
+        set_weight_attrs(w2_weight_scale_2, extra_weight_attrs)
+
+        # Input Global Scales
+        w13_input_scale = torch.nn.Parameter(torch.empty(num_experts,
+                                                         2,
+                                                         dtype=torch.float32),
+                                             requires_grad=False)
+        layer.register_parameter("w13_input_global_scale", w13_input_scale)
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value})
+        set_weight_attrs(w13_input_scale, extra_weight_attrs)
+
+        w2_input_scale = torch.nn.Parameter(torch.empty(num_experts,
+                                                        dtype=torch.float32),
+                                            requires_grad=False)
+        layer.register_parameter("w2_input_global_scale", w2_input_scale)
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value})
+        set_weight_attrs(w2_input_scale, extra_weight_attrs)
+
+    def swizzle_blockscale(self, scale: torch.tensor):
+        assert (scale.dtype == torch.float8_e4m3fn)
+        # Pad and blockwise interleave weight_scale
+        scale_ndim = scale.ndim
+        if scale.ndim == 2:
+            scale = scale.unsqueeze(0)
+        assert scale.ndim == 3
+        B, M, K = scale.shape
+        round_up_multiple = lambda x, m: (x + m - 1) // m * m
+        M_padded = round_up_multiple(M, 128)
+        K_padded = round_up_multiple(K, 4)
+        padded_scale = torch.zeros((B, M_padded, K_padded), dtype=scale.dtype)
+        padded_scale[:B, :M, :K] = scale
+        batches, rows, cols = padded_scale.shape
+        assert rows % 128 == 0
+        assert cols % 4 == 0
+        padded_scale = padded_scale.reshape(batches, rows // 128, 4, 32,
+                                            cols // 4, 4)
+        swizzled_scale = padded_scale.permute((0, 1, 4, 3, 2, 5))
+        swizzled_scale = swizzled_scale.contiguous().cuda()
+        return (swizzled_scale.reshape(M, K)
+                if scale_ndim == 2 else swizzled_scale.reshape(B, M, K))
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+
+        # From packed to weight
+        layer.w13_weight = torch.nn.Parameter(layer.w13_weight_packed.data,
+                                              requires_grad=False)
+
+        layer.w2_weight = torch.nn.Parameter(layer.w2_weight_packed.data,
+                                             requires_grad=False)
+
+        if not torch.allclose(layer.w13_weight_global_scale[:, 0],
+                              layer.w13_weight_global_scale[:, 1]):
+            logger.warning_once(
+                "w1_weight_global_scale must match w3_weight_global_scale. "
+                "Accuracy may be affected.")
+
+        # Take inverse of global scale saved to disk
+        layer.w13_weight_scale_2 = torch.nn.Parameter(
+            1 / layer.w13_weight_global_scale[:, 0], requires_grad=False)
+
+        layer.w2_weight_scale_2 = torch.nn.Parameter(
+            1 / layer.w2_weight_global_scale.data, requires_grad=False)
+
+        if self.use_marlin:
+            prepare_moe_fp4_layer_for_marlin(layer)
+            return
+
+        # swizzle weight scales
+        layer.w13_blockscale_swizzled = torch.nn.Parameter(
+            self.swizzle_blockscale(layer.w13_weight_scale),
+            requires_grad=False)
+
+        layer.w2_blockscale_swizzled = torch.nn.Parameter(
+            self.swizzle_blockscale(layer.w2_weight_scale),
+            requires_grad=False)
+
+        # w13
+        w13_input_global_scale = layer.w13_input_global_scale.max(
+            dim=1).values.to(torch.float32)
+
+        layer.g1_alphas = torch.nn.Parameter(
+            ((1 / w13_input_global_scale) * layer.w13_weight_scale_2),
+            requires_grad=False)
+
+        layer.w13_input_scale_quant = torch.nn.Parameter(
+            (w13_input_global_scale), requires_grad=False)
+
+        # w2
+        layer.g2_alphas = torch.nn.Parameter(
+            ((1 / layer.w2_input_global_scale) * layer.w2_weight_scale_2).to(
+                torch.float32),
+            requires_grad=False)
+
+        layer.w2_input_scale_quant = torch.nn.Parameter(
+            (layer.w2_input_global_scale), requires_grad=False)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool,
+        use_grouped_topk: bool = False,
+        topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
+        global_num_experts: int = -1,
+        expert_map: Optional[torch.Tensor] = None,
+        custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
+        enable_eplb: bool = False,
+        expert_load_view: Optional[torch.Tensor] = None,
+        logical_to_physical_map: Optional[torch.Tensor] = None,
+        logical_replica_count: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if enable_eplb:
+            raise NotImplementedError("EPLB not supported for "
+                                      "`CompressedTensorsW4A4MoeMethod` yet.")
+
+        topk_weights, topk_ids = FusedMoE.select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function,
+            scoring_func=scoring_func,
+            e_score_correction_bias=e_score_correction_bias,
+        )
+
+        if self.use_marlin:
+            return torch.ops.vllm.fused_marlin_moe(
+                x,
+                layer.w13_weight,
+                layer.w2_weight,
+                layer.w13_weight_scale,
+                layer.w2_weight_scale,
+                router_logits,
+                topk_weights,
+                topk_ids,
+                global_scale1=layer.w13_weight_scale_2,
+                global_scale2=layer.w2_weight_scale_2,
+                quant_type_id=scalar_types.float4_e2m1f.id,
+                global_num_experts=global_num_experts,
+                expert_map=expert_map)
+
+        assert activation == "silu", "Only SiLU activation is supported."
+        assert not apply_router_weight_on_input, (
+            "Router weight on input is not "
+            "supported for CompressedTensorsW4A4MoeMethod.")
+        assert expert_map is None, ("Expert Parallelism / expert_map "
+                                    "is currently not supported for "
+                                    "CompressedTensorsW4A4MoeMethod.")
+
+        from vllm.model_executor.layers.fused_moe.cutlass_moe import (
+            cutlass_moe_fp4)
+
+        # Cutlass moe takes in activations in BF16/Half precision
+        # and fp4 quantized weights loaded from the checkpoint
+        return cutlass_moe_fp4(a=x,
+                               w1_fp4=layer.w13_weight,
+                               w1_blockscale=layer.w13_blockscale_swizzled,
+                               w1_alphas=layer.g1_alphas,
+                               w2_fp4=layer.w2_weight,
+                               w2_blockscale=layer.w2_blockscale_swizzled,
+                               w2_alphas=layer.g2_alphas,
+                               topk_weights=topk_weights,
+                               topk_ids=topk_ids,
+                               m=x.shape[0],
+                               n=layer.w2_weight.shape[2] * 2,
+                               k=x.shape[1],
+                               e=layer.w13_weight.shape[0],
+                               a1_gscale=layer.w13_input_scale_quant,
+                               a2_gscale=layer.w2_input_scale_quant,
+                               device=x.device).to(x.dtype)
+
+
 class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
 
     def __init__(

vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w4a4_nvfp4.py

@@ -5,8 +5,7 @@ import torch
 from torch.nn.parameter import Parameter
 
 import vllm.envs as envs
-from vllm._custom_ops import (cutlass_scaled_fp4_mm,
-                              cutlass_scaled_mm_supports_fp4, scaled_fp4_quant)
+from vllm._custom_ops import cutlass_scaled_fp4_mm, scaled_fp4_quant
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization.compressed_tensors.schemes import (
     CompressedTensorsScheme)
@@ -15,7 +14,6 @@ from vllm.model_executor.layers.quantization.utils.nvfp4_emulation_utils import
 from vllm.model_executor.parameter import (GroupQuantScaleParameter,
                                            ModelWeightParameter,
                                            PerTensorScaleParameter)
-from vllm.platforms import current_platform
 
 logger = init_logger(__name__)
 
@@ -33,15 +31,6 @@ class CompressedTensorsW4A4Fp4(CompressedTensorsScheme):
             return 80
         return 100
 
-    @classmethod
-    def cutlass_fp4_supported(cls) -> bool:
-        if not current_platform.is_cuda():
-            return False
-        capability_tuple = current_platform.get_device_capability()
-        capability = -1 if capability_tuple is None else capability_tuple.to_int(  # noqa: E501
-        )
-        return cutlass_scaled_mm_supports_fp4(capability)
-
     def create_weights(self, layer: torch.nn.Module,
                        output_partition_sizes: list[int],
                        input_size_per_partition: int,

vllm/model_executor/layers/quantization/utils/nvfp4_emulation_utils.py

@@ -2,9 +2,14 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import torch
 
+from vllm._custom_ops import cutlass_scaled_mm_supports_fp4
+from vllm.platforms import current_platform
 from vllm.scalar_type import scalar_types
 
-__all__ = ["break_fp4_bytes", "dequantize_to_dtype", "ref_nvfp4_quant"]
+__all__ = [
+    "break_fp4_bytes", "dequantize_to_dtype", "ref_nvfp4_quant",
+    "cutlass_fp4_supported"
+]
 
 FLOAT4_E2M1_MAX = scalar_types.float4_e2m1f.max()
 
@@ -12,6 +17,14 @@ kE2M1ToFloat = torch.tensor([0., 0.5, 1., 1.5, 2., 3., 4., 6.],
                             dtype=torch.float32)
 
 
+def cutlass_fp4_supported() -> bool:
+    if not current_platform.is_cuda():
+        return False
+    capability_tuple = current_platform.get_device_capability()
+    capability = -1 if capability_tuple is None else capability_tuple.to_int()
+    return cutlass_scaled_mm_supports_fp4(capability)
+
+
 def break_fp4_bytes(a, dtype):
     assert a.dtype == torch.uint8
     m, n = a.shape