[Hardware/NVIDIA/Kernel] Enable nvidia/DeepSeek-R1-FP4 Model (#16362)

vllm/model_executor/layers/quantization/modelopt.py

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Callable, Dict, List, Optional, Union
 
 import torch
 from torch.nn import Module
@@ -9,6 +9,8 @@ from torch.nn.parameter import Parameter
 from vllm._custom_ops import (cutlass_scaled_fp4_mm,
                               cutlass_scaled_mm_supports_fp4, scaled_fp4_quant)
 from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe.layer import (
+    FusedMoE, FusedMoEMethodBase, FusedMoeWeightScaleSupported)
 from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
                                                UnquantizedLinearMethod)
 from vllm.model_executor.layers.quantization import QuantizationMethods
@@ -210,25 +212,37 @@ class ModelOptNvFp4Config(QuantizationConfig):
                              "`hf_quant_config.json` file for your model's "
                              "quant configuration.")
         is_checkpoint_nvfp4_serialized = ("NVFP4" in quant_method)
-        kv_cache_quant_algo = quant_config["kv_cache_quant_algo"]
-        group_size = quant_config["group_size"]
-        exclude_modules = quant_config["exclude_modules"]
-        if not (group_size and kv_cache_quant_algo and exclude_modules):
+        if ("group_size" and "kv_cache_quant_algo"
+                and "exclude_modules") not in quant_config:
             raise ValueError("NVFP4 quantization requires group size and "
                              "kv_cache_quant_algo specified in "
                              "hf_quant_config.json")
+        kv_cache_quant_algo = quant_config["kv_cache_quant_algo"]
+        group_size = quant_config["group_size"]
+        exclude_modules = quant_config["exclude_modules"]
         return cls(is_checkpoint_nvfp4_serialized, kv_cache_quant_algo,
                    exclude_modules, group_size)
 
+    def is_layer_excluded(self, prefix: str, exclude_modules: List):
+        import re
+        for pattern in exclude_modules:
+            regex_str = pattern.replace('.', r'\.').replace('*', r'.*')
+            if re.fullmatch(regex_str, prefix):
+                return True
+        return False
+
     def get_quant_method(self, layer: torch.nn.Module,
                          prefix: str) -> Optional["QuantizeMethodBase"]:
         from vllm.attention.layer import Attention  # Avoid circular import
         if isinstance(layer, LinearBase):
-            if is_layer_skipped(prefix, self.exclude_modules):
+            if (is_layer_skipped(prefix, self.exclude_modules)
+                    or self.is_layer_excluded(prefix, self.exclude_modules)):
                 return UnquantizedLinearMethod()
             return ModelOptNvFp4LinearMethod(self)
         elif isinstance(layer, Attention):
             return ModelOptFp8KVCacheMethod(self)
+        elif isinstance(layer, FusedMoE):
+            return ModelOptNvFp4FusedMoE(self)
         return None
 
 
@@ -409,3 +423,235 @@ class ModelOptNvFp4LinearMethod(LinearMethodBase):
         if bias is not None:
             out = out + bias
         return out.view(*output_shape)
+
+
+class ModelOptNvFp4FusedMoE(FusedMoEMethodBase):
+    """
+    MoE Method for FP4 Quantization.
+    Args: 
+        quant_config: NVFP4 Quant Config
+    """
+
+    def __init__(self, quant_config: ModelOptNvFp4Config):
+        self.quant_config = quant_config
+
+    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):
+        if not self.quant_config.is_checkpoint_nvfp4_serialized:
+            raise ValueError("NVFP4 quantization was selected, "
+                             " dynamic quantization is not supported.")
+
+        layer.quant_config = self.quant_config
+        weight_dtype = torch.uint8
+        weight_scale_dtype = torch.float8_e4m3fn
+        weight_loader = extra_weight_attrs.get("weight_loader")
+        # GEMM 1
+        w13_weight = ModelWeightParameter(
+            data=torch.empty(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                # 2 fp4 items are packed in the input dimension
+                hidden_size // 2,
+                dtype=weight_dtype),
+            input_dim=1,
+            output_dim=2,
+            weight_loader=weight_loader)
+        layer.register_parameter("w13_weight", w13_weight)
+
+        # GEMM 2
+        w2_weight = ModelWeightParameter(
+            data=torch.empty(
+                num_experts,
+                hidden_size,
+                # 2 fp4 items are packed in the input dimension
+                intermediate_size_per_partition // 2,
+                dtype=weight_dtype),
+            input_dim=1,
+            output_dim=2,
+            weight_loader=weight_loader)
+        layer.register_parameter("w2_weight", w2_weight)
+
+        w13_weight_scale = ModelWeightParameter(
+            data=torch.empty(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                # 2 fp4 items are packed in the input dimension
+                hidden_size // self.quant_config.group_size,
+                dtype=weight_scale_dtype),
+            input_dim=1,
+            output_dim=2,
+            weight_loader=weight_loader)
+        layer.register_parameter("w13_weight_scale", w13_weight_scale)
+
+        w2_weight_scale = ModelWeightParameter(
+            data=torch.empty(
+                num_experts,
+                hidden_size,
+                # 2 fp4 items are packed in the input dimension
+                intermediate_size_per_partition //
+                self.quant_config.group_size,
+                dtype=weight_scale_dtype),
+            input_dim=1,
+            output_dim=2,
+            weight_loader=weight_loader)
+        layer.register_parameter("w2_weight_scale", w2_weight_scale)
+
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.BLOCK.value})
+
+        w13_weight_scale_2 = PerTensorScaleParameter(
+            data=torch.empty(num_experts, 2, dtype=torch.float32),
+            weight_loader=weight_loader)
+        layer.register_parameter("w13_weight_scale_2", w13_weight_scale_2)
+
+        w2_weight_scale_2 = PerTensorScaleParameter(
+            data=torch.empty(num_experts, dtype=torch.float32),
+            weight_loader=weight_loader)
+        layer.register_parameter("w2_weight_scale_2", w2_weight_scale_2)
+
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value})
+
+        w13_input_scale = PerTensorScaleParameter(data=torch.empty(
+            num_experts, 2, dtype=torch.float32),
+                                                  weight_loader=weight_loader)
+        layer.register_parameter("w13_input_scale", w13_input_scale)
+
+        w2_input_scale = PerTensorScaleParameter(data=torch.empty(
+            num_experts, dtype=torch.float32),
+                                                 weight_loader=weight_loader)
+        layer.register_parameter("w2_input_scale", w2_input_scale)
+
+    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:
+        # GEMM 1
+
+        assert torch.allclose(
+            layer.w13_weight_scale_2[:, 0], layer.w13_weight_scale_2[:, 1]), (
+                "Expected w1_weight_scale_2 to equal w3_weight_scale_2")
+
+        w13_weight_scale_2 = layer.w13_weight_scale_2[:, 0]
+        layer.w13_weight_scale_2 = Parameter(w13_weight_scale_2,
+                                             requires_grad=False)
+
+        w13_input_scale = layer.w13_input_scale.max(dim=1).values.to(
+            torch.float32)
+        layer.g1_alphas = Parameter(
+            (w13_input_scale * w13_weight_scale_2).to(torch.float32),
+            requires_grad=False)
+
+        assert (layer.w13_weight_scale.shape[2] % 16 == 0), (
+            "Expected weight_scale.dim(1) to be divisible by 16")
+        assert (layer.w13_weight_scale.dtype == torch.float8_e4m3fn), (
+            "Weight Blockscale must be represented as FP8-E4M3")
+        w13_blockscale_swizzled = self.swizzle_blockscale(
+            layer.w13_weight_scale)
+
+        layer.w13_blockscale_swizzled = Parameter(w13_blockscale_swizzled,
+                                                  requires_grad=False)
+
+        # This is for quantization, so we need to invert it.
+        layer.w13_input_scale_quant = Parameter(
+            (1 / w13_input_scale).to(torch.float32), requires_grad=False)
+
+        # GEMM 2
+        layer.g2_alphas = Parameter(
+            (layer.w2_input_scale * layer.w2_weight_scale_2).to(torch.float32),
+            requires_grad=False)
+
+        # This is for quantization, so we need to invert it.
+        layer.w2_input_scale_quant = Parameter(
+            (1 / layer.w2_input_scale).to(torch.float32), requires_grad=False)
+
+        assert (layer.w2_weight_scale.shape[2] % 16 == 0), (
+            "Expected weight_scale.dim(1) to be divisible by 16")
+        assert (layer.w2_weight_scale.dtype == torch.float8_e4m3fn), (
+            "Weight Blockscale must be represented as FP8-E4M3")
+        w2_blockscale_swizzled = self.swizzle_blockscale(layer.w2_weight_scale)
+
+        layer.w2_blockscale_swizzled = Parameter(w2_blockscale_swizzled,
+                                                 requires_grad=False)
+        return
+
+    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",
+    ):
+        assert activation == "silu", "Only SiLU activation is supported."
+        assert not apply_router_weight_on_input, (
+            "Router weight on input is not "
+            "supported for ModelOptNvFp4FusedMoE.")
+        assert expert_map is None, ("Expert Parallelism /expert_map "
+                                    "is currently not supported for "
+                                    "ModelOptNvFp4FusedMoE.")
+
+        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)
+
+        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)