[Kernels] Support blocked fp8 quantization for compressed tensors MoE (#25219)

Signed-off-by: Bill Nell <[email protected]>
Co-authored-by: Michael Goin <[email protected]>
Signed-off-by: yewentao256 <[email protected]>

Author: 2 people

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

@@ -13,6 +13,7 @@
 import vllm.envs as envs
 import vllm.model_executor.layers.fused_moe.modular_kernel as mk
 from vllm import _custom_ops as ops
+from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe import (
     FusedMoE, FusedMoEActivationFormat, FusedMoEConfig, FusedMoEMethodBase,
@@ -31,6 +32,9 @@
 from vllm.model_executor.layers.quantization.utils.flashinfer_fp4_moe import (
     build_flashinfer_fp4_cutlass_moe_prepare_finalize, reorder_w1w3_to_w3w1,
     select_nvfp4_gemm_impl)
+from vllm.model_executor.layers.quantization.utils.fp8_utils import (
+    expert_weight_is_col_major, get_col_major_tma_aligned_tensor,
+    requant_weight_ue8m0_inplace)
 from vllm.model_executor.layers.quantization.utils.marlin_utils import (
     check_moe_marlin_supports_layer, marlin_make_workspace_new,
     marlin_moe_permute_scales)
@@ -45,6 +49,7 @@
 from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import current_platform
 from vllm.scalar_type import scalar_types
+from vllm.utils.deep_gemm import is_deep_gemm_e8m0_used
 
 logger = init_logger(__name__)
 
@@ -505,10 +510,12 @@ def __init__(
             self.weight_quant.strategy == QuantizationStrategy.CHANNEL
             and self.input_quant.strategy == QuantizationStrategy.TOKEN)
         if not (per_tensor or per_channel):
-            raise ValueError(
-                "For FP8 Fused MoE layers, we require per tensor "
-                "or channelwise, dynamic per token quantization. Found "
-                f"{self.weight_quant}, {self.input_quant}")
+            assert self.weight_quant.strategy == QuantizationStrategy.BLOCK
+            self.weight_block_size = self.weight_quant.block_structure
+            assert self.weight_quant.dynamic is not None
+        else:
+            self.weight_block_size = None
+        self.block_quant = self.weight_block_size is not None
 
         self.static_input_scales = not self.input_quant.dynamic
         if self.static_input_scales and per_channel:
@@ -519,7 +526,8 @@ def __init__(
         # For GPUs that lack FP8 hardware support, we can leverage the Marlin
         # kernel for fast weight-only FP8 quantization
         self.use_marlin = (not current_platform.has_device_capability(89)
-                           or envs.VLLM_TEST_FORCE_FP8_MARLIN)
+                           or envs.VLLM_TEST_FORCE_FP8_MARLIN
+                           and not self.block_quant)
         # Disable marlin for rocm
         if current_platform.is_rocm():
             self.use_marlin = False
@@ -531,8 +539,9 @@ def __init__(
         # cutlass path
         self.is_fp8_w8a8_sm100 = quant_config._is_fp8_w8a8_sm100(
             self.weight_quant, self.input_quant)
-        self.use_cutlass = (quant_config._is_fp8_w8a8_sm90(
-            self.weight_quant, self.input_quant) or self.is_fp8_w8a8_sm100)
+        self.use_cutlass = not self.block_quant and (
+            quant_config._is_fp8_w8a8_sm90(self.weight_quant, self.input_quant)
+            or self.is_fp8_w8a8_sm100)
         self.disable_expert_map = False
 
     def create_weights(self, layer: torch.nn.Module, num_experts: int,
@@ -547,6 +556,31 @@ def create_weights(self, layer: torch.nn.Module, num_experts: int,
 
         params_dtype = torch.float8_e4m3fn
 
+        if self.block_quant:
+            assert self.weight_block_size is not None
+            layer.weight_block_size = self.weight_block_size
+            tp_size = get_tensor_model_parallel_world_size()
+            block_n, block_k = (
+                self.weight_block_size[0],
+                self.weight_block_size[1],
+            )
+            # NOTE: To ensure proper alignment of the block-wise quantization
+            # scales, the output_size of the weights for both the gate and up
+            # layers must be divisible by block_n.
+            # Required by column parallel or enabling merged weights
+            if intermediate_size_per_partition % block_n != 0:
+                raise ValueError(
+                    f"The output_size of gate's and up's weight = "
+                    f"{intermediate_size_per_partition} is not divisible by "
+                    f"weight quantization block_n = {block_n}.")
+            if (tp_size > 1
+                    and intermediate_size_per_partition % block_k != 0):
+                # Required by row parallel
+                raise ValueError(
+                    f"The input_size of down's weight = "
+                    f"{intermediate_size_per_partition} is not divisible by "
+                    f"weight quantization block_k = {block_k}.")
+
         # WEIGHTS
         w13_weight = torch.nn.Parameter(torch.empty(
             num_experts,
@@ -602,6 +636,27 @@ def create_weights(self, layer: torch.nn.Module, num_experts: int,
             set_weight_attrs(w13_weight_scale, extra_weight_attrs)
             set_weight_attrs(w2_weight_scale, extra_weight_attrs)
 
+        elif self.weight_quant.strategy == QuantizationStrategy.BLOCK:
+            w13_weight_scale = torch.nn.Parameter(torch.ones(
+                num_experts,
+                2 *
+                ((intermediate_size_per_partition + block_n - 1) // block_n),
+                (hidden_size + block_k - 1) // block_k,
+                dtype=torch.float32),
+                                                  requires_grad=False)
+            layer.register_parameter("w13_weight_scale", w13_weight_scale)
+            w2_weight_scale = torch.nn.Parameter(torch.ones(
+                num_experts, (hidden_size + block_n - 1) // block_n,
+                (intermediate_size_per_partition + block_k - 1) // block_k,
+                dtype=torch.float32),
+                                                 requires_grad=False)
+            layer.register_parameter("w2_weight_scale", w2_weight_scale)
+            # Add PER-CHANNEL quantization for FusedMoE.weight_loader.
+            extra_weight_attrs.update(
+                {"quant_method": FusedMoeWeightScaleSupported.BLOCK.value})
+            set_weight_attrs(w13_weight_scale, extra_weight_attrs)
+            set_weight_attrs(w2_weight_scale, extra_weight_attrs)
+
         # INPUT_SCALES
         if self.static_input_scales:
             w13_input_scale = torch.nn.Parameter(torch.ones(
@@ -706,6 +761,7 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
             del layer.w2_input_scale
 
         if self.use_cutlass:
+            assert self.weight_quant.strategy != QuantizationStrategy.BLOCK
             device = layer.w13_weight.device
             # ab_strides1 and c_strides2 are the same
             self.ab_strides1_c_strides2 = torch.full(
@@ -724,6 +780,29 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
                 device=device,
                 dtype=torch.int64)
 
+        if is_deep_gemm_e8m0_used() and self.block_quant:
+            assert layer.weight_block_size is not None
+            # Re-quantise the expert weights so their scales are UE8M0.
+            block_sz = tuple(layer.weight_block_size)
+            requant_weight_ue8m0_inplace(
+                layer.w13_weight.data,
+                layer.w13_weight_scale.data,
+                block_sz,
+            )
+            requant_weight_ue8m0_inplace(
+                layer.w2_weight.data,
+                layer.w2_weight_scale.data,
+                block_sz,
+            )
+
+            # Ensure column-major TMA alignment expected by DeepGEMM.
+            if expert_weight_is_col_major(layer.w13_weight_scale):
+                layer.w13_weight_scale = get_col_major_tma_aligned_tensor(
+                    layer.w13_weight_scale)
+            if expert_weight_is_col_major(layer.w2_weight_scale):
+                layer.w2_weight_scale = get_col_major_tma_aligned_tensor(
+                    layer.w2_weight_scale)
+
     def maybe_make_prepare_finalize(
             self) -> Optional[mk.FusedMoEPrepareAndFinalize]:
         if self.use_marlin or self.rocm_aiter_moe_enabled:
@@ -777,9 +856,10 @@ def select_gemm_impl(
             return experts
 
         # triton path
-        from vllm.model_executor.layers.fused_moe import TritonExperts
-        from vllm.model_executor.layers.fused_moe.fused_batched_moe import (
-            BatchedTritonExperts)
+        from vllm.model_executor.layers.fused_moe.batched_triton_or_deep_gemm_moe import (  # noqa: E501
+            BatchedTritonOrDeepGemmExperts)
+        from vllm.model_executor.layers.fused_moe.triton_deep_gemm_moe import (
+            TritonOrDeepGemmExperts)
 
         assert not self.rocm_aiter_moe_enabled and not self.use_marlin
 
@@ -790,14 +870,16 @@ def select_gemm_impl(
             assert max_num_tokens_per_rank is not None
 
             logger.debug("BatchedTritonExperts(%s)", self.__class__.__name__)
-            return BatchedTritonExperts(
+            return BatchedTritonOrDeepGemmExperts(
                 max_num_tokens=max_num_tokens_per_rank,
                 num_dispatchers=prepare_finalize.num_dispatchers(),
                 quant_config=self.moe_quant_config,
             )
         else:
-            logger.debug("TritonExperts(%s)", self.__class__.__name__)
-            return TritonExperts(self.moe_quant_config)
+            logger.debug("TritonOrDeepGemmExperts(%s)",
+                         self.__class__.__name__)
+            return TritonOrDeepGemmExperts(self.moe_quant_config,
+                                           allow_deep_gemm=True)
 
     def get_fused_moe_quant_config(
             self, layer: torch.nn.Module) -> Optional[FusedMoEQuantConfig]:
@@ -816,6 +898,7 @@ def get_fused_moe_quant_config(
             a2_scale=layer.w2_input_scale,
             per_act_token_quant=per_act_token,
             per_out_ch_quant=per_channel_quant,
+            block_shape=layer.weight_block_size,
         )
 
     def apply(

vllm/model_executor/layers/quantization/fp8.py

@@ -33,10 +33,10 @@
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
     apply_fp8_block_linear, check_aiter_fp8_linear_support,
     create_fp8_input_scale, create_fp8_scale_parameter,
-    create_fp8_weight_parameter, get_col_major_tma_aligned_tensor,
-    maybe_post_process_fp8_weight_block, process_fp8_weight_block_strategy,
-    process_fp8_weight_tensor_strategy, requant_weight_ue8m0_inplace,
-    validate_fp8_block_shape)
+    create_fp8_weight_parameter, expert_weight_is_col_major,
+    get_col_major_tma_aligned_tensor, maybe_post_process_fp8_weight_block,
+    process_fp8_weight_block_strategy, process_fp8_weight_tensor_strategy,
+    requant_weight_ue8m0_inplace, validate_fp8_block_shape)
 from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
     apply_fp8_marlin_linear, prepare_fp8_layer_for_marlin,
     prepare_moe_fp8_layer_for_marlin)
@@ -64,12 +64,6 @@
 logger = init_logger(__name__)
 
 
-def _is_col_major(x: torch.Tensor) -> bool:
-    assert x.dim() == 3
-    b, m, n = x.shape
-    return x.stride(0) == m * n and x.stride(1) == 1 and x.stride(2) == m
-
-
 class Fp8Config(QuantizationConfig):
     """Config class for FP8."""
 
@@ -660,10 +654,10 @@ def process_weights_after_loading(self, layer: Module) -> None:
             # DeepGemm scales need to be transposed and aligned. We try to do
             # it ahead of time for performance reasons.
             if self.allow_deep_gemm and not is_deep_gemm_e8m0_used():
-                if _is_col_major(layer.w13_weight_scale_inv):
+                if expert_weight_is_col_major(layer.w13_weight_scale_inv):
                     layer.w13_weight_scale_inv = \
                         get_col_major_tma_aligned_tensor(layer.w13_weight_scale_inv)
-                if _is_col_major(layer.w2_weight_scale_inv):
+                if expert_weight_is_col_major(layer.w2_weight_scale_inv):
                     layer.w2_weight_scale_inv = \
                         get_col_major_tma_aligned_tensor(layer.w2_weight_scale_inv)
 
@@ -811,10 +805,10 @@ def process_weights_after_loading(self, layer: Module) -> None:
             )
 
             # Ensure column-major TMA alignment expected by DeepGEMM.
-            if _is_col_major(layer.w13_weight_scale_inv):
+            if expert_weight_is_col_major(layer.w13_weight_scale_inv):
                 layer.w13_weight_scale_inv = get_col_major_tma_aligned_tensor(
                     layer.w13_weight_scale_inv)
-            if _is_col_major(layer.w2_weight_scale_inv):
+            if expert_weight_is_col_major(layer.w2_weight_scale_inv):
                 layer.w2_weight_scale_inv = get_col_major_tma_aligned_tensor(
                     layer.w2_weight_scale_inv)
 

vllm/model_executor/layers/quantization/utils/fp8_utils.py

@@ -1014,3 +1014,9 @@ def apply_fp8_block_linear(layer: torch.nn.Module, input: torch.Tensor,
         cutlass_block_fp8_supported=cutlass_block_fp8_supported,
         use_aiter_and_is_supported=use_aiter_and_is_supported,
     )
+
+
+def expert_weight_is_col_major(x: torch.Tensor) -> bool:
+    assert x.dim() == 3
+    b, m, n = x.shape
+    return x.stride(0) == m * n and x.stride(1) == 1 and x.stride(2) == m

vllm/model_executor/warmup/deep_gemm_warmup.py

@@ -53,9 +53,9 @@ def _extract_data_from_fused_moe_module(
     """
     assert isinstance(m, FusedMoE)
     w13 = m.w13_weight
-    w13_s = m.w13_weight_scale_inv
+    w13_s = getattr(m, "w13_weight_scale_inv", m.w13_weight_scale)
     w2 = m.w2_weight
-    w2_s = m.w2_weight_scale_inv
+    w2_s = getattr(m, "w2_weight_scale_inv", m.w2_weight_scale)
     num_topk = m.top_k
 
     assert isinstance(w13, torch.Tensor)