Sync from v0.13

vllm/v1/attention/backends/mla/cutlass_mla.py

@@ -0,0 +1,278 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import os
+from typing import ClassVar
+
+import torch
+
+import vllm._custom_ops as ops
+from vllm.attention.backends.abstract import (
+    AttentionLayer,
+    AttentionType,
+    MultipleOf,
+    is_quantized_kv_cache,
+)
+from vllm.config.cache import CacheDType
+from vllm.logger import init_logger
+from vllm.platforms.interface import DeviceCapability
+from vllm.v1.attention.backends.mla.common import (
+    MLACommonBackend,
+    MLACommonImpl,
+    MLACommonMetadata,
+    MLACommonMetadataBuilder,
+)
+from vllm.v1.attention.backends.utils import AttentionCGSupport
+
+logger = init_logger(__name__)
+
+
+class CutlassMLAMetadataBuilder(MLACommonMetadataBuilder[MLACommonMetadata]):
+    # enable full CUDA Graph support for decode-only capture
+    _cudagraph_support: ClassVar[AttentionCGSupport] = (
+        AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE
+    )
+
+
+class CutlassMLABackend(MLACommonBackend):
+    supported_dtypes: ClassVar[list[torch.dtype]] = [torch.float16, torch.bfloat16]
+    supported_kv_cache_dtypes: ClassVar[list[CacheDType]] = [
+        "auto",
+        "fp8",
+        "fp8_e4m3",
+    ]
+
+    @staticmethod
+    def get_supported_kernel_block_sizes() -> list[int | MultipleOf]:
+        return [128]
+
+    @staticmethod
+    def get_name() -> str:
+        return "CUTLASS_MLA"
+
+    @staticmethod
+    def get_impl_cls() -> type["CutlassMLAImpl"]:
+        return CutlassMLAImpl
+
+    @staticmethod
+    def get_builder_cls() -> type["CutlassMLAMetadataBuilder"]:
+        return CutlassMLAMetadataBuilder
+
+    @classmethod
+    def supports_compute_capability(cls, capability: DeviceCapability) -> bool:
+        return capability.major == 10
+
+
+class SM100Workspace:
+    def __init__(self, initial_workspace_size):
+        self._workspace_buf = torch.empty(
+            initial_workspace_size, device="cuda", dtype=torch.uint8
+        )
+
+        self._block_size = 128  # Forced to 128
+
+        # Pre-compute sm_count to avoid recomputing it. Use device 0 as a proxy
+        # (assumes all devices are similar)
+        properties = torch.cuda.get_device_properties(torch.device("cuda:0"))
+        self._sm_count = properties.multi_processor_count
+
+    def get_buf(self):
+        return self._workspace_buf
+
+    def ensure_size(self, attn_metadata: MLACommonMetadata, num_kv_splits: int):
+        batch_size = attn_metadata.num_reqs
+        max_seq_len = attn_metadata.max_query_len
+
+        workspace_size = ops.sm100_cutlass_mla_get_workspace_size(
+            max_seq_len * self._block_size,
+            batch_size,
+            self._sm_count,
+            num_kv_splits=num_kv_splits,
+        )
+
+        if self._workspace_buf.shape[0] < workspace_size:
+            self._workspace_buf.resize_(workspace_size)
+
+
+g_sm100_workspace = SM100Workspace(128 * 1024 * 1024)  # 128MB
+
+MAX_HEADS = 128
+
+
+class CutlassMLAImpl(MLACommonImpl[MLACommonMetadata]):
+    can_return_lse_for_decode: bool = True
+
+    def __init__(
+        self,
+        num_heads: int,
+        head_size: int,
+        scale: float,
+        num_kv_heads: int,
+        alibi_slopes: list[float] | None,
+        sliding_window: int | None,
+        kv_cache_dtype: str,
+        logits_soft_cap: float | None,
+        attn_type: str,
+        kv_sharing_target_layer_name: str | None,
+        # MLA Specific Arguments
+        **mla_args,
+    ) -> None:
+        super().__init__(
+            num_heads,
+            head_size,
+            scale,
+            num_kv_heads,
+            alibi_slopes,
+            sliding_window,
+            kv_cache_dtype,
+            logits_soft_cap,
+            attn_type,
+            kv_sharing_target_layer_name,
+            q_pad_num_heads=MAX_HEADS,
+            **mla_args,
+        )
+
+        unsupported_features = [alibi_slopes, sliding_window, logits_soft_cap]
+        if any(unsupported_features):
+            raise NotImplementedError(
+                "CutlassMLAImpl does not support one of the following: "
+                "alibi_slopes, sliding_window, logits_soft_cap"
+            )
+
+        if attn_type != AttentionType.DECODER:
+            raise NotImplementedError(
+                "Encoder self-attention and "
+                "encoder/decoder cross-attention "
+                "are not implemented for "
+                "CutlassMLAImpl"
+            )
+
+        # TODO: Currently, num_kv_splits is limited to 16 to avoid hanging
+        #       issues. In case the code hangs, use:
+        #       FORCE_NUM_KV_SPLITS=1
+        force_num_kv_splits = os.environ.get("FORCE_NUM_KV_SPLITS", None)
+        if force_num_kv_splits:
+            logger.debug_once("Forcing num_kv_splits to %d", int(force_num_kv_splits))
+            self._num_kv_splits = int(force_num_kv_splits)
+        else:
+            self._num_kv_splits = -1  # => Auto-detect
+
+        # Share workspace buffer across all executions
+        self._workspace = g_sm100_workspace
+
+    def _sm100_cutlass_mla_decode(
+        self,
+        q_nope: torch.Tensor,
+        q_pe: torch.Tensor,
+        kv_c_and_k_pe_cache: torch.Tensor,
+        seq_lens: torch.Tensor,
+        page_table: torch.Tensor,
+        workspace: torch.Tensor,
+        sm_scale: float,
+        num_kv_splits: int,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        assert q_nope.ndim == 3, f"q_nope must be a 3D tensor, but got {q_nope.ndim}"
+        assert q_pe.ndim == 3, f"q_pe must be a 3D tensor, but got {q_pe.ndim}"
+        assert kv_c_and_k_pe_cache.ndim == 3, (
+            "kv_c_and_k_pe_cache must be a 3D tensor, but got {}".format(
+                kv_c_and_k_pe_cache.ndim
+            )
+        )
+
+        B_q, H, D_q_nope = q_nope.shape
+        B_q_2, H_2, D_q_pe = q_pe.shape
+        assert (B_q == B_q_2) and (H == H_2)
+
+        _, PAGE_SIZE, D_ckv = kv_c_and_k_pe_cache.shape
+
+        D_latent = 512
+        D_rope = 64
+        assert D_q_nope == D_latent
+        assert D_q_pe == D_rope
+        assert D_ckv == D_latent + D_rope
+
+        MAX_HEADS = 128
+        assert H <= MAX_HEADS, f"H must be <= {MAX_HEADS}, but got {H}"
+
+        assert len(page_table.shape) == 2
+        B_block_table, block_num = page_table.shape
+        assert B_block_table == B_q
+        assert block_num > 0, f"block num must be greater than 0, got {block_num}"
+        assert block_num % (128 / PAGE_SIZE) == 0
+
+        assert q_nope.dtype in (torch.float16, torch.bfloat16, torch.float8_e4m3fn), (
+            f"q_nope.dtype needs to be fp16 or bf16 or e4m3 but got {q_nope.dtype}."
+        )
+        assert q_nope.dtype == q_pe.dtype == kv_c_and_k_pe_cache.dtype
+        assert seq_lens.dtype == torch.int32, (
+            f"seq_lens.dtype needs to be int32 but got {seq_lens.dtype}."
+        )
+        assert page_table.dtype == torch.int32, (
+            f"page_table.dtype needs to be int32 but got {page_table.dtype}."
+        )
+
+        dtype = (
+            torch.bfloat16
+            if is_quantized_kv_cache(self.kv_cache_dtype)
+            else q_nope.dtype
+        )
+        out = q_nope.new_empty((B_q, MAX_HEADS, D_latent), dtype=dtype)
+        lse = (
+            torch.empty((B_q, MAX_HEADS), dtype=torch.float32, device=q_nope.device)
+            if self.need_to_return_lse_for_decode
+            else torch.Tensor()
+        )
+
+        ops.sm100_cutlass_mla_decode(
+            out,
+            lse,
+            q_nope,
+            q_pe,
+            kv_c_and_k_pe_cache,
+            seq_lens,
+            page_table,
+            workspace,
+            sm_scale,
+            num_kv_splits,
+        )
+
+        if H < MAX_HEADS:
+            # Extract the subsets of the outputs
+            lse = lse[:, :H] if self.need_to_return_lse_for_decode else lse
+            out = out[:, :H]
+
+        return out, lse
+
+    def _forward_decode(
+        self,
+        q: torch.Tensor | tuple[torch.Tensor, torch.Tensor],
+        kv_c_and_k_pe_cache: torch.Tensor,
+        attn_metadata: MLACommonMetadata,
+        layer: AttentionLayer,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        assert kv_c_and_k_pe_cache.numel() > 0
+        assert attn_metadata.decode is not None
+
+        if type(q) is tuple:
+            q_nope, q_pe = q
+        else:
+            q_nope, q_pe = torch.split(
+                q, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
+            )
+
+        # Adjust workspace size (if necessary)
+        self._workspace.ensure_size(attn_metadata, self._num_kv_splits)
+
+        # Run MLA
+        o, lse = self._sm100_cutlass_mla_decode(
+            q_nope,
+            q_pe,
+            kv_c_and_k_pe_cache,
+            attn_metadata.decode.seq_lens,
+            attn_metadata.decode.block_table,
+            self._workspace.get_buf(),
+            self.scale,
+            self._num_kv_splits,
+        )
+
+        return o, (lse if self.need_to_return_lse_for_decode else None)