[ROCm] Add AMD GPU support on Deepseek v3.2 and SparseMLA

csrc/cache_kernels.cu

@@ -552,7 +552,11 @@ __global__ void indexer_k_quant_and_cache_kernel(
 #ifndef USE_ROCM
   __syncwarp();
 #endif
+#if defined(__gfx942__)
+  float scale = fmaxf(amax, 1e-4) / 224.0f;
+#else
   float scale = fmaxf(amax, 1e-4) / 448.0f;
+#endif
   if (use_ue8m0) {
     scale = exp2f(ceilf(log2f(scale)));
   }

vllm/attention/ops/rocm_aiter_mla_sparse.py

@@ -0,0 +1,210 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import importlib
+from functools import lru_cache
+
+import torch
+
+from vllm._aiter_ops import rocm_aiter_ops
+from vllm.logger import init_logger
+from vllm.platforms import current_platform
+
+logger = init_logger(__name__)
+
+
+# Take from https://github.com/deepseek-ai/DeepGEMM/blob/main/tests/test_attention.py#L84
+def fp8_mqa_logits_torch(
+    q: torch.Tensor,
+    kv: tuple[torch.Tensor, torch.Tensor],
+    weights: torch.Tensor,
+    cu_seqlen_ks: torch.Tensor,
+    cu_seqlen_ke: torch.Tensor,
+) -> torch.Tensor:
+    """Compute FP8 MQA logits for a single sequence without KV paging.
+
+    Args:
+        q: Query tensor of shape [M, H, D]. Casted to
+            `torch.float8_e4m3fn` by caller.
+        kv: Tuple `(k_fp8, k_scales)` where `k_fp8` has shape [N, D] with
+            dtype `torch.float8_e4m3fn` and `k_scales` has shape [N] (or
+            [N, 1]) with dtype `torch.float32`.
+        weights: weights of shape [M, H], dtype `torch.float32`.
+        cu_seqlen_ks: Start indices (inclusive) for valid K per query position,
+            shape [M], dtype int32.
+        cu_seqlen_ke: End indices (exclusive) for valid K per query position,
+            shape [M], dtype int32.
+
+    Returns:
+        Logits tensor of shape [M, N], dtype `torch.float32`.
+    """
+    kv, scale = kv
+    seq_len_kv = kv.shape[0]
+    k = kv.to(torch.bfloat16)
+    q = q.to(torch.bfloat16)
+
+    mask_lo = (
+        torch.arange(0, seq_len_kv, device="cuda")[None, :] >= cu_seqlen_ks[:, None]
+    )
+    mask_hi = (
+        torch.arange(0, seq_len_kv, device="cuda")[None, :] < cu_seqlen_ke[:, None]
+    )
+    mask = mask_lo & mask_hi
+
+    score = torch.einsum("mhd,nd->hmn", q, k).float() * scale
+    logits = (score.relu() * weights.unsqueeze(-1).transpose(0, 1)).sum(dim=0)
+    logits = logits.masked_fill(~mask, float("-inf"))
+
+    return logits
+
+
+def rocm_fp8_mqa_logits(
+    q: torch.Tensor,
+    kv: tuple[torch.Tensor, torch.Tensor],
+    weights: torch.Tensor,
+    cu_seqlen_ks: torch.Tensor,
+    cu_seqlen_ke: torch.Tensor,
+) -> torch.Tensor:
+    """Compute FP8 MQA logits for a single sequence without KV paging.
+
+    Args:
+        q: Query tensor of shape [M, H, D]. Casted to
+            `torch.float8_e4m3fn` by caller.
+        kv: Tuple `(k_fp8, k_scales)` where `k_fp8` has shape [N, D] with
+            dtype `torch.float8_e4m3fn` and `k_scales` has shape [N] (or
+            [N, 1]) with dtype `torch.float32`.
+        weights: weights of shape [M, H], dtype `torch.float32`.
+        cu_seqlen_ks: Start indices (inclusive) for valid K per query position,
+            shape [M], dtype int32.
+        cu_seqlen_ke: End indices (exclusive) for valid K per query position,
+            shape [M], dtype int32.
+
+    Returns:
+        Logits tensor of shape [M, N], dtype `torch.float32`.
+    """
+
+    # TODO(ganyi): Temporarily workaround, will remove the module check and reference
+    # path after aiter merge this kernel into main
+    @lru_cache
+    def has_mqa_logits_module():
+        return importlib.util.find_spec("aiter.ops.triton.fp8_mqa_logits") is not None
+
+    if rocm_aiter_ops.is_enabled() and has_mqa_logits_module():
+        from aiter.ops.triton.fp8_mqa_logits import fp8_mqa_logits
+
+        kv, scale = kv
+        return fp8_mqa_logits(q, kv, scale, weights, cu_seqlen_ks, cu_seqlen_ke)
+    else:
+        return fp8_mqa_logits_torch(q, kv, weights, cu_seqlen_ks, cu_seqlen_ke)
+
+
+# Taken from https://github.com/deepseek-ai/DeepGEMM/blob/main/tests/test_attention.py#L156
+def fp8_paged_mqa_logits_torch(
+    q: torch.Tensor,
+    kv_cache: torch.Tensor,
+    weights: torch.Tensor,
+    context_lens: torch.Tensor,
+    block_tables: torch.Tensor,
+    max_model_len: int,
+):
+    from vllm.utils.math_utils import cdiv
+
+    fp8_dtype = current_platform.fp8_dtype()
+    batch_size, next_n, _, dim = q.size()
+    kv_cache, scale = kv_cache[..., :dim], kv_cache[..., dim:]
+    scale = scale.contiguous().view(torch.float)
+    q = q.float()
+    kv_cache = kv_cache.view(fp8_dtype).float() * scale
+    num_block, block_size, _, dim = kv_cache.size()
+    logits = torch.full(
+        [batch_size * next_n, max_model_len],
+        float("-inf"),
+        device=q.device,
+        dtype=torch.float32,
+    )
+    context_lens = context_lens.tolist()
+    for i in range(batch_size):
+        context_len = context_lens[i]
+        q_offsets = torch.arange(context_len - next_n, context_len, device="cuda")
+        weight_slice = (
+            weights[i * next_n : (i + 1) * next_n, :].transpose(0, 1).contiguous()
+        )
+        for block_rk in range(cdiv(context_len, block_size)):
+            block_idx = block_tables[i][block_rk]
+            qx, kx = q[i], kv_cache[block_idx]
+            k_offsets = torch.arange(
+                block_rk * block_size, (block_rk + 1) * block_size, device="cuda"
+            )
+            mask = (k_offsets[None, :] < context_len) & (
+                k_offsets[None, :] <= q_offsets[:, None]
+            )
+            s = torch.where(
+                mask[None, :, :],
+                (qx.transpose(0, 1) @ kx.transpose(0, 1).transpose(1, 2)).to(
+                    logits.dtype
+                ),
+                float("-inf"),
+            )
+            s = torch.relu(s) * weight_slice[..., None]
+            s = s.sum(dim=0)
+            logits[
+                i * next_n : (i + 1) * next_n,
+                block_rk * block_size : (block_rk + 1) * block_size,
+            ] = torch.where(k_offsets[None, :] <= q_offsets[:, None], s, float("-inf"))
+    return logits
+
+
+def rocm_fp8_paged_mqa_logits(
+    q_fp8: torch.Tensor,
+    kv_cache_fp8: torch.Tensor,
+    weights: torch.Tensor,
+    context_lens: torch.Tensor,
+    block_tables: torch.Tensor,
+    schedule_metadata: torch.Tensor,
+    max_model_len: int,
+) -> torch.Tensor:
+    """Compute FP8 MQA logits using paged KV-cache.
+
+    Args:
+        q_fp8: Query tensor of shape [B, next_n, H, D]. Casted to
+            `torch.float8_e4m3fn` by caller.
+        kv_cache_fp8: Paged KV-cache in packed FP8+scale layout with shape
+            [num_blocks, block_size, 1, D+4], dtype `torch.uint8`. The last
+            4 bytes per (block,pos) store the `float` dequant scale.
+        weights: Tensor of shape [B * next_n, H], dtype `torch.float32`.
+        context_lens: Tensor of shape [B], dtype int32; effective context length
+            for each batch element.
+        block_tables: Tensor of shape [B, max_blocks], dtype int32; maps logical
+            block indices to physical blocks in the paged cache.
+        schedule_metadata: Returned by `get_paged_mqa_logits_metadata`;
+            used to distribute work across SMs.
+        max_model_len: Maximum sequence length used to size the logits output.
+
+    Returns:
+        Logits tensor of shape [B * next_n, max_model_len], dtype
+        `torch.float32`.
+    """
+
+    if rocm_aiter_ops.is_enabled():
+        from aiter.ops.triton.pa_mqa_logits import deepgemm_fp8_paged_mqa_logits_stage1
+
+        batch_size, next_n, heads, _ = q_fp8.shape
+        out_qk = torch.full(
+            (heads, batch_size * next_n, max_model_len),
+            float("-inf"),
+            device="cuda",
+            dtype=torch.float32,
+        )
+        deepgemm_fp8_paged_mqa_logits_stage1(
+            q_fp8,
+            kv_cache_fp8,
+            weights,
+            out_qk,
+            context_lens,
+            block_tables,
+            max_model_len,
+        )
+        return out_qk.sum(dim=0)
+    else:
+        return fp8_paged_mqa_logits_torch(
+            q_fp8, kv_cache_fp8, weights, context_lens, block_tables, max_model_len
+        )

vllm/model_executor/models/deepseek_v2.py

@@ -591,6 +591,7 @@ def sparse_attn_indexer(
 ) -> torch.Tensor:
     # careful! this will be None in dummy run
     attn_metadata = get_forward_context().attn_metadata
+    fp8_dtype = current_platform.fp8_dtype()
     # assert isinstance(attn_metadata, dict)
     if not isinstance(attn_metadata, dict):
         return sparse_attn_indexer_fake(
@@ -630,7 +631,7 @@ def sparse_attn_indexer(
             k_fp8 = torch.empty(
                 [chunk.total_seq_lens, head_dim],
                 device=k.device,
-                dtype=torch.float8_e4m3fn,
+                dtype=fp8_dtype,
             )
             k_scale = torch.empty(
                 [chunk.total_seq_lens, 4],
@@ -644,7 +645,12 @@ def sparse_attn_indexer(
                 chunk.block_table,
                 chunk.cu_seq_lens,
             )
-            logits = fp8_mqa_logits(
+            fp8_mqa_logits_func = fp8_mqa_logits
+            if current_platform.is_rocm():
+                from vllm.attention.ops.rocm_aiter_mla_sparse import rocm_fp8_mqa_logits
+
+                fp8_mqa_logits_func = rocm_fp8_mqa_logits
+            logits = fp8_mqa_logits_func(
                 q_fp8[chunk.token_start : chunk.token_end],
                 (k_fp8, k_scale.view(torch.float32)),
                 weights[chunk.token_start : chunk.token_end],
@@ -689,7 +695,14 @@ def sparse_attn_indexer(
         next_n = padded_q_fp8_decode_tokens.shape[1]
         assert batch_size == decode_metadata.seq_lens.shape[0]
         num_padded_tokens = batch_size * next_n
-        logits = fp8_paged_mqa_logits(
+        fp8_paged_mqa_logits_func = fp8_paged_mqa_logits
+        if current_platform.is_rocm():
+            from vllm.attention.ops.rocm_aiter_mla_sparse import (
+                rocm_fp8_paged_mqa_logits,
+            )
+
+            fp8_paged_mqa_logits_func = rocm_fp8_paged_mqa_logits
+        logits = fp8_paged_mqa_logits_func(
             padded_q_fp8_decode_tokens,
             kv_cache,
             weights[:num_padded_tokens],
@@ -746,7 +759,8 @@ def sparse_attn_indexer_fake(
     _flattened_kv = torch.empty(
         [total_seq_lens, head_dim + 4], device=k.device, dtype=torch.uint8
     )
-    _k_fp8 = _flattened_kv[..., :head_dim].view(torch.float8_e4m3fn).contiguous()
+    fp8_dtype = current_platform.fp8_dtype()
+    _k_fp8 = _flattened_kv[..., :head_dim].view(fp8_dtype).contiguous()
     _k_scale = _flattened_kv[..., head_dim:].view(torch.float32).contiguous()
     return topk_indices_buffer
 

vllm/platforms/rocm.py

@@ -225,7 +225,18 @@ def get_attn_backend_cls(
         from vllm.attention.backends.registry import AttentionBackendEnum
 
         if use_sparse:
-            raise NotImplementedError("Sparse Attention is not supported on ROCm.")
+            if kv_cache_dtype.startswith("fp8"):
+                raise ValueError(
+                    "ROCMAiterMLASparseBackend doesn't support fp8 kv_cache_dtype."
+                )
+            assert block_size == 1, (
+                "Sparse MLA backend on ROCm only supports block size 1 for now."
+            )
+            logger.info_once("Using Sparse MLA backend on V1 engine.")
+            return (
+                "vllm.v1.attention.backends.mla.rocm_aiter_mla_sparse."
+                "ROCMAiterMLASparseBackend"
+            )
 
         if use_mla:
             if selected_backend is None:

vllm/utils/deep_gemm.py

@@ -325,6 +325,7 @@ def _align(x: int, y: int) -> int:
 def per_block_cast_to_fp8(
     x: torch.Tensor, block_size: list[int] = DEFAULT_BLOCK_SIZE, use_ue8m0: bool = False
 ) -> tuple[torch.Tensor, torch.Tensor]:
+    fp8_dtype = current_platform.fp8_dtype()
     assert x.dim() == 2
     m, n = x.shape
     block_m, block_n = block_size
@@ -334,9 +335,9 @@ def per_block_cast_to_fp8(
     x_padded[:m, :n] = x
     x_view = x_padded.view(-1, block_m, x_padded.size(1) // block_n, block_n)
     x_amax = x_view.abs().float().amax(dim=(1, 3), keepdim=True).clamp(1e-4)
-    sf = x_amax / 448.0
+    sf = x_amax / 224.0 if current_platform.is_fp8_fnuz() else x_amax / 448.0
     sf = _ceil_to_ue8m0(sf) if use_ue8m0 else sf
-    x_scaled = (x_view * (1.0 / sf)).to(torch.float8_e4m3fn)
+    x_scaled = (x_view * (1.0 / sf)).to(fp8_dtype)
     return x_scaled.view_as(x_padded)[:m, :n].contiguous(), sf.view(
         x_view.size(0), x_view.size(2)
     )

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

@@ -168,7 +168,7 @@ def _convert_req_index_to_global_index_kernel(
     inblock_off = tok % BLOCK_SIZE
 
     # Guard block_table access
-    valid_block = block_id < max_num_blocks_per_req
+    valid_block = (block_id < max_num_blocks_per_req) & (block_id >= 0)
     bt_ptr = block_table_ptr + req * bt_stride0 + block_id * bt_stride1
     base = tl.load(bt_ptr, mask=valid_block, other=0)
 

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

@@ -11,7 +11,8 @@
 )
 from vllm.config import VllmConfig
 from vllm.logger import init_logger
-from vllm.utils.deep_gemm import get_paged_mqa_logits_metadata
+from vllm.platforms import current_platform
+from vllm.utils.deep_gemm import get_paged_mqa_logits_metadata, is_deep_gemm_supported
 from vllm.v1.attention.backends.utils import (
     AttentionCGSupport,
     AttentionMetadataBuilder,
@@ -23,7 +24,9 @@
 
 
 class DeepseekV32IndexerBackend(AttentionBackend):
-    supported_kernel_block_sizes: ClassVar[list[int | MultipleOf]] = [64]
+    supported_kernel_block_sizes: ClassVar[list[int | MultipleOf]] = [
+        1 if current_platform.is_rocm() else 64
+    ]
 
     @classmethod
     def get_supported_head_sizes(cls) -> list[int]:
@@ -328,10 +331,10 @@ def build(
             requires_padding = (decode_lens_cpu.max() > decode_lens_cpu.min()).item()
 
             seq_lens = common_attn_metadata.seq_lens[:num_decodes]
-
-            self.scheduler_metadata_buffer[:] = get_paged_mqa_logits_metadata(
-                seq_lens, self.kv_cache_spec.block_size, self.num_sms
-            )
+            if is_deep_gemm_supported():
+                self.scheduler_metadata_buffer[:] = get_paged_mqa_logits_metadata(
+                    seq_lens, self.kv_cache_spec.block_size, self.num_sms
+                )
             decode_metadata = DeepSeekV32IndexerDecodeMetadata(
                 block_table=common_attn_metadata.block_table_tensor[:num_decodes, ...],
                 seq_lens=common_attn_metadata.seq_lens[:num_decodes],