[Revert] Remove CUDA torch fallbacks for fp8_mqa_logits/fp8_paged_mqa_logits_torch function

vllm/model_executor/layers/sparse_attn_indexer.py

@@ -9,13 +9,7 @@
 from vllm.logger import init_logger
 from vllm.model_executor.custom_op import CustomOp
 from vllm.platforms import current_platform
-from vllm.utils.deep_gemm import (
-    fp8_mqa_logits,
-    fp8_mqa_logits_torch,
-    fp8_paged_mqa_logits,
-    fp8_paged_mqa_logits_torch,
-    is_deep_gemm_supported,
-)
+from vllm.utils.deep_gemm import fp8_mqa_logits, fp8_paged_mqa_logits, has_deep_gemm
 from vllm.utils.torch_utils import direct_register_custom_op
 from vllm.v1.attention.backends.mla.indexer import (
     DeepseekV32IndexerMetadata,
@@ -114,23 +108,14 @@ def sparse_attn_indexer(
                 chunk.block_table,
                 chunk.cu_seq_lens,
             )
-            if is_deep_gemm_supported():
-                logits = fp8_mqa_logits(
-                    q_fp8[chunk.token_start : chunk.token_end],
-                    (k_fp8, k_scale.view(torch.float32).flatten()),
-                    weights[chunk.token_start : chunk.token_end],
-                    chunk.cu_seqlen_ks,
-                    chunk.cu_seqlen_ke,
-                    clean_logits=False,
-                )
-            else:
-                logits = fp8_mqa_logits_torch(
-                    q_fp8[chunk.token_start : chunk.token_end],
-                    (k_fp8, k_scale.view(torch.float32).flatten()),
-                    weights[chunk.token_start : chunk.token_end],
-                    chunk.cu_seqlen_ks,
-                    chunk.cu_seqlen_ke,
-                )
+            logits = fp8_mqa_logits(
+                q_fp8[chunk.token_start : chunk.token_end],
+                (k_fp8, k_scale.view(torch.float32).flatten()),
+                weights[chunk.token_start : chunk.token_end],
+                chunk.cu_seqlen_ks,
+                chunk.cu_seqlen_ke,
+                clean_logits=False,
+            )
             num_rows = logits.shape[0]
 
             topk_indices = topk_indices_buffer[
@@ -194,26 +179,16 @@ 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
-        if is_deep_gemm_supported():
-            logits = fp8_paged_mqa_logits(
-                padded_q_fp8_decode_tokens,
-                kv_cache,
-                weights[:num_padded_tokens],
-                decode_metadata.seq_lens,
-                decode_metadata.block_table,
-                decode_metadata.schedule_metadata,
-                max_model_len=max_model_len,
-                clean_logits=False,
-            )
-        else:
-            logits = fp8_paged_mqa_logits_torch(
-                padded_q_fp8_decode_tokens,
-                kv_cache,
-                weights[:num_padded_tokens],
-                decode_metadata.seq_lens,
-                decode_metadata.block_table,
-                max_model_len=max_model_len,
-            )
+        logits = fp8_paged_mqa_logits(
+            padded_q_fp8_decode_tokens,
+            kv_cache,
+            weights[:num_padded_tokens],
+            decode_metadata.seq_lens,
+            decode_metadata.block_table,
+            decode_metadata.schedule_metadata,
+            max_model_len=max_model_len,
+            clean_logits=False,
+        )
         num_rows = logits.shape[0]
         topk_indices = topk_indices_buffer[:num_padded_tokens, :topk_tokens]
 
@@ -333,12 +308,9 @@ def __init__(
         self.max_model_len = max_model_len
         self.max_total_seq_len = max_total_seq_len
         self.topk_indices_buffer = topk_indices_buffer
-        if current_platform.is_cuda() and not is_deep_gemm_supported():
-            logger.warning_once(
-                "DeepGEMM is not supported or available. SparseAttnIndexer will use a "
-                "less efficient PyTorch implementation. "
-                "Please make sure you have the required hardware and software setup "
-                "for DeepGEMM to achieve optimal performance."
+        if current_platform.is_cuda() and not has_deep_gemm():
+            raise RuntimeError(
+                "Sparse Attention Indexer CUDA op requires DeepGEMM to be installed."
             )
 
     def forward_native(

vllm/utils/deep_gemm.py

@@ -415,135 +415,14 @@ def should_use_deepgemm_for_fp8_linear(
     )
 
 
-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 (CUDA fallback).
-
-    This is a pure PyTorch fallback for CUDA when DeepGEMM is not available.
-
-    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_fp8, scale = kv
-    seq_len_kv = kv_fp8.shape[0]
-    k = kv_fp8.to(torch.bfloat16)
-    q = q.to(torch.bfloat16)
-
-    mask_lo = (
-        torch.arange(0, seq_len_kv, device=q.device)[None, :] >= cu_seqlen_ks[:, None]
-    )
-    mask_hi = (
-        torch.arange(0, seq_len_kv, device=q.device)[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 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,
-) -> torch.Tensor:
-    """Compute FP8 MQA logits using paged KV-cache (CUDA fallback).
-
-    This is a pure PyTorch fallback for CUDA when DeepGEMM is not available.
-    Handles head_dim = 132 (128 + 4 for RoPE).
-
-    Args:
-        q: Query tensor of shape [B, next_n, H, D].
-        kv_cache: 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.
-        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`.
-    """
-    fp8_dtype = current_platform.fp8_dtype()
-    batch_size, next_n, heads, 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_blocks, block_size, _, dim = kv_cache.size()
-    logits = torch.full(
-        [batch_size * next_n, max_model_len],
-        float("-inf"),
-        device=q.device,
-        dtype=torch.float32,
-    )
-    for i in range(batch_size):
-        context_len = context_lens[i].item()
-        q_offsets = torch.arange(context_len - next_n, context_len, device=q.device)
-        weight_slice = (
-            weights[i * next_n : (i + 1) * next_n, :].transpose(0, 1).contiguous()
-        )
-        for block_idx in range(cdiv(context_len, block_size)):
-            block_id = block_tables[i][block_idx]
-            qx, kx = q[i], kv_cache[block_id]
-            k_offsets = torch.arange(
-                block_idx * block_size, (block_idx + 1) * block_size, device=q.device
-            )
-            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_idx * block_size : (block_idx + 1) * block_size,
-            ] = torch.where(k_offsets[None, :] <= q_offsets[:, None], s, float("-inf"))
-    return logits
-
-
 __all__ = [
     "calc_diff",
     "DeepGemmQuantScaleFMT",
     "fp8_gemm_nt",
     "m_grouped_fp8_gemm_nt_contiguous",
     "fp8_m_grouped_gemm_nt_masked",
     "fp8_mqa_logits",
-    "fp8_mqa_logits_torch",
     "fp8_paged_mqa_logits",
-    "fp8_paged_mqa_logits_torch",
     "get_paged_mqa_logits_metadata",
     "per_block_cast_to_fp8",
     "is_deep_gemm_e8m0_used",

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

@@ -9,6 +9,7 @@
 from vllm.platforms import current_platform
 from vllm.utils.deep_gemm import (
     get_paged_mqa_logits_metadata,
+    has_deep_gemm,
     is_deep_gemm_supported,
 )
 from vllm.utils.math_utils import cdiv
@@ -449,7 +450,7 @@ def build(
                 batch_size = num_decodes
 
             # DeepGEMM is required for the paged MQA logits on CUDA devices
-            if current_platform.is_cuda() and is_deep_gemm_supported():
+            if current_platform.is_cuda() and has_deep_gemm():
                 self.scheduler_metadata_buffer[:] = get_paged_mqa_logits_metadata(
                     seq_lens,
                     self.kv_cache_spec.block_size,