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tool_server/.venv/lib/python3.12/site-packages/vllm/v1/attention/backends/mla/rocm_aiter_mla.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from dataclasses import dataclass
+from typing import ClassVar, Optional
+
+import torch
+
+import vllm.envs as envs
+from vllm.attention.ops.rocm_aiter_mla import aiter_mla_decode_fwd
+from vllm.config import VllmConfig
+from vllm.utils import cdiv
+# yapf conflicts with isort for this docstring
+# yapf: disable
+from vllm.v1.attention.backends.mla.common import (MLACommonBackend,
+                                                   MLACommonDecodeMetadata,
+                                                   MLACommonImpl,
+                                                   MLACommonMetadata,
+                                                   MLACommonMetadataBuilder)
+from vllm.v1.attention.backends.utils import AttentionCGSupport
+from vllm.v1.kv_cache_interface import AttentionSpec
+
+# yapf: enable
+
+
+def is_aiter_mla_enabled() -> bool:
+    return envs.VLLM_ROCM_USE_AITER \
+        and envs.VLLM_ROCM_USE_AITER_MLA
+
+
+class AiterMLABackend(MLACommonBackend):
+
+    @staticmethod
+    def get_name() -> str:
+        return "ROCM_AITER_MLA_VLLM_V1"
+
+    @staticmethod
+    def get_impl_cls() -> type["AiterMLAImpl"]:
+        return AiterMLAImpl
+
+    @staticmethod
+    def get_metadata_cls() -> type["AiterMLAMetadata"]:
+        return AiterMLAMetadata
+
+    @staticmethod
+    def get_builder_cls() -> type["AiterMLAMetadataBuilder"]:
+        return AiterMLAMetadataBuilder
+
+
+@dataclass
+class AiterMLADecodeMetadata(MLACommonDecodeMetadata):
+    # The indptr of the paged kv cache, shape: [batch_size + 1]
+    paged_kv_indptr: Optional[torch.Tensor] = None
+    # The page indices of the paged kv cache
+    paged_kv_indices: Optional[torch.Tensor] = None
+    # The number of entries in the last page of each request in
+    # the paged kv cache, shape: [batch_size]
+    paged_kv_last_page_len: Optional[torch.Tensor] = None
+    # The query indptr, shape : [num_decode + 1]
+    qo_indptr: Optional[torch.Tensor] = None
+
+
+class AiterMLAMetadata(MLACommonMetadata[AiterMLADecodeMetadata]):
+    pass
+
+
+class AiterMLAMetadataBuilder(MLACommonMetadataBuilder[AiterMLAMetadata]):
+    # TODO(luka, lucas): audit this as part of:
+    #  https://github.com/vllm-project/vllm/issues/22945
+    cudagraph_support: ClassVar[AttentionCGSupport] = \
+        AttentionCGSupport.UNIFORM_SINGLE_TOKEN_DECODE
+
+    def __init__(self, kv_cache_spec: AttentionSpec, layer_names: list[str],
+                 vllm_config: VllmConfig, device: torch.device):
+        super().__init__(kv_cache_spec, layer_names, vllm_config, device,
+                         AiterMLAMetadata)
+        assert self.kv_cache_spec.block_size == 1, "AITER MLA" \
+            "only supports block size 1."
+
+        self.compilation_config = vllm_config.compilation_config
+        max_num_pages_per_req = cdiv(vllm_config.model_config.max_model_len,
+                                     self.kv_cache_spec.block_size)
+        max_num_reqs = vllm_config.scheduler_config.max_num_seqs
+        max_num_pages = max_num_reqs * max_num_pages_per_req
+
+        # Preparing persistent buffers
+        # TODO: we can disambiguate between decode and mixed-prefill decode here
+        # so we can only use the persistent buffer if a cudagraph is actually
+        # being used.
+        if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
+            self.paged_kv_indptr = torch.zeros(max_num_reqs + 1,
+                                               dtype=torch.int32,
+                                               device=device)
+            self.paged_kv_indices = torch.zeros(max_num_pages,
+                                                dtype=torch.int32,
+                                                device=device)
+            self.paged_kv_last_page_len = torch.zeros(max_num_reqs,
+                                                      dtype=torch.int32,
+                                                      device=device)
+
+            self.qo_indptr = torch.arange(0,
+                                          max_num_reqs + 1,
+                                          dtype=torch.int32,
+                                          device=device)
+
+    def _build_decode(self, block_table_tensor: torch.Tensor,
+                      seq_lens: torch.Tensor) -> AiterMLADecodeMetadata:
+        page_size = self.kv_cache_spec.block_size
+        block_table_bounds = (seq_lens + page_size - 1) // page_size
+        device = self.device
+        num_reqs = seq_lens.size(0)
+
+        mask = (torch.arange(block_table_tensor.size(1),
+                             dtype=block_table_tensor.dtype,
+                             device=device).unsqueeze(0)
+                < block_table_bounds.unsqueeze(1))
+        paged_kv_indices = block_table_tensor[mask]
+
+        paged_kv_last_page_len = seq_lens % page_size
+        paged_kv_last_page_len = torch.where(paged_kv_last_page_len == 0,
+                                             page_size, paged_kv_last_page_len)
+
+        paged_kv_indptr = torch.cat([
+            torch.zeros(1, dtype=block_table_bounds.dtype, device=device),
+            block_table_bounds.cumsum(dim=0, dtype=torch.int32)
+        ])
+
+        if self.compilation_config.cudagraph_mode.has_full_cudagraphs():
+
+            num_actual_pages = paged_kv_indices.size(0)
+
+            self.paged_kv_indices[:num_actual_pages].copy_(paged_kv_indices,
+                                                           non_blocking=True)
+            self.paged_kv_indices[num_actual_pages:].fill_(-1)
+            paged_kv_indices = self.paged_kv_indices[:num_actual_pages]
+
+            self.paged_kv_indptr[:1 + num_reqs].copy_(paged_kv_indptr,
+                                                      non_blocking=True)
+            self.paged_kv_indptr[1 + num_reqs:].fill_(paged_kv_indptr[-1])
+            paged_kv_indptr = self.paged_kv_indptr[:1 + num_reqs]
+
+            self.paged_kv_last_page_len[:num_reqs].copy_(
+                paged_kv_last_page_len, non_blocking=True)
+            self.paged_kv_last_page_len[num_reqs:].fill_(1)
+            paged_kv_last_page_len = self.paged_kv_last_page_len[:num_reqs]
+
+            qo_indptr = self.qo_indptr[:1 + num_reqs]
+
+        else:
+            qo_indptr = torch.arange(0,
+                                     num_reqs + 1,
+                                     step=1,
+                                     dtype=torch.int32,
+                                     device=device)
+
+        attn_metadata = AiterMLADecodeMetadata(
+            block_table=block_table_tensor,
+            seq_lens=seq_lens,
+            paged_kv_indptr=paged_kv_indptr,
+            paged_kv_indices=paged_kv_indices,
+            paged_kv_last_page_len=paged_kv_last_page_len,
+            qo_indptr=qo_indptr)
+
+        return attn_metadata
+
+
+class AiterMLAImpl(MLACommonImpl[AiterMLAMetadata]):
+
+    def __init__(
+            self,
+            num_heads: int,
+            head_size: int,
+            scale: float,
+            num_kv_heads: int,
+            alibi_slopes: Optional[list[float]],
+            sliding_window: Optional[int],
+            kv_cache_dtype: str,
+            logits_soft_cap: Optional[float],
+            attn_type: str,
+            kv_sharing_target_layer_name: Optional[str],
+            # 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, **mla_args)
+        assert (num_heads == 16 or num_heads == 128), (
+            f"Aiter MLA only supports 16 or 128 number of heads.\n"
+            f"Provided {num_heads} number of heads.\n"
+            "Try adjusting tensor_parallel_size value.")
+        unsupported_features = [alibi_slopes, sliding_window, logits_soft_cap]
+        if any(unsupported_features):
+            raise NotImplementedError(
+                "Aiter MLA does not support one of the following: "
+                "alibi_slopes, sliding_window, logits_soft_cap")
+
+        from aiter import flash_attn_varlen_func
+        self.flash_attn_varlen_func = flash_attn_varlen_func
+
+    def _flash_attn_varlen_diff_headdims(self,
+                                         q,
+                                         k,
+                                         v,
+                                         return_softmax_lse=False,
+                                         softmax_scale=None,
+                                         **kwargs):
+        output = self.flash_attn_varlen_func(
+            q=q,
+            k=k,
+            v=v,
+            softmax_scale=softmax_scale,
+            return_lse=return_softmax_lse,
+            **kwargs,
+        )
+
+        return output
+
+    def _forward_decode(
+        self,
+        q_nope: torch.Tensor,
+        q_pe: torch.Tensor,
+        kv_c_and_k_pe_cache: torch.Tensor,
+        attn_metadata: AiterMLAMetadata,
+    ) -> torch.Tensor:
+        assert kv_c_and_k_pe_cache.numel() > 0
+        assert attn_metadata.decode is not None
+
+        B = q_nope.shape[0]
+
+        q = torch.cat([q_nope, q_pe], dim=-1)
+        o = torch.zeros(B,
+                        self.num_heads,
+                        self.kv_lora_rank,
+                        dtype=q.dtype,
+                        device=q.device)
+
+        kv_buffer = kv_c_and_k_pe_cache.unsqueeze(2)
+
+        # max_seqlen_qo must be 1 except for MTP
+        # TODO: Find the best value for MTP
+        max_seqlen_qo = 1
+        aiter_mla_decode_fwd(q, kv_buffer, o, self.scale,
+                             attn_metadata.decode.qo_indptr, max_seqlen_qo,
+                             attn_metadata.decode.paged_kv_indptr,
+                             attn_metadata.decode.paged_kv_indices,
+                             attn_metadata.decode.paged_kv_last_page_len)
+
+        return self._v_up_proj(o)

tool_server/.venv/lib/python3.12/site-packages/vllm/v1/attention/backends/mla/triton_mla.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Optional
+
+import torch
+
+from vllm import envs
+from vllm.attention.backends.abstract import (AttentionType,
+                                              is_quantized_kv_cache)
+from vllm.attention.ops.triton_decode_attention import decode_attention_fwd
+from vllm.attention.ops.triton_flash_attention import triton_attention
+from vllm.logger import init_logger
+from vllm.platforms import current_platform
+from vllm.triton_utils import HAS_TRITON
+from vllm.v1.attention.backends.mla.common import (MLACommonBackend,
+                                                   MLACommonImpl,
+                                                   MLACommonMetadata)
+
+logger = init_logger(__name__)
+
+
+class TritonMLABackend(MLACommonBackend):
+
+    @staticmethod
+    def get_name() -> str:
+        return "TRITON_MLA_VLLM_V1"
+
+    @staticmethod
+    def get_impl_cls() -> type["TritonMLAImpl"]:
+        return TritonMLAImpl
+
+
+class TritonMLAImpl(MLACommonImpl[MLACommonMetadata]):
+
+    def __init__(
+            self,
+            num_heads: int,
+            head_size: int,
+            scale: float,
+            num_kv_heads: int,
+            alibi_slopes: Optional[list[float]],
+            sliding_window: Optional[int],
+            kv_cache_dtype: str,
+            logits_soft_cap: Optional[float],
+            attn_type: str,
+            kv_sharing_target_layer_name: Optional[str],
+            # 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, **mla_args)
+
+        unsupported_features = [alibi_slopes, sliding_window, logits_soft_cap]
+        if any(unsupported_features):
+            raise NotImplementedError(
+                "TritonMLAImpl 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 "
+                                      "TritonMLAImpl")
+
+        if is_quantized_kv_cache(self.kv_cache_dtype):
+            raise NotImplementedError(
+                "TritonMLA V1 with FP8 KV cache not yet supported")
+
+        self.use_triton_flash_attn = envs.VLLM_USE_TRITON_FLASH_ATTN
+        self.triton_fa_func = triton_attention if HAS_TRITON else None
+
+    def _flash_attn_varlen_diff_headdims_rocm(self,
+                                              q,
+                                              k,
+                                              v,
+                                              softmax_scale=None,
+                                              **kwargs):
+        assert self.triton_fa_func is not None
+
+        # Triton Attention requires a padded V
+        padded_v = torch.nn.functional.pad(v, [0, q.shape[-1] - v.shape[-1]],
+                                           value=0)
+        # The output of triton_attention is a tuple of
+        # [output_tensor, encoded_softmax] where encoded_softmax is always None
+        output_tensor, _ = self.triton_fa_func(
+            q,
+            k,
+            padded_v,
+            None,  # output
+            kwargs["cu_seqlens_q"],
+            kwargs["cu_seqlens_k"],
+            kwargs["max_seqlen_q"],
+            kwargs["max_seqlen_k"],
+            kwargs["causal"],
+            softmax_scale,
+            None,  # bias
+        )
+
+        return output_tensor
+
+    def _flash_attn_varlen_diff_headdims(self,
+                                         q,
+                                         k,
+                                         v,
+                                         return_softmax_lse=False,
+                                         softmax_scale=None,
+                                         **kwargs):
+        if current_platform.is_rocm() \
+            and self.use_triton_flash_attn \
+            and not return_softmax_lse:
+            return self._flash_attn_varlen_diff_headdims_rocm(
+                q, k, v, softmax_scale=softmax_scale, **kwargs)
+        else:
+            return super()._flash_attn_varlen_diff_headdims(
+                q,
+                k,
+                v,
+                return_softmax_lse=return_softmax_lse,
+                softmax_scale=softmax_scale,
+                **kwargs)
+
+    def _forward_decode(
+        self,
+        q_nope: torch.Tensor,
+        q_pe: torch.Tensor,
+        kv_c_and_k_pe_cache: torch.Tensor,
+        attn_metadata: MLACommonMetadata,
+    ) -> torch.Tensor:
+        assert kv_c_and_k_pe_cache.numel() > 0
+        assert attn_metadata.decode is not None
+
+        if self.kv_cache_dtype.startswith("fp8"):
+            raise NotImplementedError("FP8 Triton MLA not yet supported")
+
+        B = q_nope.shape[0]
+
+        q = torch.cat([q_nope, q_pe], dim=-1)
+        o = torch.zeros(B,
+                        self.num_heads,
+                        self.kv_lora_rank,
+                        dtype=q.dtype,
+                        device=q.device)
+
+        num_kv_splits = 4  # TODO: heuristic
+
+        # TODO(lucas) Allocate ahead of time
+        attn_logits = torch.empty(
+            (
+                B,
+                self.num_heads,
+                num_kv_splits,
+                # NOTE(lucas) idk why the +1 is here but sglang has it so we
+                # just mirror that
+                self.kv_lora_rank + 1,
+            ),
+            dtype=torch.float32,
+            device=q.device,
+        )
+
+        # Add a head dim of 1
+        kv_c_and_k_pe_cache = kv_c_and_k_pe_cache.unsqueeze(2)
+        kv_c_cache = kv_c_and_k_pe_cache[..., :self.kv_lora_rank]
+        PAGE_SIZE = kv_c_and_k_pe_cache.size(1)
+
+        # Run MQA
+        decode_attention_fwd(q, kv_c_and_k_pe_cache, kv_c_cache, o,
+                             attn_metadata.decode.block_table,
+                             attn_metadata.decode.seq_lens, attn_logits,
+                             num_kv_splits, self.scale, PAGE_SIZE)
+
+        return self._v_up_proj(o)