Iluvatar-mrv100 SDK 4.3.0

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

@@ -0,0 +1,154 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Any, Optional
+
+import torch
+
+from vllm.attention.backends.abstract import (AttentionType,
+                                              is_quantized_kv_cache)
+from vllm.attention.ops.triton_decode_attention import decode_attention_fwd
+from vllm.logger import init_logger
+from vllm.v1.attention.backends.mla.common import (MLACommonBackend,
+                                                   MLACommonImpl,
+                                                   MLACommonMetadata)
+import ixformer.inference.functions as ixf_ops
+import vllm.envs as envs
+from vllm import _custom_ops as ops
+
+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,
+            blocksparse_params: Optional[dict[str, Any]],
+            logits_soft_cap: Optional[float],
+            attn_type: str,
+            # MLA Specific Arguments
+            **mla_args) -> None:
+        super().__init__(num_heads, head_size, scale, num_kv_heads,
+                         alibi_slopes, sliding_window, kv_cache_dtype,
+                         blocksparse_params, logits_soft_cap, attn_type,
+                         **mla_args)
+
+        unsupported_features = [
+            alibi_slopes, sliding_window, blocksparse_params, logits_soft_cap
+        ]
+        if any(unsupported_features):
+            raise NotImplementedError(
+                "TritonMLAImpl does not support one of the following: "
+                "alibi_slopes, sliding_window, blocksparse_params, "
+                "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._k_scale = torch.tensor(1.0, dtype=torch.float32)
+
+    def _forward_decode(
+        self,
+        q_nope: torch.Tensor,
+        q_pe: torch.Tensor,
+        kv_c_and_k_pe_cache: torch.Tensor,
+        kv_c_and_k_pe_cache_scale: torch.Tensor,
+        attn_metadata: MLACommonMetadata,
+        k_c_normed: torch.Tensor=None,
+        k_pe: torch.Tensor=None,
+    ) -> 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.empty(B,
+                        self.num_heads,
+                        self.kv_lora_rank,
+                        dtype=q_nope.dtype,
+                        device=q_nope.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)
+        if envs.VLLM_USE_INT8_MLA:
+            q_int8, q_scale = ops.quant_kv(q)
+            ixf_ops.vllm_paged_attention_mla_int8(
+                o,
+                q_int8,
+                q_scale,
+                kv_c_and_k_pe_cache,
+                kv_c_and_k_pe_cache_scale, 
+                self.scale,
+                attn_metadata.decode.block_table,
+                attn_metadata.decode.seq_lens,
+                attn_metadata.decode.max_decode_seq_len,
+                attn_metadata.decode.use_cuda_graph
+            )
+            
+        else:
+            # fused q concat & cache write
+            ixf_ops.vllm_paged_attention_mla_fused(
+                output=o,
+                q_nope=q_nope,
+                q_pe=q_pe.contiguous(),
+                kv_cache=kv_c_and_k_pe_cache,
+                scale=self.scale,
+                block_tables=attn_metadata.decode.block_table,
+                context_lens=attn_metadata.decode.seq_lens,
+                max_context_len=attn_metadata.decode.max_decode_seq_len,
+                k_c_normed=k_c_normed,
+                k_pe=k_pe,
+                use_cuda_graph=attn_metadata.decode.use_cuda_graph
+            )
+        return self._v_up_proj_and_o_proj(o)