under test, not sure no errors

ixformer_sdk/contrib/flashinfer/decode.py

@@ -0,0 +1,101 @@
+import math
+from typing import Optional, Tuple, Union
+
+import ixformer.inference.functions as ops
+import torch
+
+
+def _grouped_size_compiled_for_decode_kernels(
+    num_qo_heads: int, num_kv_heads: int
+) -> bool:
+    return (num_qo_heads // num_kv_heads) in [1, 2, 4, 8]
+
+
+class BatchDecodeWithPagedKVCacheWrapper:
+    def __init__(
+        self,
+        float_workspace_buffer: torch.Tensor,
+        kv_layout: str = "NHD",
+        use_cuda_graph: bool = False,
+        use_tensor_cores: bool = False,
+    ) -> None:
+        pass
+
+    def plan(
+        self,
+        indptr: torch.Tensor,
+        indices: torch.Tensor,
+        last_page_len: torch.Tensor,
+        num_qo_heads: int,
+        num_kv_heads: int,
+        head_dim: int,
+        page_size: int,
+        # pos_encoding_mode: str = "NONE",
+        # window_left: int = -1,
+        # logits_soft_cap: Optional[float] = None,
+        data_type: Union[str, torch.dtype] = "float16",
+        q_data_type: Optional[Union[str, torch.dtype]] = None,
+        sm_scale: Optional[float] = None,
+        # rope_scale: Optional[float] = None,
+        # rope_theta: Optional[float] = None,
+        max_seqlen_q: int = None,
+        max_seqlen_k: int = None,
+    ) -> None:
+        self.indptr = indptr
+        self.indices = indices
+        self.last_page_len = last_page_len
+        self.num_qo_heads = num_qo_heads
+        self.num_kv_heads = num_kv_heads
+        self.head_dim = head_dim
+
+        assert page_size == 1
+
+        self.cu_seqlens_q = torch.ones_like(indptr)
+        self.cu_seqlens_q[0] = 0
+        self.cu_seqlens_q = torch.cumsum(self.cu_seqlens_q, dim=0).int()
+
+        self.cu_seqlens_k = indptr
+        if sm_scale is None:
+            sm_scale = 1.0 / math.sqrt(head_dim)
+
+            self.sm_scale = sm_scale
+            self.max_seqlen_q = max_seqlen_q
+            self.max_seqlen_k = max_seqlen_k
+
+    begin_forward = plan
+
+    def forward(
+        self,
+        q: torch.Tensor,
+        paged_kv_cache: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]],
+        pos_encoding_mode: str = "NONE",
+        q_scale: Optional[float] = None,
+        k_scale: Optional[float] = None,
+        v_scale: Optional[float] = None,
+        window_left: int = -1,
+        logits_soft_cap: Optional[float] = None,
+        sm_scale: Optional[float] = None,
+        rope_scale: Optional[float] = None,
+        rope_theta: Optional[float] = None,
+    ) -> torch.Tensor:
+        k_cache, v_cache = paged_kv_cache
+
+        out = torch.empty_like(q)
+
+        ops.paged_attention_flashinfer(
+            output=out,
+            query=q,
+            paged_kv_data=(k_cache.unsqueeze(1), v_cache.unsqueeze(1)),
+            paged_kv_indptr=self.indptr,
+            paged_kv_indices=self.indices,
+            paged_kv_last_page_len=self.last_page_len,
+            scale=self.sm_scale,
+            max_seq_len=self.max_seqlen_k,
+            kv_cache_format="NHD",
+        )
+
+        return out
+
+    def end_forward(self) -> None:
+        r"""Warning: this function is deprecated and has no effect."""
+        pass