jit: don't run ninja in build_and_load() if the .so is already there

benchmarks/README.md

@@ -5,11 +5,11 @@ The aim of `flashinfer_benchmark.py` is to provide a single framework for benchm
 ## Overview
 
 This framework provides tools to:
-- Benchmark FlashInfer's Attention, GEMM, MOE, Norm, Quantization, Sampling, RoPE, and Mamba API performance from different kernel backends such as FlashAttention2/3, cuDNN, cuBLAS, CUTLASS, CuTe-DSL, TensorRT-LLM, and Triton
+- Benchmark FlashInfer's Attention, GEMM, MOE, Norm, Quantization, Sampling, RoPE, Mamba, and GDN API performance from different kernel backends such as FlashAttention2/3, cuDNN, cuBLAS, CUTLASS, CuTe-DSL, TensorRT-LLM, and Triton
 - Compare performance across different configurations
 - Batch performance test multiple test cases
 
-Currently supports testing attention, gemm, fused MOE, normalization, quantization, sampling, RoPE, and Mamba APIs:
+Currently supports testing attention, gemm, fused MOE, normalization, quantization, sampling, RoPE, Mamba, and GDN (Gated Delta Net) APIs:
 - Attention:
     - `BatchDecodeWithPagedKVCacheWrapper` - Decode attention with paged KV cache.
         - Also supports computationally similar `cudnn_batch_decode_with_kv_cache` and `trtllm_batch_decode_with_kv_cache`.
@@ -77,6 +77,10 @@ Currently supports testing attention, gemm, fused MOE, normalization, quantizati
     - `rope_quantize_fp8_append_paged_kv_cache` - RoPE with FP8 quantization and paged KV cache append (SM8.9+).
 - Mamba (Selective State Space Models):
     - `selective_state_update` - Selective state update for Mamba layers (generation phase). Supports both single-token prediction (STP) and multi-token prediction (MTP) via `--cache_steps`. Backends: `flashinfer` (CUDA, architecture-specific kernels for base/SM90/SM100+) and `triton` (reference).
+- GDN (Gated Delta Net linear attention, SM90+):
+    - `gated_delta_rule_decode` - Single-token (T=1) gated delta rule decode. `--state_layout` selects between `gated_delta_rule_decode_pretranspose` ([B, HV, V, K] state, default) and `gated_delta_rule_decode` ([B, HV, K, V] state). `--state_dtype bfloat16` selects the BF16 state kernels (head_size=128, pretranspose only). Backends: `flashinfer` (CuTe-DSL) and `triton` (reference).
+    - `gated_delta_rule_mtp` - Multi-token (T>=2) gated delta rule for speculative-decoding verification, with a state pool + indices. `--state_dtype float32` uses `gated_delta_rule_mtp`; `--state_dtype bfloat16` uses the BF16 MTP kernel via `gated_delta_rule_decode_pretranspose`. Backends: `flashinfer`, `triton`.
+    - `chunk_gated_delta_rule` - Chunked GDN prefill over varlen sequences (uniform per-sequence length `--s_qo`). Backends: `flashinfer` (SM90 C++ / SM100 CuTe-DSL) and `fla` (flash-linear-attention Triton baseline, perf-only).
 
 ## Quick Start
 ### Single Test Run
@@ -445,6 +449,31 @@ mpirun -np 8 python benchmarks/flashinfer_benchmark.py \
 | `--dt_softplus`          | Apply softplus to dt before use                                                                            |
 | `--backends`             | Backends to test: `flashinfer` (default), `triton` (reference). Refcheck compares against Triton reference |
 
+### GDN Flags
+Applies to `gated_delta_rule_decode`, `gated_delta_rule_mtp`, and `chunk_gated_delta_rule` (SM90+).
+
+| Flag                          | Description                                                                                                 |
+|-------------------------------|-------------------------------------------------------------------------------------------------------------|
+| `--batch_size`                | Decode/MTP: number of concurrent requests. Prefill: number of sequences                                    |
+| `--num_q_heads`               | Number of query heads. Default: 16                                                                         |
+| `--num_k_heads`               | Number of key heads. Default: 16                                                                           |
+| `--num_v_heads`               | Number of value heads (GVA when > `num_q_heads`). Default: 32                                              |
+| `--head_size`                 | Head dimension (K = V = head_size). Default: 128                                                           |
+| `--input_dtype`               | Data type for q/k/v/a/b tensors: `bfloat16` (default) or `float16`                                         |
+| `--state_dtype`               | Recurrent state dtype: `float32` (default) or `bfloat16` (BF16 state kernels; decode/MTP, head_size=128, pretranspose) |
+| `--state_layout`              | Decode only: `pretranspose` ([B, HV, V, K], default) or `nontranspose` ([B, HV, K, V])                     |
+| `--pool_mode`                 | `single` (default, read == write slots) or `split` (pool of 2B; reads slots [0..B), writes [B..2B))        |
+| `--seq_len`                   | MTP only: tokens per request (>= 2). Default: 2                                                            |
+| `--s_qo`                      | Prefill only: per-sequence length (uniform). Default: 2048                                                 |
+| `--update_state`              | MTP only: write the final state back (`disable_state_update=False`). BF16 state always updates in-place    |
+| `--cache_intermediate_states` | MTP with `float32` state only: cache per-token intermediate states                                         |
+| `--no_qk_l2norm`              | Decode/MTP: disable in-kernel Q/K L2 normalization                                                         |
+| `--backends`                  | Decode/MTP: `flashinfer` (default), `triton`. Prefill: `flashinfer` (default), `fla` (requires `pip install flash-linear-attention`; perf-only, excluded from refcheck) |
+
+Notes:
+- Refcheck compares against the torch reference in `tests/gdn/reference_delta_rule.py`.
+- Prefill pre-L2-normalizes k and calls the kernel with `use_qk_l2norm_in_kernel=False` so the kernel and reference see identical inputs.
+
 ## `flashinfer_benchmark.py` Routine & Backend Support Matrix
 The following table summarizes the support surface of each routine & backend's on various [CUDA Compute Capabilities](https://developer.nvidia.com/cuda-gpus).
 
@@ -516,6 +545,9 @@ Legend:
 | **rope_quantize_fp8** |  |  |  | cuda | cuda | cuda | cuda | cuda |
 | **rope_quantize_fp8_append_paged_kv_cache** |  |  |  | cuda | cuda | cuda | cuda | cuda |
 | **selective_state_update** | flashinfer, triton | flashinfer, triton | flashinfer, triton | flashinfer, triton | flashinfer, triton | flashinfer, triton | flashinfer, triton | flashinfer, triton |
+| **gated_delta_rule_decode** |  |  |  |  | flashinfer, triton | flashinfer, triton | flashinfer, triton | triton |
+| **gated_delta_rule_mtp** |  |  |  |  | flashinfer, triton | flashinfer, triton | flashinfer, triton | triton |
+| **chunk_gated_delta_rule** |  |  |  |  | flashinfer, fla | flashinfer, fla | flashinfer, fla |  |
 
 Backend Legend:
 - fa2: FlashAttention2
@@ -533,4 +565,5 @@ Backend Legend:
 - cute-dsl: FlashInfer CuTe-DSL kernels (Blackwell SM10.0+)
 - moe_a2a: MoE All-to-All communication (requires mpirun, Blackwell SM10.0+ with MNNVL)
 - allreduce: AllReduce fusion communication (requires mpirun, Blackwell SM10.0+ with MNNVL)
-- triton: Triton reference kernels (used for Mamba selective_state_update)
+- triton: Triton reference kernels (used for Mamba selective_state_update and GDN decode/MTP)
+- fla: flash-linear-attention Triton kernels (GDN prefill baseline)