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86591de476
[CK] [CK_Tile] Add FMHA scaffolding to CK kernel dispatcher (#5260) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Motivation The CK Tile dispatcher currently supports GEMM and Grouped Convolution but has no support for Fused Multi-Head Attention (FMHA). The example/ck_tile/01_fmha folder contains a comprehensive FMHA implementation with forward, backward, split-KV, paged-KV, append-KV, and batch-prefill kernels across multiple GPU architectures — but there is no unified dispatch layer for it. This PR ports the FMHA stack into the dispatcher, following the same architectural patterns established by GEMM and Grouped Convolution, enabling runtime kernel selection, JIT compilation from Python, and a declarative C++ example flow. Autotuning heuristics to follow. ## Technical Details This PR adds FMHA scaffolding to the CK dispatcher framework, mirroring GEMM's layered architecture. Seven new C++ runtime headers provide type definitions (coexisting with upstream headers via __has_include, requiring zero modifications to example/ck_tile/01_fmha/), a problem builder with 18+ setters, Signature + Algorithm kernel key matching, a virtual kernel instance, a DECL_FMHA_KERNEL_SET macro with wildcard support and named tile/wave/warp setters, arch-aware registry with JSON export, and a dispatcher with seqtune-aware selection, configurable timing, and multi-stage execution plans for split-KV (two-stage) and backward (three-stage). The codegen pipeline is driven by a fmha_arch_specs.json capturing per-arch tile tables and pipeline constraints for five architectures (gfx90a/942/950/1100/1201), migrated from hardcoded logic in 01_fmha/codegen/, with supporting modules for C++ symbol mappings, validation rules, and named receipt profiles (ck_default, flash, pytorch, aiter, fp32, fp8). Python integration (fmha_utils.py) mirrors the C++ layer with JIT compilation, parallel multi-kernel builds, HIP memory management via ctypes, tolerance-based validation, and a NumPy CPU reference with GQA support. Twenty-seven C++ and thirty-two Python examples cover the full feature surface — forward, split-KV, masks, bias, dropout, GQA, backward, append-KV, batch prefill, fp8, logits soft cap, sink tokens, and parameter sweeps — all JIT-compiled on the fly. ## Test Plan Seven test files cover the runtime types, codegen, and end-to-end correctness. C++ unit tests validate the problem builder, dispatcher planning (single-stage for forward/paged-KV/append-KV; multi-stage for split-KV and backward), registry operations, and the kernel-set declaration macro. Python unit tests verify codegen emission, profile filtering, and 15 validation rules for masks, hdim constraints, and pipeline requirements. GPU execution validation in 01_basic_fmha --validate reports zero errors across 65,536 elements with max absolute error of 7.29e-05. A gold-standard parity suite (test_fmha_parity.py) runs 14 configurations through both the upstream tile_example_fmha_fwd and the dispatcher, comparing exit codes to confirm behavioral parity — all 14 match. ## Test Result The C++ smoke test builds and passes all 9 compiled examples, and a Python JIT sweep (29_sweep_seqlen.py) passes 7/7 configurations reaching up to 375 TFLOPS at seqlen 2048. ## Submission Checklist - [x] Look over the contributing guidelines at https://github.com/ROCm/ROCm/blob/develop/CONTRIBUTING.md#pull-requests.
467 lines
18 KiB
C++
467 lines
18 KiB
C++
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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//
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// Example 18: GPU Split-KV FMHA Forward
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//
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// Demonstrates split-KV attention with GPU execution:
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// 1. Declare both fwd_splitkv and fwd_splitkv_combine kernels
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// 2. Show 2-stage execution plan
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// 3. Allocate Q, K, V, O plus workspace (lse_acc, o_acc)
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// 4. Run the split-KV forward pass on GPU
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// 5. Copy output to host and validate against CPU reference
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#include <hip/hip_runtime.h>
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#include <cmath>
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#include <iomanip>
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#include <iostream>
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#include <random>
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#include <vector>
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#include "ck_tile/dispatcher.hpp"
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#include "ck_tile/dispatcher/example_args.hpp"
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using namespace ck_tile::dispatcher;
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using namespace ck_tile::dispatcher::utils;
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DECL_FMHA_KERNEL_SET(splitkv_gpu_kernels,
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.add(FmhaSignature()
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.family("fwd_splitkv")
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.dtype("fp16")
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.mode("batch")
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.vlayout("r")
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.hdim(128)
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.mask("no")
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.bias("no")
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.lse(true)
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.dropout(false)
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.qscale("no")
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.paged_kv(false),
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FmhaAlgorithm()
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// Stage 0 (Q*K^T): m0=seqlen_q, n0=seqlen_k, k0=hdim_q
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.tile_m0(64)
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.tile_n0(128)
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.tile_k0(32)
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// Stage 1 (Attn*V): n1=hdim_v, k1=seqlen_k, k0max=alignment
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.tile_n1(128)
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.tile_k1(32)
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.tile_k0max(128)
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.wave_m0(4)
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.wave_n0(1)
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.wave_k0(1)
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.wave_m1(4)
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.wave_n1(1)
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.wave_k1(1)
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.warp_m0(16)
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.warp_n0(16)
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.warp_k0(16)
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.warp_m1(16)
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.warp_n1(16)
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.warp_k1(16)
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.pipeline("qr_nwarp_sshuffle")
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.padding(true, true, true, true)
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.max_splits_log2(6)
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.selection_rank(0),
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"gfx950")
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.add(FmhaSignature()
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.family("fwd_splitkv_combine")
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.dtype("fp16")
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.mode("batch")
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.vlayout("r")
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.hdim(128)
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.mask("no")
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.bias("no")
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.lse(true)
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.dropout(false)
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.qscale("no")
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.paged_kv(false),
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FmhaAlgorithm()
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.tile_m0(64)
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.tile_n0(128)
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.tile_k0(32)
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.tile_n1(32)
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.tile_k1(32)
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.tile_k0max(128)
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.wave_m0(4)
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.wave_n0(1)
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.wave_k0(1)
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.wave_m1(4)
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.wave_n1(1)
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.wave_k1(1)
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.warp_m0(16)
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.warp_n0(16)
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.warp_k0(16)
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.warp_m1(16)
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.warp_n1(16)
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.warp_k1(16)
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.pipeline("qr")
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.padding(true, true, true, true)
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.max_splits_log2(6)
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.selection_rank(0),
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"gfx950"));
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namespace {
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using FmhaDataType = ck_tile::fp16_t;
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void cpu_attention_fwd(const std::vector<float>& Q,
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const std::vector<float>& K,
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const std::vector<float>& V,
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std::vector<float>& O,
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int batch,
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int nhead,
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int seqlen_q,
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int seqlen_k,
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int hdim_q,
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int hdim_v,
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float scale)
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{
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for(int b = 0; b < batch; ++b)
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{
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for(int h = 0; h < nhead; ++h)
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{
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for(int sq = 0; sq < seqlen_q; ++sq)
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{
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std::vector<float> scores(seqlen_k, 0.0f);
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float max_score = -1e30f;
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for(int sk = 0; sk < seqlen_k; ++sk)
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{
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float dot = 0.0f;
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for(int d = 0; d < hdim_q; ++d)
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{
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int q_idx = ((b * nhead + h) * seqlen_q + sq) * hdim_q + d;
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int k_idx = ((b * nhead + h) * seqlen_k + sk) * hdim_q + d;
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dot += Q[q_idx] * K[k_idx];
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}
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scores[sk] = dot * scale;
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max_score = std::max(max_score, scores[sk]);
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}
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float sum_exp = 0.0f;
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for(int sk = 0; sk < seqlen_k; ++sk)
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{
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scores[sk] = std::exp(scores[sk] - max_score);
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sum_exp += scores[sk];
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}
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for(int sk = 0; sk < seqlen_k; ++sk)
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{
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scores[sk] /= sum_exp;
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}
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for(int dv = 0; dv < hdim_v; ++dv)
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{
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float acc = 0.0f;
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for(int sk = 0; sk < seqlen_k; ++sk)
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{
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int v_idx = ((b * nhead + h) * seqlen_k + sk) * hdim_v + dv;
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acc += scores[sk] * V[v_idx];
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}
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int o_idx = ((b * nhead + h) * seqlen_q + sq) * hdim_v + dv;
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O[o_idx] = acc;
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}
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}
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}
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}
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}
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} // namespace
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int main(int argc, char* argv[])
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{
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ExampleArgs args("Example 18: GPU Split-KV FMHA Forward", "Split-KV with GPU execution");
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args.add_option("--arch", "gfx950", "GPU architecture");
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args.add_option("--batch", "2", "Batch size");
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args.add_option("--nhead", "4", "Number of heads");
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args.add_option("--seqlen_q", "64", "Query sequence length");
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args.add_option("--seqlen_k", "2048", "KV sequence length");
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args.add_option("--hdim", "128", "Head dimension");
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args.add_option("--splits", "2", "Number of KV splits");
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args.add_flag("--validate", "Validate against CPU reference");
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if(!args.parse(argc, argv))
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return 0;
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const std::string gfx_arch = args.get("--arch", "gfx950");
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const int batch = args.get_int("--batch", 2);
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const int nhead = args.get_int("--nhead", 4);
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const int seqlen_q = args.get_int("--seqlen_q", 64);
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const int seqlen_k = args.get_int("--seqlen_k", 2048);
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const int hdim = args.get_int("--hdim", 128);
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const int num_splits = args.get_int("--splits", 2);
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print_header("Example 18: GPU Split-KV FMHA Forward");
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// Step 1: Register kernels
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std::cout << "\nStep 1: Register Kernels\n";
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FmhaKernelSetRegistry::instance().print();
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FmhaRegistry registry;
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registry.set_name("splitkv_gpu_fmha");
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REGISTER_GENERATED_KERNELS(registry, gfx_arch);
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std::cout << " Registered " << registry.size() << " kernel(s)\n";
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FmhaDispatcher dispatcher(®istry);
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dispatcher.set_benchmarking(true);
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dispatcher.set_timing(1, 3);
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// Step 2: Set up traits and plan
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const float scale = 1.0f / std::sqrt(static_cast<float>(hdim));
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fmha_fwd_splitkv_traits traits{};
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traits.hdim_q = hdim;
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traits.hdim_v = hdim;
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traits.data_type = "fp16";
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traits.is_group_mode = false;
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traits.is_v_rowmajor = true;
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traits.has_logits_soft_cap = false;
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traits.mask_type = mask_enum::no_mask;
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traits.bias_type = bias_enum::no_bias;
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traits.has_lse = true;
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traits.do_fp8_static_quant = false;
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traits.has_sink = false;
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// Workspace sizes: lse_acc [batch, nhead, num_splits, seqlen_q]
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// o_acc [batch, nhead, num_splits, seqlen_q, hdim]
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const int64_t q_elems = static_cast<int64_t>(batch) * nhead * seqlen_q * hdim;
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const int64_t k_elems = static_cast<int64_t>(batch) * nhead * seqlen_k * hdim;
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const int64_t v_elems = k_elems;
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const int64_t o_elems = q_elems;
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const int64_t lse_elems = static_cast<int64_t>(batch) * nhead * seqlen_q;
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const int64_t lse_acc_elems = static_cast<int64_t>(batch) * nhead * num_splits * seqlen_q;
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const int64_t o_acc_elems = static_cast<int64_t>(batch) * nhead * num_splits * seqlen_q * hdim;
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// Show the 2-stage plan
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std::cout << "\nStep 2: Plan (2-stage split-KV)\n";
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fmha_fwd_splitkv_args plan_args{};
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plan_args.seqlen_q = seqlen_q;
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plan_args.seqlen_k = seqlen_k;
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plan_args.batch = batch;
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plan_args.max_seqlen_q = seqlen_q;
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plan_args.hdim_q = hdim;
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plan_args.hdim_v = hdim;
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plan_args.nhead_q = nhead;
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plan_args.nhead_k = nhead;
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plan_args.num_splits = num_splits;
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auto problem = FmhaProblem::from_invocation(FmhaInvocation::make(traits, plan_args), gfx_arch);
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auto plan = dispatcher.plan(problem);
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if(!plan.is_valid() || plan.stages.size() != 2)
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{
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std::cerr << " WARNING: Expected a two-stage split-KV plan, got " << plan.stages.size()
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<< " stage(s)\n";
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if(!plan.is_valid())
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{
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std::cerr << " Plan is invalid -- no matching kernels found\n";
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print_separator();
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return 1;
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}
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}
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for(const auto& stage : plan.stages)
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{
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std::cout << " " << to_string(stage.family) << " -> " << stage.kernel_id << "\n";
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}
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// Step 3: Allocate GPU buffers
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std::cout << "\nStep 3: Allocate GPU Buffers\n";
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std::cout << " Q: [" << batch << ", " << nhead << ", " << seqlen_q << ", " << hdim
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<< "]\n";
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std::cout << " K/V: [" << batch << ", " << nhead << ", " << seqlen_k << ", " << hdim
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<< "]\n";
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std::cout << " O: [" << batch << ", " << nhead << ", " << seqlen_q << ", " << hdim
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<< "]\n";
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std::cout << " lse_acc: [" << batch << ", " << nhead << ", " << num_splits << ", " << seqlen_q
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<< "]\n";
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std::cout << " o_acc: [" << batch << ", " << nhead << ", " << num_splits << ", " << seqlen_q
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<< ", " << hdim << "]\n";
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GpuBuffer<FmhaDataType> q_dev(q_elems);
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GpuBuffer<FmhaDataType> k_dev(k_elems);
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GpuBuffer<FmhaDataType> v_dev(v_elems);
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GpuBuffer<FmhaDataType> o_dev(o_elems);
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GpuBuffer<float> lse_dev(lse_elems);
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GpuBuffer<float> lse_acc_dev(lse_acc_elems);
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GpuBuffer<float> o_acc_dev(o_acc_elems);
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// Fill Q, K, V with random data
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std::mt19937 rng(42);
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std::uniform_real_distribution<float> dist(-0.5f, 0.5f);
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std::vector<FmhaDataType> q_host(q_elems);
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std::vector<FmhaDataType> k_host(k_elems);
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std::vector<FmhaDataType> v_host(v_elems);
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for(auto& x : q_host)
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x = FmhaDataType(dist(rng));
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for(auto& x : k_host)
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x = FmhaDataType(dist(rng));
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for(auto& x : v_host)
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x = FmhaDataType(dist(rng));
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q_dev.copy_from_host(q_host.data());
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k_dev.copy_from_host(k_host.data());
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v_dev.copy_from_host(v_host.data());
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o_dev.zero();
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lse_acc_dev.zero();
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o_acc_dev.zero();
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// Step 4: Set up splitkv args with device pointers and strides
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fmha_fwd_splitkv_args fmha_args{};
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fmha_args.q_ptr = q_dev.get();
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fmha_args.k_ptr = k_dev.get();
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fmha_args.v_ptr = v_dev.get();
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fmha_args.o_ptr = o_dev.get();
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fmha_args.bias_ptr = nullptr;
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fmha_args.lse_acc_ptr = lse_acc_dev.get();
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fmha_args.o_acc_ptr = o_acc_dev.get();
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fmha_args.lse_ptr = lse_dev.get();
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fmha_args.block_table_ptr = nullptr;
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fmha_args.batch_stride_block_table = 0;
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fmha_args.page_block_size = 0;
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fmha_args.is_gappy = false;
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fmha_args.cache_batch_idx = nullptr;
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fmha_args.seqstart_q_ptr = nullptr;
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fmha_args.seqstart_k_ptr = nullptr;
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fmha_args.seqlen_k_ptr = nullptr;
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fmha_args.sink_ptr = nullptr;
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fmha_args.seqlen_q = seqlen_q;
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fmha_args.seqlen_k = seqlen_k;
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fmha_args.batch = batch;
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fmha_args.max_seqlen_q = seqlen_q;
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fmha_args.hdim_q = hdim;
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fmha_args.hdim_v = hdim;
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fmha_args.nhead_q = nhead;
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fmha_args.nhead_k = nhead;
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fmha_args.num_splits = num_splits;
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fmha_args.scale_s = scale;
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fmha_args.scale_p = 1.0f;
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fmha_args.scale_o = 1.0f;
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fmha_args.logits_soft_cap = 0.0f;
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// bhsd layout strides
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fmha_args.stride_q = hdim;
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fmha_args.stride_k = hdim;
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fmha_args.stride_v = hdim;
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fmha_args.stride_bias = 0;
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fmha_args.stride_o_acc = hdim;
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fmha_args.stride_o = hdim;
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fmha_args.nhead_stride_q = seqlen_q * hdim;
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fmha_args.nhead_stride_k = seqlen_k * hdim;
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fmha_args.nhead_stride_v = seqlen_k * hdim;
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fmha_args.nhead_stride_bias = 0;
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fmha_args.nhead_stride_lse = seqlen_q;
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fmha_args.nhead_stride_lse_acc = num_splits * seqlen_q;
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fmha_args.nhead_stride_o_acc = num_splits * seqlen_q * hdim;
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fmha_args.nhead_stride_o = seqlen_q * hdim;
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fmha_args.batch_stride_q = nhead * seqlen_q * hdim;
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fmha_args.batch_stride_k = nhead * seqlen_k * hdim;
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fmha_args.batch_stride_v = nhead * seqlen_k * hdim;
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fmha_args.batch_stride_bias = 0;
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fmha_args.batch_stride_lse = nhead * seqlen_q;
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fmha_args.batch_stride_lse_acc = nhead * num_splits * seqlen_q;
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fmha_args.batch_stride_o_acc = nhead * num_splits * seqlen_q * hdim;
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fmha_args.batch_stride_o = nhead * seqlen_q * hdim;
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fmha_args.split_stride_lse_acc = seqlen_q;
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fmha_args.split_stride_o_acc = seqlen_q * hdim;
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fmha_args.window_size_left = -1;
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fmha_args.window_size_right = -1;
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fmha_args.sink_size = 0;
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fmha_args.mask_type = 0;
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// Step 5: Run on GPU
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std::cout << "\nStep 4: Run Split-KV FMHA Forward on GPU\n";
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float time_ms = 0.0f;
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try
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{
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time_ms = dispatcher.run_fwd_splitkv(traits, fmha_args, nullptr);
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}
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catch(const std::exception& e)
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{
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|
std::cerr << " WARNING: GPU execution failed: " << e.what() << "\n";
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|
std::cerr << " Falling back to planning-only mode (split-KV compilation can be complex)\n";
|
|
std::cout << "\n Plan summary (2 stages):\n";
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|
for(const auto& stage : plan.stages)
|
|
{
|
|
std::cout << " " << to_string(stage.family) << " -> " << stage.kernel_id << "\n";
|
|
}
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|
print_separator();
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|
std::cout << "Status: PLAN_ONLY\n";
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|
print_separator();
|
|
return 0;
|
|
}
|
|
|
|
auto run_problem =
|
|
FmhaProblem::from_invocation(FmhaInvocation::make(traits, fmha_args), gfx_arch);
|
|
double tflops = static_cast<double>(run_problem.num_ops()) / (time_ms * 1e-3) / 1e12;
|
|
|
|
std::cout << " Time: " << std::fixed << std::setprecision(4) << time_ms << " ms\n";
|
|
std::cout << " TFLOPS: " << std::setprecision(2) << tflops << "\n";
|
|
|
|
// Step 6: Copy output and validate
|
|
std::cout << "\nStep 5: Validate\n";
|
|
std::vector<FmhaDataType> o_host(o_elems);
|
|
o_dev.copy_to_host(o_host.data());
|
|
|
|
int nonzero = 0;
|
|
for(int64_t i = 0; i < o_elems; ++i)
|
|
{
|
|
if(static_cast<float>(o_host[i]) != 0.0f)
|
|
++nonzero;
|
|
}
|
|
std::cout << " Non-zero outputs: " << nonzero << " / " << o_elems << "\n";
|
|
|
|
bool passed = (nonzero > 0);
|
|
|
|
if(args.has("--validate"))
|
|
{
|
|
std::vector<float> q_f32(q_elems), k_f32(k_elems), v_f32(v_elems), o_ref(o_elems, 0.0f);
|
|
for(int64_t i = 0; i < q_elems; ++i)
|
|
q_f32[i] = static_cast<float>(q_host[i]);
|
|
for(int64_t i = 0; i < k_elems; ++i)
|
|
k_f32[i] = static_cast<float>(k_host[i]);
|
|
for(int64_t i = 0; i < v_elems; ++i)
|
|
v_f32[i] = static_cast<float>(v_host[i]);
|
|
|
|
cpu_attention_fwd(
|
|
q_f32, k_f32, v_f32, o_ref, batch, nhead, seqlen_q, seqlen_k, hdim, hdim, scale);
|
|
|
|
double max_abs_err = 0.0;
|
|
double max_rel_err = 0.0;
|
|
int errors = 0;
|
|
const double rtol = 1e-2;
|
|
const double atol = 1e-2;
|
|
|
|
for(int64_t i = 0; i < o_elems; ++i)
|
|
{
|
|
float gpu_val = static_cast<float>(o_host[i]);
|
|
float ref_val = o_ref[i];
|
|
double abs_err = std::abs(gpu_val - ref_val);
|
|
double rel_err = abs_err / (std::abs(ref_val) + 1e-6);
|
|
max_abs_err = std::max(max_abs_err, abs_err);
|
|
max_rel_err = std::max(max_rel_err, rel_err);
|
|
if(abs_err > atol + rtol * std::abs(ref_val))
|
|
++errors;
|
|
}
|
|
|
|
std::cout << " Max abs error: " << std::scientific << max_abs_err << "\n";
|
|
std::cout << " Max rel error: " << max_rel_err << "\n";
|
|
std::cout << " Errors: " << errors << " / " << o_elems << "\n";
|
|
passed = (errors == 0);
|
|
}
|
|
|
|
print_separator();
|
|
std::cout << "Status: " << (passed ? "PASS" : "FAIL") << "\n";
|
|
print_separator();
|
|
|
|
return passed ? 0 : 1;
|
|
}
|