ROCm/composable_kernel
1
0
Fork 0
mirror of https://github.com/ROCm/composable_kernel.git synced 2026-05-21 05:19:20 +00:00
Code Issues Packages Projects Releases Wiki Activity
Files
86591de476dbf8d16a3dce849fda0d2887220ffa
composable_kernel/dispatcher/examples/fmha/cpp/21_gpu_features_fmha.cpp
Vidyasagar Ananthan 86591de476 [rocm-libraries] ROCm/rocm-libraries#5260 (commit a1834d2) 
[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.
2026-05-17 07:30:33 +00:00

698 lines
27 KiB
C++
Raw Blame History

// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
//
// Example 21: GPU Features FMHA
//
// Tests multiple FMHA features with real GPU execution:
// 1. Dropout (with LSE, rand_val buffer)
// 2. GQA (nhead_q=16, nhead_k=4, same kernel)
// 3. LSE output (verify log-sum-exp values)
//
// Mirrors 01_basic_fmha.cpp for each feature variant.
#include <hip/hip_runtime.h>
#include <cmath>
#include <iomanip>
#include <iostream>
#include <random>
#include <vector>
#include "ck_tile/dispatcher.hpp"
#include "ck_tile/dispatcher/example_args.hpp"
using namespace ck_tile::dispatcher;
using namespace ck_tile::dispatcher::utils;
DECL_FMHA_KERNEL_SET(gpu_features_fmha_kernels,
// Basic fp16 kernel (used for GQA -- GQA is a runtime concern, same kernel)
.add(FmhaSignature()
.family("fwd")
.dtype("fp16")
.mode("batch")
.vlayout("r")
.hdim(128)
.mask("no")
.bias("no")
.lse(false)
.dropout(false)
.qscale("no"),
FmhaAlgorithm()
// Stage 0 (Q*K^T): m0=seqlen_q, n0=seqlen_k, k0=hdim_q
.tile_m0(128)
.tile_n0(128)
.tile_k0(32)
// Stage 1 (Attn*V): n1=hdim_v, k1=seqlen_k, k0max=alignment
.tile_n1(128)
.tile_k1(32)
.tile_k0max(128)
.wave_m0(4)
.wave_n0(1)
.wave_k0(1)
.wave_m1(4)
.wave_n1(1)
.wave_k1(1)
.warp_m0(32)
.warp_n0(32)
.warp_k0(16)
.warp_m1(32)
.warp_n1(32)
.warp_k1(16)
.pipeline("qr_async")
.padding(true, true, true, true)
.alignments(128, 128)
.selection_rank(0),
"gfx950")
// Dropout kernel (requires LSE)
.add(FmhaSignature()
.family("fwd")
.dtype("fp16")
.mode("batch")
.vlayout("r")
.hdim(128)
.mask("no")
.bias("no")
.lse(true)
.dropout(true)
.qscale("no"),
FmhaAlgorithm()
.tile_m0(128)
.tile_n0(128)
.tile_k0(32)
.tile_n1(128)
.tile_k1(32)
.tile_k0max(128)
.wave_m0(4)
.wave_n0(1)
.wave_k0(1)
.wave_m1(4)
.wave_n1(1)
.wave_k1(1)
.warp_m0(32)
.warp_n0(32)
.warp_k0(16)
.warp_m1(32)
.warp_n1(32)
.warp_k1(16)
.pipeline("qr_async")
.padding(true, true, true, true)
.alignments(128, 128)
.selection_rank(0),
"gfx950")
// LSE-only kernel
.add(FmhaSignature()
.family("fwd")
.dtype("fp16")
.mode("batch")
.vlayout("r")
.hdim(128)
.mask("no")
.bias("no")
.lse(true)
.dropout(false)
.qscale("no"),
FmhaAlgorithm()
.tile_m0(128)
.tile_n0(128)
.tile_k0(32)
.tile_n1(128)
.tile_k1(32)
.tile_k0max(128)
.wave_m0(4)
.wave_n0(1)
.wave_k0(1)
.wave_m1(4)
.wave_n1(1)
.wave_k1(1)
.warp_m0(32)
.warp_n0(32)
.warp_k0(16)
.warp_m1(32)
.warp_n1(32)
.warp_k1(16)
.pipeline("qr_async")
.padding(true, true, true, true)
.alignments(128, 128)
.selection_rank(0),
"gfx950"));
namespace {
using FmhaDataType = ck_tile::fp16_t;
void cpu_attention_fwd(const std::vector<float>& Q,
const std::vector<float>& K,
const std::vector<float>& V,
std::vector<float>& O,
int batch,
int nhead_q,
int nhead_k,
int seqlen_q,
int seqlen_k,
int hdim_q,
int hdim_v,
float scale,
std::vector<float>* lse_out = nullptr)
{
const int nhead_ratio = nhead_q / nhead_k;
for(int b = 0; b < batch; ++b)
{
for(int hq = 0; hq < nhead_q; ++hq)
{
const int hk = hq / nhead_ratio;
for(int sq = 0; sq < seqlen_q; ++sq)
{
std::vector<float> scores(seqlen_k, 0.0f);
float max_score = -1e30f;
for(int sk = 0; sk < seqlen_k; ++sk)
{
float dot = 0.0f;
for(int d = 0; d < hdim_q; ++d)
{
int q_idx = ((b * nhead_q + hq) * seqlen_q + sq) * hdim_q + d;
int k_idx = ((b * nhead_k + hk) * seqlen_k + sk) * hdim_q + d;
dot += Q[q_idx] * K[k_idx];
}
scores[sk] = dot * scale;
max_score = std::max(max_score, scores[sk]);
}
float sum_exp = 0.0f;
for(int sk = 0; sk < seqlen_k; ++sk)
{
scores[sk] = std::exp(scores[sk] - max_score);
sum_exp += scores[sk];
}
if(lse_out)
{
int lse_idx = (b * nhead_q + hq) * seqlen_q + sq;
(*lse_out)[lse_idx] = max_score + std::log(sum_exp);
}
for(int sk = 0; sk < seqlen_k; ++sk)
{
scores[sk] /= sum_exp;
}
for(int dv = 0; dv < hdim_v; ++dv)
{
float acc = 0.0f;
for(int sk = 0; sk < seqlen_k; ++sk)
{
int v_idx = ((b * nhead_k + hk) * seqlen_k + sk) * hdim_v + dv;
acc += scores[sk] * V[v_idx];
}
int o_idx = ((b * nhead_q + hq) * seqlen_q + sq) * hdim_v + dv;
O[o_idx] = acc;
}
}
}
}
}
struct FeatureResult
{
std::string name;
bool passed;
float time_ms;
};
fmha_fwd_args make_base_args(void* q,
void* k,
void* v,
void* o,
int batch,
int nhead_q,
int nhead_k,
int seqlen,
int hdim,
float scale)
{
fmha_fwd_args a{};
a.q_ptr = q;
a.k_ptr = k;
a.v_ptr = v;
a.o_ptr = o;
a.bias_ptr = nullptr;
a.q_descale_ptr = nullptr;
a.k_descale_ptr = nullptr;
a.v_descale_ptr = nullptr;
a.rand_val_ptr = nullptr;
a.lse_ptr = nullptr;
a.sink_ptr = nullptr;
a.block_scale_seqstart_q_ptr = nullptr;
a.block_scale_seqstart_k_ptr = nullptr;
a.seqlen_q = seqlen;
a.seqlen_k = seqlen;
a.batch = batch;
a.max_seqlen_q = seqlen;
a.hdim_q = hdim;
a.hdim_v = hdim;
a.nhead_q = nhead_q;
a.nhead_k = nhead_k;
a.scale_s = scale;
a.logits_soft_cap = 0.0f;
a.stride_q = hdim;
a.stride_k = hdim;
a.stride_v = hdim;
a.stride_bias = 0;
a.stride_randval = 0;
a.stride_o = hdim;
a.nhead_stride_q = seqlen * hdim;
a.nhead_stride_k = seqlen * hdim;
a.nhead_stride_v = seqlen * hdim;
a.nhead_stride_bias = 0;
a.nhead_stride_randval = 0;
a.nhead_stride_lse = 0;
a.nhead_stride_o = seqlen * hdim;
a.nhead_stride_q_descale = 0;
a.nhead_stride_k_descale = 0;
a.nhead_stride_v_descale = 0;
a.batch_stride_q = nhead_q * seqlen * hdim;
a.batch_stride_k = nhead_k * seqlen * hdim;
a.batch_stride_v = nhead_k * seqlen * hdim;
a.batch_stride_bias = 0;
a.batch_stride_randval = 0;
a.batch_stride_lse = 0;
a.batch_stride_o = nhead_q * seqlen * hdim;
a.batch_stride_q_descale = 0;
a.batch_stride_k_descale = 0;
a.batch_stride_v_descale = 0;
a.window_size_left = -1;
a.window_size_right = -1;
a.sink_size = 0;
a.mask_type = 0;
a.min_seqlen_q = 0;
a.p_drop = 0.0f;
a.s_randval = false;
a.drop_seed_offset = std::make_pair(uint64_t(0), uint64_t(0));
a.block_scale_size_q = 0;
a.block_scale_size_kv = 0;
return a;
}
} // namespace
int main(int argc, char* argv[])
{
ExampleArgs args("Example 21: GPU Features FMHA", "Dropout, GQA, LSE with real GPU data");
args.add_option("--arch", "gfx950", "GPU architecture");
args.add_option("--batch", "2", "Batch size");
args.add_option("--seqlen", "64", "Sequence length");
args.add_option("--hdim", "128", "Head dimension");
if(!args.parse(argc, argv))
return 0;
const std::string gfx_arch = args.get("--arch", "gfx950");
const int batch = args.get_int("--batch", 2);
const int seqlen = args.get_int("--seqlen", 64);
const int hdim = args.get_int("--hdim", 128);
const float scale = 1.0f / std::sqrt(static_cast<float>(hdim));
print_header("Example 21: GPU Features FMHA");
// Step 1: Register kernels
std::cout << "\nStep 1: Register Kernels\n";
FmhaKernelSetRegistry::instance().print();
FmhaRegistry registry;
registry.set_name("gpu_features_fmha");
REGISTER_GENERATED_KERNELS(registry, gfx_arch);
std::cout << " Registered " << registry.size() << " kernel(s)\n";
FmhaDispatcher dispatcher(&registry);
dispatcher.set_benchmarking(true);
dispatcher.set_timing(1, 3);
std::mt19937 rng(42);
std::uniform_real_distribution<float> dist(-0.5f, 0.5f);
std::vector<FeatureResult> results;
// -----------------------------------------------------------------------
// Feature A: GQA (nhead_q=16, nhead_k=4, same basic kernel)
// -----------------------------------------------------------------------
{
std::cout << "\nStep 2a: GQA (nhead_q=16, nhead_k=4)\n";
const int nhead_q = 16;
const int nhead_k = 4;
const int64_t q_elems = static_cast<int64_t>(batch) * nhead_q * seqlen * hdim;
const int64_t k_elems = static_cast<int64_t>(batch) * nhead_k * seqlen * hdim;
const int64_t o_elems = q_elems;
GpuBuffer<FmhaDataType> q_dev(q_elems);
GpuBuffer<FmhaDataType> k_dev(k_elems);
GpuBuffer<FmhaDataType> v_dev(k_elems);
GpuBuffer<FmhaDataType> o_dev(o_elems);
std::vector<FmhaDataType> q_host(q_elems), k_host(k_elems), v_host(k_elems);
for(auto& x : q_host)
x = FmhaDataType(dist(rng));
for(auto& x : k_host)
x = FmhaDataType(dist(rng));
for(auto& x : v_host)
x = FmhaDataType(dist(rng));
q_dev.copy_from_host(q_host.data());
k_dev.copy_from_host(k_host.data());
v_dev.copy_from_host(v_host.data());
o_dev.zero();
fmha_fwd_traits traits{};
traits.hdim_q = hdim;
traits.hdim_v = hdim;
traits.data_type = "fp16";
traits.is_group_mode = false;
traits.is_v_rowmajor = true;
traits.has_logits_soft_cap = false;
traits.mask_type = mask_enum::no_mask;
traits.bias_type = bias_enum::no_bias;
traits.has_lse = false;
traits.has_dropout = false;
traits.qscale_type = quant_scale_enum::no_scale;
auto fmha_args = make_base_args(q_dev.get(),
k_dev.get(),
v_dev.get(),
o_dev.get(),
batch,
nhead_q,
nhead_k,
seqlen,
hdim,
scale);
bool passed = false;
float time_ms = 0.0f;
try
{
time_ms = dispatcher.run_fwd(traits, fmha_args, nullptr);
std::cout << " Time: " << std::fixed << std::setprecision(4) << time_ms << " ms\n";
// Validate against CPU reference with GQA head repetition
std::vector<float> q_f32(q_elems), k_f32(k_elems), v_f32(k_elems);
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 < k_elems; ++i)
v_f32[i] = static_cast<float>(v_host[i]);
std::vector<float> o_ref(o_elems, 0.0f);
cpu_attention_fwd(q_f32,
k_f32,
v_f32,
o_ref,
batch,
nhead_q,
nhead_k,
seqlen,
seqlen,
hdim,
hdim,
scale);
std::vector<FmhaDataType> o_host(o_elems);
o_dev.copy_to_host(o_host.data());
double max_abs_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);
max_abs_err = std::max(max_abs_err, abs_err);
if(abs_err > atol + rtol * std::abs(ref_val))
++errors;
}
std::cout << " Max abs error: " << std::scientific << max_abs_err << "\n";
std::cout << " Errors: " << errors << " / " << o_elems << "\n";
passed = (errors == 0);
}
catch(const std::exception& e)
{
std::cerr << " ERROR: " << e.what() << "\n";
}
results.push_back({"GQA (16q/4k)", passed, time_ms});
}
// -----------------------------------------------------------------------
// Feature B: LSE output
// -----------------------------------------------------------------------
{
std::cout << "\nStep 2b: LSE Output\n";
const int nhead = 4;
const int64_t qkv_elems = static_cast<int64_t>(batch) * nhead * seqlen * hdim;
const int64_t lse_elems = static_cast<int64_t>(batch) * nhead * seqlen;
GpuBuffer<FmhaDataType> q_dev(qkv_elems);
GpuBuffer<FmhaDataType> k_dev(qkv_elems);
GpuBuffer<FmhaDataType> v_dev(qkv_elems);
GpuBuffer<FmhaDataType> o_dev(qkv_elems);
GpuBuffer<float> lse_dev(lse_elems);
std::vector<FmhaDataType> q_host(qkv_elems), k_host(qkv_elems), v_host(qkv_elems);
for(auto& x : q_host)
x = FmhaDataType(dist(rng));
for(auto& x : k_host)
x = FmhaDataType(dist(rng));
for(auto& x : v_host)
x = FmhaDataType(dist(rng));
q_dev.copy_from_host(q_host.data());
k_dev.copy_from_host(k_host.data());
v_dev.copy_from_host(v_host.data());
o_dev.zero();
lse_dev.zero();
fmha_fwd_traits traits{};
traits.hdim_q = hdim;
traits.hdim_v = hdim;
traits.data_type = "fp16";
traits.is_group_mode = false;
traits.is_v_rowmajor = true;
traits.has_logits_soft_cap = false;
traits.mask_type = mask_enum::no_mask;
traits.bias_type = bias_enum::no_bias;
traits.has_lse = true;
traits.has_dropout = false;
traits.qscale_type = quant_scale_enum::no_scale;
auto fmha_args = make_base_args(q_dev.get(),
k_dev.get(),
v_dev.get(),
o_dev.get(),
batch,
nhead,
nhead,
seqlen,
hdim,
scale);
fmha_args.lse_ptr = lse_dev.get();
fmha_args.nhead_stride_lse = seqlen;
fmha_args.batch_stride_lse = nhead * seqlen;
bool passed = false;
float time_ms = 0.0f;
try
{
time_ms = dispatcher.run_fwd(traits, fmha_args, nullptr);
std::cout << " Time: " << std::fixed << std::setprecision(4) << time_ms << " ms\n";
// Compute CPU reference LSE
std::vector<float> q_f32(qkv_elems), k_f32(qkv_elems), v_f32(qkv_elems);
for(int64_t i = 0; i < qkv_elems; ++i)
q_f32[i] = static_cast<float>(q_host[i]);
for(int64_t i = 0; i < qkv_elems; ++i)
k_f32[i] = static_cast<float>(k_host[i]);
for(int64_t i = 0; i < qkv_elems; ++i)
v_f32[i] = static_cast<float>(v_host[i]);
std::vector<float> o_ref(qkv_elems, 0.0f);
std::vector<float> lse_ref(lse_elems, 0.0f);
cpu_attention_fwd(q_f32,
k_f32,
v_f32,
o_ref,
batch,
nhead,
nhead,
seqlen,
seqlen,
hdim,
hdim,
scale,
&lse_ref);
std::vector<float> lse_host(lse_elems);
lse_dev.copy_to_host(lse_host.data());
int lse_reasonable = 0;
double max_lse_err = 0.0;
for(int64_t i = 0; i < lse_elems; ++i)
{
if(std::isfinite(lse_host[i]) && std::abs(lse_host[i]) < 100.0f)
++lse_reasonable;
double err = std::abs(lse_host[i] - lse_ref[i]);
max_lse_err = std::max(max_lse_err, err);
}
std::cout << " LSE reasonable: " << lse_reasonable << " / " << lse_elems << "\n";
std::cout << " LSE max error vs ref: " << std::scientific << max_lse_err << "\n";
std::cout << " LSE sample [0..3]: ";
for(int i = 0; i < std::min<int>(4, lse_elems); ++i)
std::cout << std::fixed << std::setprecision(4) << lse_host[i] << " ";
std::cout << "\n";
passed = (lse_reasonable == lse_elems) && (max_lse_err < 1.0);
}
catch(const std::exception& e)
{
std::cerr << " ERROR: " << e.what() << "\n";
}
results.push_back({"LSE", passed, time_ms});
}
// -----------------------------------------------------------------------
// Feature C: Dropout
// -----------------------------------------------------------------------
{
std::cout << "\nStep 2c: Dropout (p_drop=0.2)\n";
const int nhead = 4;
const int64_t qkv_elems = static_cast<int64_t>(batch) * nhead * seqlen * hdim;
const int64_t lse_elems = static_cast<int64_t>(batch) * nhead * seqlen;
const int64_t randval_elems = static_cast<int64_t>(batch) * nhead * seqlen * seqlen;
GpuBuffer<FmhaDataType> q_dev(qkv_elems);
GpuBuffer<FmhaDataType> k_dev(qkv_elems);
GpuBuffer<FmhaDataType> v_dev(qkv_elems);
GpuBuffer<FmhaDataType> o_dev(qkv_elems);
GpuBuffer<float> lse_dev(lse_elems);
GpuBuffer<uint8_t> rand_val_dev(randval_elems);
std::vector<FmhaDataType> q_host(qkv_elems), k_host(qkv_elems), v_host(qkv_elems);
for(auto& x : q_host)
x = FmhaDataType(dist(rng));
for(auto& x : k_host)
x = FmhaDataType(dist(rng));
for(auto& x : v_host)
x = FmhaDataType(dist(rng));
q_dev.copy_from_host(q_host.data());
k_dev.copy_from_host(k_host.data());
v_dev.copy_from_host(v_host.data());
o_dev.zero();
lse_dev.zero();
rand_val_dev.zero();
fmha_fwd_traits traits{};
traits.hdim_q = hdim;
traits.hdim_v = hdim;
traits.data_type = "fp16";
traits.is_group_mode = false;
traits.is_v_rowmajor = true;
traits.has_logits_soft_cap = false;
traits.mask_type = mask_enum::no_mask;
traits.bias_type = bias_enum::no_bias;
traits.has_lse = true;
traits.has_dropout = true;
traits.qscale_type = quant_scale_enum::no_scale;
auto fmha_args = make_base_args(q_dev.get(),
k_dev.get(),
v_dev.get(),
o_dev.get(),
batch,
nhead,
nhead,
seqlen,
hdim,
scale);
fmha_args.lse_ptr = lse_dev.get();
fmha_args.rand_val_ptr = rand_val_dev.get();
fmha_args.nhead_stride_lse = seqlen;
fmha_args.batch_stride_lse = nhead * seqlen;
fmha_args.stride_randval = seqlen;
fmha_args.nhead_stride_randval = seqlen * seqlen;
fmha_args.batch_stride_randval = nhead * seqlen * seqlen;
fmha_args.p_drop = 0.2f;
fmha_args.s_randval = true;
fmha_args.drop_seed_offset = std::make_pair(uint64_t(42), uint64_t(0));
bool passed = false;
float time_ms = 0.0f;
try
{
time_ms = dispatcher.run_fwd(traits, fmha_args, nullptr);
std::cout << " Time: " << std::fixed << std::setprecision(4) << time_ms << " ms\n";
std::vector<FmhaDataType> o_host(qkv_elems);
o_dev.copy_to_host(o_host.data());
int nonzero = 0;
for(int64_t i = 0; i < qkv_elems; ++i)
{
if(static_cast<float>(o_host[i]) != 0.0f)
++nonzero;
}
std::cout << " Non-zero outputs: " << nonzero << " / " << qkv_elems << "\n";
std::vector<float> lse_host(lse_elems);
lse_dev.copy_to_host(lse_host.data());
int lse_reasonable = 0;
for(int64_t i = 0; i < lse_elems; ++i)
{
if(std::isfinite(lse_host[i]) && std::abs(lse_host[i]) < 100.0f)
++lse_reasonable;
}
std::cout << " LSE reasonable: " << lse_reasonable << " / " << lse_elems << "\n";
passed = (nonzero > 0) && (lse_reasonable == lse_elems);
}
catch(const std::exception& e)
{
std::cerr << " ERROR: " << e.what() << "\n";
}
results.push_back({"Dropout", passed, time_ms});
}
// -----------------------------------------------------------------------
// Summary
// -----------------------------------------------------------------------
std::cout << "\nStep 3: Summary\n";
std::cout << " " << std::setw(16) << "Feature" << " | " << std::setw(10) << "Time(ms)" << " | "
<< std::setw(8) << "Status" << "\n";
std::cout << " " << std::string(42, '-') << "\n";
bool all_passed = true;
for(const auto& r : results)
{
std::cout << " " << std::setw(16) << r.name << " | " << std::fixed << std::setprecision(4)
<< std::setw(10) << r.time_ms << " | " << std::setw(8)
<< (r.passed ? "PASS" : "FAIL") << "\n";
if(!r.passed)
all_passed = false;
}
print_separator();
std::cout << "Status: " << (all_passed ? "PASS" : "FAIL") << "\n";
print_separator();
return all_passed ? 0 : 1;
}
Reference in New Issue View Git Blame Copy Permalink