mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-06-28 02:37:01 +00:00
[CK] [CK_Tile] Add FMHA scaffolding to CK kernel dispatcher (#5260) ## 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. --------- Co-authored-by: Yaswanth Raparti <113389104+yraparti@users.noreply.github.com> Co-authored-by: Mohsen Saffari <mohsen.saffari@amd.com> Co-authored-by: Maksim (Max) Podkorytov <Maksim.Podkorytov@amd.com> Co-authored-by: yashagar <yashagar@amd.com>
492 lines
17 KiB
C++
492 lines
17 KiB
C++
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
|
|
// SPDX-License-Identifier: MIT
|
|
|
|
#include <gtest/gtest.h>
|
|
|
|
#include "ck_tile/dispatcher.hpp"
|
|
|
|
using namespace ck_tile::dispatcher;
|
|
|
|
namespace {
|
|
|
|
class MockFmhaKernel : public FmhaKernelInstance
|
|
{
|
|
public:
|
|
MockFmhaKernel(FmhaKernelKey key, std::string name)
|
|
: key_(std::move(key)), name_(std::move(name))
|
|
{
|
|
}
|
|
|
|
const FmhaKernelKey& get_key() const override { return key_; }
|
|
|
|
bool supports(const FmhaProblem& problem) const override
|
|
{
|
|
return key_.signature.family == problem.requested_family &&
|
|
key_.signature.data_type == problem.data_type &&
|
|
problem.hdim_q <= key_.signature.hdim_q && problem.hdim_v <= key_.signature.hdim_v;
|
|
}
|
|
|
|
std::string get_name() const override { return name_; }
|
|
|
|
void launch(const FmhaInvocation&, const ck_tile::stream_config&) const override {}
|
|
|
|
private:
|
|
FmhaKernelKey key_;
|
|
std::string name_;
|
|
};
|
|
|
|
FmhaKernelKey make_key(FmhaKernelFamily family, const std::string& name, int rank = 0)
|
|
{
|
|
(void)name;
|
|
FmhaKernelKey key;
|
|
key.signature.family = family;
|
|
key.signature.data_type = "fp16";
|
|
key.signature.is_group_mode = false;
|
|
key.signature.is_v_rowmajor = true;
|
|
key.signature.hdim_q = 128;
|
|
key.signature.hdim_v = 128;
|
|
key.algorithm.selection_rank = rank;
|
|
key.algorithm.tile_shape = {128, 128, 32, 128, 32, 128};
|
|
key.algorithm.pad_s = true;
|
|
key.algorithm.pad_sk = true;
|
|
key.algorithm.pad_d = true;
|
|
key.algorithm.pad_dv = true;
|
|
return key;
|
|
}
|
|
|
|
FmhaProblem make_splitkv_problem()
|
|
{
|
|
fmha_fwd_splitkv_traits traits{};
|
|
traits.hdim_q = 128;
|
|
traits.hdim_v = 128;
|
|
traits.data_type = "fp16";
|
|
traits.is_group_mode = false;
|
|
traits.is_v_rowmajor = true;
|
|
traits.mask_type = mask_enum::no_mask;
|
|
traits.bias_type = bias_enum::no_bias;
|
|
traits.has_lse = true;
|
|
|
|
fmha_fwd_splitkv_args args{};
|
|
args.batch = 1;
|
|
args.seqlen_q = 128;
|
|
args.seqlen_k = 1024;
|
|
args.max_seqlen_q = 128;
|
|
args.hdim_q = 128;
|
|
args.hdim_v = 128;
|
|
args.nhead_q = 16;
|
|
args.nhead_k = 16;
|
|
args.num_splits = 8;
|
|
|
|
return FmhaProblem::from_invocation(FmhaInvocation::make(traits, args), "gfx942");
|
|
}
|
|
|
|
FmhaProblem make_bwd_problem()
|
|
{
|
|
fmha_bwd_traits traits{};
|
|
traits.hdim_q = 128;
|
|
traits.hdim_v = 128;
|
|
traits.data_type = "fp16";
|
|
traits.is_group_mode = false;
|
|
traits.mask_type = mask_enum::no_mask;
|
|
traits.bias_type = bias_enum::no_bias;
|
|
|
|
fmha_bwd_args args{};
|
|
args.batch = 1;
|
|
args.seqlen_q = 128;
|
|
args.seqlen_k = 128;
|
|
args.max_seqlen_q = 128;
|
|
args.max_seqlen_k = 128;
|
|
args.hdim_q = 128;
|
|
args.hdim_v = 128;
|
|
args.nhead_q = 16;
|
|
args.nhead_k = 16;
|
|
|
|
return FmhaProblem::from_invocation(FmhaInvocation::make(traits, args), "gfx942");
|
|
}
|
|
|
|
} // namespace
|
|
|
|
TEST(FmhaDispatcherTest, PlansSplitKvAsTwoStages)
|
|
{
|
|
FmhaRegistry registry;
|
|
registry.register_kernel(
|
|
std::make_shared<MockFmhaKernel>(make_key(FmhaKernelFamily::FwdSplitKv, "split"), "split"));
|
|
registry.register_kernel(std::make_shared<MockFmhaKernel>(
|
|
make_key(FmhaKernelFamily::FwdSplitKvCombine, "combine"), "combine"));
|
|
|
|
FmhaDispatcher dispatcher(®istry);
|
|
auto plan = dispatcher.plan(make_splitkv_problem());
|
|
ASSERT_TRUE(plan.is_valid());
|
|
ASSERT_EQ(plan.stages.size(), 2u);
|
|
EXPECT_EQ(plan.stages[0].family, FmhaKernelFamily::FwdSplitKv);
|
|
EXPECT_EQ(plan.stages[1].family, FmhaKernelFamily::FwdSplitKvCombine);
|
|
}
|
|
|
|
TEST(FmhaDispatcherTest, PlansSingleStageFwd)
|
|
{
|
|
FmhaRegistry registry;
|
|
registry.register_kernel(
|
|
std::make_shared<MockFmhaKernel>(make_key(FmhaKernelFamily::Fwd, "fwd"), "fwd"));
|
|
|
|
FmhaDispatcher dispatcher(®istry);
|
|
|
|
fmha_fwd_traits traits{};
|
|
traits.hdim_q = 128;
|
|
traits.hdim_v = 128;
|
|
traits.data_type = "fp16";
|
|
traits.is_group_mode = false;
|
|
traits.is_v_rowmajor = true;
|
|
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;
|
|
|
|
fmha_fwd_args args{};
|
|
args.batch = 1;
|
|
args.seqlen_q = 128;
|
|
args.seqlen_k = 128;
|
|
args.max_seqlen_q = 128;
|
|
args.hdim_q = 128;
|
|
args.hdim_v = 128;
|
|
args.nhead_q = 16;
|
|
args.nhead_k = 16;
|
|
|
|
auto problem = FmhaProblem::from_invocation(FmhaInvocation::make(traits, args), "gfx942");
|
|
auto plan = dispatcher.plan(problem);
|
|
ASSERT_TRUE(plan.is_valid());
|
|
ASSERT_EQ(plan.stages.size(), 1u);
|
|
EXPECT_EQ(plan.stages[0].family, FmhaKernelFamily::Fwd);
|
|
}
|
|
|
|
TEST(FmhaDispatcherTest, PlansSingleStagePagedKv)
|
|
{
|
|
FmhaRegistry registry;
|
|
registry.register_kernel(std::make_shared<MockFmhaKernel>(
|
|
make_key(FmhaKernelFamily::FwdPagedKv, "pagedkv"), "pagedkv"));
|
|
|
|
FmhaDispatcher dispatcher(®istry);
|
|
|
|
fmha_fwd_pagedkv_traits traits{};
|
|
traits.hdim_q = 128;
|
|
traits.hdim_v = 128;
|
|
traits.data_type = "fp16";
|
|
traits.is_group_mode = false;
|
|
traits.is_v_rowmajor = true;
|
|
traits.mask_type = mask_enum::no_mask;
|
|
traits.bias_type = bias_enum::no_bias;
|
|
|
|
fmha_fwd_pagedkv_args args{};
|
|
args.batch = 1;
|
|
args.seqlen_q = 128;
|
|
args.seqlen_k = 128;
|
|
args.max_seqlen_q = 128;
|
|
args.hdim_q = 128;
|
|
args.hdim_v = 128;
|
|
args.nhead_q = 16;
|
|
args.nhead_k = 16;
|
|
|
|
auto problem = FmhaProblem::from_invocation(FmhaInvocation::make(traits, args), "gfx942");
|
|
auto plan = dispatcher.plan(problem);
|
|
ASSERT_TRUE(plan.is_valid());
|
|
ASSERT_EQ(plan.stages.size(), 1u);
|
|
EXPECT_EQ(plan.stages[0].family, FmhaKernelFamily::FwdPagedKv);
|
|
}
|
|
|
|
TEST(FmhaDispatcherTest, PlansSingleStageAppendKv)
|
|
{
|
|
FmhaRegistry registry;
|
|
auto key = make_key(FmhaKernelFamily::FwdAppendKv, "appendkv");
|
|
registry.register_kernel(std::make_shared<MockFmhaKernel>(key, "appendkv"));
|
|
|
|
FmhaDispatcher dispatcher(®istry);
|
|
|
|
fmha_fwd_appendkv_traits traits{};
|
|
traits.hdim_q = 128;
|
|
traits.hdim_v = 128;
|
|
traits.data_type = "fp16";
|
|
traits.is_v_rowmajor = true;
|
|
traits.rope_type = rope_enum::none;
|
|
|
|
fmha_fwd_appendkv_args args{};
|
|
args.batch = 1;
|
|
args.seqlen_q = 128;
|
|
args.seqlen_knew = 64;
|
|
args.hdim_q = 128;
|
|
args.hdim_v = 128;
|
|
args.nhead_q = 16;
|
|
args.nhead_k = 16;
|
|
|
|
auto problem = FmhaProblem::from_invocation(FmhaInvocation::make(traits, args), "gfx942");
|
|
auto plan = dispatcher.plan(problem);
|
|
ASSERT_TRUE(plan.is_valid());
|
|
ASSERT_EQ(plan.stages.size(), 1u);
|
|
EXPECT_EQ(plan.stages[0].family, FmhaKernelFamily::FwdAppendKv);
|
|
}
|
|
|
|
TEST(FmhaDispatcherTest, SeqtunePrefersSmallerAlignedTile)
|
|
{
|
|
FmhaRegistry registry;
|
|
|
|
auto key_big = make_key(FmhaKernelFamily::Fwd, "big", /*rank=*/0);
|
|
key_big.algorithm.tile_shape.m0 = 128;
|
|
key_big.algorithm.pad_s = false;
|
|
auto key_small = make_key(FmhaKernelFamily::Fwd, "small", /*rank=*/0);
|
|
key_small.algorithm.tile_shape.m0 = 64;
|
|
key_small.algorithm.pad_s = false;
|
|
|
|
registry.register_kernel(std::make_shared<MockFmhaKernel>(key_big, "big"));
|
|
registry.register_kernel(std::make_shared<MockFmhaKernel>(key_small, "small"));
|
|
|
|
FmhaDispatcher dispatcher(®istry);
|
|
|
|
fmha_fwd_traits traits{};
|
|
traits.hdim_q = 128;
|
|
traits.hdim_v = 128;
|
|
traits.data_type = "fp16";
|
|
traits.is_v_rowmajor = true;
|
|
traits.mask_type = mask_enum::no_mask;
|
|
traits.bias_type = bias_enum::no_bias;
|
|
|
|
fmha_fwd_args args{};
|
|
args.batch = 1;
|
|
args.seqlen_q = 128;
|
|
args.seqlen_k = 128;
|
|
args.max_seqlen_q = 128;
|
|
args.hdim_q = 128;
|
|
args.hdim_v = 128;
|
|
args.nhead_q = 16;
|
|
args.nhead_k = 16;
|
|
|
|
auto problem = FmhaProblem::from_invocation(FmhaInvocation::make(traits, args), "gfx942");
|
|
auto selected = dispatcher.select_kernel(problem);
|
|
ASSERT_NE(selected, nullptr);
|
|
// Both tiles align to 128; seqtune prefers the smaller tile_m0
|
|
EXPECT_EQ(selected->get_name(), "small");
|
|
}
|
|
|
|
TEST(FmhaDispatcherTest, PlansBackwardAsThreeStagesWhenConvertExists)
|
|
{
|
|
FmhaRegistry registry;
|
|
registry.register_kernel(
|
|
std::make_shared<MockFmhaKernel>(make_key(FmhaKernelFamily::BwdDotDoO, "dot"), "dot"));
|
|
registry.register_kernel(
|
|
std::make_shared<MockFmhaKernel>(make_key(FmhaKernelFamily::BwdDqDkDv, "dq"), "dq"));
|
|
registry.register_kernel(std::make_shared<MockFmhaKernel>(
|
|
make_key(FmhaKernelFamily::BwdConvertDq, "convert"), "convert"));
|
|
|
|
FmhaDispatcher dispatcher(®istry);
|
|
auto plan = dispatcher.plan(make_bwd_problem());
|
|
ASSERT_TRUE(plan.is_valid());
|
|
ASSERT_EQ(plan.stages.size(), 3u);
|
|
EXPECT_EQ(plan.stages[0].family, FmhaKernelFamily::BwdDotDoO);
|
|
EXPECT_EQ(plan.stages[1].family, FmhaKernelFamily::BwdDqDkDv);
|
|
EXPECT_EQ(plan.stages[2].family, FmhaKernelFamily::BwdConvertDq);
|
|
}
|
|
|
|
// #15: BWD with asymmetric head dimensions (hdim_q != hdim_v)
|
|
TEST(FmhaDispatcherTest, PlansBackwardWithAsymmetricHdim)
|
|
{
|
|
FmhaRegistry registry;
|
|
registry.set_name("test_bwd_asym");
|
|
|
|
auto asym_key = [](FmhaKernelFamily family, const std::string& n) {
|
|
auto key = make_key(family, n);
|
|
key.signature.hdim_q = 96;
|
|
key.signature.hdim_v = 128;
|
|
return key;
|
|
};
|
|
|
|
registry.register_kernel(
|
|
std::make_shared<MockFmhaKernel>(asym_key(FmhaKernelFamily::BwdDotDoO, "dot96"), "dot96"));
|
|
registry.register_kernel(
|
|
std::make_shared<MockFmhaKernel>(asym_key(FmhaKernelFamily::BwdDqDkDv, "dq96"), "dq96"));
|
|
|
|
FmhaDispatcher dispatcher(®istry);
|
|
auto problem = make_bwd_problem();
|
|
problem.hdim_q = 96;
|
|
problem.hdim_v = 128;
|
|
auto plan = dispatcher.plan(problem);
|
|
ASSERT_TRUE(plan.is_valid());
|
|
EXPECT_GE(plan.stages.size(), 2u);
|
|
EXPECT_EQ(plan.stages[0].family, FmhaKernelFamily::BwdDotDoO);
|
|
EXPECT_EQ(plan.stages[1].family, FmhaKernelFamily::BwdDqDkDv);
|
|
}
|
|
|
|
// #16: BWD negative test -- no matching kernel returns invalid plan
|
|
TEST(FmhaDispatcherTest, PlansBackwardReturnsInvalidWhenNoKernel)
|
|
{
|
|
FmhaRegistry registry;
|
|
registry.set_name("test_bwd_neg");
|
|
|
|
// Register only a fwd kernel, no bwd kernels
|
|
registry.register_kernel(
|
|
std::make_shared<MockFmhaKernel>(make_key(FmhaKernelFamily::Fwd, "fwd"), "fwd"));
|
|
|
|
FmhaDispatcher dispatcher(®istry);
|
|
auto plan = dispatcher.plan(make_bwd_problem());
|
|
EXPECT_FALSE(plan.is_valid());
|
|
}
|
|
|
|
// #17: Canonical key distinguishes dropout seed differences
|
|
TEST(FmhaDispatcherTest, CanonicalKeyDistinguishesDropout)
|
|
{
|
|
FmhaProblem p1;
|
|
p1.data_type = "fp16";
|
|
p1.hdim_q = 128;
|
|
p1.hdim_v = 128;
|
|
p1.has_dropout = false;
|
|
|
|
FmhaProblem p2 = p1;
|
|
p2.has_dropout = true;
|
|
|
|
EXPECT_NE(p1.canonical_key(), p2.canonical_key());
|
|
}
|
|
|
|
// Canonical key covers all signature fields
|
|
TEST(FmhaDispatcherTest, CanonicalKeyCoversAllFields)
|
|
{
|
|
FmhaProblem base;
|
|
base.data_type = "fp16";
|
|
base.hdim_q = 128;
|
|
base.hdim_v = 128;
|
|
|
|
auto check_differs = [&](auto mutator) {
|
|
FmhaProblem p = base;
|
|
mutator(p);
|
|
EXPECT_NE(base.canonical_key(), p.canonical_key());
|
|
};
|
|
|
|
check_differs([](FmhaProblem& p) { p.has_lse = true; });
|
|
check_differs([](FmhaProblem& p) { p.has_dropout = true; });
|
|
check_differs([](FmhaProblem& p) { p.has_logits_soft_cap = true; });
|
|
check_differs([](FmhaProblem& p) { p.has_sink = true; });
|
|
check_differs([](FmhaProblem& p) { p.is_deterministic = true; });
|
|
check_differs([](FmhaProblem& p) { p.has_dbias = true; });
|
|
check_differs([](FmhaProblem& p) { p.is_store_randval = true; });
|
|
check_differs([](FmhaProblem& p) { p.mask_type = 1; });
|
|
check_differs([](FmhaProblem& p) { p.bias_type = 2; });
|
|
check_differs([](FmhaProblem& p) { p.is_group_mode = true; });
|
|
}
|
|
|
|
// BWD workspace sizing
|
|
TEST(FmhaDispatcherTest, BwdWorkspaceInfoComputation)
|
|
{
|
|
FmhaProblem p;
|
|
p.batch = 2;
|
|
p.nhead_q = 8;
|
|
p.seqlen_q = 256;
|
|
p.seqlen_k = 256;
|
|
p.hdim_q = 128;
|
|
|
|
auto info = bwd_workspace_info(p);
|
|
EXPECT_EQ(info.d_bytes, 2u * 8 * 256 * sizeof(float));
|
|
EXPECT_EQ(info.dq_acc_bytes, 2u * 8 * 256 * 128 * sizeof(float));
|
|
EXPECT_EQ(info.d_offset, 0u);
|
|
EXPECT_GT(info.dq_acc_offset, 0u);
|
|
EXPECT_GE(info.dq_acc_offset, info.d_bytes);
|
|
EXPECT_EQ(info.dq_acc_offset % 256, 0u);
|
|
EXPECT_GT(info.total_bytes, info.dq_acc_offset + info.dq_acc_bytes - 1);
|
|
}
|
|
|
|
// Benchmarking control
|
|
TEST(FmhaDispatcherTest, SetBenchmarkingControlsTimingFlag)
|
|
{
|
|
FmhaRegistry registry;
|
|
FmhaDispatcher dispatcher(®istry);
|
|
|
|
EXPECT_FALSE(dispatcher.benchmarking_enabled());
|
|
dispatcher.set_benchmarking(true);
|
|
EXPECT_TRUE(dispatcher.benchmarking_enabled());
|
|
dispatcher.set_benchmarking(false);
|
|
EXPECT_FALSE(dispatcher.benchmarking_enabled());
|
|
}
|
|
|
|
// Verify tie() covers all Signature and Algorithm fields.
|
|
// If a new field is added to FmhaKernelKey but not to tie(),
|
|
// two keys differing only in that field would compare equal (silent bug).
|
|
TEST(FmhaKernelKeyTest, TieCoversAllSignatureFields)
|
|
{
|
|
FmhaKernelKey a{};
|
|
a.signature.data_type = "fp16";
|
|
a.gfx_arch = "gfx950";
|
|
|
|
auto flip = [&](auto mutator) {
|
|
FmhaKernelKey b = a;
|
|
mutator(b);
|
|
EXPECT_NE(a, b) << "tie() does not distinguish a Signature/Algorithm field";
|
|
};
|
|
|
|
flip([](FmhaKernelKey& k) { k.signature.family = FmhaKernelFamily::BwdDqDkDv; });
|
|
flip([](FmhaKernelKey& k) { k.signature.data_type = "bf16"; });
|
|
flip([](FmhaKernelKey& k) { k.signature.is_group_mode = true; });
|
|
flip([](FmhaKernelKey& k) { k.signature.is_v_rowmajor = false; });
|
|
flip([](FmhaKernelKey& k) { k.signature.has_logits_soft_cap = true; });
|
|
flip([](FmhaKernelKey& k) { k.signature.mask_type = 1; });
|
|
flip([](FmhaKernelKey& k) { k.signature.bias_type = 1; });
|
|
flip([](FmhaKernelKey& k) { k.signature.has_lse = true; });
|
|
flip([](FmhaKernelKey& k) { k.signature.has_dropout = true; });
|
|
flip([](FmhaKernelKey& k) { k.signature.qscale_type = 1; });
|
|
flip([](FmhaKernelKey& k) { k.signature.rope_type = 1; });
|
|
flip([](FmhaKernelKey& k) { k.signature.use_paged_kv = true; });
|
|
flip([](FmhaKernelKey& k) { k.signature.do_fp8_static_quant = true; });
|
|
flip([](FmhaKernelKey& k) { k.signature.skip_min_seqlen_q = true; });
|
|
flip([](FmhaKernelKey& k) { k.signature.has_sink = true; });
|
|
flip([](FmhaKernelKey& k) { k.signature.has_dbias = true; });
|
|
flip([](FmhaKernelKey& k) { k.signature.is_store_randval = true; });
|
|
flip([](FmhaKernelKey& k) { k.signature.is_deterministic = true; });
|
|
flip([](FmhaKernelKey& k) { k.signature.kv_memory_layout = 1; });
|
|
flip([](FmhaKernelKey& k) { k.signature.kv_lookup_table = 1; });
|
|
flip([](FmhaKernelKey& k) { k.signature.page_size = 64; });
|
|
flip([](FmhaKernelKey& k) { k.signature.hdim_q = 256; });
|
|
flip([](FmhaKernelKey& k) { k.signature.hdim_v = 256; });
|
|
flip([](FmhaKernelKey& k) { k.signature.receipt = 1; });
|
|
|
|
flip([](FmhaKernelKey& k) { k.algorithm.tile_shape.m0 = 64; });
|
|
flip([](FmhaKernelKey& k) { k.algorithm.pipeline = "qr_async"; });
|
|
flip([](FmhaKernelKey& k) { k.algorithm.pad_s = false; });
|
|
flip([](FmhaKernelKey& k) { k.algorithm.selection_rank = 5; });
|
|
flip([](FmhaKernelKey& k) { k.algorithm.constraint_tag = "special"; });
|
|
flip([](FmhaKernelKey& k) { k.gfx_arch = "gfx942"; });
|
|
}
|
|
|
|
TEST(FmhaDispatcherTest, SelectKernelReturnsNullptrOnEmptyRegistry)
|
|
{
|
|
FmhaRegistry registry;
|
|
FmhaDispatcher dispatcher(®istry);
|
|
|
|
fmha_fwd_traits traits{};
|
|
traits.hdim_q = 128;
|
|
traits.hdim_v = 128;
|
|
traits.data_type = "fp16";
|
|
traits.mask_type = mask_enum::no_mask;
|
|
traits.bias_type = bias_enum::no_bias;
|
|
|
|
auto problem =
|
|
FmhaProblem::from_invocation(FmhaInvocation::make(traits, fmha_fwd_args{}), "gfx950");
|
|
auto selected = dispatcher.select_kernel(problem);
|
|
EXPECT_EQ(selected, nullptr);
|
|
}
|
|
|
|
TEST(FmhaDispatcherTest, SelectKernelReturnsNullptrOnNoMatch)
|
|
{
|
|
FmhaRegistry registry;
|
|
auto key = make_fwd_key(128, 128, "fp16", "gfx950");
|
|
auto mock = std::make_shared<MockFmhaKernel>(key, "fp16_h128");
|
|
registry.register_kernel(mock);
|
|
|
|
FmhaDispatcher dispatcher(®istry);
|
|
|
|
fmha_fwd_traits traits{};
|
|
traits.hdim_q = 256;
|
|
traits.hdim_v = 256;
|
|
traits.data_type = "bf16";
|
|
traits.mask_type = mask_enum::no_mask;
|
|
traits.bias_type = bias_enum::no_bias;
|
|
|
|
auto problem =
|
|
FmhaProblem::from_invocation(FmhaInvocation::make(traits, fmha_fwd_args{}), "gfx950");
|
|
auto selected = dispatcher.select_kernel(problem);
|
|
EXPECT_EQ(selected, nullptr);
|
|
}
|