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b1975951d4
[CK] Add rocm_ck schema engine: Signature, resolve(), ArchProperties (#7179) ## Summary A `Signature` is a directed compute graph: tensors are nodes, operators are edges. Shared names between operator outputs and inputs form the graph structure. `resolve()` walks this graph at compile time (`consteval`), inferring dtype, rank, and layout for every tensor — invalid configs become compiler errors, not runtime crashes. **Key design decisions:** - **Operators teach the system about tensors.** `GemmOp` implies rank 2 and Row/Col/Row layout. `AddOp` and `ReluOp` propagate from connected slots. The dtype cascade fills in the rest: per-tensor → signature-wide → error. - **Adding a new op is zero lines in the resolution engine** if it's structurally binary (`lhs/rhs/out`) or unary (`in/out`) — C++20 concepts handle dispatch automatically. Only ops with special semantics need explicit branches. - **TargetSet is a compile-time bitset over GPU targets.** The wave tile validation table is the single source of truth for valid instruction shapes, traced from CK Tile's WarpGemmDispatcher. FP8 tiles are available on gfx942+ via IterateK composition, not gfx950-only. **Reading order:** signature.hpp (data model) → arch_properties.hpp (TargetSet, wave tiles) → resolve.hpp (resolution engine). 3 new headers, 3 unit tests (including diamond DAG coverage), 3 compile-fail tests. Introduces tests/compile_fail/ infrastructure. **Stack**: PR 2 of 3 porting the rocm_ck constexpr schema from experimental to production. 1. Foundation types — DataType, Layout, Args, Ops (#7114) 2. **This PR** — Schema engine (graph resolution) 3. Spec factories — GemmSpec, makeSpec() (#7180 ) Note: We also removed `FmhaBwdOp` for clarity, since that was introduced early and doesn't have tests set up. **Depends on**: #7114 ## Test plan - [x] ctest --test-dir build --output-on-failure — unit tests + compile-fail tests pass - [x] Compile-fail tests correctly reject: mixed CDNA+RDNA TargetSet, conflicting layouts, empty quantization scale names --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
536 lines
20 KiB
C++
536 lines
20 KiB
C++
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#include <rocm_ck/resolve.hpp>
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#include <gtest/gtest.h>
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using ::rocm_ck::AddOp;
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using ::rocm_ck::BinaryOpLike;
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using ::rocm_ck::DataType;
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using ::rocm_ck::FastGeluOp;
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using ::rocm_ck::GeluOp;
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using ::rocm_ck::GemmOp;
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using ::rocm_ck::kMaxTensors;
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using ::rocm_ck::Layout;
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using ::rocm_ck::MulOp;
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using ::rocm_ck::Quantization;
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using ::rocm_ck::ReluOp;
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using ::rocm_ck::resolve;
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using ::rocm_ck::Scalar;
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using ::rocm_ck::ScaleOp;
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using ::rocm_ck::SigmoidOp;
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using ::rocm_ck::Signature;
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using ::rocm_ck::SiluOp;
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using ::rocm_ck::SoftmaxOp;
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using ::rocm_ck::Tensor;
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using ::rocm_ck::UnaryOpLike;
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// ============================================================================
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// Simple GemmOp resolution
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// ============================================================================
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TEST(Resolve, ResolvesSimpleGemmToThreeTensors)
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{
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constexpr auto r = resolve(
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Signature{.dtype = DataType::FP16, .ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"}}});
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EXPECT_EQ(r.num_tensors, 3);
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}
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TEST(Resolve, CascadesSignatureDtypeToAllGemmTensors)
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{
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constexpr auto r = resolve(
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Signature{.dtype = DataType::FP16, .ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"}}});
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EXPECT_EQ(r.tensor("A").dtype, DataType::FP16);
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EXPECT_EQ(r.tensor("B").dtype, DataType::FP16);
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EXPECT_EQ(r.tensor("C").dtype, DataType::FP16);
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}
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TEST(Resolve, AssignsRank2ToGemmTensors)
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{
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constexpr auto r = resolve(
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Signature{.dtype = DataType::FP16, .ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"}}});
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EXPECT_EQ(r.tensor("A").rank, 2);
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EXPECT_EQ(r.tensor("B").rank, 2);
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EXPECT_EQ(r.tensor("C").rank, 2);
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}
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TEST(Resolve, AssignsRowColRowLayoutToGemmTensors)
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{
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constexpr auto r = resolve(
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Signature{.dtype = DataType::FP16, .ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"}}});
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EXPECT_EQ(r.tensor("A").layout, Layout::Row);
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EXPECT_EQ(r.tensor("B").layout, Layout::Col);
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EXPECT_EQ(r.tensor("C").layout, Layout::Row);
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}
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// ============================================================================
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// Custom tensor names
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// ============================================================================
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TEST(Resolve, AcceptsCustomTensorNames)
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{
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constexpr auto r = resolve(
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Signature{.dtype = DataType::FP16, .ops = {GemmOp{.lhs = "X", .rhs = "Y", .out = "Z"}}});
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EXPECT_EQ(r.tensor("X").rank, 2);
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EXPECT_EQ(r.tensor("Y").rank, 2);
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EXPECT_EQ(r.tensor("Z").rank, 2);
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}
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// ============================================================================
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// dtype cascade
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// ============================================================================
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TEST(Resolve, CascadesBF16DtypeToAllTensors)
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{
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constexpr auto r = resolve(
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Signature{.dtype = DataType::BF16, .ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"}}});
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EXPECT_EQ(r.tensor("A").dtype, DataType::BF16);
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EXPECT_EQ(r.tensor("C").dtype, DataType::BF16);
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}
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TEST(Resolve, AllowsPerTensorDtypeOverride)
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{
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constexpr auto r = resolve( //
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Signature{.dtype = DataType::FP16,
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.tensors = {Tensor{.name = "C", .dtype = DataType::FP32}},
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.ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"}}});
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EXPECT_EQ(r.tensor("C").dtype, DataType::FP32);
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EXPECT_EQ(r.tensor("A").dtype, DataType::FP16); // cascade still applies to A
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}
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// ============================================================================
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// Explicit tensor rank/layout overrides
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// ============================================================================
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TEST(Resolve, AllowsPerTensorRankOverride)
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{
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constexpr auto r = resolve( //
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Signature{.dtype = DataType::FP16,
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.tensors = {Tensor{.name = "A", .rank = 3}},
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.ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"}}});
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EXPECT_EQ(r.tensor("A").rank, 3);
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}
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TEST(Resolve, AllowsPerTensorLayoutOverride)
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{
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// Override B from default Col to Row (R×R layout)
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constexpr auto r = resolve( //
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Signature{.dtype = DataType::FP16,
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.tensors = {Tensor{.name = "B", .layout = Layout::Row}},
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.ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"}}});
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EXPECT_EQ(r.tensor("A").layout, Layout::Row); // default preserved
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EXPECT_EQ(r.tensor("B").layout, Layout::Row); // overridden from Col
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EXPECT_EQ(r.tensor("C").layout, Layout::Row); // default preserved
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}
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TEST(Resolve, AllowsMultipleLayoutOverrides)
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{
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// Override both A and B (C×C layout)
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constexpr auto r = resolve( //
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Signature{.dtype = DataType::FP16,
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.tensors = {Tensor{.name = "A", .layout = Layout::Col},
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Tensor{.name = "B", .layout = Layout::Col}},
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.ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"}}});
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EXPECT_EQ(r.tensor("A").layout, Layout::Col);
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EXPECT_EQ(r.tensor("B").layout, Layout::Col);
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EXPECT_EQ(r.tensor("C").layout, Layout::Row); // default preserved
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}
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// ============================================================================
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// GEMM + Add + Relu chain
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// ============================================================================
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TEST(Resolve, ResolvesGemmAddReluToSixTensors)
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{
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constexpr auto r = resolve( //
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Signature{.dtype = DataType::FP16,
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.ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"},
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AddOp{.lhs = "C", .rhs = "bias", .out = "D"},
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ReluOp{.in = "D", .out = "E"}}});
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EXPECT_EQ(r.num_tensors, 6); // A, B, C, bias, D, E
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}
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TEST(Resolve, PropagatesRankAndLayoutThroughEpilogueChain)
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{
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constexpr auto r = resolve( //
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Signature{.dtype = DataType::FP16,
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.ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"},
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AddOp{.lhs = "C", .rhs = "bias", .out = "D"},
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ReluOp{.in = "D", .out = "E"}}});
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EXPECT_EQ(r.tensor("C").rank, 2);
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EXPECT_EQ(r.tensor("bias").rank, 2);
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EXPECT_EQ(r.tensor("bias").layout, Layout::Row);
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EXPECT_EQ(r.tensor("D").rank, 2);
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EXPECT_EQ(r.tensor("D").layout, Layout::Row);
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EXPECT_EQ(r.tensor("E").rank, 2);
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EXPECT_EQ(r.tensor("E").layout, Layout::Row);
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}
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TEST(Resolve, PropagatesRankAndLayoutThroughDiamondDAG)
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{
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// Diamond: GEMM→C splits into two Add paths, then joins.
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// C → Add(C,bias1)→D1 ─→ Add(D1,D2)→E
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// C → Add(C,bias2)→D2 ─┘
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constexpr auto r = resolve( //
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Signature{.dtype = DataType::FP16,
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.ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"},
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AddOp{.lhs = "C", .rhs = "bias1", .out = "D1"},
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AddOp{.lhs = "C", .rhs = "bias2", .out = "D2"},
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AddOp{.lhs = "D1", .rhs = "D2", .out = "E"}}});
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EXPECT_EQ(r.num_tensors, 8); // A, B, C, bias1, D1, bias2, D2, E
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EXPECT_EQ(r.tensor("D1").rank, 2);
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EXPECT_EQ(r.tensor("D2").rank, 2);
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EXPECT_EQ(r.tensor("E").rank, 2);
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EXPECT_EQ(r.tensor("bias1").layout, Layout::Row);
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EXPECT_EQ(r.tensor("E").layout, Layout::Row);
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}
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TEST(Resolve, AssignsSequentialIndicesToChainedOps)
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{
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constexpr auto r = resolve( //
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Signature{.dtype = DataType::FP16,
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.ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"},
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AddOp{.lhs = "C", .rhs = "bias", .out = "D"},
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ReluOp{.in = "D", .out = "E"}}});
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EXPECT_EQ(r.tensorIndex("A"), 0);
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EXPECT_EQ(r.tensorIndex("B"), 1);
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EXPECT_EQ(r.tensorIndex("C"), 2);
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EXPECT_EQ(r.tensorIndex("bias"), 3);
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EXPECT_EQ(r.tensorIndex("D"), 4);
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EXPECT_EQ(r.tensorIndex("E"), 5);
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}
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// ============================================================================
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// Standalone AddOp
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// ============================================================================
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TEST(Resolve, ResolvesStandaloneAddWithoutImpliedRank)
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{
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constexpr auto r = resolve(
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Signature{.dtype = DataType::FP32, .ops = {AddOp{.lhs = "A", .rhs = "B", .out = "C"}}});
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EXPECT_EQ(r.num_tensors, 3);
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EXPECT_EQ(r.tensor("A").rank, 0); // no op implies rank
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EXPECT_EQ(r.tensor("A").layout, Layout::Auto); // no op implies layout
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}
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// ============================================================================
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// Conflict detection — redundant identical sets are silent
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// ============================================================================
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TEST(Resolve, AllowsRedundantIdenticalLayoutFromTwoGemmOps)
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{
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// GemmOp1 outputs "C" as Row. GemmOp2 uses "C" as lhs (also Row).
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// Two ops set the same layout → no conflict.
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constexpr auto r = resolve( //
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Signature{.dtype = DataType::FP16,
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.ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"},
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GemmOp{.lhs = "C", .rhs = "D", .out = "E"}}});
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EXPECT_EQ(r.tensor("C").layout, Layout::Row);
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EXPECT_EQ(r.tensor("C").rank, 2);
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}
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TEST(Resolve, AllowsPropagationThroughAddWithConsistentLayout)
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{
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// GemmOp sets C=Row. AddOp connects C to bias and D.
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// Propagation sets bias and D to Row (matching C) → no conflict.
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constexpr auto r = resolve( //
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Signature{.dtype = DataType::FP16,
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.ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"},
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AddOp{.lhs = "C", .rhs = "bias", .out = "D"}}});
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EXPECT_EQ(r.tensor("C").layout, Layout::Row);
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EXPECT_EQ(r.tensor("bias").layout, Layout::Row);
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EXPECT_EQ(r.tensor("D").layout, Layout::Row);
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}
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// ============================================================================
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// FMHA pattern: two GemmOps + SoftmaxOp
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// ============================================================================
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TEST(Resolve, ResolvesFMHATwoGemmSoftmaxPattern)
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{
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constexpr auto r = resolve( //
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Signature{.dtype = DataType::FP16,
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.ops = {GemmOp{.lhs = "Q", .rhs = "K", .out = "S"},
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SoftmaxOp{.in = "S", .out = "P"},
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GemmOp{.lhs = "P", .rhs = "V", .out = "O"}}});
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EXPECT_EQ(r.num_tensors, 6); // Q, K, S, P, V, O
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EXPECT_EQ(r.tensor("Q").rank, 2);
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EXPECT_EQ(r.tensor("S").rank, 2);
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EXPECT_EQ(r.tensor("P").rank, 2); // propagated via SoftmaxOp
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EXPECT_EQ(r.tensor("O").rank, 2);
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}
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// ============================================================================
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// Scalar tracking
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// ============================================================================
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TEST(Resolve, PreservesScalarNamesAndDtypes)
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{
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constexpr auto r = resolve( //
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Signature{.dtype = DataType::FP16,
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.scalars = {Scalar{.name = "alpha", .dtype = DataType::FP32},
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Scalar{.name = "beta", .dtype = DataType::FP32}},
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.ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"}}});
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EXPECT_EQ(r.num_scalars, 2);
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EXPECT_EQ(r.scalar("alpha").dtype, DataType::FP32);
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EXPECT_EQ(r.scalar("beta").dtype, DataType::FP32);
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EXPECT_EQ(r.scalarIndex("alpha"), 0);
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EXPECT_EQ(r.scalarIndex("beta"), 1);
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}
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TEST(Resolve, ReportsZeroScalarsWhenNoneDeclared)
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{
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constexpr auto r = resolve(
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Signature{.dtype = DataType::FP16, .ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"}}});
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EXPECT_EQ(r.num_scalars, 0);
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}
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// ============================================================================
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// findTensor / findScalar (constexpr, not consteval — returns -1 on miss)
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// ============================================================================
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TEST(Resolve, FindsTensorByName)
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{
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constexpr auto r = resolve(
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Signature{.dtype = DataType::FP16, .ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"}}});
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EXPECT_EQ(r.findTensor("A"), 0);
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EXPECT_EQ(r.findTensor("C"), 2);
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}
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TEST(Resolve, ReturnsNegativeOneForUnknownTensor)
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{
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constexpr auto r = resolve(
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Signature{.dtype = DataType::FP16, .ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"}}});
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EXPECT_EQ(r.findTensor("Z"), -1);
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}
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TEST(Resolve, ReturnsNegativeOneForUnknownScalar)
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{
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constexpr auto r = resolve(
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Signature{.dtype = DataType::FP16, .ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"}}});
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EXPECT_EQ(r.findScalar("nonexistent"), -1);
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}
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// ============================================================================
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// Quantized tensors
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// ============================================================================
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TEST(Resolve, QuantizedBAutoRegistersScaleTensor)
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{
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constexpr auto r = resolve( //
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Signature{.dtype = DataType::FP16,
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.tensors = {Tensor{.name = "B",
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.dtype = DataType::I4,
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.quantize = Quantization{.scale_name = "scale",
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.scale_dtype = DataType::FP32,
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.group_size = 128}}},
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.ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"}}});
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// A, B, C from GemmOp + scale auto-registered = 4 tensors
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EXPECT_EQ(r.num_tensors, 4);
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}
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TEST(Resolve, ScaleTensorGetsDtypeFromQuantization)
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{
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constexpr auto r = resolve(
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Signature{.dtype = DataType::FP16,
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.tensors = {Tensor{.name = "B",
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.dtype = DataType::I4,
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.quantize = Quantization{.scale_name = "scale",
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.scale_dtype = DataType::FP32}}},
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.ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"}}});
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// Scale tensor dtype comes from Quantization, not the signature cascade
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EXPECT_EQ(r.tensor("scale").dtype, DataType::FP32);
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}
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TEST(Resolve, ScaleTensorGetsRank2RowLayout)
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{
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constexpr auto r = resolve( //
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Signature{.dtype = DataType::FP16,
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.tensors = {Tensor{.name = "B",
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.dtype = DataType::I4,
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.quantize = Quantization{.scale_name = "scale"}}},
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.ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"}}});
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EXPECT_EQ(r.tensor("scale").rank, 2);
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EXPECT_EQ(r.tensor("scale").layout, Layout::Row);
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}
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TEST(Resolve, QuantizedTensorKeepsOwnDtype)
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{
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constexpr auto r = resolve( //
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Signature{.dtype = DataType::FP16,
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.tensors = {Tensor{.name = "B",
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.dtype = DataType::I4,
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.quantize = Quantization{.scale_name = "scale"}}},
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.ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"}}});
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EXPECT_EQ(r.tensor("B").dtype, DataType::I4);
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EXPECT_EQ(r.tensor("A").dtype, DataType::FP16); // cascade still works
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}
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TEST(Resolve, QuantizedResolvedTensorCarriesQuantizeInfo)
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{
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constexpr auto r = resolve( //
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Signature{.dtype = DataType::FP16,
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.tensors = {Tensor{.name = "B",
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.dtype = DataType::I4,
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.quantize = Quantization{.scale_name = "scale",
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.scale_dtype = DataType::FP32,
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.group_size = 64}}},
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.ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"}}});
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EXPECT_TRUE(r.tensor("B").quantize.has_value());
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EXPECT_EQ(r.tensor("B").quantize->scale_name, "scale");
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EXPECT_EQ(r.tensor("B").quantize->scale_dtype, DataType::FP32);
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EXPECT_EQ(r.tensor("B").quantize->group_size, 64);
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}
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||
TEST(Resolve, NonQuantizedTensorHasNoQuantizeInfo)
|
||
{
|
||
constexpr auto r = resolve( //
|
||
Signature{.dtype = DataType::FP16,
|
||
.tensors = {Tensor{.name = "B",
|
||
.dtype = DataType::I4,
|
||
.quantize = Quantization{.scale_name = "scale"}}},
|
||
.ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"}}});
|
||
|
||
EXPECT_FALSE(r.tensor("A").quantize.has_value());
|
||
EXPECT_FALSE(r.tensor("C").quantize.has_value());
|
||
EXPECT_FALSE(r.tensor("scale").quantize.has_value());
|
||
}
|
||
|
||
TEST(Resolve, QuantizedGemmWithEpiloguePreservesScaleTensor)
|
||
{
|
||
constexpr auto r = resolve( //
|
||
Signature{.dtype = DataType::FP16,
|
||
.tensors = {Tensor{.name = "B",
|
||
.dtype = DataType::I4,
|
||
.quantize = Quantization{.scale_name = "scale"}}},
|
||
.ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"},
|
||
AddOp{.lhs = "C", .rhs = "bias", .out = "D"},
|
||
ReluOp{.in = "D", .out = "E"}}});
|
||
|
||
// A, B, C, bias, D, E from ops + scale auto-registered = 7
|
||
EXPECT_EQ(r.num_tensors, 7);
|
||
EXPECT_EQ(r.tensor("scale").dtype, DataType::FP32);
|
||
EXPECT_TRUE(r.tensor("B").quantize.has_value());
|
||
}
|
||
|
||
// ============================================================================
|
||
// C++20 concepts
|
||
// ============================================================================
|
||
|
||
TEST(Concepts, ClassifiesAddAndMulAsBinaryOpLike)
|
||
{
|
||
EXPECT_TRUE(BinaryOpLike<AddOp>);
|
||
EXPECT_TRUE(BinaryOpLike<MulOp>);
|
||
EXPECT_FALSE(BinaryOpLike<ReluOp>);
|
||
EXPECT_FALSE(BinaryOpLike<SoftmaxOp>);
|
||
}
|
||
|
||
TEST(Concepts, ClassifiesActivationsAsUnaryOpLike)
|
||
{
|
||
EXPECT_TRUE(UnaryOpLike<ReluOp>);
|
||
EXPECT_TRUE(UnaryOpLike<FastGeluOp>);
|
||
EXPECT_TRUE(UnaryOpLike<GeluOp>);
|
||
EXPECT_TRUE(UnaryOpLike<SiluOp>);
|
||
EXPECT_TRUE(UnaryOpLike<SigmoidOp>);
|
||
EXPECT_TRUE(UnaryOpLike<SoftmaxOp>);
|
||
EXPECT_FALSE(UnaryOpLike<AddOp>);
|
||
EXPECT_FALSE(UnaryOpLike<GemmOp>);
|
||
}
|
||
|
||
TEST(Concepts, ClassifiesGemmOpAsBinaryButNotUnary)
|
||
{
|
||
// GemmOp has lhs/rhs/out AND is special-cased, not generic BinaryOpLike
|
||
// (it has .lhs, .rhs, .out but is handled separately in registerSlots)
|
||
EXPECT_TRUE(BinaryOpLike<GemmOp>); // structurally matches, but dispatch special-cases it
|
||
EXPECT_FALSE(UnaryOpLike<GemmOp>);
|
||
}
|
||
|
||
TEST(Concepts, ClassifiesScaleOpAsUnaryNotBinary)
|
||
{
|
||
EXPECT_TRUE(UnaryOpLike<ScaleOp>);
|
||
EXPECT_FALSE(BinaryOpLike<ScaleOp>);
|
||
}
|
||
|
||
// ============================================================================
|
||
// ScaleOp with explicit Scalar
|
||
// ============================================================================
|
||
|
||
TEST(Resolve, ScaleOpReferencesExplicitScalar)
|
||
{
|
||
constexpr auto r = resolve( //
|
||
Signature{.dtype = DataType::FP16,
|
||
.scalars = {Scalar{.name = "alpha", .dtype = DataType::FP32}},
|
||
.ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"},
|
||
ScaleOp{.in = "C", .out = "D", .scale = "alpha"}}});
|
||
|
||
EXPECT_EQ(r.num_tensors, 4); // A, B, C, D
|
||
EXPECT_EQ(r.num_scalars, 1);
|
||
EXPECT_EQ(r.scalar("alpha").dtype, DataType::FP32);
|
||
EXPECT_EQ(r.scalarIndex("alpha"), 0);
|
||
}
|
||
|
||
TEST(Resolve, ScaleOpPreservesScalarDtype)
|
||
{
|
||
constexpr auto r = resolve( //
|
||
Signature{.dtype = DataType::FP16,
|
||
.scalars = {Scalar{.name = "scale_factor", .dtype = DataType::FP16}},
|
||
.ops = {GemmOp{.lhs = "A", .rhs = "B", .out = "C"},
|
||
ScaleOp{.in = "C", .out = "D", .scale = "scale_factor"}}});
|
||
|
||
EXPECT_EQ(r.scalar("scale_factor").dtype, DataType::FP16);
|
||
}
|
||
|
||
// ============================================================================
|
||
// Boundary: signature at kMaxTensors
|
||
// ============================================================================
|
||
|
||
TEST(Resolve, HandlesSignatureWithManyTensors)
|
||
{
|
||
// Create a chain of AddOps to generate many tensors (close to kMaxTensors).
|
||
// Each AddOp creates 3 tensors (lhs, rhs, out). We'll create a chain that
|
||
// approaches the limit.
|
||
// kMaxTensors is 16, so a signature with 3 GEMMs (each with 3 tensors = 9)
|
||
// plus some adds should get close.
|
||
constexpr auto r = resolve( //
|
||
Signature{.dtype = DataType::FP16,
|
||
.ops = {GemmOp{.lhs = "A1", .rhs = "B1", .out = "C1"},
|
||
GemmOp{.lhs = "A2", .rhs = "B2", .out = "C2"},
|
||
GemmOp{.lhs = "A3", .rhs = "B3", .out = "C3"},
|
||
AddOp{.lhs = "C1", .rhs = "C2", .out = "D1"},
|
||
AddOp{.lhs = "D1", .rhs = "C3", .out = "D2"}}});
|
||
|
||
// A1, B1, C1, A2, B2, C2, A3, B3, C3, D1, D2 = 11 tensors
|
||
EXPECT_EQ(r.num_tensors, 11);
|
||
EXPECT_EQ(r.tensor("A1").dtype, DataType::FP16);
|
||
EXPECT_EQ(r.tensor("D2").dtype, DataType::FP16);
|
||
}
|