diff options
author | Håvard Pettersen <havardpe@oath.com> | 2020-04-27 14:07:26 +0000 |
---|---|---|
committer | Håvard Pettersen <havardpe@oath.com> | 2020-05-04 08:13:56 +0000 |
commit | a38091735759c16e86cab1353db1f439a3cd35c4 (patch) | |
tree | de913055842b7e54461867360bde42d15fb43b25 /eval | |
parent | e3f135fc9ccad48719e462397915c6efeaf450b6 (diff) |
multi-matmul
Diffstat (limited to 'eval')
11 files changed, 522 insertions, 24 deletions
diff --git a/eval/CMakeLists.txt b/eval/CMakeLists.txt index bb13638cf1d..9cd5f396144 100644 --- a/eval/CMakeLists.txt +++ b/eval/CMakeLists.txt @@ -35,7 +35,8 @@ vespa_define_module( src/tests/tensor/dense_generic_join src/tests/tensor/dense_inplace_join_function src/tests/tensor/dense_inplace_map_function - src/tests/tensor/dense_matmul_function + src/tests/tensor/dense_matmul_function + src/tests/tensor/dense_multi_matmul_function src/tests/tensor/dense_remove_dimension_optimizer src/tests/tensor/dense_replace_type_function src/tests/tensor/dense_tensor_create_function diff --git a/eval/src/tests/tensor/dense_matmul_function/dense_matmul_function_test.cpp b/eval/src/tests/tensor/dense_matmul_function/dense_matmul_function_test.cpp index 5d7c0be704e..a571837b8e9 100644 --- a/eval/src/tests/tensor/dense_matmul_function/dense_matmul_function_test.cpp +++ b/eval/src/tests/tensor/dense_matmul_function/dense_matmul_function_test.cpp @@ -23,30 +23,14 @@ using namespace vespalib::eval::tensor_function; const TensorEngine &prod_engine = DefaultTensorEngine::ref(); -void add_matrix(EvalFixture::ParamRepo &repo, const char *d1, size_t s1, const char *d2, size_t s2) { - for (bool float_cells: {false, true}) { - auto name = make_string("%s%zu%s%zu%s", d1, s1, d2, s2, float_cells ? "f" : ""); - auto type_str = make_string("tensor%s(%s[%zu],%s[%zu])", float_cells ? "<float>" : "", d1, s1, d2, s2); - TensorSpec matrix(type_str); - for (size_t i = 0; i < s1; ++i) { - for (size_t j = 0; j < s2; ++j) { - double value = (i + s1 + s2) * 3.0 + (j + s2) * 7.0; - matrix.add({{d1, i}, {d2, j}}, value); - } - } - repo.add(name, matrix); - } -} - EvalFixture::ParamRepo make_params() { - EvalFixture::ParamRepo repo; - add_matrix(repo, "a", 2, "d", 3); // inner/inner - add_matrix(repo, "a", 2, "b", 5); // inner/outer - add_matrix(repo, "b", 5, "c", 2); // outer/outer - add_matrix(repo, "a", 2, "c", 3); // not matching - //----------------------------------------------- - add_matrix(repo, "b", 5, "d", 3); // fixed param - return repo; + return EvalFixture::ParamRepo() + .add_matrix("a", 2, "d", 3) // inner/inner + .add_matrix("a", 2, "b", 5) // inner/outer + .add_matrix("b", 5, "c", 2) // outer/outer + .add_matrix("a", 2, "c", 3) // not matching + //------------------------------------------ + .add_matrix("b", 5, "d", 3); // fixed param } EvalFixture::ParamRepo param_repo = make_params(); diff --git a/eval/src/tests/tensor/dense_multi_matmul_function/CMakeLists.txt b/eval/src/tests/tensor/dense_multi_matmul_function/CMakeLists.txt new file mode 100644 index 00000000000..1619f42c897 --- /dev/null +++ b/eval/src/tests/tensor/dense_multi_matmul_function/CMakeLists.txt @@ -0,0 +1,8 @@ +# Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. +vespa_add_executable(eval_dense_multi_matmul_function_test_app TEST + SOURCES + dense_multi_matmul_function_test.cpp + DEPENDS + vespaeval +) +vespa_add_test(NAME eval_dense_multi_matmul_function_test_app COMMAND eval_dense_multi_matmul_function_test_app) diff --git a/eval/src/tests/tensor/dense_multi_matmul_function/dense_multi_matmul_function_test.cpp b/eval/src/tests/tensor/dense_multi_matmul_function/dense_multi_matmul_function_test.cpp new file mode 100644 index 00000000000..c0823248538 --- /dev/null +++ b/eval/src/tests/tensor/dense_multi_matmul_function/dense_multi_matmul_function_test.cpp @@ -0,0 +1,155 @@ +// Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. + +#include <vespa/vespalib/testkit/test_kit.h> +#include <vespa/eval/eval/tensor_function.h> +#include <vespa/eval/eval/operation.h> +#include <vespa/eval/eval/simple_tensor.h> +#include <vespa/eval/eval/simple_tensor_engine.h> +#include <vespa/eval/tensor/default_tensor_engine.h> +#include <vespa/eval/tensor/dense/dense_multi_matmul_function.h> +#include <vespa/eval/tensor/dense/dense_tensor.h> +#include <vespa/eval/tensor/dense/dense_tensor_view.h> +#include <vespa/eval/eval/test/tensor_model.hpp> +#include <vespa/eval/eval/test/eval_fixture.h> + +#include <vespa/vespalib/util/stringfmt.h> +#include <vespa/vespalib/util/stash.h> + +using namespace vespalib; +using namespace vespalib::eval; +using namespace vespalib::eval::test; +using namespace vespalib::tensor; +using namespace vespalib::eval::tensor_function; + +const TensorEngine &prod_engine = DefaultTensorEngine::ref(); + +EvalFixture::ParamRepo make_params() { + return EvalFixture::ParamRepo() + .add_dense({{"A", 2}, {"B", 1}, {"C", 3}, {"a", 2}, {"d", 3}}) // inner/inner + .add_dense({{"B", 1}, {"C", 3}, {"a", 2}, {"d", 3}}) // inner/inner, missing A + .add_dense({{"A", 1}, {"a", 2}, {"d", 3}}) // inner/inner, single mat + .add_dense({{"A", 2}, {"D", 3}, {"a", 2}, {"b", 1}, {"c", 3}}) // inner/inner, inverted + .add_dense({{"A", 2}, {"B", 1}, {"C", 3}, {"a", 2}, {"b", 5}}) // inner/outer + .add_dense({{"A", 2}, {"B", 1}, {"C", 3}, {"b", 5}, {"c", 2}}) // outer/outer + .add_dense({{"A", 2}, {"B", 1}, {"C", 3}, {"a", 2}, {"c", 3}}) // not matching + //---------------------------------------------------------------------------------------- + .add_dense({{"A", 2}, {"B", 1}, {"C", 3}, {"b", 5}, {"d", 3}}) // fixed param + .add_dense({{"B", 1}, {"C", 3}, {"b", 5}, {"d", 3}}) // fixed param, missing A + .add_dense({{"A", 1}, {"b", 5}, {"d", 3}}) // fixed param, single mat + .add_dense({{"B", 5}, {"D", 3}, {"a", 2}, {"b", 1}, {"c", 3}}); // fixed param, inverted +} +EvalFixture::ParamRepo param_repo = make_params(); + +void verify_optimized(const vespalib::string &expr, + size_t lhs_size, size_t common_size, size_t rhs_size, size_t matmul_cnt, + bool lhs_inner, bool rhs_inner) +{ + EvalFixture slow_fixture(prod_engine, expr, param_repo, false); + EvalFixture fixture(prod_engine, expr, param_repo, true); + EXPECT_EQUAL(fixture.result(), EvalFixture::ref(expr, param_repo)); + EXPECT_EQUAL(fixture.result(), slow_fixture.result()); + auto info = fixture.find_all<DenseMultiMatMulFunction>(); + ASSERT_EQUAL(info.size(), 1u); + EXPECT_TRUE(info[0]->result_is_mutable()); + EXPECT_EQUAL(info[0]->lhs_size(), lhs_size); + EXPECT_EQUAL(info[0]->common_size(), common_size); + EXPECT_EQUAL(info[0]->rhs_size(), rhs_size); + EXPECT_EQUAL(info[0]->matmul_cnt(), matmul_cnt); + EXPECT_EQUAL(info[0]->lhs_common_inner(), lhs_inner); + EXPECT_EQUAL(info[0]->rhs_common_inner(), rhs_inner); +} + +void verify_not_optimized(const vespalib::string &expr) { + EvalFixture slow_fixture(prod_engine, expr, param_repo, false); + EvalFixture fixture(prod_engine, expr, param_repo, true); + EXPECT_EQUAL(fixture.result(), EvalFixture::ref(expr, param_repo)); + EXPECT_EQUAL(fixture.result(), slow_fixture.result()); + auto info = fixture.find_all<DenseMultiMatMulFunction>(); + EXPECT_TRUE(info.empty()); +} + +TEST("require that multi matmul can be optimized") { + TEST_DO(verify_optimized("reduce(A2B1C3a2d3*A2B1C3b5d3,sum,d)", 2, 3, 5, 6, true, true)); +} + +TEST("require that single multi matmul can be optimized") { + TEST_DO(verify_optimized("reduce(A1a2d3*A1b5d3,sum,d)", 2, 3, 5, 1, true, true)); +} + +TEST("require that multi matmul with lambda can be optimized") { + TEST_DO(verify_optimized("reduce(join(A2B1C3a2d3,A2B1C3b5d3,f(x,y)(x*y)),sum,d)", 2, 3, 5, 6, true, true)); + TEST_DO(verify_optimized("reduce(join(A2B1C3a2d3,A2B1C3b5d3,f(x,y)(y*x)),sum,d)", 2, 3, 5, 6, true, true)); +} + +TEST("require that expressions similar to multi matmul are not optimized") { + TEST_DO(verify_not_optimized("reduce(A2B1C3a2d3*A2B1C3b5d3,sum,a)")); + TEST_DO(verify_not_optimized("reduce(A2B1C3a2d3*A2B1C3b5d3,sum,b)")); + TEST_DO(verify_not_optimized("reduce(A2B1C3a2d3*A2B1C3b5d3,prod,d)")); + TEST_DO(verify_not_optimized("reduce(A2B1C3a2d3*A2B1C3b5d3,sum)")); + TEST_DO(verify_not_optimized("reduce(join(A2B1C3a2d3,A2B1C3b5d3,f(x,y)(x+y)),sum,d)")); + TEST_DO(verify_not_optimized("reduce(join(A2B1C3a2d3,A2B1C3b5d3,f(x,y)(x*x)),sum,d)")); + TEST_DO(verify_not_optimized("reduce(join(A2B1C3a2d3,A2B1C3b5d3,f(x,y)(y*y)),sum,d)")); + TEST_DO(verify_not_optimized("reduce(join(A2B1C3a2d3,A2B1C3b5d3,f(x,y)(x*y*1)),sum,d)")); + TEST_DO(verify_not_optimized("reduce(A2B1C3a2c3*A2B1C3b5d3,sum,d)")); + TEST_DO(verify_not_optimized("reduce(A2B1C3a2c3*A2B1C3b5d3,sum,c)")); +} + +TEST("require that multi matmul must have matching cell type") { + TEST_DO(verify_not_optimized("reduce(A2B1C3a2d3f*A2B1C3b5d3,sum,d)")); + TEST_DO(verify_not_optimized("reduce(A2B1C3a2d3*A2B1C3b5d3f,sum,d)")); +} + +TEST("require that multi matmul must have matching dimension prefix") { + TEST_DO(verify_not_optimized("reduce(B1C3a2d3*A2B1C3b5d3,sum,d)")); + TEST_DO(verify_not_optimized("reduce(A2B1C3a2d3*B1C3b5d3,sum,d)")); +} + +TEST("require that multi matmul must have inner nesting of matmul dimensions") { + TEST_DO(verify_not_optimized("reduce(A2D3a2b1c3*B5D3a2b1c3,sum,D)")); + TEST_DO(verify_not_optimized("reduce(B5D3a2b1c3*A2D3a2b1c3,sum,D)")); +} + +TEST("require that multi matmul function can be debug dumped") { + EvalFixture fixture(prod_engine, "reduce(A2B1C3a2d3*A2B1C3b5d3,sum,d)", param_repo, true); + auto info = fixture.find_all<DenseMultiMatMulFunction>(); + ASSERT_EQUAL(info.size(), 1u); + fprintf(stderr, "%s\n", info[0]->as_string().c_str()); +} + +vespalib::string make_expr(const vespalib::string &a, const vespalib::string &b, const vespalib::string &common, + bool float_cells) +{ + return make_string("reduce(%s%s*%s%s,sum,%s)", a.c_str(), float_cells ? "f" : "", b.c_str(), float_cells ? "f" : "", common.c_str()); +} + +void verify_optimized_multi(const vespalib::string &a, const vespalib::string &b, const vespalib::string &common, + size_t lhs_size, size_t common_size, size_t rhs_size, size_t matmul_cnt, + bool lhs_inner, bool rhs_inner) +{ + for (bool float_cells: {false, true}) { + { + auto expr = make_expr(a, b, common, float_cells); + TEST_STATE(expr.c_str()); + TEST_DO(verify_optimized(expr, lhs_size, common_size, rhs_size, matmul_cnt, lhs_inner, rhs_inner)); + } + { + auto expr = make_expr(b, a, common, float_cells); + TEST_STATE(expr.c_str()); + TEST_DO(verify_optimized(expr, lhs_size, common_size, rhs_size, matmul_cnt, lhs_inner, rhs_inner)); + } + } +} + +TEST("require that multi matmul inner/inner works correctly") { + TEST_DO(verify_optimized_multi("A2B1C3a2d3", "A2B1C3b5d3", "d", 2, 3, 5, 6, true, true)); +} + +TEST("require that multi matmul inner/outer works correctly") { + TEST_DO(verify_optimized_multi("A2B1C3a2b5", "A2B1C3b5d3", "b", 2, 5, 3, 6, true, false)); +} + +TEST("require that multi matmul outer/outer works correctly") { + TEST_DO(verify_optimized_multi("A2B1C3b5c2", "A2B1C3b5d3", "b", 2, 5, 3, 6, false, false)); +} + +TEST_MAIN() { TEST_RUN_ALL(); } diff --git a/eval/src/vespa/eval/eval/test/eval_fixture.cpp b/eval/src/vespa/eval/eval/test/eval_fixture.cpp index 325fb208319..7ce05ccea8f 100644 --- a/eval/src/vespa/eval/eval/test/eval_fixture.cpp +++ b/eval/src/vespa/eval/eval/test/eval_fixture.cpp @@ -3,6 +3,9 @@ #include <vespa/vespalib/testkit/test_kit.h> #include "eval_fixture.h" #include <vespa/eval/eval/make_tensor_function.h> +#include <vespa/vespalib/util/stringfmt.h> + +using vespalib::make_string_short::fmt; namespace vespalib::eval::test { @@ -96,8 +99,71 @@ std::vector<Value::CREF> get_refs(const std::vector<Value::UP> &values) { return result; } +void add_cell_values(TensorSpec &spec, TensorSpec::Address &addr, + const std::vector<std::pair<vespalib::string, size_t> > &dims, + size_t idx, size_t &seq) +{ + if (idx < dims.size()) { + for (size_t i = 0; i < dims[idx].second; ++i) { + addr.emplace(dims[idx].first, TensorSpec::Label(i)).first->second = TensorSpec::Label(i); + add_cell_values(spec, addr, dims, idx + 1, seq); + } + } else { + spec.add(addr, seq++); + } +} + +TensorSpec make_dense(const vespalib::string &type, + const std::vector<std::pair<vespalib::string, size_t> > &dims, + size_t seed) +{ + TensorSpec spec(type); + TensorSpec::Address addr; + size_t seq = seed; + add_cell_values(spec, addr, dims, 0, seq); + return spec; +} + } // namespace vespalib::eval::test +ParamRepo & +EvalFixture::ParamRepo::add_vector(const char *d1, size_t s1, size_t seed) +{ + return add_dense({{d1, s1}}, seed); +} + +ParamRepo & +EvalFixture::ParamRepo::add_matrix(const char *d1, size_t s1, const char *d2, size_t s2, size_t seed) +{ + return add_dense({{d1, s1}, {d2, s2}}, seed); +} + +ParamRepo & +EvalFixture::ParamRepo::add_cube(const char *d1, size_t s1, const char *d2, size_t s2, const char *d3, size_t s3, size_t seed) +{ + return add_dense({{d1, s1}, {d2, s2}, {d3, s3}}, seed); +} + +ParamRepo & +EvalFixture::ParamRepo::add_dense(const std::vector<std::pair<vespalib::string, size_t> > &dims, size_t seed) +{ + vespalib::string prev; + vespalib::string name; + vespalib::string type; + for (const auto &dim: dims) { + if (!prev.empty()) { + ASSERT_LESS(prev, dim.first); + type += ","; + } + name += fmt("%s%zu", dim.first.c_str(), dim.second); + type += fmt("%s[%zu]", dim.first.c_str(), dim.second); + prev = dim.first; + } + add(name, make_dense(fmt("tensor(%s)", type.c_str()), dims, seed)); + add(name + "f", make_dense(fmt("tensor<float>(%s)", type.c_str()), dims, seed)); + return *this; +} + void EvalFixture::detect_param_tampering(const ParamRepo ¶m_repo, bool allow_mutable) const { diff --git a/eval/src/vespa/eval/eval/test/eval_fixture.h b/eval/src/vespa/eval/eval/test/eval_fixture.h index 1d39dc52cba..f11fb8ebf22 100644 --- a/eval/src/vespa/eval/eval/test/eval_fixture.h +++ b/eval/src/vespa/eval/eval/test/eval_fixture.h @@ -37,6 +37,10 @@ public: ParamRepo &add_mutable(const vespalib::string &name, const TensorSpec &value) { return add(name, value, true); } + ParamRepo &add_vector(const char *d1, size_t s1, size_t seed = 1); + ParamRepo &add_matrix(const char *d1, size_t s1, const char *d2, size_t s2, size_t seed = 1); + ParamRepo &add_cube(const char *d1, size_t s1, const char *d2, size_t s2, const char *d3, size_t s3, size_t seed = 1); + ParamRepo &add_dense(const std::vector<std::pair<vespalib::string, size_t> > &dims, size_t seed = 1); ~ParamRepo() {} }; diff --git a/eval/src/vespa/eval/eval/test/tensor_model.hpp b/eval/src/vespa/eval/eval/test/tensor_model.hpp index 2466701df62..42f0dc7e996 100644 --- a/eval/src/vespa/eval/eval/test/tensor_model.hpp +++ b/eval/src/vespa/eval/eval/test/tensor_model.hpp @@ -1,5 +1,7 @@ // Copyright 2017 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. +#pragma once + #include <vespa/vespalib/testkit/test_kit.h> #include <vespa/eval/eval/tensor_spec.h> #include <vespa/eval/eval/value_type.h> diff --git a/eval/src/vespa/eval/tensor/default_tensor_engine.cpp b/eval/src/vespa/eval/tensor/default_tensor_engine.cpp index b16241fe5e5..0cadbd64372 100644 --- a/eval/src/vespa/eval/tensor/default_tensor_engine.cpp +++ b/eval/src/vespa/eval/tensor/default_tensor_engine.cpp @@ -11,6 +11,7 @@ #include "dense/dense_dot_product_function.h" #include "dense/dense_xw_product_function.h" #include "dense/dense_matmul_function.h" +#include "dense/dense_multi_matmul_function.h" #include "dense/dense_fast_rename_optimizer.h" #include "dense/dense_add_dimension_optimizer.h" #include "dense/dense_remove_dimension_optimizer.h" @@ -273,6 +274,7 @@ DefaultTensorEngine::optimize(const TensorFunction &expr, Stash &stash) const child.set(DenseDotProductFunction::optimize(child.get(), stash)); child.set(DenseXWProductFunction::optimize(child.get(), stash)); child.set(DenseMatMulFunction::optimize(child.get(), stash)); + child.set(DenseMultiMatMulFunction::optimize(child.get(), stash)); child.set(DenseFastRenameOptimizer::optimize(child.get(), stash)); child.set(DenseAddDimensionOptimizer::optimize(child.get(), stash)); child.set(DenseRemoveDimensionOptimizer::optimize(child.get(), stash)); diff --git a/eval/src/vespa/eval/tensor/dense/CMakeLists.txt b/eval/src/vespa/eval/tensor/dense/CMakeLists.txt index 1b9b51d6ad2..7019749e123 100644 --- a/eval/src/vespa/eval/tensor/dense/CMakeLists.txt +++ b/eval/src/vespa/eval/tensor/dense/CMakeLists.txt @@ -11,6 +11,7 @@ vespa_add_library(eval_tensor_dense OBJECT dense_lambda_peek_function.cpp dense_lambda_peek_optimizer.cpp dense_matmul_function.cpp + dense_multi_matmul_function.cpp dense_remove_dimension_optimizer.cpp dense_replace_type_function.cpp dense_tensor.cpp diff --git a/eval/src/vespa/eval/tensor/dense/dense_multi_matmul_function.cpp b/eval/src/vespa/eval/tensor/dense/dense_multi_matmul_function.cpp new file mode 100644 index 00000000000..73942f7f044 --- /dev/null +++ b/eval/src/vespa/eval/tensor/dense/dense_multi_matmul_function.cpp @@ -0,0 +1,223 @@ +// Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. + +#include "dense_multi_matmul_function.h" +#include "dense_tensor_view.h" +#include <vespa/vespalib/objects/objectvisitor.h> +#include <vespa/eval/eval/value.h> +#include <vespa/eval/eval/operation.h> +#include <cassert> + +#include <cblas.h> + +namespace vespalib::tensor { + +using eval::ValueType; +using eval::TensorFunction; +using eval::InterpretedFunction; +using eval::TensorEngine; +using eval::as; +using eval::Aggr; +using namespace eval::tensor_function; +using namespace eval::operation; + +namespace { + +void my_cblas_double_multi_matmul_op(InterpretedFunction::State &state, uint64_t param) { + using CT = double; + const DenseMultiMatMulFunction &self = *((const DenseMultiMatMulFunction *)(param)); + size_t lhs_block_size = self.lhs_size() * self.common_size(); + size_t rhs_block_size = self.rhs_size() * self.common_size(); + size_t dst_block_size = self.lhs_size() * self.rhs_size(); + size_t num_blocks = self.matmul_cnt(); + const CT *lhs = DenseTensorView::typify_cells<CT>(state.peek(1)).cbegin(); + const CT *rhs = DenseTensorView::typify_cells<CT>(state.peek(0)).cbegin(); + auto dst_cells = state.stash.create_array<CT>(dst_block_size * num_blocks); + CT *dst = dst_cells.begin(); + for (size_t i = 0; i < num_blocks; ++i, lhs += lhs_block_size, rhs += rhs_block_size, dst += dst_block_size) { + cblas_dgemm(CblasRowMajor, self.lhs_common_inner() ? CblasNoTrans : CblasTrans, self.rhs_common_inner() ? CblasTrans : CblasNoTrans, + self.lhs_size(), self.rhs_size(), self.common_size(), 1.0, + lhs, self.lhs_common_inner() ? self.common_size() : self.lhs_size(), + rhs, self.rhs_common_inner() ? self.common_size() : self.rhs_size(), + 0.0, dst, self.rhs_size()); + } + state.pop_pop_push(state.stash.create<DenseTensorView>(self.result_type(), TypedCells(dst_cells))); +} + +void my_cblas_float_multi_matmul_op(InterpretedFunction::State &state, uint64_t param) { + using CT = float; + const DenseMultiMatMulFunction &self = *((const DenseMultiMatMulFunction *)(param)); + size_t lhs_block_size = self.lhs_size() * self.common_size(); + size_t rhs_block_size = self.rhs_size() * self.common_size(); + size_t dst_block_size = self.lhs_size() * self.rhs_size(); + size_t num_blocks = self.matmul_cnt(); + const CT *lhs = DenseTensorView::typify_cells<CT>(state.peek(1)).cbegin(); + const CT *rhs = DenseTensorView::typify_cells<CT>(state.peek(0)).cbegin(); + auto dst_cells = state.stash.create_array<CT>(dst_block_size * num_blocks); + CT *dst = dst_cells.begin(); + for (size_t i = 0; i < num_blocks; ++i, lhs += lhs_block_size, rhs += rhs_block_size, dst += dst_block_size) { + cblas_sgemm(CblasRowMajor, self.lhs_common_inner() ? CblasNoTrans : CblasTrans, self.rhs_common_inner() ? CblasTrans : CblasNoTrans, + self.lhs_size(), self.rhs_size(), self.common_size(), 1.0, + lhs, self.lhs_common_inner() ? self.common_size() : self.lhs_size(), + rhs, self.rhs_common_inner() ? self.common_size() : self.rhs_size(), + 0.0, dst, self.rhs_size()); + } + state.pop_pop_push(state.stash.create<DenseTensorView>(self.result_type(), TypedCells(dst_cells))); +} + +InterpretedFunction::op_function my_select(CellType cell_type) { + if (cell_type == ValueType::CellType::DOUBLE) { + return my_cblas_double_multi_matmul_op; + } + if (cell_type == ValueType::CellType::FLOAT) { + return my_cblas_float_multi_matmul_op; + } + abort(); +} + +struct CommonDim { + bool valid; + bool inner; + CommonDim(const ValueType &type, const vespalib::string &dim) + : valid(true), inner(false) + { + size_t size = type.dimensions().size(); + if (type.dimensions()[size - 1].name == dim) { + inner = true; + } else if (type.dimensions()[size - 2].name != dim) { + valid = false; + } + } + const ValueType::Dimension &get(const ValueType &type) const { + size_t size = type.dimensions().size(); + return type.dimensions()[size - (inner ? 1 : 2)]; + } + const ValueType::Dimension &get(const TensorFunction &expr) const { + return get(expr.result_type()); + } + const ValueType::Dimension &inv(const ValueType &type) const { + size_t size = type.dimensions().size(); + return type.dimensions()[size - (inner ? 2 : 1)]; + } + const ValueType::Dimension &inv(const TensorFunction &expr) const { + return inv(expr.result_type()); + } +}; + +// Currently, non-matmul dimensions are required to be identical. This +// restriction is added to reduce complexity and might be removed in +// the future if/when a relevant use-case arises. +struct DimPrefix { + bool valid; + size_t size; + DimPrefix(const ValueType &a, const ValueType &b) + : valid(true), size(1) + { + if (a.dimensions().size() == b.dimensions().size()) { + for (size_t i = 0; i < (a.dimensions().size() - 2); ++i) { + if (a.dimensions()[i] == b.dimensions()[i]) { + size *= a.dimensions()[i].size; + } else { + valid = false; + } + } + } else { + valid = false; + } + } +}; + +bool check_input_type(const ValueType &type) { + return (type.is_dense() && + (type.dimensions().size() >= 2) && + ((type.cell_type() == CellType::FLOAT) || (type.cell_type() == CellType::DOUBLE))); +} + +bool is_multi_matmul(const ValueType &a, const ValueType &b, const vespalib::string &reduce_dim) { + if (check_input_type(a) && check_input_type(b) && (a.cell_type() == b.cell_type())) { + CommonDim cd_a(a, reduce_dim); + CommonDim cd_b(b, reduce_dim); + DimPrefix prefix(a, b); + return (cd_a.valid && cd_b.valid && prefix.valid && + (b.dimension_index(cd_a.inv(a).name) == ValueType::Dimension::npos) && + (a.dimension_index(cd_b.inv(b).name) == ValueType::Dimension::npos)); + } + return false; +} + +const TensorFunction &create_multi_matmul(const TensorFunction &a, const TensorFunction &b, + const vespalib::string &reduce_dim, const ValueType &result_type, Stash &stash) +{ + CommonDim cd_a(a.result_type(), reduce_dim); + CommonDim cd_b(b.result_type(), reduce_dim); + DimPrefix prefix(a.result_type(), b.result_type()); + size_t a_size = cd_a.inv(a).size; + size_t b_size = cd_b.inv(b).size; + size_t common_size = cd_a.get(a).size; + bool a_is_lhs = (cd_a.inv(a).name < cd_b.inv(b).name); + if (a_is_lhs) { + return stash.create<DenseMultiMatMulFunction>(result_type, a, b, a_size, common_size, b_size, prefix.size, cd_a.inner, cd_b.inner); + } else { + return stash.create<DenseMultiMatMulFunction>(result_type, b, a, b_size, common_size, a_size, prefix.size, cd_b.inner, cd_a.inner); + } +} + +} // namespace vespalib::tensor::<unnamed> + +DenseMultiMatMulFunction::DenseMultiMatMulFunction(const ValueType &result_type, + const TensorFunction &lhs_in, + const TensorFunction &rhs_in, + size_t lhs_size, + size_t common_size, + size_t rhs_size, + size_t matmul_cnt, + bool lhs_common_inner, + bool rhs_common_inner) + : Super(result_type, lhs_in, rhs_in), + _lhs_size(lhs_size), + _common_size(common_size), + _rhs_size(rhs_size), + _matmul_cnt(matmul_cnt), + _lhs_common_inner(lhs_common_inner), + _rhs_common_inner(rhs_common_inner) +{ +} + +DenseMultiMatMulFunction::~DenseMultiMatMulFunction() = default; + +InterpretedFunction::Instruction +DenseMultiMatMulFunction::compile_self(const TensorEngine &, Stash &) const +{ + auto op = my_select(lhs().result_type().cell_type()); + return InterpretedFunction::Instruction(op, (uint64_t)(this)); +} + +void +DenseMultiMatMulFunction::visit_self(vespalib::ObjectVisitor &visitor) const +{ + Super::visit_self(visitor); + visitor.visitInt("lhs_size", _lhs_size); + visitor.visitInt("common_size", _common_size); + visitor.visitInt("rhs_size", _rhs_size); + visitor.visitInt("matmul_cnt", _matmul_cnt); + visitor.visitBool("lhs_common_inner", _lhs_common_inner); + visitor.visitBool("rhs_common_inner", _rhs_common_inner); +} + +const TensorFunction & +DenseMultiMatMulFunction::optimize(const TensorFunction &expr, Stash &stash) +{ + auto reduce = as<Reduce>(expr); + if (reduce && (reduce->aggr() == Aggr::SUM) && (reduce->dimensions().size() == 1)) { + auto join = as<Join>(reduce->child()); + if (join && (join->function() == Mul::f)) { + const TensorFunction &a = join->lhs(); + const TensorFunction &b = join->rhs(); + if (is_multi_matmul(a.result_type(), b.result_type(), reduce->dimensions()[0])) { + return create_multi_matmul(a, b, reduce->dimensions()[0], expr.result_type(), stash); + } + } + } + return expr; +} + +} // namespace vespalib::tensor diff --git a/eval/src/vespa/eval/tensor/dense/dense_multi_matmul_function.h b/eval/src/vespa/eval/tensor/dense/dense_multi_matmul_function.h new file mode 100644 index 00000000000..f80ca307a59 --- /dev/null +++ b/eval/src/vespa/eval/tensor/dense/dense_multi_matmul_function.h @@ -0,0 +1,52 @@ +// Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. + +#pragma once + +#include <vespa/eval/eval/tensor_function.h> +#include "dense_tensor_view.h" + +namespace vespalib::tensor { + +/** + * Tensor function for multiple dense matrix multiplications. This is + * an extension to normal matrix multiplication where the tensors + * combined may have more than 2 dimensions. + **/ +class DenseMultiMatMulFunction : public eval::tensor_function::Op2 +{ + using Super = eval::tensor_function::Op2; +private: + size_t _lhs_size; + size_t _common_size; + size_t _rhs_size; + size_t _matmul_cnt; + bool _lhs_common_inner; + bool _rhs_common_inner; + +public: + DenseMultiMatMulFunction(const eval::ValueType &result_type, + const eval::TensorFunction &lhs_in, + const eval::TensorFunction &rhs_in, + size_t lhs_size, + size_t common_size, + size_t rhs_size, + size_t matmul_cnt, + bool lhs_common_inner, + bool rhs_common_inner); + ~DenseMultiMatMulFunction() override; + + bool result_is_mutable() const override { return true; } + + size_t lhs_size() const { return _lhs_size; } + size_t common_size() const { return _common_size; } + size_t rhs_size() const { return _rhs_size; } + size_t matmul_cnt() const { return _matmul_cnt; } + bool lhs_common_inner() const { return _lhs_common_inner; } + bool rhs_common_inner() const { return _rhs_common_inner; } + + eval::InterpretedFunction::Instruction compile_self(const eval::TensorEngine &engine, Stash &stash) const override; + void visit_self(vespalib::ObjectVisitor &visitor) const override; + static const eval::TensorFunction &optimize(const eval::TensorFunction &expr, Stash &stash); +}; + +} // namespace vespalib::tensor |