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Use less memory in multi_normal_cholesky_lpdf #2983

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Use less memory in multi_normal_cholesky_lpdf #2983

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0434a7b
use less memory
spinkney Dec 6, 2023
ccf5783
[Jenkins] auto-formatting by clang-format version 10.0.0-4ubuntu1
stan-buildbot Dec 6, 2023
1284e64
fix double semi-colon
spinkney Dec 6, 2023
226e865
Merge branch 'multi_normal_cholesky_less_memory' of https://github.co…
spinkney Dec 6, 2023
a08db5e
Update stan/math/prim/prob/multi_normal_cholesky_lpdf.hpp
spinkney Dec 20, 2023
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61 changes: 27 additions & 34 deletions stan/math/prim/prob/multi_normal_cholesky_lpdf.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -53,6 +53,9 @@ return_type_t<T_y, T_loc, T_covar> multi_normal_cholesky_lpdf(
using T_partials_return = partials_return_t<T_y, T_loc, T_covar>;
using matrix_partials_t
= Eigen::Matrix<T_partials_return, Eigen::Dynamic, Eigen::Dynamic>;
using vector_partials_t = Eigen::Matrix<T_partials_return, Eigen::Dynamic, 1>;
using row_vector_partials_t
= Eigen::Matrix<T_partials_return, 1, Eigen::Dynamic>;
using T_y_ref = ref_type_t<T_y>;
using T_mu_ref = ref_type_t<T_loc>;
using T_L_ref = ref_type_t<T_covar>;
Expand Down Expand Up @@ -119,59 +122,49 @@ return_type_t<T_y, T_loc, T_covar> multi_normal_cholesky_lpdf(
}

if (include_summand<propto, T_y, T_loc, T_covar_elem>::value) {
Eigen::Matrix<T_partials_return, Eigen::Dynamic, Eigen::Dynamic>
y_val_minus_mu_val(size_y, size_vec);
row_vector_partials_t half(size_vec);
vector_partials_t y_val_minus_mu_val(size_vec);
vector_partials_t scaled_diff(size_vec);
matrix_partials_t L_val = value_of(L_ref);

T_partials_return sum_lp_vec(0.0);

for (size_t i = 0; i < size_vec; i++) {
decltype(auto) y_val = as_value_column_vector_or_scalar(y_vec[i]);
decltype(auto) mu_val = as_value_column_vector_or_scalar(mu_vec[i]);
y_val_minus_mu_val.col(i) = y_val - mu_val;
y_val_minus_mu_val = y_val - mu_val;
half = mdivide_left_tri<Eigen::Lower>(L_val, y_val_minus_mu_val)
.transpose();
scaled_diff = mdivide_right_tri<Eigen::Lower>(half, L_val).transpose();
Comment on lines +136 to +138
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@andrjohns andrjohns Dec 11, 2023 •
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This is the part that concerns me, since it's gone from a single solve each (with a matrix) to size_vec solves (with a vector) for half and scaled_diff each. Especially when the single larger solve can be better vectorised with SIMD & other compiler opts

Is there enough of a memory hit to justify to extra operations?

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Definitely agreed. We do this sort of thing already in

  • math/stan/math/prim/prob/multi_normal_lpdf.hpp

    Lines 94 to 98 in 0c16b89

    lp_type sum_lp_vec(0.0);
    for (size_t i = 0; i < size_vec; i++) {
    const auto& y_col = as_column_vector_or_scalar(y_vec[i]);
    const auto& mu_col = as_column_vector_or_scalar(mu_vec[i]);
    sum_lp_vec += trace_inv_quad_form_ldlt(ldlt_Sigma, y_col - mu_col);

  • math/stan/math/prim/prob/multi_student_t_lpdf.hpp

    Lines 124 to 128 in 0c16b89

    for (size_t i = 0; i < size_vec; i++) {
    const auto& y_col = as_column_vector_or_scalar(y_vec[i]);
    const auto& mu_col = as_column_vector_or_scalar(mu_vec[i]);
    sum_lp_vec
    += log1p(trace_inv_quad_form_ldlt(ldlt_Sigma, y_col - mu_col) / nu);

I'm really not sure what's best here.

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If it's alright with you, I'd prefer not to implement this. The current implementation is likely to scale better to larger inputs, and the changes would also reduce any benefits from OpenCL accelerated-ops.

But also completely happy for you to call someone in for a tie-breaker if you feel strongly about it!

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I'm fine with closing it but I want someone to weigh in on if we should change the other distributions. I can update the mvn derivatives pr to follow the same.

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@SteveBronder - as the Chief of Memory Police, what do you think?

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I have an M1max. Is there someone who could benchmark on a windows and linux machine?

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I got the library setup but I don't have taskset. Also, how can I set up the script to run the two branches?

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You don't need taskset to run the benchmarks, only if you want to isolate in a single core.

You can add another branch in your benchmarks cmake file like

FetchContent_Declare(
  stanmathalt
  GIT_REPOSITORY https://github.com/stan-dev/math
  GIT_TAG  mybranch # replace with the version you want to use
)

FetchContent_GetProperties(stanmathalt)
if(NOT stanmathalt_POPULATED)
  FetchContent_Populate(stanmathalt)
endif()

Then you can include it in your executible build like ${stanmathalt_SOURCE_DIR}

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Then how do I just run benchmarks for this distribution?

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There's an example of how to add a benchmark on the readme and an example benchmark folder below. You need to write a little Cmake file for compiling the benchmark and should be able to use that folder as an example

https://github.com/SteveBronder/stan-perf/tree/main/benchmarks/matmul_aos_soa


sum_lp_vec += dot_self(half);

if (!is_constant_all<T_y>::value) {
partials_vec<0>(ops_partials)[i] += -scaled_diff;
}
if (!is_constant_all<T_loc>::value) {
partials_vec<1>(ops_partials)[i] += scaled_diff;
}
if (!is_constant<T_covar_elem>::value) {
partials_vec<2>(ops_partials)[i] += scaled_diff * half;
}
}

matrix_partials_t half;
matrix_partials_t scaled_diff;
logp += -0.5 * sum_lp_vec;

// If the covariance is not autodiff, we can avoid computing a matrix
// inverse
if (is_constant<T_covar_elem>::value) {
matrix_partials_t L_val = value_of(L_ref);

half = mdivide_left_tri<Eigen::Lower>(L_val, y_val_minus_mu_val)
.transpose();

scaled_diff = mdivide_right_tri<Eigen::Lower>(half, L_val).transpose();

if (include_summand<propto>::value) {
logp -= sum(log(L_val.diagonal())) * size_vec;
}
} else {
matrix_partials_t inv_L_val
= mdivide_left_tri<Eigen::Lower>(value_of(L_ref));

half = (inv_L_val.template triangularView<Eigen::Lower>()
* y_val_minus_mu_val)
.transpose();

scaled_diff = (half * inv_L_val.template triangularView<Eigen::Lower>())
.transpose();

logp += sum(log(inv_L_val.diagonal())) * size_vec;
partials<2>(ops_partials) -= size_vec * inv_L_val.transpose();

for (size_t i = 0; i < size_vec; i++) {
partials_vec<2>(ops_partials)[i] += scaled_diff.col(i) * half.row(i);
}
}

logp -= 0.5 * sum(columns_dot_self(half));

for (size_t i = 0; i < size_vec; i++) {
if (!is_constant_all<T_y>::value) {
partials_vec<0>(ops_partials)[i] -= scaled_diff.col(i);
}
if (!is_constant_all<T_loc>::value) {
partials_vec<1>(ops_partials)[i] += scaled_diff.col(i);
}
partials<2>(ops_partials) -= size_vec * inv_L_val.transpose();
}
}

Expand Down