406 lines
12 KiB
C++
406 lines
12 KiB
C++
/*
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* Copyright (c) Meta Platforms, Inc. and affiliates.
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* All rights reserved.
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*
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* This source code is licensed under the BSD-style license found in the
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* LICENSE file in the root directory of this source tree.
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*/
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#if defined(__x86_64__) || defined(__i386__) || \
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(defined(_MSC_VER) && (defined(_M_X64) || defined(_M_IX86)))
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#include <immintrin.h>
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#endif
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#include <algorithm>
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#include <cassert>
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#include <chrono>
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#include <cmath>
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#include <cstdint>
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#include <iomanip>
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#include <iostream>
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#include <map>
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#include <numeric>
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#include <random>
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#include <set>
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#include <vector>
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#include "./BenchUtils.h"
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#include "fbgemm/Fbgemm.h"
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#include "fbgemm/FbgemmConvert.h"
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#include "src/RefImplementations.h"
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using namespace std;
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using namespace fbgemm;
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void print_fused_table(int rows, int embedding_dim, const uint8_t* table) {
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for (int i = 0; i < rows; i++) {
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std::cout << "row: " << i << " : " << std::endl;
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for (int ii = 0; ii < embedding_dim; ii++) {
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std::cout << (int)table[i * (embedding_dim + 2 * sizeof(float)) + ii]
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<< ",";
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}
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std::cout << std::endl;
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}
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}
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static vector<vector<int>> GetInputs_() {
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vector<vector<int>> input_dims = {
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// batch size, number of rows of table, emb dim , avg lengthl
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// TODO: Add more inputs
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// Use these -- but they are slow.
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{10, 4000000, 32, 100},
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{10, 4000000, 64, 100},
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{10, 4000000, 128, 100},
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{10, 4000000, 256, 100},
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// Use these for debugging
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// {2, 16, 128, 10},
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// {10, 4000, 128, 100},
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// {10, 4000, 128, 100},
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// {10, 4000, 128, 100},
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};
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return input_dims;
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}
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int run_benchmark(
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int bit_rate,
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int batch_size,
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int num_rows,
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int embedding_dim,
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int average_len,
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bool normalize_by_lengths,
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bool use_32_bit_indices = false,
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bool prefetch = false) {
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// Generate mapping table
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default_random_engine generator;
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constexpr float sparsity = 0.7;
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vector<int32_t> mapping_table(num_rows);
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bernoulli_distribution row_prune_dist(sparsity);
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int num_compressed_rows = 0;
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for (int i = 0; i < num_rows; ++i) {
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if (row_prune_dist(generator)) {
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// pruned
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mapping_table[i] = -1;
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} else {
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mapping_table[i] = num_compressed_rows;
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++num_compressed_rows;
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}
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}
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// Create embedding table
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int num_elem_per_byte = 8 / bit_rate;
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int fused_embedding_dim =
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(embedding_dim + num_elem_per_byte - 1) / num_elem_per_byte +
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2 * sizeof(float16);
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normal_distribution<float> embedding_distribution;
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vector<uint8_t> fused_embedding_table(
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num_compressed_rows * fused_embedding_dim);
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for (int i = 0; i < num_compressed_rows; i++) {
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for (int ii = 0;
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ii < (embedding_dim + num_elem_per_byte - 1) / num_elem_per_byte;
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ii++) {
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fused_embedding_table[i * fused_embedding_dim + ii] = 2;
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}
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float16* scale_bias = reinterpret_cast<float16*>(
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&fused_embedding_table[i * fused_embedding_dim] +
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(embedding_dim + num_elem_per_byte - 1) / num_elem_per_byte);
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float scale = 2.0f;
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float bias = 1.0f;
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FloatToFloat16_ref(&scale, scale_bias, 1, true /* clip */);
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FloatToFloat16_ref(&bias, scale_bias + 1, 1, true /* clip */);
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}
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// print_fused_table(num_rows, embedding_dim, fused_embedding_table);
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// Generate lengths
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uniform_int_distribution<int> length_distribution(
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1, std::min(2 * average_len + 1, num_rows));
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vector<int> offsets(batch_size + 1);
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offsets[0] = 0;
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for (int i = 0; i < batch_size; ++i) {
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offsets[i + 1] = offsets[i] + length_distribution(generator);
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}
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// Compute the number of indices
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int lengths_sum = offsets[batch_size];
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// Generate indices
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vector<int64_t> indices;
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vector<int32_t> indices_32;
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vector<int> container(num_rows);
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map<int64_t, set<int>> dedup_map; // index -> set(output index)
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// please note we generate unique indices
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for (int i = 0; i < batch_size; ++i) {
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iota(container.begin(), container.end(), 0);
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shuffle(container.begin(), container.end(), generator);
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copy(
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container.begin(),
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container.begin() + (offsets[i + 1] - offsets[i]),
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back_inserter(indices));
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}
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copy(begin(indices), end(indices), back_inserter(indices_32));
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// Compute the number of valid indices
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int num_valid_indices = 0;
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for (int index : indices) {
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if (mapping_table[index] != -1) {
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++num_valid_indices;
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}
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}
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cout << "lengths_sum " << lengths_sum << " num_valid_indices "
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<< num_valid_indices << endl;
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// Generate weights
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vector<float> weights(lengths_sum);
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for (int i = 0; i < lengths_sum; ++i) {
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weights[i] = embedding_distribution(generator);
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}
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vector<float> output_sls_ref(batch_size * embedding_dim);
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vector<float> output_slws_ref(output_sls_ref.size()),
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output_sls(output_sls_ref.size()), output_slws(output_sls_ref.size());
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constexpr int NUM_WARMUP = 4;
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constexpr int NUM_ITER = 10;
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// Only counts the number of bytes for reading embedding table and ignore
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// others. Should be good enough as long as embdding_dim is big enough.
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constexpr int CACHE_LINE_SIZE = 64;
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double bytes = lengths_sum * 2 *
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(use_32_bit_indices ? sizeof(int32_t) : sizeof(int64_t)) +
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num_valid_indices * fused_embedding_dim;
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double bytes_padded = lengths_sum *
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((use_32_bit_indices ? sizeof(int32_t) : sizeof(int64_t)) +
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CACHE_LINE_SIZE) +
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num_valid_indices * CACHE_LINE_SIZE *
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static_cast<int>(
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(fused_embedding_dim + CACHE_LINE_SIZE - 1) / CACHE_LINE_SIZE);
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for (bool has_weight : {false, true}) {
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vector<float>& output_ref = has_weight ? output_slws_ref : output_sls_ref;
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bool success = false, success_ref = false;
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for (int i = 0; i < NUM_WARMUP + NUM_ITER; ++i) {
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if (use_32_bit_indices) {
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success_ref = EmbeddingSpMDMNBitRowWiseSparse_ref(
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bit_rate,
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embedding_dim,
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batch_size,
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lengths_sum,
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num_rows,
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fused_embedding_table.data(),
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indices_32.data(),
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mapping_table.data(),
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offsets.data(),
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has_weight ? weights.data() : nullptr,
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normalize_by_lengths,
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output_ref.data());
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} else {
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success_ref = EmbeddingSpMDMNBitRowWiseSparse_ref(
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bit_rate,
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embedding_dim,
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batch_size,
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lengths_sum,
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num_rows,
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fused_embedding_table.data(),
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indices.data(),
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mapping_table.data(),
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offsets.data(),
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has_weight ? weights.data() : nullptr,
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normalize_by_lengths,
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output_ref.data());
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}
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}
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vector<float>& output = has_weight ? output_slws : output_sls;
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auto kernel_32 = GenerateEmbeddingSpMDMNBitRowWiseSparse<int32_t>(
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bit_rate,
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embedding_dim,
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has_weight,
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normalize_by_lengths,
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prefetch ? 16 : 0);
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auto kernel_64 = GenerateEmbeddingSpMDMNBitRowWiseSparse<int64_t>(
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bit_rate,
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embedding_dim,
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has_weight,
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normalize_by_lengths,
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prefetch ? 16 : 0);
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for (bool flush_cache : {false, true}) {
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double t = measureWithWarmup(
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[&]() {
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if (use_32_bit_indices) {
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success = kernel_32(
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batch_size,
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lengths_sum,
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num_rows,
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fused_embedding_table.data(),
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indices_32.data(),
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offsets.data(),
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has_weight ? weights.data() : nullptr,
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output.data(),
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mapping_table.data());
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} else {
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success = kernel_64(
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batch_size,
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lengths_sum,
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num_rows,
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fused_embedding_table.data(),
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indices.data(),
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offsets.data(),
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has_weight ? weights.data() : nullptr,
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output.data(),
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mapping_table.data());
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}
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},
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NUM_WARMUP,
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NUM_ITER,
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[&]() {
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if (flush_cache) {
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cache_evict(fused_embedding_table);
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cache_evict(indices);
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cache_evict(indices_32);
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cache_evict(offsets);
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cache_evict(weights);
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cache_evict(output);
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}
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});
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// printMatrix(
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// matrix_op_t::NoTranspose,
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// output.data(),
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// batch_size,
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// embedding_dim,
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// embedding_dim,
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// "");
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// printMatrix(
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// matrix_op_t::NoTranspose,
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// output_ref.data(),
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// batch_size,
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// embedding_dim,
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// embedding_dim,
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// "");
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// Check correctness
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if (!flush_cache) {
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if (success != success_ref) {
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assert(
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false && "ERROR: refernce impl and JIT imp did not both succeed");
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} else if (success) {
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for (size_t i = 0; i < output.size(); ++i) {
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assert(fabs(output[i] - output_ref[i]) < 1e-3);
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if (fabs(output[i] - output_ref[i]) >= 1e-3) {
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cout << i << " " << output[i] << " " << output_ref[i] << endl;
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}
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}
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}
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}
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if (has_weight) {
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cout << setw(16) << "SLW(WEIGHTED) ";
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} else {
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cout << setw(16) << "SLS ";
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}
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if (flush_cache) {
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cout << setw(20) << "cache flushed";
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} else {
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cout << setw(20) << "cache not flushed";
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}
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if (prefetch) {
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cout << setw(16) << "prefetch on";
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} else {
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cout << setw(16) << "prefetch off";
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}
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cout << setw(8) << "b/w" << setw(10) << bytes / 1e9 / t << " GB/s"
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<< setw(20) << "effective b/w: " << setw(16)
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<< bytes_padded / 1e9 / t << "GB/s" << setw(8) << " time "
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<< setw(16) << t << endl;
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} // flush_cache
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} // has_weight
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return 0;
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}
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int main() {
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int batch_size;
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int num_rows;
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int embedding_dim;
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int average_len;
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vector<vector<int>> inputs(GetInputs_());
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for (int bit_rate : {2, 4}) {
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for (auto& input : inputs) {
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assert(input.size() > 3);
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batch_size = input[0];
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num_rows = input[1];
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embedding_dim = input[2];
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average_len = input[3];
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cout << "bit_rate" << setw(6) << bit_rate << "batch size" << setw(6)
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<< batch_size << setw(10) << "num rows" << setw(16) << num_rows
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<< setw(10) << "emb dim" << setw(6) << embedding_dim << setw(16)
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<< "avg length" << setw(6) << average_len << endl;
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// args: batch sz, num rows, emb dim, avg len, normalize, use 32b,
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// prefetch
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cout << "64 bit indices, ";
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run_benchmark(
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bit_rate,
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batch_size,
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num_rows,
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embedding_dim,
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average_len,
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false); // normalize_by_lengths
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cout << "64 bit indices with prefetching, ";
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run_benchmark(
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bit_rate,
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batch_size,
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num_rows,
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embedding_dim,
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average_len,
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false, // normalize_by_lengths
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false, // use_32_bit_indices
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true); // prefetch
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cout << "32 bit indices, ";
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run_benchmark(
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bit_rate,
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batch_size,
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num_rows,
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embedding_dim,
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average_len,
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false, // normalize_by_lengths
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true); // use_32_bit_indices
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cout << "32 bit indices with prefetching, ";
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run_benchmark(
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bit_rate,
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batch_size,
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num_rows,
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embedding_dim,
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average_len,
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false, // normalize_by_lengths
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true, // use_32_bit_indices
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true); // prefetch
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// running with normalize by lengths
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// run_benchmark(batch_size, num_rows, embedding_dim, average_len,
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// true); run_benchmark(
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// batch_size, num_rows, embedding_dim, average_len, true,
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// true);
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// run_benchmark(
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// batch_size,
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// num_rows,
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// embedding_dim,
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// average_len,
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// false,
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// true,
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// true);
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}
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}
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return 0;
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}
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