563 lines
21 KiB
C++
563 lines
21 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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#include <algorithm>
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#include <numeric>
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#include <ostream>
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#include <random>
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#include <stdexcept>
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#include <gtest/gtest.h>
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#include "./EmbeddingSpMDMTestUtils.h"
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#include "fbgemm/Fbgemm.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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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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{1, 8, 8, 4},
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{2, 8, 16, 4},
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{10, 4000, 32, 100},
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{100, 4000, 32, 100},
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{10, 4000, 64, 100},
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{10, 4000, 128, 100},
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{4, 400, 256, 10},
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{10, 4000, 48, 100},
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{10, 4000, 40, 100},
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{10, 4000, 56, 100},
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{10, 4000, 1, 100},
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{10, 4000, 4, 100},
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// These were from C2 tests
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{10, 40, 16, 10},
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{10, 40, 85, 10},
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{10, 40, 8, 10},
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{10, 40, 96, 10},
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{10, 40, 163, 10},
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};
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return input_dims;
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}
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vector<int> prefetch_distances{0, 16, 1000000};
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namespace {
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class Fused8BitRowwiseEmbeddingLookupTest
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: public testing::TestWithParam<tuple<
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int,
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EmbeddingSpMDMWeightChoice,
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EmbeddingSpMDMCornerCase,
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EmbeddingSpMDMOutputDtypeChoice>> {};
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}; // namespace
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INSTANTIATE_TEST_CASE_P(
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InstantiationName,
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Fused8BitRowwiseEmbeddingLookupTest,
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::testing::Combine(
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::testing::ValuesIn(prefetch_distances),
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::testing::Values(
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UNWEIGHTED,
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WEIGHTED,
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POSITIONAL_WEIGHTED), // use_weight
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::testing::Values(
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NONE,
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EMPTY_INDICES,
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OUT_OF_BOUND_INDICES,
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UNMATCHED_NUM_INDICES_AND_LENGTHS_SUM),
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::testing::Values(FLOAT, FLOAT16, BFLOAT16)));
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TEST_P(Fused8BitRowwiseEmbeddingLookupTest, basicTest) {
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vector<vector<int>> inputs(GetInputs_());
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random_device r;
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default_random_engine generator(r());
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uniform_int_distribution<> bool_dist(0, 1);
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bool isIndex64b = bool_dist(generator);
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bool isOffset64b = bool_dist(generator);
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bool normalize_by_lengths = bool_dist(generator);
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bool use_offsets = bool_dist(generator);
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bool scale_bias_last = bool_dist(generator);
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int prefetch;
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EmbeddingSpMDMWeightChoice weight_choice;
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EmbeddingSpMDMCornerCase corner_case;
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EmbeddingSpMDMOutputDtypeChoice out_type;
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tie(prefetch, weight_choice, corner_case, out_type) = GetParam();
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bool is_wt_positional = weight_choice == POSITIONAL_WEIGHTED;
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bool use_weight = weight_choice != UNWEIGHTED;
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if (corner_case != NONE || weight_choice == POSITIONAL_WEIGHTED) {
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// Check corner case only for subset of tests.
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if (normalize_by_lengths || out_type != FLOAT || !scale_bias_last) {
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return;
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}
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}
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if (is_wt_positional && !use_weight) {
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// weight positional only makes sense when use_weight is true
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return;
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}
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for (auto input : inputs) {
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int batch_size = input[0];
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int num_rows = input[1];
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int embedding_dim = input[2];
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int average_len = input[3];
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// Create embedding table
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normal_distribution<float> embedding_distribution;
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uniform_int_distribution<int> entries(0, 16);
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int fused_embedding_dim =
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embedding_dim + 2 * (scale_bias_last ? sizeof(float) : sizeof(float16));
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vector<uint8_t> fused_embedding_table(num_rows * fused_embedding_dim);
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for (int i = 0; i < num_rows; i++) {
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for (int ii = 0; ii < embedding_dim; ii++) {
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fused_embedding_table
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[i * fused_embedding_dim + ii +
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(scale_bias_last ? 0 : 2 * sizeof(float16))] = entries(generator);
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}
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float* scale_bias = reinterpret_cast<float*>(
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fused_embedding_table.data() + i * fused_embedding_dim +
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(scale_bias_last ? embedding_dim : 0));
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if (scale_bias_last) {
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scale_bias[0] = embedding_distribution(generator);
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scale_bias[1] = embedding_distribution(generator);
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} else {
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reinterpret_cast<float16*>(scale_bias)[0] =
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cpu_float2half_rn(embedding_distribution(generator));
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reinterpret_cast<float16*>(scale_bias)[1] =
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cpu_float2half_rn(embedding_distribution(generator));
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}
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}
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vector<int64_t> lengths, offsets, indices;
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vector<int32_t> lengths_32, offsets_32, indices_32;
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vector<float> weights;
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int lengths_sum = GenerateLengthsIndicesWeights(
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lengths,
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lengths_32,
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offsets,
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offsets_32,
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indices,
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indices_32,
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weights,
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batch_size,
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num_rows,
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average_len,
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corner_case);
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const int64_t* offsets_or_lengths =
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(use_offsets ? offsets : lengths).data();
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const int32_t* offsets_or_lengths_32 =
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(use_offsets ? offsets_32 : lengths_32).data();
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if (!scale_bias_last && use_weight) {
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// When scale_bias_last == false, assume this is for table batched
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// embedding (TBE) that can get -1 for pruned rows.
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uniform_int_distribution<int> pruned_indices_distribution(
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0, indices.size() - 1);
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constexpr float PRUNED_INDICES_PROPORTION = 0.1;
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for (int i = 0; i < indices.size() * PRUNED_INDICES_PROPORTION; ++i) {
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auto idx = pruned_indices_distribution(generator);
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indices[idx] = -1;
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indices_32[idx] = -1;
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}
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}
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// Sentries at the end to make sure masking is done correctly not to write
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// out of bounds.
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constexpr int num_sentries = 10;
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const float sentry_value = 1.0f;
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int output_size_wo_sentries = batch_size * embedding_dim;
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vector<float> output_ref(output_size_wo_sentries + num_sentries);
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vector<float> output(output_ref.size());
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vector<uint16_t> output_ref_16b(output.size()), output_16b(output.size());
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for (size_t i = output_size_wo_sentries; i < output.size(); ++i) {
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output_ref[i] = sentry_value;
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output[i] = sentry_value;
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output_ref_16b[i] =
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convert_from_float_ref<uint16_t>(sentry_value, out_type == BFLOAT16);
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output_16b[i] =
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convert_from_float_ref<uint16_t>(sentry_value, out_type == BFLOAT16);
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}
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bool success, success_ref;
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#define TEST_BASE( \
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indices, \
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offsets_or_lengths, \
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output_ref, \
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output, \
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IndexType, \
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OffsetType, \
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OutType) \
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success_ref = EmbeddingSpMDM_ref<uint8_t>( \
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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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corner_case == EMPTY_INDICES ? nullptr : indices.data(), \
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offsets_or_lengths, \
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use_weight ? weights.data() : nullptr, \
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normalize_by_lengths, \
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output_ref.data(), \
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is_wt_positional, \
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use_offsets, \
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/*output_stride=*/-1, \
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/*input_stride=*/-1, \
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scale_bias_last, \
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/*is_bf16_out=*/out_type == BFLOAT16, \
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/*is_bf16_in=*/false); \
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\
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auto kernel = GenerateEmbeddingSpMDMWithStrides< \
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uint8_t, \
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IndexType, \
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OffsetType, \
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OutType>( \
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embedding_dim, \
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use_weight, \
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normalize_by_lengths, \
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prefetch, \
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is_wt_positional, \
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use_offsets, \
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/*output_stride=*/-1, \
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/*input_stride=*/-1, \
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scale_bias_last, \
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/*is_bf16_out=*/out_type == BFLOAT16, \
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/*is_bf16_in=*/false); \
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success = kernel( \
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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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corner_case == EMPTY_INDICES ? nullptr : indices.data(), \
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offsets_or_lengths, \
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use_weight ? weights.data() : nullptr, \
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output.data());
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#define TEST_OUT_TYPE(indices, offsets_or_lengths, IndexType, OffsetType) \
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if (out_type == FLOAT) { \
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TEST_BASE( \
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indices, \
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offsets_or_lengths, \
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output_ref, \
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output, \
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IndexType, \
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OffsetType, \
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float); \
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} else { \
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TEST_BASE( \
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indices, \
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offsets_or_lengths, \
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output_ref_16b, \
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output_16b, \
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IndexType, \
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OffsetType, \
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float16); \
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}
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#define TEST_OFFSET_TYPE(indices, IndexType) \
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if (isOffset64b) { \
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TEST_OUT_TYPE(indices, offsets_or_lengths, IndexType, int64_t); \
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} else { \
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TEST_OUT_TYPE(indices, offsets_or_lengths_32, IndexType, int32_t); \
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}
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if (isIndex64b) {
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TEST_OFFSET_TYPE(indices, int64_t);
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} else {
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TEST_OFFSET_TYPE(indices_32, int32_t);
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}
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#undef TEST_OFFSET_TYPE
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#undef TEST_OUT_TYPE
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#undef TEST_BASE
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// Check correctness
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EXPECT_EQ(success, success_ref)
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<< "Reference and JIT impl did not both succeed";
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if (corner_case == OUT_OF_BOUND_INDICES ||
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corner_case == UNMATCHED_NUM_INDICES_AND_LENGTHS_SUM) {
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EXPECT_EQ(success, false);
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}
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if (success) {
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for (size_t i = 0; i < output.size(); ++i) {
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float actual = (out_type == FLOAT)
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? output[i]
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: convert_to_float_ref(output_16b[i], out_type == BFLOAT16);
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float expected = (out_type == FLOAT)
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? output_ref[i]
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: convert_to_float_ref(output_ref_16b[i], out_type == BFLOAT16);
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EXPECT_EQ(actual, expected)
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<< "results differ at (" << i << ") reference: " << expected
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<< ", FBGEMM: " << actual << " emb dim :" << embedding_dim;
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}
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for (int offset = output_size_wo_sentries;
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offset < output_size_wo_sentries + num_sentries;
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++offset) {
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float actual = (out_type == FLOAT)
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? output[offset]
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: convert_to_float_ref(output_16b[offset], out_type == BFLOAT16);
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float expected = (out_type == FLOAT)
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? output_ref[offset]
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: convert_to_float_ref(
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output_ref_16b[offset], out_type == BFLOAT16);
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EXPECT_EQ(actual, expected)
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<< "results differ at (" << offset << ") reference: " << expected
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<< ", FBGEMM: " << actual << " emb dim :" << embedding_dim;
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}
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}
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} // end for input
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}
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TEST_P(Fused8BitRowwiseEmbeddingLookupTest, rowwiseSparseTest) {
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vector<vector<int>> inputs(GetInputs_());
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random_device r;
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default_random_engine generator(r());
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uniform_int_distribution<> bool_dist(0, 1);
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bool isIndex64b = bool_dist(generator);
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bool isOffset64b = bool_dist(generator);
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bool normalize_by_lengths = bool_dist(generator);
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bool use_offsets = bool_dist(generator);
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bool scale_bias_last = bool_dist(generator);
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int prefetch;
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EmbeddingSpMDMWeightChoice weight_choice;
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EmbeddingSpMDMCornerCase corner_case;
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EmbeddingSpMDMDtypeChoice out_type;
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tie(prefetch, weight_choice, corner_case, out_type) = GetParam();
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bool is_wt_positional = weight_choice == POSITIONAL_WEIGHTED;
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bool use_weight = weight_choice != UNWEIGHTED;
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if (out_type != FLOAT || !scale_bias_last) {
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return;
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}
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constexpr float sparsity = 0.7;
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for (auto input : inputs) {
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int batch_size = input[0];
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int num_rows = input[1];
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int embedding_dim = input[2];
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int average_len = input[3];
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// Create mapping table for rowwise sparsity
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vector<int32_t> mapping_table;
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int num_compressed_rows =
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CreateMappingTableForRowWiseSparsity(mapping_table, num_rows, sparsity);
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// Create embedding table
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normal_distribution<float> embedding_distribution;
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uniform_int_distribution<int> entries(0, 16);
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int fused_embedding_dim = embedding_dim + 2 * sizeof(float);
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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; ii < embedding_dim; ii++) {
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fused_embedding_table[i * fused_embedding_dim + ii] =
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entries(generator);
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}
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float* scale_bias = reinterpret_cast<float*>(
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fused_embedding_table.data() + i * fused_embedding_dim +
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embedding_dim);
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scale_bias[0] = embedding_distribution(generator);
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scale_bias[1] = embedding_distribution(generator);
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}
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vector<int64_t> lengths, offsets, indices;
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vector<int32_t> lengths_32, offsets_32, indices_32;
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vector<float> weights;
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int lengths_sum = GenerateLengthsIndicesWeights(
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lengths,
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lengths_32,
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offsets,
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offsets_32,
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indices,
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indices_32,
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weights,
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batch_size,
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num_rows,
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average_len,
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corner_case);
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const int64_t* offsets_or_lengths =
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(use_offsets ? offsets : lengths).data();
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const int32_t* offsets_or_lengths_32 =
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(use_offsets ? offsets_32 : lengths_32).data();
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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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vector<float>& output_ref = use_weight ? output_slws_ref : output_sls_ref;
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vector<float>& output = use_weight ? output_slws : output_sls;
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bool success, success_ref;
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if (isOffset64b) {
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if (isIndex64b) {
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success_ref = EmbeddingSpMDMRowWiseSparse_ref<uint8_t, int64_t>(
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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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corner_case == EMPTY_INDICES ? nullptr : indices.data(),
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mapping_table.data(),
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offsets_or_lengths,
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use_weight ? weights.data() : nullptr,
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normalize_by_lengths,
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output_ref.data(),
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is_wt_positional,
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use_offsets);
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auto kernel =
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GenerateEmbeddingSpMDMRowWiseSparse<uint8_t, int64_t, int64_t>(
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embedding_dim,
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use_weight,
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normalize_by_lengths,
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prefetch,
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is_wt_positional,
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use_offsets);
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success = kernel(
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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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corner_case == EMPTY_INDICES ? nullptr : indices.data(),
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offsets_or_lengths,
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use_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_ref = EmbeddingSpMDMRowWiseSparse_ref<uint8_t, int32_t>(
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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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corner_case == EMPTY_INDICES ? nullptr : indices_32.data(),
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mapping_table.data(),
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offsets_or_lengths,
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use_weight ? weights.data() : nullptr,
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normalize_by_lengths,
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output_ref.data(),
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is_wt_positional,
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use_offsets);
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auto kernel =
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GenerateEmbeddingSpMDMRowWiseSparse<uint8_t, int32_t, int64_t>(
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embedding_dim,
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use_weight,
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normalize_by_lengths,
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prefetch,
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is_wt_positional,
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use_offsets);
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success = kernel(
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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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corner_case == EMPTY_INDICES ? nullptr : indices_32.data(),
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offsets_or_lengths,
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use_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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} else {
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if (isIndex64b) {
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success_ref = EmbeddingSpMDMRowWiseSparse_ref<uint8_t, int64_t>(
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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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corner_case == EMPTY_INDICES ? nullptr : indices.data(),
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mapping_table.data(),
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offsets_or_lengths,
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use_weight ? weights.data() : nullptr,
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normalize_by_lengths,
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output_ref.data(),
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is_wt_positional,
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use_offsets);
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|
|
|
auto kernel = GenerateEmbeddingSpMDMRowWiseSparse<uint8_t, int64_t>(
|
|
embedding_dim,
|
|
use_weight,
|
|
normalize_by_lengths,
|
|
prefetch,
|
|
is_wt_positional,
|
|
use_offsets);
|
|
success = kernel(
|
|
batch_size,
|
|
lengths_sum,
|
|
num_rows,
|
|
fused_embedding_table.data(),
|
|
corner_case == EMPTY_INDICES ? nullptr : indices.data(),
|
|
offsets_or_lengths_32,
|
|
use_weight ? weights.data() : nullptr,
|
|
output.data(),
|
|
mapping_table.data());
|
|
} else {
|
|
success_ref = EmbeddingSpMDMRowWiseSparse_ref<uint8_t, int32_t>(
|
|
embedding_dim,
|
|
batch_size,
|
|
lengths_sum,
|
|
num_rows,
|
|
fused_embedding_table.data(),
|
|
corner_case == EMPTY_INDICES ? nullptr : indices_32.data(),
|
|
mapping_table.data(),
|
|
offsets_or_lengths,
|
|
use_weight ? weights.data() : nullptr,
|
|
normalize_by_lengths,
|
|
output_ref.data(),
|
|
is_wt_positional,
|
|
use_offsets);
|
|
|
|
auto kernel = GenerateEmbeddingSpMDMRowWiseSparse<uint8_t, int32_t>(
|
|
embedding_dim,
|
|
use_weight,
|
|
normalize_by_lengths,
|
|
prefetch,
|
|
is_wt_positional,
|
|
use_offsets);
|
|
success = kernel(
|
|
batch_size,
|
|
lengths_sum,
|
|
num_rows,
|
|
fused_embedding_table.data(),
|
|
corner_case == EMPTY_INDICES ? nullptr : indices_32.data(),
|
|
offsets_or_lengths_32,
|
|
use_weight ? weights.data() : nullptr,
|
|
output.data(),
|
|
mapping_table.data());
|
|
}
|
|
}
|
|
|
|
// Check correctness
|
|
EXPECT_EQ(success, success_ref)
|
|
<< "Reference and JIT impl did not both succeed";
|
|
if (corner_case == OUT_OF_BOUND_INDICES ||
|
|
corner_case == UNMATCHED_NUM_INDICES_AND_LENGTHS_SUM) {
|
|
EXPECT_EQ(success, false);
|
|
}
|
|
if (success) {
|
|
for (size_t i = 0; i < output.size(); ++i) {
|
|
EXPECT_EQ(output[i], output_ref[i])
|
|
<< "results differ at (" << i << ") reference: " << output_ref[i]
|
|
<< ", FBGEMM: " << output[i] << " emb dim :" << embedding_dim;
|
|
}
|
|
}
|
|
} // end for input
|
|
}
|