638 lines
24 KiB
C++
638 lines
24 KiB
C++
/*
|
|
* Copyright (c) Meta Platforms, Inc. and affiliates.
|
|
* All rights reserved.
|
|
*
|
|
* This source code is licensed under the BSD-style license found in the
|
|
* LICENSE file in the root directory of this source tree.
|
|
*/
|
|
|
|
#include <algorithm>
|
|
#include <numeric>
|
|
#include <ostream>
|
|
#include <random>
|
|
#include <stdexcept>
|
|
|
|
#include <gtest/gtest.h>
|
|
|
|
#include "./EmbeddingSpMDMTestUtils.h"
|
|
#include "fbgemm/Fbgemm.h"
|
|
#include "fbgemm/FbgemmConvert.h"
|
|
#include "src/RefImplementations.h"
|
|
|
|
using namespace std;
|
|
using namespace fbgemm;
|
|
|
|
static vector<vector<int>> GetInputs_() {
|
|
vector<vector<int>> input_dims = {
|
|
// batch size, number of rows of table, emb dim , avg length
|
|
{1, 8, 8, 4},
|
|
{2, 8, 16, 4},
|
|
{10, 4000, 32, 100},
|
|
{100, 4000, 32, 100},
|
|
{10, 4000, 64, 100},
|
|
{10, 4000, 128, 100},
|
|
{4, 400, 256, 10},
|
|
{4, 400, 512, 10},
|
|
{10, 4000, 48, 100},
|
|
{10, 4000, 40, 100},
|
|
{10, 4000, 56, 100},
|
|
{10, 4000, 2, 100},
|
|
{10, 4000, 4, 100},
|
|
{10, 4000, 7, 100},
|
|
// These were from C2 tests
|
|
{10, 40, 16, 10},
|
|
{10, 40, 86, 10},
|
|
{10, 40, 8, 10},
|
|
{10, 40, 96, 10},
|
|
{10, 40, 164, 10},
|
|
};
|
|
return input_dims;
|
|
}
|
|
|
|
vector<int> prefetch_distances{0, 16, 1000000};
|
|
|
|
namespace {
|
|
|
|
class FusedNBitRowwiseEmbeddingLookupTest : public testing::TestWithParam<tuple<
|
|
int,
|
|
int,
|
|
EmbeddingSpMDMWeightChoice,
|
|
EmbeddingSpMDMCornerCase,
|
|
EmbeddingSpMDMDtypeChoice>> {};
|
|
}; // namespace
|
|
|
|
INSTANTIATE_TEST_CASE_P(
|
|
InstantiationName,
|
|
FusedNBitRowwiseEmbeddingLookupTest,
|
|
::testing::Combine(
|
|
::testing::Values(2, 4), // bit_rate
|
|
::testing::ValuesIn(prefetch_distances),
|
|
::testing::Values(
|
|
UNWEIGHTED,
|
|
WEIGHTED,
|
|
POSITIONAL_WEIGHTED), // use_weight
|
|
::testing::Values(
|
|
NONE,
|
|
EMPTY_INDICES,
|
|
OUT_OF_BOUND_INDICES,
|
|
UNMATCHED_NUM_INDICES_AND_LENGTHS_SUM),
|
|
::testing::Values(FLOAT, FLOAT16, BFLOAT16)));
|
|
|
|
TEST_P(FusedNBitRowwiseEmbeddingLookupTest, basicTest) {
|
|
vector<vector<int>> inputs(GetInputs_());
|
|
|
|
random_device r;
|
|
default_random_engine generator(r());
|
|
uniform_int_distribution<> bool_dist(0, 1);
|
|
|
|
bool isIndex64b = bool_dist(generator);
|
|
bool isOffset64b = bool_dist(generator);
|
|
bool normalize_by_lengths = bool_dist(generator);
|
|
bool use_offsets = bool_dist(generator);
|
|
bool scale_bias_last = bool_dist(generator);
|
|
bool test_thread_local = bool_dist(generator);
|
|
int bit_rate, prefetch;
|
|
EmbeddingSpMDMWeightChoice weight_choice;
|
|
EmbeddingSpMDMCornerCase corner_case;
|
|
EmbeddingSpMDMDtypeChoice out_type;
|
|
tie(bit_rate, prefetch, weight_choice, corner_case, out_type) = GetParam();
|
|
bool is_wt_positional = weight_choice == POSITIONAL_WEIGHTED;
|
|
bool use_weight = weight_choice != UNWEIGHTED;
|
|
bool is_bf16_out = out_type == BFLOAT16;
|
|
|
|
if (corner_case != NONE || weight_choice == POSITIONAL_WEIGHTED) {
|
|
// Check corner case only for subset of tests.
|
|
if (normalize_by_lengths || out_type != FLOAT || !scale_bias_last ||
|
|
test_thread_local) {
|
|
return;
|
|
}
|
|
}
|
|
if (is_wt_positional && !use_weight) {
|
|
// weight positional only makes sense when use_weight is true
|
|
return;
|
|
}
|
|
|
|
int num_elem_per_byte = 8 / bit_rate;
|
|
|
|
for (auto input : inputs) {
|
|
int batch_size = input[0];
|
|
int num_rows = input[1];
|
|
int embedding_dim = input[2];
|
|
int average_len = input[3];
|
|
|
|
// Create embedding table
|
|
normal_distribution<float> embedding_distribution;
|
|
uniform_int_distribution<int> entries(0, 16);
|
|
|
|
int fused_embedding_dim =
|
|
(embedding_dim + num_elem_per_byte - 1) / num_elem_per_byte +
|
|
2 * sizeof(float16);
|
|
vector<uint8_t> fused_embedding_table(num_rows * fused_embedding_dim);
|
|
for (int i = 0; i < num_rows; i++) {
|
|
for (int ii = 0;
|
|
ii < (embedding_dim + num_elem_per_byte - 1) / num_elem_per_byte;
|
|
ii++) {
|
|
fused_embedding_table
|
|
[i * fused_embedding_dim + ii +
|
|
(scale_bias_last ? 0 : 2 * sizeof(float16))] = entries(generator);
|
|
}
|
|
float16* scale_bias = reinterpret_cast<float16*>(
|
|
fused_embedding_table.data() + i * fused_embedding_dim +
|
|
(scale_bias_last
|
|
? (embedding_dim + num_elem_per_byte - 1) / num_elem_per_byte
|
|
: 0));
|
|
float scale = embedding_distribution(generator);
|
|
float bias = embedding_distribution(generator);
|
|
FloatToFloat16_ref(&scale, scale_bias, 1, true /* clip */);
|
|
FloatToFloat16_ref(&bias, scale_bias + 1, 1, true /* clip */);
|
|
}
|
|
|
|
vector<int64_t> lengths, offsets, indices;
|
|
vector<int32_t> lengths_32, offsets_32, indices_32;
|
|
vector<float> weights;
|
|
int lengths_sum = GenerateLengthsIndicesWeights(
|
|
lengths,
|
|
lengths_32,
|
|
offsets,
|
|
offsets_32,
|
|
indices,
|
|
indices_32,
|
|
weights,
|
|
batch_size,
|
|
num_rows,
|
|
average_len,
|
|
corner_case);
|
|
const int64_t* offsets_or_lengths =
|
|
(use_offsets ? offsets : lengths).data();
|
|
const int32_t* offsets_or_lengths_32 =
|
|
(use_offsets ? offsets_32 : lengths_32).data();
|
|
|
|
if (!scale_bias_last && use_weight) {
|
|
// When scale_bias_last == false, assume this is for table batched
|
|
// embedding (TBE) that can get -1 for pruned rows.
|
|
uniform_int_distribution<int> pruned_indices_distribution(
|
|
0, indices.size() - 1);
|
|
constexpr float PRUNED_INDICES_PROPORTION = 0.1;
|
|
for (int i = 0; i < indices.size() * PRUNED_INDICES_PROPORTION; ++i) {
|
|
auto idx = pruned_indices_distribution(generator);
|
|
indices[idx] = -1;
|
|
indices_32[idx] = -1;
|
|
}
|
|
}
|
|
|
|
// Sentries at the end to make sure masking is done correctly not to write
|
|
// out of bounds.
|
|
constexpr int num_sentries = 10;
|
|
const float sentry_value = 1.0f;
|
|
int output_size_wo_sentries = batch_size * embedding_dim;
|
|
vector<float> output_ref(output_size_wo_sentries + num_sentries);
|
|
vector<float> output(output_ref.size());
|
|
vector<float16> output_ref_fp16(output.size()), output_fp16(output.size());
|
|
vector<bfloat16> output_ref_bf16(output.size()), output_bf16(output.size());
|
|
for (size_t i = output_size_wo_sentries; i < output.size(); ++i) {
|
|
output_ref[i] = sentry_value;
|
|
output[i] = sentry_value;
|
|
output_ref_fp16[i] = cpu_float2half_rn(sentry_value);
|
|
output_fp16[i] = cpu_float2half_rn(sentry_value);
|
|
FloatToBfloat16_ref(&sentry_value, &output_ref_bf16[i], 1);
|
|
FloatToBfloat16_ref(&sentry_value, &output_bf16[i], 1);
|
|
}
|
|
|
|
bool success, success_ref;
|
|
|
|
#define TEST_BASE( \
|
|
indices, \
|
|
offsets_or_lengths, \
|
|
output_ref, \
|
|
output, \
|
|
IndexType, \
|
|
OffsetType, \
|
|
OutType, \
|
|
THREAD_LOCAL) \
|
|
success_ref = EmbeddingSpMDMNBit_ref<IndexType, OffsetType, OutType>( \
|
|
bit_rate, \
|
|
embedding_dim, \
|
|
batch_size, \
|
|
lengths_sum, \
|
|
num_rows, \
|
|
fused_embedding_table.data(), \
|
|
corner_case == EMPTY_INDICES ? nullptr : indices.data(), \
|
|
offsets_or_lengths, \
|
|
use_weight ? weights.data() : nullptr, \
|
|
normalize_by_lengths, \
|
|
output_ref.data(), \
|
|
is_wt_positional, \
|
|
use_offsets, \
|
|
/*output_stride=*/-1, \
|
|
/*input_stride=*/-1, \
|
|
scale_bias_last, \
|
|
is_bf16_out); \
|
|
\
|
|
auto kernel = GenerateEmbeddingSpMDMNBitWithStrides< \
|
|
IndexType, \
|
|
OffsetType, \
|
|
OutType, \
|
|
THREAD_LOCAL>( \
|
|
bit_rate, \
|
|
embedding_dim, \
|
|
use_weight, \
|
|
normalize_by_lengths, \
|
|
prefetch, \
|
|
is_wt_positional, \
|
|
use_offsets, \
|
|
/*output_stride=*/-1, \
|
|
/*input_stride=*/-1, \
|
|
scale_bias_last, \
|
|
is_bf16_out); \
|
|
success = kernel( \
|
|
batch_size, \
|
|
lengths_sum, \
|
|
num_rows, \
|
|
fused_embedding_table.data(), \
|
|
corner_case == EMPTY_INDICES ? nullptr : indices.data(), \
|
|
offsets_or_lengths, \
|
|
use_weight ? weights.data() : nullptr, \
|
|
output.data());
|
|
|
|
#define TEST_THREAD_LOCAL( \
|
|
indices, \
|
|
offsets_or_lengths, \
|
|
output_ref, \
|
|
output, \
|
|
IndexType, \
|
|
OffsetType, \
|
|
OutType) \
|
|
if (test_thread_local) { \
|
|
TEST_BASE( \
|
|
indices, \
|
|
offsets_or_lengths, \
|
|
output_ref, \
|
|
output, \
|
|
IndexType, \
|
|
OffsetType, \
|
|
OutType, \
|
|
true); \
|
|
} else { \
|
|
TEST_BASE( \
|
|
indices, \
|
|
offsets_or_lengths, \
|
|
output_ref, \
|
|
output, \
|
|
IndexType, \
|
|
OffsetType, \
|
|
OutType, \
|
|
false); \
|
|
}
|
|
|
|
#define TEST_OUT_TYPE(indices, offsets_or_lengths, IndexType, OffsetType) \
|
|
if (out_type == FLOAT) { \
|
|
TEST_THREAD_LOCAL( \
|
|
indices, \
|
|
offsets_or_lengths, \
|
|
output_ref, \
|
|
output, \
|
|
IndexType, \
|
|
OffsetType, \
|
|
float); \
|
|
} else if (out_type == BFLOAT16) { \
|
|
TEST_THREAD_LOCAL( \
|
|
indices, \
|
|
offsets_or_lengths, \
|
|
output_ref_bf16, \
|
|
output_bf16, \
|
|
IndexType, \
|
|
OffsetType, \
|
|
bfloat16); \
|
|
} else { \
|
|
TEST_THREAD_LOCAL( \
|
|
indices, \
|
|
offsets_or_lengths, \
|
|
output_ref_fp16, \
|
|
output_fp16, \
|
|
IndexType, \
|
|
OffsetType, \
|
|
float16); \
|
|
}
|
|
|
|
#define TEST_OFFSET_TYPE(indices, IndexType) \
|
|
if (isOffset64b) { \
|
|
TEST_OUT_TYPE(indices, offsets_or_lengths, IndexType, int64_t); \
|
|
} else { \
|
|
TEST_OUT_TYPE(indices, offsets_or_lengths_32, IndexType, int32_t); \
|
|
}
|
|
|
|
if (isIndex64b) {
|
|
TEST_OFFSET_TYPE(indices, int64_t);
|
|
} else {
|
|
TEST_OFFSET_TYPE(indices_32, int32_t);
|
|
}
|
|
|
|
#undef TEST_OFFSET_TYPE
|
|
#undef TEST_OUT_TYPE
|
|
#undef TEST_THREAD_LOCAL
|
|
#undef TEST_BASE
|
|
|
|
// 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);
|
|
}
|
|
|
|
auto get_actual = [&](int offset) {
|
|
if (out_type == FLOAT) {
|
|
return output[offset];
|
|
} else if (out_type == BFLOAT16) {
|
|
return cpu_bf162float(output[offset]);
|
|
} else {
|
|
return cpu_half2float(output[offset]);
|
|
}
|
|
};
|
|
|
|
auto get_expected = [&](int offset) {
|
|
if (out_type == FLOAT) {
|
|
return output_ref[offset];
|
|
} else if (out_type == BFLOAT16) {
|
|
return cpu_bf162float(output_ref[offset]);
|
|
} else {
|
|
return cpu_half2float(output_ref[offset]);
|
|
}
|
|
};
|
|
|
|
if (success) {
|
|
for (size_t i = 0; i < output.size(); ++i) {
|
|
float actual = get_actual(i);
|
|
float expected = get_expected(i);
|
|
EXPECT_EQ(actual, expected)
|
|
<< "results differ at (" << i << ") reference: " << expected
|
|
<< ", FBGEMM: " << actual << " emb dim :" << embedding_dim;
|
|
}
|
|
for (int offset = output_size_wo_sentries;
|
|
offset < output_size_wo_sentries + num_sentries;
|
|
++offset) {
|
|
float actual = get_actual(offset);
|
|
float expected = get_expected(offset);
|
|
EXPECT_EQ(actual, expected)
|
|
<< "results differ at (" << offset << ") reference: " << expected
|
|
<< ", FBGEMM: " << actual << " emb dim :" << embedding_dim;
|
|
}
|
|
}
|
|
} // end for input
|
|
}
|
|
|
|
TEST_P(FusedNBitRowwiseEmbeddingLookupTest, rowwiseSparseTest) {
|
|
vector<vector<int>> inputs(GetInputs_());
|
|
|
|
random_device r;
|
|
default_random_engine generator(r());
|
|
uniform_int_distribution<> bool_dist(0, 1);
|
|
|
|
bool isIndex64b = bool_dist(generator);
|
|
bool isOffset64b = bool_dist(generator);
|
|
bool normalize_by_lengths = bool_dist(generator);
|
|
bool use_offsets = bool_dist(generator);
|
|
bool scale_bias_last = bool_dist(generator);
|
|
|
|
int bit_rate, prefetch;
|
|
EmbeddingSpMDMWeightChoice weight_choice;
|
|
EmbeddingSpMDMCornerCase corner_case;
|
|
EmbeddingSpMDMDtypeChoice out_type;
|
|
tie(bit_rate, prefetch, weight_choice, corner_case, out_type) = GetParam();
|
|
bool is_wt_positional = weight_choice == POSITIONAL_WEIGHTED;
|
|
bool use_weight = weight_choice != UNWEIGHTED;
|
|
|
|
if (out_type != FLOAT || !scale_bias_last) {
|
|
return;
|
|
}
|
|
|
|
int num_elem_per_byte = 8 / bit_rate;
|
|
constexpr float sparsity = 0.7;
|
|
|
|
for (auto input : inputs) {
|
|
int batch_size = input[0];
|
|
int num_rows = input[1];
|
|
int embedding_dim = input[2];
|
|
int average_len = input[3];
|
|
|
|
// Create mapping table for rowwise sparsity
|
|
vector<int32_t> mapping_table;
|
|
int num_compressed_rows =
|
|
CreateMappingTableForRowWiseSparsity(mapping_table, num_rows, sparsity);
|
|
|
|
// Create embedding table
|
|
normal_distribution<float> embedding_distribution;
|
|
uniform_int_distribution<int> entries(0, 16);
|
|
|
|
int fused_embedding_dim =
|
|
(embedding_dim + num_elem_per_byte - 1) / num_elem_per_byte +
|
|
2 * sizeof(float16);
|
|
vector<uint8_t> fused_embedding_table(
|
|
num_compressed_rows * fused_embedding_dim);
|
|
for (int i = 0; i < num_compressed_rows; i++) {
|
|
for (int ii = 0;
|
|
ii < (embedding_dim + num_elem_per_byte - 1) / num_elem_per_byte;
|
|
ii++) {
|
|
fused_embedding_table[i * fused_embedding_dim + ii] =
|
|
entries(generator);
|
|
}
|
|
float16* scale_bias = reinterpret_cast<float16*>(
|
|
fused_embedding_table.data() + i * fused_embedding_dim +
|
|
(embedding_dim + num_elem_per_byte - 1) / num_elem_per_byte);
|
|
float scale = embedding_distribution(generator);
|
|
float bias = embedding_distribution(generator);
|
|
FloatToFloat16_ref(&scale, scale_bias, 1, true /* clip */);
|
|
FloatToFloat16_ref(&bias, scale_bias + 1, 1, true /* clip */);
|
|
}
|
|
|
|
vector<int64_t> lengths, offsets, indices;
|
|
vector<int32_t> lengths_32, offsets_32, indices_32;
|
|
vector<float> weights;
|
|
int lengths_sum = GenerateLengthsIndicesWeights(
|
|
lengths,
|
|
lengths_32,
|
|
offsets,
|
|
offsets_32,
|
|
indices,
|
|
indices_32,
|
|
weights,
|
|
batch_size,
|
|
num_rows,
|
|
average_len,
|
|
corner_case);
|
|
const int64_t* offsets_or_lengths =
|
|
(use_offsets ? offsets : lengths).data();
|
|
const int32_t* offsets_or_lengths_32 =
|
|
(use_offsets ? offsets_32 : lengths_32).data();
|
|
|
|
vector<float> output_sls_ref(batch_size * embedding_dim);
|
|
vector<float> output_slws_ref(output_sls_ref.size()),
|
|
output_sls(output_sls_ref.size()), output_slws(output_sls_ref.size());
|
|
|
|
vector<float>& output_ref = use_weight ? output_slws_ref : output_sls_ref;
|
|
vector<float>& output = use_weight ? output_slws : output_sls;
|
|
bool success, success_ref;
|
|
|
|
if (isOffset64b) {
|
|
if (isIndex64b) {
|
|
success_ref = fbgemm::EmbeddingSpMDMNBitRowWiseSparse_ref<int64_t>(
|
|
bit_rate,
|
|
embedding_dim,
|
|
batch_size,
|
|
lengths_sum,
|
|
num_rows,
|
|
fused_embedding_table.data(),
|
|
corner_case == EMPTY_INDICES ? nullptr : indices.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 = GenerateEmbeddingSpMDMNBitRowWiseSparse<int64_t, int64_t>(
|
|
bit_rate,
|
|
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,
|
|
use_weight ? weights.data() : nullptr,
|
|
output.data(),
|
|
mapping_table.data());
|
|
} else {
|
|
success_ref = EmbeddingSpMDMNBitRowWiseSparse_ref<int32_t>(
|
|
bit_rate,
|
|
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 = GenerateEmbeddingSpMDMNBitRowWiseSparse<int32_t, int64_t>(
|
|
bit_rate,
|
|
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,
|
|
use_weight ? weights.data() : nullptr,
|
|
output.data(),
|
|
mapping_table.data());
|
|
}
|
|
} else {
|
|
if (isIndex64b) {
|
|
success_ref = fbgemm::EmbeddingSpMDMNBitRowWiseSparse_ref<int64_t>(
|
|
bit_rate,
|
|
embedding_dim,
|
|
batch_size,
|
|
lengths_sum,
|
|
num_rows,
|
|
fused_embedding_table.data(),
|
|
corner_case == EMPTY_INDICES ? nullptr : indices.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 = GenerateEmbeddingSpMDMNBitRowWiseSparse<int64_t>(
|
|
bit_rate,
|
|
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 = EmbeddingSpMDMNBitRowWiseSparse_ref<int32_t>(
|
|
bit_rate,
|
|
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 = GenerateEmbeddingSpMDMNBitRowWiseSparse<int32_t>(
|
|
bit_rate,
|
|
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
|
|
}
|