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gemma.cpp
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Add MQA support

This commit is contained in:
RangerUFO 2024-03-20 18:14:09 +08:00
parent 130e1f678f
commit 6923aec853
3 changed files with 44 additions and 32 deletions
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2
configs.h
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@ -54,7 +54,7 @@ struct ConfigGemma2B {
static constexpr int kModelDim = 2048;
static constexpr int kFFHiddenDim = 16 * 2048 / 2; // = 16384
static constexpr int kHeads = 8;
static constexpr int kKVHeads = 8; // TODO(austinvhuang): add MQA support
static constexpr int kKVHeads = 1;
static constexpr int kQKVDim = 256; // query size == key size == value size
static constexpr int kTopK = gcpp::kTopK;
};

52
gemma.cc
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@ -70,12 +70,13 @@ template <class TConfig>
struct Layer {
Layer() = default;
static constexpr size_t kHeads = TConfig::kHeads;
static constexpr size_t kKVHeads = TConfig::kKVHeads;
static constexpr size_t kModelDim = TConfig::kModelDim;
static constexpr size_t kQKVDim = TConfig::kQKVDim;
static constexpr size_t kFFHiddenDim = TConfig::kFFHiddenDim;
static constexpr size_t kAttVecEinsumWSize = kHeads * kQKVDim * kModelDim;
// 3x for (query, key, value)
static constexpr size_t kQKVEinsumWSize = 3 * kHeads * kQKVDim * kModelDim;
static constexpr size_t kQKVEinsumWSize =
(kHeads + 2 * kKVHeads) * kQKVDim * kModelDim;
// 2x for (gelu gating vector, gated vector)
static constexpr size_t kGatingEinsumWSize = 2 * kFFHiddenDim * kModelDim;
@ -313,26 +314,28 @@ HWY_NOINLINE void Attention(size_t batch_start, size_t batch_idx, size_t layer,
static constexpr size_t kModelDim =
gcpp::Activations<TConfig, kBatchSize>::kModelDim;
static constexpr size_t kHeads = TConfig::kHeads;
static constexpr size_t kKVHeads = TConfig::kKVHeads;
static const float kQueryScale =
static_cast<float>(1.0 / sqrt(static_cast<double>(kQKVDim)));
const size_t batch_offset = batch_idx * kModelDim;
pool.Run(0, kHeads, [&](const uint64_t head, size_t /*thread*/) HWY_ATTR {
// linear projections to QKV
const size_t head_offset =
3 * kQKVDim * kModelDim; // 3x for QKV dimensions
constexpr const size_t head_offset =
kHeads == kKVHeads ? 3 * kQKVDim * kModelDim : kQKVDim * kModelDim;
const size_t q_offset = head * head_offset + 0 * kQKVDim * kModelDim;
const size_t k_offset = head * head_offset + 1 * kQKVDim * kModelDim;
const size_t v_offset = head * head_offset + 2 * kQKVDim * kModelDim;
float* HWY_RESTRICT q =
activations.q.data() + head * kQKVDim + batch_idx * kHeads * kQKVDim;
const size_t batch_offset = batch_idx * kModelDim;
MatVecLoop<kQKVDim, kModelDim>(
c_layer->c_qkv_einsum_w, q_offset,
activations.pre_att_rms_out.data() + batch_offset, q);
if constexpr (kHeads == kKVHeads) {
const size_t k_offset = head * head_offset + 1 * kQKVDim * kModelDim;
const size_t v_offset = head * head_offset + 2 * kQKVDim * kModelDim;
const size_t kv_offset =
pos * kCachePosSize + layer * kCacheLayerSize + head * kQKVDim;
@ -342,18 +345,40 @@ HWY_NOINLINE void Attention(size_t batch_start, size_t batch_idx, size_t layer,
kv_cache.key_cache.get() + kv_offset,
kv_cache.value_cache.get() + kv_offset);
Rope(kv_cache.key_cache.get() + kv_offset, kQKVDim, pos);
}
});
if constexpr (kHeads != kKVHeads) {
constexpr const size_t q_offset = kHeads * kQKVDim * kModelDim;
constexpr const size_t k_offset = q_offset + 0 * kQKVDim * kModelDim;
constexpr const size_t v_offset = q_offset + 1 * kQKVDim * kModelDim;
const size_t kv_offset = pos * kCachePosSize + layer * kCacheLayerSize;
TwoOfsMatVecLoop<kQKVDim, kModelDim>(
c_layer->c_qkv_einsum_w, k_offset, v_offset,
activations.pre_att_rms_out.data() + batch_offset,
kv_cache.key_cache.get() + kv_offset,
kv_cache.value_cache.get() + kv_offset);
Rope(kv_cache.key_cache.get() + kv_offset, kQKVDim, pos);
}
pool.Run(0, kHeads, [&](const uint64_t head, size_t /*thread*/) HWY_ATTR {
// Calculate scores
float* HWY_RESTRICT q =
activations.q.data() + head * kQKVDim + batch_idx * kHeads * kQKVDim;
float* HWY_RESTRICT head_att = activations.att.data() +
head * TConfig::kSeqLen +
batch_idx * kHeads * kQKVDim;
Rope(q, kQKVDim, pos);
Rope(kv_cache.key_cache.get() + kv_offset, kQKVDim, pos);
MulByConst(kQueryScale, q, kQKVDim);
// Compute Q dot K scores
for (size_t pos2 = 0; pos2 <= pos; ++pos2) {
const size_t cache_offset =
pos2 * kCachePosSize + layer * kCacheLayerSize + head * kQKVDim;
const size_t cache_offset = kHeads == kKVHeads
? pos2 * kCachePosSize + layer * kCacheLayerSize + head * kQKVDim
: pos2 * kCachePosSize + layer * kCacheLayerSize;
const float* HWY_RESTRICT k2 = kv_cache.key_cache.get() + cache_offset;
const float score = Dot(q, k2, kQKVDim);
head_att[pos2] = score;
@ -365,8 +390,9 @@ HWY_NOINLINE void Attention(size_t batch_start, size_t batch_idx, size_t layer,
batch_idx * kHeads * kQKVDim;
hwy::ZeroBytes(att_out, kQKVDim * sizeof(*att_out));
for (size_t pos2 = 0; pos2 <= pos; ++pos2) {
const size_t cache_offset =
pos2 * kCachePosSize + layer * kCacheLayerSize + head * kQKVDim;
const size_t cache_offset = kHeads == kKVHeads
? pos2 * kCachePosSize + layer * kCacheLayerSize + head * kQKVDim
: pos2 * kCachePosSize + layer * kCacheLayerSize;
float* HWY_RESTRICT v2 = kv_cache.value_cache.get() + cache_offset;
MulByConstAndAdd(head_att[pos2], v2, att_out, kQKVDim);
}

18
util/convert_weights.py
View File

@ -72,26 +72,12 @@ parser.add_argument(
args = parser.parse_args()
def expand_qkv(qkv_proj: np.array) -> np.array:
"""This won't be needed anymore when MQA is implemented"""
assert qkv_proj.shape == (2560, 2048)
qkv = qkv_proj.reshape((10, 256, 2048))
q_proj = qkv[:8].reshape((1,8,256,2048))
kv_proj = qkv[8:]
kv_proj = kv_proj[:, np.newaxis, :, :]
kv_proj = np.repeat(kv_proj, 8, axis=1)
qkv = np.concatenate([q_proj, kv_proj])
qkv = np.transpose(qkv, axes=[1,0,2,3])
return qkv
TRANSFORMATIONS = {
"2b":defaultdict(
lambda: lambda x: x,
{
"embedder.weight": lambda x: x,
"self_attn.qkv_proj.weight": expand_qkv,
"self_attn.qkv_proj.weight": lambda x: x.reshape((10, 256, 2048)),
"self_attn.o_proj.weight": lambda x: x.reshape((2048, 8, 256)).transpose([1,0,2]),
"mlp.gate_proj.weight": lambda x: x[np.newaxis, :, :],
"mlp.up_proj.weight": lambda x: x[np.newaxis, :, :],
@ -115,7 +101,7 @@ VALIDATIONS = {
"2b": {
"embedder.weight": lambda x: x.shape == (256000, 2048),
"model.norm.weight": lambda x: x.shape == (2048,),
"self_attn.qkv_proj.weight": lambda x: x.shape == (8, 3, 256, 2048),
"self_attn.qkv_proj.weight": lambda x: x.shape == (10, 256, 2048),
"self_attn.o_proj.weight": lambda x: x.shape == (8, 2048, 256),
"mlp.gate_proj.weight": lambda x: x.shape == (1, 16384, 2048),
"mlp.up_proj.weight": lambda x: x.shape == (1, 16384, 2048),