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llama : support RWKV v6 models (#8980)

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* convert_hf_to_gguf: Add support for RWKV v6

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Add RWKV tokenization

* Fix build

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Do not use special tokens when matching in RWKV tokenizer

* Fix model loading

* Add (broken) placeholder graph builder for RWKV

* Add workaround for kv cache

* Add logits conversion to rwkv5

* Add rwkv5 layer norms

* Add time mix KVRG & correct merge mistake

* Add remaining time mix parameters

* Add time mix output loading

* Add placeholder llm_build_time_mix

* Fix build

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Load more tensors for rwkv v6

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Fix rwkv tokenizer

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* ggml: Add unary operator Exp

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* RWKV v6 graph building

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Add ``rescale_every_n_layers`` parameter

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Add ``wkv.head_size`` key for RWKV

so it doesn't reuse Mamba ssm parameters

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Fix offloading layers to CUDA

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Fix parallel inferencing for RWKV

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Remove trailing whitespaces

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* build_rwkv: Avoid using inplace operations

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* convert_hf_to_gguf: rwkv: Avoid using ``eval``

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* convert_hf_to_gguf: rwkv tokenizer: Don't escape sequences manually

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Update convert_hf_to_gguf.py

Co-authored-by: compilade <git@compilade.net>

* ggml: Add backward computation for unary op ``exp``

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Update convert_hf_to_gguf.py

Co-authored-by: compilade <git@compilade.net>

* Update convert_hf_to_gguf.py

Co-authored-by: compilade <git@compilade.net>

* Use MODEL_ARCH.RWKV6 instead of MODEL_ARCH.RWKV

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* build_rwkv6: Simplify graph

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* llama: rwkv6: Detect model.type

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* llama: rwkv6: Fix tensor loading for 7B/14B models

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* llama: rwkv6: Fix group_norm assertion failure with Metal

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* llama: rwkv6: Clean up

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* llama: rwkv6: Add quantization tensor exclusion

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* llama: rwkv6: Use the new advanced batch splits

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Update src/llama.cpp

Co-authored-by: compilade <git@compilade.net>

* llama: rwkv6: Use ``ggml_norm`` instead of ``ggml_group_norm``

Co-authored-by: compilade <git@compilade.net>

* llama: rwkv6: Apply code style and misc changes

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* converter: Use class name ``Rwkv6Model``

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* llama: rwkv6: Make use of key ``feed_forward_length``

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* llama: rwkv6: Add kv ``time_mix_extra_dim`` and ``time_decay_extra_dim``

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* converter: Match ``new_name`` instead of ``name`` for float32 explicit tensors

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* llama: rwkv6: Keep ``time_mix_w1/w2`` as F32

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* llama: rwkv6: Remove unused nodes

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* llama: rwkv6: Apply code format changes

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* llama: rwkv6: Add lora for some supported tensors

Currently att.key/receptance/value/gate/output, ffn.receptance/key/value, as well as head.weight

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* rwkv : speed-up tokenization using trie

* minor : style + indentation

* llama: rwkv6: Avoid division by zero

Co-authored-by: compilade <git@compilade.net>

* ggml: rwkv_wkv: Avoid copying the state

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

---------

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
Co-authored-by: Layl Bongers <3094382+LaylBongers@users.noreply.github.com>
Co-authored-by: compilade <git@compilade.net>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Molly Sophia 2024-09-01 22:38:17 +08:00 • committed by GitHub
parent a47667cff4
commit 8f1d81a0b6
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GPG key ID: B5690EEEBB952194
9 changed files with 1266 additions and 103 deletions
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145
src/llama-vocab.cpp
View file

@ -58,17 +58,17 @@ struct naive_trie {
auto res = children.find(c);
if (res != children.end()) {
return res->second.get_longest_prefix(key, len, offset + 1);
} else {
return std::make_pair(key, offset);
}
return std::make_pair(key, offset);
}
struct naive_trie * traverse(const char c) {
const struct naive_trie * traverse(const char c) const {
auto res = children.find(c);
if (res != children.end()) {
return &res->second;
} else {
return NULL;
}
return NULL;
}
std::map<char, struct naive_trie> children;
bool has_value;
@ -843,7 +843,7 @@ struct llm_tokenizer_ugm {
// traverse the token matcher trie to find a matching token
bool single_codepoint_token_found = false;
const struct best_tokenization & current_best = tokenization_results[input_offset];
struct naive_trie * node = token_matcher.traverse(normalized[prefix_offset++]);
const struct naive_trie * node = token_matcher.traverse(normalized[prefix_offset++]);
while (prefix_offset <= input_len && node != NULL) {
// check if we found valid token in prefix
@ -1097,6 +1097,111 @@ private:
struct naive_trie token_matcher;
};
//
// RWKV tokenizer
//
static std::vector<uint8_t> llama_unescape_rwkv_token(const std::string & escaped) {
std::vector<uint8_t> output;
output.reserve(escaped.size());
// Parser state
bool escaping = false;
uint8_t hex_remaining = 0;
uint8_t hex_acc = 0;
// Step through characters, performing parsing
for (const char & c : escaped) {
// If we're parsing a hex code, interpret the next character
if (hex_remaining != 0) {
uint8_t value = (c >= 'a') ? (c - 'a' + 10) : (c - '0');
hex_acc = (hex_acc << 4) + value;
hex_remaining -= 1;
if (hex_remaining == 0) {
output.push_back(hex_acc);
hex_acc = 0;
}
continue;
}
// If we got an escape character, interpret it
if (escaping) {
if (c == 't') {
output.push_back('\t');
} else if (c == 'n') {
output.push_back('\n');
} else if (c == 'r') {
output.push_back('\r');
} else if (c == 'x') {
hex_remaining = 2;
} else {
output.push_back(c);
}
escaping = false;
continue;
}
if (c == '\\') {
escaping = true;
continue;
}
output.push_back(c);
}
return output;
}
struct llm_tokenizer_rwkv {
llm_tokenizer_rwkv(const llama_vocab & vocab): vocab(vocab) {
// RWKV supports arbitrary byte tokens, but the vocab struct only supports string tokens.
// For now, we decode the vocab here into the lookup we'll use for tokenization.
// build trie
for (unsigned int id = 0; id < vocab.id_to_token.size(); ++id) {
const auto & token = vocab.id_to_token[id];
const auto data = llama_unescape_rwkv_token(token.text);
token_matcher.insert((const char *) data.data(), data.size(), id);
}
}
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
uint32_t position = 0;
while (position < text.size()) {
const struct naive_trie * node = token_matcher.traverse(text[position]);
if (node == NULL) {
// no matching token found, add unknown token
output.push_back(vocab.special_unk_id);
position += 1;
continue;
}
// traverse the trie to find the longest matching token
uint32_t token_id = 0;
uint32_t token_length = 0;
while (node != NULL) {
if (node->has_value) {
token_id = node->value;
token_length = position + 1;
}
node = node->traverse(text[++position]);
}
// add the longest matching token
output.push_back(token_id);
position = token_length;
}
}
const llama_vocab & vocab;
struct naive_trie token_matcher;
};
//
// (de-) tokenize
//
@ -1401,6 +1506,23 @@ std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & vocab,
output.push_back(vocab.special_eos_id);
}
} break;
case LLAMA_VOCAB_TYPE_RWKV:
{
for (const auto & fragment : fragment_buffer) {
if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
#ifdef PRETOKENIZERDEBUG
LLAMA_LOG_WARN("TT: (%ld %ld %ld) '%s'\n", raw_text.length(), fragment.offset, fragment.length, raw_text.c_str());
#endif
llm_tokenizer_rwkv tokenizer(vocab);
tokenizer.tokenize(raw_text, output);
} else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
output.push_back(fragment.token);
}
}
} break;
case LLAMA_VOCAB_TYPE_NONE:
GGML_ABORT("fatal error");
}
@ -1616,6 +1738,17 @@ int32_t llama_token_to_piece_impl(const struct llama_vocab & vocab, llama_token
}
break;
}
case LLAMA_VOCAB_TYPE_RWKV: {
std::vector<uint8_t> result = llama_unescape_rwkv_token(token_text);
// If we don't have enough space, return an error
if (result.size() > (size_t)length) {
return -(int)result.size();
}
memcpy(buf, result.data(), result.size());
return (int)result.size();
}
default:
GGML_ABORT("fatal error");
}

533
src/llama.cpp
View file

@ -212,6 +212,7 @@ enum llm_arch {
LLM_ARCH_JAIS,
LLM_ARCH_NEMOTRON,
LLM_ARCH_EXAONE,
LLM_ARCH_RWKV6,
LLM_ARCH_UNKNOWN,
};
@ -259,6 +260,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_JAIS, "jais" },
{ LLM_ARCH_NEMOTRON, "nemotron" },
{ LLM_ARCH_EXAONE, "exaone" },
{ LLM_ARCH_RWKV6, "rwkv6" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
@ -295,6 +297,9 @@ enum llm_kv {
LLM_KV_DECODER_START_TOKEN_ID,
LLM_KV_ATTN_LOGIT_SOFTCAPPING,
LLM_KV_FINAL_LOGIT_SOFTCAPPING,
LLM_KV_RESCALE_EVERY_N_LAYERS,
LLM_KV_TIME_MIX_EXTRA_DIM,
LLM_KV_TIME_DECAY_EXTRA_DIM,
LLM_KV_ATTENTION_HEAD_COUNT,
LLM_KV_ATTENTION_HEAD_COUNT_KV,
@ -330,6 +335,8 @@ enum llm_kv {
LLM_KV_SSM_TIME_STEP_RANK,
LLM_KV_SSM_DT_B_C_RMS,
LLM_KV_WKV_HEAD_SIZE,
LLM_KV_TOKENIZER_MODEL,
LLM_KV_TOKENIZER_PRE,
LLM_KV_TOKENIZER_LIST,
@ -389,11 +396,14 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" },
{ LLM_KV_EXPERT_SHARED_COUNT, "%s.expert_shared_count" },
{ LLM_KV_EXPERT_WEIGHTS_SCALE, "%s.expert_weights_scale" },
{ LLM_KV_POOLING_TYPE , "%s.pooling_type" },
{ LLM_KV_POOLING_TYPE, "%s.pooling_type" },
{ LLM_KV_LOGIT_SCALE, "%s.logit_scale" },
{ LLM_KV_DECODER_START_TOKEN_ID, "%s.decoder_start_token_id" },
{ LLM_KV_ATTN_LOGIT_SOFTCAPPING, "%s.attn_logit_softcapping" },
{ LLM_KV_FINAL_LOGIT_SOFTCAPPING, "%s.final_logit_softcapping" },
{ LLM_KV_RESCALE_EVERY_N_LAYERS, "%s.rescale_every_n_layers" },
{ LLM_KV_TIME_MIX_EXTRA_DIM, "%s.time_mix_extra_dim" },
{ LLM_KV_TIME_DECAY_EXTRA_DIM, "%s.time_decay_extra_dim" },
{ LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" },
{ LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" },
@ -429,6 +439,8 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_SSM_TIME_STEP_RANK, "%s.ssm.time_step_rank" },
{ LLM_KV_SSM_DT_B_C_RMS, "%s.ssm.dt_b_c_rms" },
{ LLM_KV_WKV_HEAD_SIZE, "%s.wkv.head_size" },
{ LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" },
{ LLM_KV_TOKENIZER_PRE, "tokenizer.ggml.pre" },
{ LLM_KV_TOKENIZER_LIST, "tokenizer.ggml.tokens" },
@ -518,6 +530,29 @@ enum llm_tensor {
LLM_TENSOR_SSM_A,
LLM_TENSOR_SSM_D,
LLM_TENSOR_SSM_OUT,
LLM_TENSOR_TIME_MIX_W1,
LLM_TENSOR_TIME_MIX_W2,
LLM_TENSOR_TIME_MIX_LERP_X,
LLM_TENSOR_TIME_MIX_LERP_W,
LLM_TENSOR_TIME_MIX_LERP_K,
LLM_TENSOR_TIME_MIX_LERP_V,
LLM_TENSOR_TIME_MIX_LERP_R,
LLM_TENSOR_TIME_MIX_LERP_G,
LLM_TENSOR_TIME_MIX_FIRST,
LLM_TENSOR_TIME_MIX_DECAY,
LLM_TENSOR_TIME_MIX_DECAY_W1,
LLM_TENSOR_TIME_MIX_DECAY_W2,
LLM_TENSOR_TIME_MIX_KEY,
LLM_TENSOR_TIME_MIX_VALUE,
LLM_TENSOR_TIME_MIX_RECEPTANCE,
LLM_TENSOR_TIME_MIX_GATE,
LLM_TENSOR_TIME_MIX_LN,
LLM_TENSOR_TIME_MIX_OUTPUT,
LLM_TENSOR_CHANNEL_MIX_LERP_K,
LLM_TENSOR_CHANNEL_MIX_LERP_R,
LLM_TENSOR_CHANNEL_MIX_KEY,
LLM_TENSOR_CHANNEL_MIX_RECEPTANCE,
LLM_TENSOR_CHANNEL_MIX_VALUE,
LLM_TENSOR_ATTN_Q_A,
LLM_TENSOR_ATTN_Q_B,
LLM_TENSOR_ATTN_KV_A_MQA,
@ -1339,6 +1374,40 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_RWKV6,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_NORM_2, "blk.%d.attn_norm_2" },
{ LLM_TENSOR_TIME_MIX_W1, "blk.%d.time_mix_w1" },
{ LLM_TENSOR_TIME_MIX_W2, "blk.%d.time_mix_w2" },
{ LLM_TENSOR_TIME_MIX_LERP_X, "blk.%d.time_mix_lerp_x" },
{ LLM_TENSOR_TIME_MIX_LERP_W, "blk.%d.time_mix_lerp_w" },
{ LLM_TENSOR_TIME_MIX_LERP_K, "blk.%d.time_mix_lerp_k" },
{ LLM_TENSOR_TIME_MIX_LERP_V, "blk.%d.time_mix_lerp_v" },
{ LLM_TENSOR_TIME_MIX_LERP_R, "blk.%d.time_mix_lerp_r" },
{ LLM_TENSOR_TIME_MIX_LERP_G, "blk.%d.time_mix_lerp_g" },
{ LLM_TENSOR_TIME_MIX_FIRST, "blk.%d.time_mix_first" },
{ LLM_TENSOR_TIME_MIX_DECAY, "blk.%d.time_mix_decay" },
{ LLM_TENSOR_TIME_MIX_DECAY_W1, "blk.%d.time_mix_decay_w1" },
{ LLM_TENSOR_TIME_MIX_DECAY_W2, "blk.%d.time_mix_decay_w2" },
{ LLM_TENSOR_TIME_MIX_KEY, "blk.%d.time_mix_key" },
{ LLM_TENSOR_TIME_MIX_VALUE, "blk.%d.time_mix_value" },
{ LLM_TENSOR_TIME_MIX_RECEPTANCE, "blk.%d.time_mix_receptance" },
{ LLM_TENSOR_TIME_MIX_GATE, "blk.%d.time_mix_gate" },
{ LLM_TENSOR_TIME_MIX_LN, "blk.%d.time_mix_ln" },
{ LLM_TENSOR_TIME_MIX_OUTPUT, "blk.%d.time_mix_output" },
{ LLM_TENSOR_CHANNEL_MIX_LERP_K, "blk.%d.channel_mix_lerp_k" },
{ LLM_TENSOR_CHANNEL_MIX_LERP_R, "blk.%d.channel_mix_lerp_r" },
{ LLM_TENSOR_CHANNEL_MIX_KEY, "blk.%d.channel_mix_key" },
{ LLM_TENSOR_CHANNEL_MIX_VALUE, "blk.%d.channel_mix_value" },
{ LLM_TENSOR_CHANNEL_MIX_RECEPTANCE, "blk.%d.channel_mix_receptance" },
},
},
{
LLM_ARCH_UNKNOWN,
{
@ -2151,6 +2220,7 @@ enum e_model {
MODEL_1B,
MODEL_1_3B,
MODEL_1_4B,
MODEL_1_6B,
MODEL_2B,
MODEL_2_8B,
MODEL_3B,
@ -2228,6 +2298,12 @@ struct llama_hparams {
float f_attn_logit_softcapping = 50.0f;
float f_final_logit_softcapping = 30.0f;
// for RWKV
uint32_t rescale_every_n_layers = 0;
uint32_t time_mix_extra_dim = 0;
uint32_t time_decay_extra_dim = 0;
uint32_t wkv_head_size = 0;
float rope_attn_factor = 1.0f;
float rope_freq_base_train;
float rope_freq_scale_train;
@ -2291,6 +2367,11 @@ struct llama_hparams {
if (this->ssm_dt_rank != other.ssm_dt_rank) return true;
if (this->ssm_dt_b_c_rms != other.ssm_dt_b_c_rms) return true;
if (this->rescale_every_n_layers != other.rescale_every_n_layers) return true;
if (this->time_mix_extra_dim != other.time_mix_extra_dim) return true;
if (this->time_decay_extra_dim != other.time_decay_extra_dim) return true;
if (this->wkv_head_size != other.wkv_head_size) return true;
if (this->dec_start_token_id != other.dec_start_token_id) return true;
const float EPSILON = 1e-9f;
@ -2354,15 +2435,25 @@ struct llama_hparams {
}
uint32_t n_embd_k_s() const { // dimension of the rolling state embeddings
// corresponds to Mamba's conv_states size
// TODO: maybe support other convolution strides than 1
// NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed
return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * ssm_d_inner;
// corresponds to Mamba's conv_states size or RWKV's token_shift states size
if (wkv_head_size != 0) {
// for RWKV models
return 2 * n_embd;
} else {
// TODO: maybe support other convolution strides than 1
// NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed
return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * ssm_d_inner;
}
}
uint32_t n_embd_v_s() const { // dimension of the recurrent state embeddings
// corresponds to Mamba's ssm_states size
return ssm_d_state * ssm_d_inner;
if (wkv_head_size != 0) {
// corresponds to RWKV's wkv_states size
return n_embd * wkv_head_size;
} else {
// corresponds to Mamba's ssm_states size
return ssm_d_state * ssm_d_inner;
}
}
};
@ -2501,6 +2592,36 @@ struct llama_layer {
struct ggml_tensor * ssm_conv1d_b;
struct ggml_tensor * ssm_dt_b;
// rwkv
struct ggml_tensor * time_mix_w1;
struct ggml_tensor * time_mix_w2;
struct ggml_tensor * time_mix_lerp_x;
struct ggml_tensor * time_mix_lerp_w;
struct ggml_tensor * time_mix_lerp_k;
struct ggml_tensor * time_mix_lerp_v;
struct ggml_tensor * time_mix_lerp_r;
struct ggml_tensor * time_mix_lerp_g;
struct ggml_tensor * time_mix_first;
struct ggml_tensor * time_mix_decay;
struct ggml_tensor * time_mix_decay_w1;
struct ggml_tensor * time_mix_decay_w2;
struct ggml_tensor * time_mix_key;
struct ggml_tensor * time_mix_value;
struct ggml_tensor * time_mix_receptance;
struct ggml_tensor * time_mix_gate;
struct ggml_tensor * time_mix_ln;
struct ggml_tensor * time_mix_ln_b;
struct ggml_tensor * time_mix_output;
struct ggml_tensor * channel_mix_lerp_k;
struct ggml_tensor * channel_mix_lerp_r;
struct ggml_tensor * channel_mix_key;
struct ggml_tensor * channel_mix_receptance;
struct ggml_tensor * channel_mix_value;
// long rope factors
struct ggml_tensor * rope_long = nullptr;
struct ggml_tensor * rope_short = nullptr;
@ -3426,7 +3547,7 @@ static bool llama_kv_cache_find_slot(
const uint32_t n_seq_tokens = batch.n_seq_tokens;
if (cache.recurrent) {
// For recurrent state architectures (like Mamba),
// For recurrent state architectures (like Mamba or RWKV),
// each cache cell can store the state for a whole sequence.
// A slot should be always be contiguous.
@ -3675,7 +3796,7 @@ static bool llama_kv_cache_seq_rm(
if (p0 < 0) p0 = 0;
if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
// models like Mamba can't have a state partially erased
// models like Mamba or RWKV can't have a state partially erased
if (cache.recurrent) {
if (seq_id >= (int64_t) cache.size) {
// could be fatal
@ -3811,7 +3932,7 @@ static void llama_kv_cache_seq_add(
if (p0 == p1) return;
if (cache.recurrent) {
// for Mamba-like models, only the pos needs to be shifted
// for Mamba-like or RWKV models, only the pos needs to be shifted
if (0 <= seq_id && seq_id < (int64_t) cache.size) {
const int32_t tail_id = cache.cells[seq_id].tail;
if (tail_id >= 0) {
@ -3860,7 +3981,7 @@ static void llama_kv_cache_seq_div(
if (p0 == p1) return;
if (cache.recurrent) {
// for Mamba-like models, only the pos needs to be changed
// for Mamba-like or RWKV models, only the pos needs to be changed
if (0 <= seq_id && seq_id < (int64_t) cache.size) {
const int32_t tail_id = cache.cells[seq_id].tail;
if (tail_id >= 0) {
@ -5051,6 +5172,7 @@ static const char * llama_model_type_name(e_model type) {
case MODEL_1B: return "1B";
case MODEL_1_3B: return "1.3B";
case MODEL_1_4B: return "1.4B";
case MODEL_1_6B: return "1.6B";
case MODEL_2B: return "2B";
case MODEL_2_8B: return "2.8B";
case MODEL_3B: return "3B";
@ -5097,6 +5219,7 @@ static const char * llama_model_vocab_type_name(enum llama_vocab_type type){
case LLAMA_VOCAB_TYPE_BPE: return "BPE";
case LLAMA_VOCAB_TYPE_WPM: return "WPM";
case LLAMA_VOCAB_TYPE_UGM: return "UGM";
case LLAMA_VOCAB_TYPE_RWKV: return "RWKV";
default: return "unknown";
}
}
@ -5793,6 +5916,26 @@ static void llm_load_hparams(
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_RWKV6:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
ml.get_key(LLM_KV_WKV_HEAD_SIZE, hparams.wkv_head_size);
ml.get_key(LLM_KV_TIME_MIX_EXTRA_DIM, hparams.time_mix_extra_dim);
ml.get_key(LLM_KV_TIME_DECAY_EXTRA_DIM, hparams.time_decay_extra_dim);
ml.get_key(LLM_KV_RESCALE_EVERY_N_LAYERS, hparams.rescale_every_n_layers, false);
switch (hparams.n_layer) {
case 24: model.type = e_model::MODEL_1_6B; break;
case 32:
switch (hparams.n_embd) {
case 2560: model.type = e_model::MODEL_3B; break;
case 4096: model.type = e_model::MODEL_7B; break;
default: model.type = e_model::MODEL_UNKNOWN;
} break;
case 61: model.type = e_model::MODEL_14B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
default: (void)0;
}
@ -5922,6 +6065,15 @@ static void llm_load_vocab(
}
#endif
}
} else if (tokenizer_model == "rwkv") {
vocab.type = LLAMA_VOCAB_TYPE_RWKV;
// default special tokens
vocab.special_bos_id = -1;
vocab.special_eos_id = -1;
vocab.special_unk_id = -1;
vocab.special_sep_id = -1;
vocab.special_pad_id = -1;
} else {
throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str()));
}
@ -6053,6 +6205,12 @@ static void llm_load_vocab(
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
vocab.tokenizer_add_bos = false;
vocab.tokenizer_add_eos = true;
} else if (vocab.type == LLAMA_VOCAB_TYPE_RWKV) {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
vocab.tokenizer_add_space_prefix = false;
vocab.tokenizer_clean_spaces = false;
vocab.tokenizer_add_bos = false;
vocab.tokenizer_add_eos = false;
} else {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
}
@ -6157,6 +6315,10 @@ static void llm_load_vocab(
}
} else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) {
vocab.linefeed_id = vocab.special_pad_id;
} else if (vocab.type == LLAMA_VOCAB_TYPE_RWKV) {
const std::vector<int> ids = llama_tokenize_internal(vocab, "\n", false);
GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
vocab.linefeed_id = ids[0];
} else {
const std::vector<int> ids = llama_tokenize_internal(vocab, "\xC4\x8A", false); // U+010A
GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
@ -8203,6 +8365,68 @@ static bool llm_load_tensors(
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
}
} break;
case LLM_ARCH_RWKV6:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
// Block 0, LN0
model.tok_norm = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
model.tok_norm_b = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd});
// output
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
const int time_mix_extra_dim = hparams.time_mix_extra_dim;
const int time_decay_extra_dim = hparams.time_decay_extra_dim;
const int head_size = hparams.wkv_head_size;
const int attn_hidden_size = n_embd;
const int ffn_size = hparams.n_ff_arr[0];
for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd});
layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd});
layer.time_mix_w1 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_W1, "weight", i), {n_embd, time_mix_extra_dim * 5});
layer.time_mix_w2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {time_mix_extra_dim, n_embd, 5});
layer.time_mix_lerp_x = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_X, "weight", i), {n_embd, 1, 1});
layer.time_mix_lerp_w = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_W, "weight", i), {n_embd, 1, 1});
layer.time_mix_lerp_k = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_K, "weight", i), {n_embd, 1, 1});
layer.time_mix_lerp_v = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_V, "weight", i), {n_embd, 1, 1});
layer.time_mix_lerp_r = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_R, "weight", i), {n_embd, 1, 1});
layer.time_mix_lerp_g = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_G, "weight", i), {n_embd, 1, 1});
layer.time_mix_first = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_FIRST, "weight", i), {head_size, n_embd / head_size});
layer.time_mix_decay = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_DECAY, "weight", i), {n_embd});
layer.time_mix_decay_w1 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_DECAY_W1, "weight", i), {n_embd, time_decay_extra_dim});
layer.time_mix_decay_w2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_DECAY_W2, "weight", i), {time_decay_extra_dim, attn_hidden_size});
layer.time_mix_key = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {attn_hidden_size, n_embd});
layer.time_mix_value = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {attn_hidden_size, n_embd});
layer.time_mix_receptance = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {attn_hidden_size, n_embd});
layer.time_mix_gate = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_GATE, "weight", i), {attn_hidden_size, n_embd});
layer.time_mix_ln = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LN, "weight", i), {n_embd});
layer.time_mix_ln_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LN, "bias", i), {n_embd});
layer.time_mix_output = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_OUTPUT, "weight", i), {n_embd, attn_hidden_size});
layer.channel_mix_lerp_k = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_CHANNEL_MIX_LERP_K, "weight", i), {n_embd, 1, 1});
layer.channel_mix_lerp_r = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_CHANNEL_MIX_LERP_R, "weight", i), {n_embd, 1, 1});
layer.channel_mix_key = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_CHANNEL_MIX_KEY, "weight", i), {n_embd, ffn_size});
layer.channel_mix_value = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_CHANNEL_MIX_VALUE, "weight", i), {ffn_size, n_embd});
layer.channel_mix_receptance = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_CHANNEL_MIX_RECEPTANCE, "weight", i), {n_embd, n_embd});
}
} break;
default:
throw std::runtime_error("unknown architecture");
}
@ -9162,6 +9386,171 @@ static struct ggml_tensor * llm_build_mamba(
return cur;
}
static struct ggml_tensor * llm_build_rwkv6_time_mix(
struct llama_context & lctx,
struct ggml_context * ctx,
const struct llama_layer * layer,
struct ggml_tensor * cur,
struct ggml_tensor * x_prev,
struct ggml_tensor ** wkv_state) {
size_t n_embed = cur->ne[0];
size_t n_seq_tokens = cur->ne[1];
size_t n_seqs = cur->ne[2];
size_t head_size = layer->time_mix_first->ne[0];
size_t head_count = layer->time_mix_first->ne[1];
size_t n_tokens = n_seqs * n_seq_tokens;
struct ggml_tensor * sx = ggml_sub(ctx, x_prev, cur);
sx = ggml_reshape_2d(ctx, sx, n_embed, n_tokens);
cur = ggml_reshape_2d(ctx, cur, n_embed, n_tokens);
struct ggml_tensor * xxx = ggml_add(ctx, ggml_mul(ctx, sx, layer->time_mix_lerp_x), cur);
xxx = ggml_reshape_4d(
ctx,
ggml_tanh(
ctx,
ggml_mul_mat(ctx, layer->time_mix_w1, xxx)
),
layer->time_mix_w1->ne[1] / 5, 1, 5, n_tokens
);
xxx = ggml_cont(ctx, ggml_permute(ctx, xxx, 0, 1, 3, 2));
xxx = ggml_mul_mat(
ctx,
ggml_reshape_4d(
ctx,
layer->time_mix_w2,
layer->time_mix_w2->ne[0], layer->time_mix_w2->ne[1], 1, 5
),
xxx
);
struct ggml_tensor *mw = ggml_view_2d(ctx, xxx, n_embed, n_tokens, xxx->nb[1], 0);
struct ggml_tensor *mk = ggml_view_2d(ctx, xxx, n_embed, n_tokens, xxx->nb[1], n_embed * n_tokens * sizeof(float));
struct ggml_tensor *mv = ggml_view_2d(ctx, xxx, n_embed, n_tokens, xxx->nb[1], n_embed * n_tokens * 2 * sizeof(float));
struct ggml_tensor *mr = ggml_view_2d(ctx, xxx, n_embed, n_tokens, xxx->nb[1], n_embed * n_tokens * 3 * sizeof(float));
struct ggml_tensor *mg = ggml_view_2d(ctx, xxx, n_embed, n_tokens, xxx->nb[1], n_embed * n_tokens * 4 * sizeof(float));
struct ggml_tensor * xw = ggml_add(
ctx,
ggml_mul(
ctx,
ggml_add(ctx, mw, layer->time_mix_lerp_w),
sx
),
cur
);
struct ggml_tensor * xk = ggml_add(
ctx,
ggml_mul(
ctx,
ggml_add(ctx, mk, layer->time_mix_lerp_k),
sx
),
cur
);
struct ggml_tensor * xv = ggml_add(
ctx,
ggml_mul(
ctx,
ggml_add(ctx, mv, layer->time_mix_lerp_v),
sx
),
cur
);
struct ggml_tensor * xr = ggml_add(
ctx,
ggml_mul(
ctx,
ggml_add(ctx, mr, layer->time_mix_lerp_r),
sx
),
cur
);
struct ggml_tensor * xg = ggml_add(
ctx,
ggml_mul(
ctx,
ggml_add(ctx, mg, layer->time_mix_lerp_g),
sx
),
cur
);
struct ggml_tensor * r = ggml_reshape_4d(ctx, llm_build_lora_mm(lctx, ctx, layer->time_mix_receptance, xr), head_size, 1, head_count, n_tokens);
struct ggml_tensor * k = ggml_reshape_4d(ctx, llm_build_lora_mm(lctx, ctx, layer->time_mix_key, xk), 1, head_size, head_count, n_tokens);
struct ggml_tensor * v = ggml_reshape_4d(ctx, llm_build_lora_mm(lctx, ctx, layer->time_mix_value, xv), head_size, 1, head_count, n_tokens);
struct ggml_tensor * g = ggml_silu(
ctx,
llm_build_lora_mm(lctx, ctx, layer->time_mix_gate, xg)
);
struct ggml_tensor * w = ggml_mul_mat(
ctx,
layer->time_mix_decay_w2,
ggml_tanh(
ctx,
ggml_mul_mat(ctx, layer->time_mix_decay_w1, xw)
)
);
w = ggml_add(ctx, w, ggml_reshape_1d(ctx, layer->time_mix_decay, n_embed));
w = ggml_exp(ctx, ggml_neg(ctx, ggml_exp(ctx, w)));
w = ggml_reshape_4d(ctx, w, 1, head_size, head_count, n_tokens);
k = ggml_transpose(ctx, k);
v = ggml_transpose(ctx, v);
r = ggml_transpose(ctx, r);
struct ggml_tensor * wkv_output = ggml_rwkv_wkv(ctx, k, v, r, layer->time_mix_first, w, *wkv_state);
cur = ggml_view_1d(ctx, wkv_output, n_embed * n_tokens, 0);
*wkv_state = ggml_view_1d(ctx, wkv_output, n_embed * head_size * n_seqs, n_embed * n_tokens * sizeof(float));
// group norm with head_count groups
cur = ggml_reshape_3d(ctx, cur, n_embed / head_count, head_count, n_tokens);
cur = ggml_norm(ctx, cur, 64e-5f);
// Convert back to regular vectors.
cur = ggml_reshape_2d(ctx, cur, n_embed, n_tokens);
cur = ggml_add(ctx, ggml_mul(ctx, cur, layer->time_mix_ln), layer->time_mix_ln_b);
cur = ggml_mul(ctx, cur, g);
cur = llm_build_lora_mm(lctx, ctx, layer->time_mix_output, cur);
return ggml_reshape_3d(ctx, cur, n_embed, n_seq_tokens, n_seqs);
}
static struct ggml_tensor * llm_build_rwkv6_channel_mix(
struct llama_context & lctx,
struct ggml_context * ctx,
const struct llama_layer * layer,
struct ggml_tensor * cur,
struct ggml_tensor * x_prev) {
struct ggml_tensor * sx = ggml_sub(ctx, x_prev, cur);
struct ggml_tensor * xk = ggml_add(ctx, ggml_mul(ctx, sx, layer->channel_mix_lerp_k), cur);
struct ggml_tensor * xr = ggml_add(ctx, ggml_mul(ctx, sx, layer->channel_mix_lerp_r), cur);
struct ggml_tensor * r = ggml_sigmoid(ctx, llm_build_lora_mm(lctx, ctx, layer->channel_mix_receptance, xr));
struct ggml_tensor * k = ggml_sqr(
ctx,
ggml_relu(
ctx,
llm_build_lora_mm(lctx, ctx, layer->channel_mix_key, xk)
)
);
return ggml_mul(ctx, r, llm_build_lora_mm(lctx, ctx, layer->channel_mix_value, k));
}
struct llm_build_context {
const llama_model & model;
llama_context & lctx;
@ -14683,6 +15072,117 @@ struct llm_build_context {
return gf;
}
ggml_cgraph * build_rwkv6() {
ggml_cgraph *gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
// Token shift state dimensions should be 2 * n_emb
GGML_ASSERT(n_embd == hparams.n_embd_k_s() / 2);
const int64_t n_seqs = batch.n_seqs;
const int64_t n_seq_tokens = batch.n_seq_tokens;
const int64_t n_tokens = batch.n_tokens;
GGML_ASSERT(n_seqs != 0);
GGML_ASSERT(batch.equal_seqs);
GGML_ASSERT(n_tokens == n_seq_tokens * n_seqs);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
struct ggml_tensor * state_copy = build_inp_s_copy();
struct ggml_tensor * state_mask = build_inp_s_mask();
inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
inpL = llm_build_norm(ctx0, inpL, hparams, model.tok_norm, model.tok_norm_b, LLM_NORM, cb, -1);
for (int il = 0; il < n_layer; ++il) {
const llama_layer * layer = &model.layers[il];
// (ab)using the KV cache to store the states
struct ggml_tensor * token_shift = llm_build_copy_mask_state(ctx0,
gf, kv_self.k_l[il], state_copy, state_mask,
hparams.n_embd_k_s(), kv_self.size, kv_head, n_kv, n_seqs);
struct ggml_tensor * wkv_states = llm_build_copy_mask_state(ctx0,
gf, kv_self.v_l[il], state_copy, state_mask,
hparams.n_embd_v_s(), kv_self.size, kv_head, n_kv, n_seqs);
cur = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
token_shift = ggml_reshape_3d(ctx0, token_shift, n_embd, 2, n_seqs);
struct ggml_tensor * att_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], 0);
struct ggml_tensor * ffn_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], n_embd * ggml_element_size(token_shift));
struct ggml_tensor * x_norm_att = llm_build_norm(ctx0, cur, hparams, layer->attn_norm, layer->attn_norm_b, LLM_NORM, cb, il);
struct ggml_tensor * x_prev = ggml_concat(
ctx0,
att_shift,
ggml_view_3d(ctx0, x_norm_att, n_embd, n_seq_tokens - 1, n_seqs, x_norm_att->nb[1], x_norm_att->nb[2], 0),
1
);
cur = ggml_add(ctx0, cur, llm_build_rwkv6_time_mix(lctx, ctx0, layer, x_norm_att, x_prev, &wkv_states));
ggml_build_forward_expand(gf, cur);
ggml_build_forward_expand(
gf,
ggml_cpy(
ctx0,
wkv_states,
ggml_view_1d(
ctx0,
kv_self.v_l[il],
hparams.n_embd_v_s() * n_seqs,
hparams.n_embd_v_s() * kv_head * ggml_element_size(kv_self.v_l[il])
)
)
);
struct ggml_tensor * x_norm_ffn = llm_build_norm(ctx0, cur, hparams, layer->attn_norm_2, layer->attn_norm_2_b, LLM_NORM, cb, il);
x_prev = ggml_concat(
ctx0,
ffn_shift,
ggml_view_3d(ctx0, x_norm_ffn, n_embd, n_seq_tokens - 1, n_seqs, x_norm_ffn->nb[1], x_norm_ffn->nb[2], 0),
1
);
cur = ggml_add(ctx0, cur, llm_build_rwkv6_channel_mix(lctx, ctx0, layer, x_norm_ffn, x_prev));
ggml_build_forward_expand(gf, cur);
struct ggml_tensor * last_norm_att = ggml_view_3d(ctx0, x_norm_att, n_embd, 1, n_seqs, x_norm_att->nb[1], x_norm_att->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(x_norm_att));
struct ggml_tensor * last_norm_ffn = ggml_view_3d(ctx0, x_norm_ffn, n_embd, 1, n_seqs, x_norm_ffn->nb[1], x_norm_ffn->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(x_norm_ffn));
token_shift = ggml_concat(ctx0, last_norm_att, last_norm_ffn, 1);
ggml_build_forward_expand(
gf,
ggml_cpy(
ctx0,
ggml_view_1d(ctx0, token_shift, n_embd * n_seqs * 2, 0),
ggml_view_1d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s() * n_seqs, hparams.n_embd_k_s() * kv_head * ggml_element_size(kv_self.k_l[il]))
)
);
if (hparams.rescale_every_n_layers != 0 && (il + 1) % hparams.rescale_every_n_layers == 0) {
cur = ggml_scale(ctx0, cur, 0.5F);
}
cur = lctx.cvec.apply_to(ctx0, cur, il);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, model.output_norm_b, LLM_NORM, cb, -1);
cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
};
static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
@ -14929,6 +15429,10 @@ static struct ggml_cgraph * llama_build_graph(
{
result = llm.build_exaone();
} break;
case LLM_ARCH_RWKV6:
{
result = llm.build_rwkv6();
} break;
default:
GGML_ABORT("fatal error");
}
@ -16973,6 +17477,11 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
// NOTE: can't use LLM_TN here because the layer number is not known
quantize &= name.find("ssm_conv1d.weight") == std::string::npos;
// do not quantize RWKV's time_mix_first tensors
quantize &= name.find("time_mix_first.weight") == std::string::npos;
quantize &= name.find("time_mix_w1.weight") == std::string::npos;
quantize &= name.find("time_mix_w2.weight") == std::string::npos;
// do not quantize relative position bias (T5)
quantize &= name.find("attn_rel_b.weight") == std::string::npos;
@ -17977,6 +18486,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
case LLM_ARCH_T5:
case LLM_ARCH_T5ENCODER:
case LLM_ARCH_JAIS:
case LLM_ARCH_RWKV6:
return LLAMA_ROPE_TYPE_NONE;
// use what we call a normal RoPE, operating on pairs of consecutive head values
@ -18145,6 +18655,7 @@ llama_token llama_model_decoder_start_token(const struct llama_model * model) {
bool llama_model_is_recurrent(const struct llama_model * model) {
switch (model->arch) {
case LLM_ARCH_MAMBA: return true;
case LLM_ARCH_RWKV6: return true;
default: return false;
}
}