vbatts/llama.cpp
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llama : support for falcon-mamba architecture (#9074)

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* feat: initial support for llama.cpp

* fix: lint

* refactor: better refactor

* Update src/llama.cpp

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

* Update src/llama.cpp

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

* fix: address comments

* Update convert_hf_to_gguf.py

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

* fix: add more cleanup and harmonization

* fix: lint

* Update gguf-py/gguf/gguf_writer.py

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

* fix: change name

* Apply suggestions from code review

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

* add in operator

* fix: add `dt_b_c_rms` in `llm_load_print_meta`

* fix: correct printf format for bool

* fix: correct print format

* Update src/llama.cpp

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

* llama : quantize more Mamba tensors

* llama : use f16 as the fallback of fallback quant types

---------

Co-authored-by: compilade <git@compilade.net>
This commit is contained in:
Younes Belkada 2024-08-21 12:06:36 +04:00 • committed by GitHub
parent f63f603c87
commit b40eb84895
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5 changed files with 36 additions and 24 deletions
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22
src/llama.cpp
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@ -328,6 +328,7 @@ enum llm_kv {
LLM_KV_SSM_CONV_KERNEL,
LLM_KV_SSM_STATE_SIZE,
LLM_KV_SSM_TIME_STEP_RANK,
LLM_KV_SSM_DT_B_C_RMS,
LLM_KV_TOKENIZER_MODEL,
LLM_KV_TOKENIZER_PRE,
@ -426,6 +427,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_SSM_INNER_SIZE, "%s.ssm.inner_size" },
{ LLM_KV_SSM_STATE_SIZE, "%s.ssm.state_size" },
{ 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_TOKENIZER_MODEL, "tokenizer.ggml.model" },
{ LLM_KV_TOKENIZER_PRE, "tokenizer.ggml.pre" },
@ -2237,6 +2239,7 @@ struct llama_hparams {
uint32_t ssm_d_inner = 0;
uint32_t ssm_d_state = 0;
uint32_t ssm_dt_rank = 0;
bool ssm_dt_b_c_rms = false;
float f_clamp_kqv = 0.0f;
float f_max_alibi_bias = 0.0f;
@ -2286,6 +2289,7 @@ struct llama_hparams {
if (this->ssm_d_inner != other.ssm_d_inner) return true;
if (this->ssm_d_state != other.ssm_d_state) return true;
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->dec_start_token_id != other.dec_start_token_id) return true;
@ -5052,6 +5056,7 @@ static void llm_load_hparams(
ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
ml.get_key(LLM_KV_SSM_DT_B_C_RMS, hparams.ssm_dt_b_c_rms, false);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@ -5907,6 +5912,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
LLAMA_LOG_INFO("%s: ssm_d_inner = %u\n", __func__, hparams.ssm_d_inner);
LLAMA_LOG_INFO("%s: ssm_d_state = %u\n", __func__, hparams.ssm_d_state);
LLAMA_LOG_INFO("%s: ssm_dt_rank = %u\n", __func__, hparams.ssm_dt_rank);
LLAMA_LOG_INFO("%s: ssm_dt_b_c_rms = %d\n", __func__, hparams.ssm_dt_b_c_rms);
}
LLAMA_LOG_INFO("%s: model type = %s\n", __func__, llama_model_type_name(model.type));
@ -12161,6 +12167,10 @@ struct llm_build_context {
GGML_ASSERT(2 * d_model == d_inner);
const int64_t d_state = hparams.ssm_d_state;
const int64_t dt_rank = hparams.ssm_dt_rank;
// Some variants of Mamba arch (e.g. FalconMamba do apply layer norm on B and Dt layers)
const bool ssm_dt_b_c_rms = hparams.ssm_dt_b_c_rms;
// Use the same RMS norm as the final layer norm
const float norm_rms_eps = hparams.f_norm_rms_eps;
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
@ -12241,6 +12251,13 @@ struct llm_build_context {
struct ggml_tensor * B = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*dt_rank);
struct ggml_tensor * C = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*(dt_rank+d_state));
// Some Mamba variants (e.g. FalconMamba) apply RMS norm in B, C & Dt layers
if (ssm_dt_b_c_rms) {
dt = ggml_rms_norm(ctx0, dt, norm_rms_eps);
B = ggml_rms_norm(ctx0, B, norm_rms_eps);
C = ggml_rms_norm(ctx0, C, norm_rms_eps);
}
// {dt_rank, d_inner} * {dt_rank, n_tokens} => {d_inner, n_tokens}
dt = llm_build_lora_mm(lctx, ctx0, model.layers[il].ssm_dt, dt);
dt = ggml_add(ctx0, dt, model.layers[il].ssm_dt_b);
@ -16105,6 +16122,9 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n
case GGML_TYPE_Q6_K: new_type = GGML_TYPE_Q8_0; break;
default: throw std::runtime_error("\nUnsupported tensor size encountered\n");
}
if (tensor->ne[0] % ggml_blck_size(new_type) != 0) {
new_type = GGML_TYPE_F16;
}
LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type));
++qs.n_fallback;
}
@ -16433,8 +16453,6 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
// do not quantize Mamba's small yet 2D weights
// NOTE: can't use LLM_TN here because the layer number is not known
quantize &= name.find("ssm_conv1d.weight") == std::string::npos;
quantize &= name.find("ssm_x.weight") == std::string::npos;
quantize &= name.find("ssm_dt.weight") == std::string::npos;
// do not quantize relative position bias (T5)
quantize &= name.find("attn_rel_b.weight") == std::string::npos;