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younesbelkada
2025-07-03 15:05:01 +04:00
parent 991de6cbe4
commit f897efdaf6
9 changed files with 504 additions and 10 deletions
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25
src/llama-arch.h
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@@ -50,6 +50,7 @@ enum llm_arch {
LLM_ARCH_STARCODER2,
LLM_ARCH_MAMBA,
LLM_ARCH_MAMBA2,
LLM_ARCH_FALCON_H1,
LLM_ARCH_XVERSE,
LLM_ARCH_COMMAND_R,
LLM_ARCH_COHERE2,
@@ -156,6 +157,27 @@ enum llm_kv {
LLM_KV_ATTENTION_VALUE_LENGTH_MLA,
LLM_KV_ATTENTION_LAYER_INDICES,
// Falcon-H1 specific
LLM_KV_ATTN_HEAD_DIM,
LLM_KV_SSM_HEAD_DIM,
LLM_KV_MAMBA_D_SSM,
LLM_KV_N_LAYER,
LLM_KV_FALCON_H1_USE_MLP,
LLM_KV_FALCON_H1_ATTENTION_IN_MULTIPLIER,
LLM_KV_FALCON_H1_ATTENTION_OUT_MULTIPLIER,
LLM_KV_FALCON_H1_SSM_IN_MULTIPLIER,
LLM_KV_FALCON_H1_SSM_OUT_MULTIPLIER,
LLM_KV_FALCON_H1_MLP_GATE_MULTIPLIER,
LLM_KV_FALCON_H1_MLP_DOWN_MULTIPLIER,
LLM_KV_FALCON_H1_SSM_HAS_MUP,
LLM_KV_FALCON_H1_MAMBA_NORM_BEFORE_GATE,
LLM_KV_FALCON_H1_MAMBA_RMS_NORM,
LLM_KV_FALCON_H1_ROPE_THETA,
LLM_KV_FALCON_H1_KEY_MULTIPLIER,
LLM_KV_FALCON_H1_LM_HEAD_MULTIPLIER,
LLM_KV_FALCON_H1_EMBEDDING_MULTIPLIER,
LLM_KV_FALCON_H1_MAMBA_CHUNK_SIZE,
LLM_KV_ROPE_DIMENSION_COUNT,
LLM_KV_ROPE_DIMENSION_SECTIONS,
LLM_KV_ROPE_FREQ_BASE,
@@ -389,6 +411,9 @@ enum llm_tensor {
LLM_TENSOR_POS_NET_ATTN_K,
LLM_TENSOR_POS_NET_ATTN_V,
LLM_TENSOR_POS_NET_ATTN_OUT,
LLM_TENSOR_SSM_MUP_VEC,
LLM_TENSOR_FFN_PRE_NORM,
LLM_TENSOR_FINAL_NORM,
};
enum llm_tensor_layer {

10
src/llama-context.cpp
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@@ -2220,14 +2220,14 @@ llama_context * llama_init_from_model(
return nullptr;
}
try {
// try {
auto * ctx = new llama_context(*model, params);
return ctx;
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: failed to initialize the context: %s\n", __func__, err.what());
}
// } catch (const std::exception & err) {
// LLAMA_LOG_ERROR("%s: failed to initialize the context: %s\n", __func__, err.what());
// }
return nullptr;
// return nullptr;
}
// deprecated

7
src/llama-graph.cpp
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@@ -545,6 +545,10 @@ ggml_tensor * llm_graph_context::build_ffn(
case LLM_FFN_PAR:
{
cur = build_lora_mm(gate, cur);
if (arch == LLM_ARCH_FALCON_H1) {
cur = ggml_scale(ctx0, cur, hparams.mlp_gate_multiplier);
}
cb(cur, "ffn_gate", il);
} break;
}
@@ -631,6 +635,9 @@ ggml_tensor * llm_graph_context::build_ffn(
// GLM4 seems to have numerical issues with half-precision accumulators
ggml_mul_mat_set_prec(cur, GGML_PREC_F32);
}
if (arch == LLM_ARCH_FALCON_H1) {
cur = ggml_scale(ctx0, cur, hparams.mlp_down_multiplier);
}
}
if (down_b) {

9
src/llama-hparams.cpp
View File

@@ -74,7 +74,14 @@ uint32_t llama_hparams::n_embd_r() const {
// 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
// Corresponds to Mamba's conv_states size
// check if the architecture is using d_ssm
if (ssm_mamba_d_ssm > 0) {
return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * (ssm_mamba_d_ssm + 2*ssm_n_group*ssm_d_state);
} else {
return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * (ssm_d_inner + 2*ssm_n_group*ssm_d_state);
}
}
uint32_t llama_hparams::n_embd_s() const {
@@ -84,7 +91,7 @@ uint32_t llama_hparams::n_embd_s() const {
}
// corresponds to Mamba's ssm_states size
return ssm_d_state * ssm_d_inner;
return (ssm_mamba_d_ssm > 0 ? ssm_d_state * ssm_mamba_d_ssm : ssm_d_state * ssm_d_inner);
}
bool llama_hparams::is_recurrent(uint32_t il) const {

25
src/llama-hparams.h
View File

@@ -115,6 +115,31 @@ struct llama_hparams {
uint32_t ssm_d_state = 0;
uint32_t ssm_dt_rank = 0;
uint32_t ssm_n_group = 0;
uint32_t ssm_head_dim = 0;
uint32_t ssm_mamba_d_ssm = 0;
uint32_t attn_head_dim = 0;
bool mamba_use_mlp = false;
bool mamba_norm_before_gate = false;
bool mamba_rms_norm = false;
float attention_in_multiplier = 1.0f;
float attention_out_multiplier = 1.0f;
float ssm_in_multiplier = 1.0f;
float ssm_out_multiplier = 1.0f;
float mlp_gate_multiplier = 1.0f;
float mlp_down_multiplier = 1.0f;
float key_multiplier = 1.0f;
float lm_head_multiplier = 1.0f;
float rope_theta = 10000.0f;
bool ssm_has_mup = false;
float embedding_multiplier = 1.0f;
uint32_t vocab_size = 0;
uint32_t intermediate_size = 0;
float mamba_expand = 0.0f;
bool ssm_rms_norm = false;
bool ssm_conv_bias = false;
bool ssm_proj_bias = false;
uint32_t chunk_size = 0;
// for hybrid state space models
std::array<bool, LLAMA_MAX_LAYERS> recurrent_layer_arr;

2
src/llama-memory-hybrid.cpp
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@@ -32,7 +32,7 @@ llama_memory_hybrid::llama_memory_hybrid(
mem_attn(new llama_kv_cache_unified(
model,
filter_attn == nullptr ?
[&](int32_t il) { return !hparams.is_recurrent(il); }
[&](int32_t il) { return hparams.is_recurrent(il); }
: filter_attn,
type_k,
type_v,

422
src/llama-model.cpp
View File

@@ -1549,6 +1549,53 @@ void llama_model::load_hparams(llama_model_loader & ml) {
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_FALCON_H1:
{
// Common parameters
ml.get_key(LLM_KV_VOCAB_SIZE, hparams.vocab_size);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
// SSM parameters
ml.get_key(LLM_KV_MAMBA_D_SSM, hparams.ssm_mamba_d_ssm);
ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
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_GROUP_COUNT, hparams.ssm_n_group);
ml.get_key(LLM_KV_SSM_HEAD_DIM, hparams.ssm_head_dim);
ml.get_key(LLM_KV_FALCON_H1_MAMBA_CHUNK_SIZE, hparams.chunk_size);
// Falcon-H1 parameters
ml.get_key(LLM_KV_ATTN_HEAD_DIM, hparams.attn_head_dim);
ml.get_key(LLM_KV_FALCON_H1_USE_MLP, hparams.mamba_use_mlp);
ml.get_key(LLM_KV_FALCON_H1_ATTENTION_IN_MULTIPLIER, hparams.attention_in_multiplier);
ml.get_key(LLM_KV_FALCON_H1_ATTENTION_OUT_MULTIPLIER, hparams.attention_out_multiplier);
ml.get_key(LLM_KV_FALCON_H1_SSM_IN_MULTIPLIER, hparams.ssm_in_multiplier);
ml.get_key(LLM_KV_FALCON_H1_SSM_OUT_MULTIPLIER, hparams.ssm_out_multiplier);
ml.get_key(LLM_KV_FALCON_H1_MLP_GATE_MULTIPLIER, hparams.mlp_gate_multiplier);
ml.get_key(LLM_KV_FALCON_H1_MLP_DOWN_MULTIPLIER, hparams.mlp_down_multiplier);
ml.get_key(LLM_KV_FALCON_H1_SSM_HAS_MUP, hparams.ssm_has_mup);
ml.get_key(LLM_KV_FALCON_H1_MAMBA_NORM_BEFORE_GATE, hparams.mamba_norm_before_gate);
ml.get_key(LLM_KV_FALCON_H1_MAMBA_RMS_NORM, hparams.mamba_rms_norm);
ml.get_key(LLM_KV_FALCON_H1_ROPE_THETA, hparams.rope_theta);
ml.get_key(LLM_KV_FALCON_H1_KEY_MULTIPLIER, hparams.key_multiplier);
ml.get_key(LLM_KV_FALCON_H1_LM_HEAD_MULTIPLIER, hparams.lm_head_multiplier);
ml.get_key(LLM_KV_FALCON_H1_EMBEDDING_MULTIPLIER, hparams.embedding_multiplier);
switch (hparams.n_layer) {
case 36:
type = LLM_TYPE_0_5B; break;
case 24:
type = LLM_TYPE_1_5B; break;
case 66:
type = LLM_TYPE_1B; break;
case 32:
type = LLM_TYPE_3B; break;
case 44:
type = LLM_TYPE_7B; break;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
default: throw std::runtime_error("unsupported model architecture");
}
@@ -4475,6 +4522,88 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
}
} break;
case LLM_ARCH_FALCON_H1:
{
// Common
const int64_t hidden_size = hparams.n_embd; // hidden_size
const int64_t vocab_size = hparams.vocab_size; // vocab_size
// mamba2 Mixer SSM params
const int64_t ssm_conv_kernel_size = hparams.ssm_d_conv; // ssm_conv_kernel_size
const int64_t ssm_n_groups = hparams.ssm_n_group; // ssm_n_groups
const int64_t ssm_state_size = hparams.ssm_d_state; // ssm_state_size
const int64_t ssm_intermediate_size = hparams.ssm_mamba_d_ssm > 0 ? hparams.ssm_mamba_d_ssm : int(hparams.mamba_expand * hidden_size); // TODO expand
const int64_t ssm_num_heads = hparams.ssm_dt_rank; // ssm_num_heads
const int64_t ssm_conv_dim = ssm_intermediate_size + 2 * ssm_n_groups * ssm_state_size;
const int64_t ssm_projection_size = ssm_intermediate_size + ssm_conv_dim + ssm_num_heads;
// attn params
const int64_t attn_num_attention_head = hparams.n_head(0); // rename to: attn_num_attention_head
const int64_t attn_num_key_value_head = hparams.n_head_kv(0);
const int64_t attn_head_dim = hparams.attn_head_dim > 0 ? hparams.attn_head_dim : hidden_size / attn_num_attention_head;
// ffn params
const int64_t ffn_intermediate_size = hparams.n_ff(0);
// embeddings
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {hidden_size, vocab_size}, 0);
// output
{
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {hidden_size, vocab_size}, TENSOR_NOT_REQUIRED);
final_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {hidden_size}, 0);
}
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
/*SSM LAYERS*/
// ssm in
layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {hidden_size, ssm_projection_size}, 0);
layer.ssm_in_b = create_tensor(tn(LLM_TENSOR_SSM_IN, "bias", i), {n_embd, ssm_projection_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
// ssm 1d conv
layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {ssm_conv_kernel_size, ssm_conv_dim}, 0);
layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {ssm_conv_dim}, llama_model_loader::TENSOR_NOT_REQUIRED);
// ssm_dt
layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {ssm_num_heads}, 0);
// no "weight" suffix for these
layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, ssm_num_heads}, 0);
layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {1, ssm_num_heads}, 0);
if (hparams.ssm_has_mup == true) {
layer.ssm_mup_vec = create_tensor(tn(LLM_TENSOR_SSM_MUP_VEC, i), {2*ssm_intermediate_size + 2*ssm_n_groups*ssm_state_size + ssm_num_heads}, 0);
}
// ssm_norm
if (hparams.mamba_rms_norm == true) {
layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {ssm_intermediate_size / ssm_n_groups, ssm_n_groups}, 0);
}
// out_proj
layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {ssm_intermediate_size, hidden_size}, 0);
/*ATTENTION LAYERS*/
// attention layers (with optional bias)
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {hidden_size, attn_head_dim * attn_num_attention_head}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {hidden_size, attn_num_key_value_head * attn_head_dim}, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {hidden_size, attn_num_key_value_head * attn_head_dim}, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {attn_head_dim * attn_num_attention_head, hidden_size}, 0);
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {hidden_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {attn_num_key_value_head * attn_head_dim}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {attn_num_key_value_head * attn_head_dim}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {hidden_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {hidden_size}, 0);
// feed forward (w/ optional biases)
layer.ffn_pre_norm = create_tensor(tn(LLM_TENSOR_FFN_PRE_NORM, i), {hidden_size}, 0);
layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {hidden_size, ffn_intermediate_size}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { ffn_intermediate_size, hidden_size}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {hidden_size, ffn_intermediate_size}, 0);
layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {ffn_intermediate_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {hidden_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {ffn_intermediate_size}, llama_model_loader::TENSOR_NOT_REQUIRED);
}
} break;
default:
throw std::runtime_error("unknown architecture");
}
@@ -14525,6 +14654,285 @@ struct llm_build_ernie4_5 : public llm_graph_context {
}
};
struct llm_build_falcon_h1 : public llm_graph_context {
const llama_model & model;
llm_build_falcon_h1(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params), model(model) {
const int64_t n_embd_head = hparams.n_embd_head_v;
ggml_tensor * cur;
ggml_tensor * inpL;
inpL = build_inp_embd(model.tok_embd);
inpL = ggml_scale(ctx0, inpL, hparams.embedding_multiplier);
// inp_pos - contains the positions
ggml_tensor * inp_pos = build_inp_pos();
// Build the inputs in the recurrent & kv cache
auto * inp = build_inp_mem_hybrid();
auto * inp_attn = build_attn_inp_kv_unified();
const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
cur = build_norm(inpL,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
cur = ggml_scale(ctx0, cur, hparams.attention_in_multiplier);
// self-attention
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
cb(Qcur, "Qcur", il);
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Kcur = ggml_scale(ctx0, Kcur, hparams.key_multiplier);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, nullptr,
n_rot, 0, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow);
Kcur = ggml_rope_ext(
ctx0, Kcur, inp_pos, nullptr,
n_rot, 0, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
ggml_tensor * attn_out = build_attn(inp_attn, gf,
model.layers[il].wo, NULL,
Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
attn_out = ggml_scale(ctx0, attn_out, hparams.attention_out_multiplier);
cb(attn_out, "attn_out", il);
cur = build_norm(inpL,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, il);
// Mamba2 layer
cur = ggml_scale(ctx0, cur, hparams.ssm_in_multiplier);
cb(cur, "ssm_in", il);
ggml_tensor * ssm_out = build_mamba2_layer(inp, gf, cur, ubatch, il);
ssm_out = ggml_scale(ctx0, ssm_out, hparams.ssm_out_multiplier);
cb(ssm_out, "ssm_out", il);
// // Aggregation
cur = ggml_add(ctx0, attn_out, ssm_out);
inpSA = ggml_add(ctx0, cur, inpSA);
cb(cur, "layer_out", il);
if (il == n_layer - 1) {
// skip computing output for unused tokens
ggml_tensor * inp_out_ids = build_inp_out_ids();
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
ggml_tensor * ffn_inp = inpSA;
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
cur = build_norm(ffn_inp,
model.layers[il].ffn_pre_norm, NULL,
LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
cur = build_ffn(cur,
model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
cur = ggml_add(ctx0, cur, inpSA);
cur = build_cvec(cur, il);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = build_norm(cur,
model.final_norm, NULL,
LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1);
res->t_embd = cur;
// lm_head
cur = build_lora_mm(model.output, cur);
cur = ggml_scale(ctx0, cur, hparams.lm_head_multiplier);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
ggml_tensor * build_mamba2_layer(
llm_graph_input_mem_hybrid * inp,
ggml_cgraph * gf,
ggml_tensor * cur,
const llama_ubatch & ubatch,
int il) const {
const auto * kv_state = static_cast<const llama_memory_hybrid_context *>(mctx)->get_recr();
const auto kv_head = kv_state->get_head();
const int64_t d_conv = hparams.ssm_d_conv;
const int64_t d_ssm = hparams.ssm_mamba_d_ssm;
const int64_t d_inner = hparams.ssm_d_inner;
const int64_t d_state = hparams.ssm_d_state;
const int64_t n_head = hparams.ssm_dt_rank;
const int64_t head_dim = hparams.ssm_head_dim == 0 ? d_inner / n_head : hparams.ssm_head_dim;
const int64_t n_group = hparams.ssm_n_group;
const int64_t n_seqs = ubatch.n_seqs;
const int64_t n_seq_tokens = ubatch.n_seq_tokens;
GGML_ASSERT(n_seqs != 0);
GGML_ASSERT(ubatch.equal_seqs);
GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
ggml_tensor * conv_states_all = kv_state->get_r_l(il);
ggml_tensor * ssm_states_all = kv_state->get_s_l(il);
ggml_tensor * conv = build_rs(inp, gf, conv_states_all, hparams.n_embd_r(), n_seqs);
conv = ggml_reshape_3d(ctx0, conv, d_conv - 1, d_ssm + 2*n_group*d_state, n_seqs);
// {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs}
cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs);
// d_in_proj = 2 * self.d_inner + 2 * self.ngroups * self.d_state + self.nheads
// {n_embd, d_in_proj} @ {n_embd, n_seq_tokens, n_seqs} => {d_in_proj, n_seq_tokens, n_seqs}
ggml_tensor * zxBCdt = build_lora_mm(model.layers[il].ssm_in, cur);
// check if the models has ssm_multipliers (MuP)
if (hparams.ssm_has_mup) {
struct ggml_tensor * mup_vec = model.layers[il].ssm_mup_vec;
cur = ggml_mul(ctx0, zxBCdt, mup_vec);
zxBCdt = cur;
}
// split the above in three
ggml_tensor * z = ggml_view_4d(ctx0, zxBCdt, head_dim, n_head, n_seq_tokens, n_seqs, head_dim*zxBCdt->nb[0], zxBCdt->nb[1], zxBCdt->nb[2], 0);
ggml_tensor * xBC = ggml_view_3d(ctx0, zxBCdt, d_ssm + 2*n_group*d_state, n_seq_tokens, n_seqs, zxBCdt->nb[1], zxBCdt->nb[2], d_ssm*ggml_element_size(zxBCdt));
ggml_tensor * dt = ggml_view_3d(ctx0, zxBCdt, n_head, n_seq_tokens, n_seqs, zxBCdt->nb[1], zxBCdt->nb[2], (2*d_ssm + 2*n_group*d_state)*ggml_element_size(zxBCdt));
// conv
{
// => {d_conv - 1 + n_seq_tokens, d_inner + 2*n_group*d_state, n_seqs}
ggml_tensor * conv_x = ggml_concat(ctx0, conv, ggml_transpose(ctx0, xBC), 0);
// copy last (d_conv - 1) columns back into the state cache
ggml_tensor * last_conv = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_ssm + 2*n_group*d_state, n_seqs, conv_x->nb[1], conv_x->nb[2], n_seq_tokens*(conv_x->nb[0]));
ggml_build_forward_expand(gf,
ggml_cpy(ctx0, last_conv,
ggml_view_1d(ctx0, conv_states_all,
(d_conv - 1)*(d_ssm + 2*n_group*d_state)*(n_seqs),
kv_head*(d_conv - 1)*(d_ssm + 2*n_group*d_state)*ggml_element_size(conv_states_all))));
// 1D convolution
// The equivalent is to make a self-overlapping view of conv_x
// over d_conv columns at each stride in the 3rd dimension,
// then element-wise multiply that with the conv1d weight,
// then sum the elements of each row,
// (the last two steps are a dot product over rows (also doable with mul_mat))
// then permute away the ne[0] dimension,
// and then you're left with the resulting x tensor.
// For simultaneous sequences, all sequences need to have the same length.
xBC = ggml_ssm_conv(ctx0, conv_x, model.layers[il].ssm_conv1d);
// bias
xBC = ggml_add(ctx0, xBC, model.layers[il].ssm_conv1d_b);
xBC = ggml_silu(ctx0, xBC);
}
// ssm
{
// These correspond to V K Q in SSM/attention duality
ggml_tensor * x = ggml_view_4d(ctx0, xBC, head_dim, n_head, n_seq_tokens, n_seqs, head_dim*xBC->nb[0], xBC->nb[1], xBC->nb[2], 0);
ggml_tensor * B = ggml_view_4d(ctx0, xBC, d_state, n_group, n_seq_tokens, n_seqs, d_state*xBC->nb[0], xBC->nb[1], xBC->nb[2], d_ssm*ggml_element_size(xBC));
ggml_tensor * C = ggml_view_4d(ctx0, xBC, d_state, n_group, n_seq_tokens, n_seqs, d_state*xBC->nb[0], xBC->nb[1], xBC->nb[2], (d_ssm + n_group*d_state)*ggml_element_size(xBC));
// {n_head, n_seq_tokens, n_seqs}
dt = ggml_add(ctx0, ggml_cont(ctx0, dt), model.layers[il].ssm_dt_b);
ggml_tensor * A = model.layers[il].ssm_a;
// use the states and the indices provided by build_rs
// (this is necessary in order to properly use the states before they are overwritten,
// while avoiding to make unnecessary copies of the states)
auto get_ssm_rows = [&](ggml_context * ctx, ggml_tensor * states, ggml_tensor * ids) {
ggml_tensor * ssm = ggml_reshape_4d(ctx, states, d_state, head_dim, n_head, kv_state->get_size());
// TODO: use semistructured matrices to implement state-space duality
// => {d_inner, n_seq_tokens, n_seqs} and {d_state, d_inner, n_seqs}
return ggml_ssm_scan(ctx, ssm, x, dt, A, B, C, ids);
};
ggml_tensor * y_ssm = build_rs(inp, gf, ssm_states_all, hparams.n_embd_s(), ubatch.n_seqs, get_ssm_rows);
// store last states
ggml_build_forward_expand(gf,
ggml_cpy(ctx0,
ggml_view_1d(ctx0, y_ssm, d_state*d_ssm*n_seqs, ggml_nelements(x)*x->nb[0]),
ggml_view_1d(ctx0, ssm_states_all, d_state*d_ssm*n_seqs, kv_head*d_state*d_ssm*ggml_element_size(ssm_states_all))));
ggml_tensor * y = ggml_view_4d(ctx0, y_ssm, head_dim, n_head, n_seq_tokens, n_seqs, x->nb[1], n_head*x->nb[1], n_seq_tokens*n_head*x->nb[1], 0);
// TODO: skip computing output earlier for unused tokens
y = ggml_add(ctx0, y, ggml_mul(ctx0, x, model.layers[il].ssm_d));
y = ggml_mul(ctx0, y, ggml_silu(ctx0, ggml_cont(ctx0, z)));
// grouped RMS norm
if (hparams.mamba_rms_norm){
y = ggml_reshape_4d(ctx0, y, d_ssm / n_group, n_group, n_seq_tokens, n_seqs);
y = build_norm(y, model.layers[il].ssm_norm, NULL, LLM_NORM_RMS, il);
}
y = ggml_reshape_3d(ctx0, y, d_ssm, n_seq_tokens, n_seqs);
// {d_inner, n_embd} @ {d_inner, n_seq_tokens, n_seqs} => {n_embd, n_seq_tokens, n_seqs}
cur = build_lora_mm(model.layers[il].ssm_out, y);
}
// {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens}
cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], n_seq_tokens * n_seqs);
cb(cur, "mamba_out", il);
return cur;
}
};
struct llm_build_arcee : public llm_graph_context {
llm_build_arcee(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v;
@@ -14693,6 +15101,15 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
cparams.n_ctx = GGML_PAD(cparams.n_ctx, padding);
// -> attn_filter
// if falcon-h1 -> [&](int32_t il) { return true; }
// case LLM_ARCH_FALCON_H1:
// llama_memory_hybrid::layer_filter_cb filter_attn = [](int32_t /*il*/) { return true; };
// llama_memory_hybrid::layer_filter_cb filter_recr = [](int32_t /*il*/) { return true; };
// default:
// llama_memory_hybrid::layer_filter_cb filter_attn = nullptr;
// llama_memory_hybrid::layer_filter_cb filter_recr = nullptr;
res = new llama_memory_hybrid(
/* model */ *this,
/* attn_type_k */ params.type_k,
@@ -15040,6 +15457,10 @@ llm_graph_result_ptr llama_model::build_graph(
{
llm = std::make_unique<llm_build_ernie4_5>(*this, params, gf);
} break;
case LLM_ARCH_FALCON_H1:
{
llm = std::make_unique<llm_build_falcon_h1>(*this, params, gf);
} break;
default:
GGML_ABORT("fatal error");
}
@@ -15193,6 +15614,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_NEO_BERT:
case LLM_ARCH_ARCEE:
case LLM_ARCH_ERNIE4_5:
case LLM_ARCH_FALCON_H1:
return LLAMA_ROPE_TYPE_NORM;
// the pairs of head values are offset by n_rot/2

7
src/llama-model.h
View File

@@ -173,6 +173,7 @@ struct llama_layer {
struct ggml_tensor * attn_norm_cross = nullptr;
struct ggml_tensor * attn_norm_enc = nullptr;
struct ggml_tensor * ssm_norm = nullptr;
struct ggml_tensor * final_norm = nullptr;
// attention
struct ggml_tensor * wq = nullptr;
@@ -215,6 +216,7 @@ struct llama_layer {
struct ggml_tensor * layer_out_norm_b = nullptr;
struct ggml_tensor * ffn_norm_exps = nullptr;
struct ggml_tensor * ffn_norm_enc = nullptr;
struct ggml_tensor * ffn_pre_norm = nullptr;
// ff
struct ggml_tensor * ffn_gate = nullptr; // w1
@@ -224,6 +226,10 @@ struct llama_layer {
struct ggml_tensor * ffn_down_enc = nullptr;
struct ggml_tensor * ffn_up_enc = nullptr;
// falcon-h1
struct ggml_tensor * ssm_in_b = nullptr;
struct ggml_tensor * ssm_mup_vec = nullptr;
// ff MoE
struct ggml_tensor * ffn_gate_inp = nullptr;
struct ggml_tensor * ffn_gate_exps = nullptr;
@@ -361,6 +367,7 @@ struct llama_model {
struct ggml_tensor * output = nullptr;
struct ggml_tensor * output_b = nullptr;
struct ggml_tensor * output_norm_enc = nullptr;
struct ggml_tensor * final_norm = nullptr;
// classifier
struct ggml_tensor * cls = nullptr;

1
src/llama-vocab.cpp
View File

@@ -1522,6 +1522,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
tokenizer_pre == "llama-v3" ||
tokenizer_pre == "llama-bpe"||
tokenizer_pre == "falcon3" ||
tokenizer_pre == "falcon-h1" ||
tokenizer_pre == "pixtral") {
pre_type = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
ignore_merges = true;