vbatts/llama.cpp
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Load more tensors for rwkv v6

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Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
This commit is contained in:
Molly Sophia 2024-08-01 21:45:02 +08:00
parent 700dad1b86
commit a180b63b49
1 changed files with 89 additions and 28 deletions
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105
src/llama.cpp
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@ -520,18 +520,29 @@ enum llm_tensor {
LLM_TENSOR_SSM_A, LLM_TENSOR_SSM_A,
LLM_TENSOR_SSM_D, LLM_TENSOR_SSM_D,
LLM_TENSOR_SSM_OUT, 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_K,
LLM_TENSOR_TIME_MIX_LERP_V, LLM_TENSOR_TIME_MIX_LERP_V,
LLM_TENSOR_TIME_MIX_LERP_R, LLM_TENSOR_TIME_MIX_LERP_R,
LLM_TENSOR_TIME_MIX_LERP_G, LLM_TENSOR_TIME_MIX_LERP_G,
LLM_TENSOR_TIME_MIX_FIRST, LLM_TENSOR_TIME_MIX_FIRST,
LLM_TENSOR_TIME_MIX_DECAY, 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_KEY,
LLM_TENSOR_TIME_MIX_VALUE, LLM_TENSOR_TIME_MIX_VALUE,
LLM_TENSOR_TIME_MIX_RECEPTANCE, LLM_TENSOR_TIME_MIX_RECEPTANCE,
LLM_TENSOR_TIME_MIX_GATE, LLM_TENSOR_TIME_MIX_GATE,
LLM_TENSOR_TIME_MIX_LN, LLM_TENSOR_TIME_MIX_LN,
LLM_TENSOR_TIME_MIX_OUTPUT, 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_A,
LLM_TENSOR_ATTN_Q_B, LLM_TENSOR_ATTN_Q_B,
LLM_TENSOR_ATTN_KV_A_MQA, LLM_TENSOR_ATTN_KV_A_MQA,
@ -1362,18 +1373,29 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_OUTPUT, "output" }, { LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_NORM_2, "blk.%d.attn_norm_2" }, { LLM_TENSOR_ATTN_NORM_2, "blk.%d.attn_norm_2" },
{ LLM_TENSOR_TIME_MIX_LERP_K, "blk.%d.time_mix.lerp_k" }, { LLM_TENSOR_TIME_MIX_W1, "blk.%d.time_mix_w1" },
{ LLM_TENSOR_TIME_MIX_LERP_V, "blk.%d.time_mix.lerp_v" }, { LLM_TENSOR_TIME_MIX_W2, "blk.%d.time_mix_w2" },
{ LLM_TENSOR_TIME_MIX_LERP_R, "blk.%d.time_mix.lerp_r" }, { LLM_TENSOR_TIME_MIX_LERP_X, "blk.%d.time_mix_lerp_x" },
{ LLM_TENSOR_TIME_MIX_LERP_G, "blk.%d.time_mix.lerp_g" }, { LLM_TENSOR_TIME_MIX_LERP_W, "blk.%d.time_mix_lerp_w" },
{ LLM_TENSOR_TIME_MIX_FIRST, "blk.%d.time_mix.first" }, { LLM_TENSOR_TIME_MIX_LERP_K, "blk.%d.time_mix_lerp_k" },
{ LLM_TENSOR_TIME_MIX_DECAY, "blk.%d.time_mix.decay" }, { LLM_TENSOR_TIME_MIX_LERP_V, "blk.%d.time_mix_lerp_v" },
{ LLM_TENSOR_TIME_MIX_KEY, "blk.%d.time_mix.key" }, { LLM_TENSOR_TIME_MIX_LERP_R, "blk.%d.time_mix_lerp_r" },
{ LLM_TENSOR_TIME_MIX_VALUE, "blk.%d.time_mix.value" }, { LLM_TENSOR_TIME_MIX_LERP_G, "blk.%d.time_mix_lerp_g" },
{ LLM_TENSOR_TIME_MIX_RECEPTANCE, "blk.%d.time_mix.receptance" }, { LLM_TENSOR_TIME_MIX_FIRST, "blk.%d.time_mix_first" },
{ LLM_TENSOR_TIME_MIX_GATE, "blk.%d.time_mix.gate" }, { LLM_TENSOR_TIME_MIX_DECAY, "blk.%d.time_mix_decay" },
{ LLM_TENSOR_TIME_MIX_LN, "blk.%d.time_mix.ln" }, { LLM_TENSOR_TIME_MIX_DECAY_W1, "blk.%d.time_mix_decay_w1" },
{ LLM_TENSOR_TIME_MIX_OUTPUT, "blk.%d.time_mix.output" }, { 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" },
}, },
}, },
{ {
@ -2539,6 +2561,10 @@ struct llama_layer {
struct ggml_tensor * ssm_dt_b; struct ggml_tensor * ssm_dt_b;
// rwkv // 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_k;
struct ggml_tensor * time_mix_lerp_v; struct ggml_tensor * time_mix_lerp_v;
struct ggml_tensor * time_mix_lerp_r; struct ggml_tensor * time_mix_lerp_r;
@ -2546,6 +2572,8 @@ struct llama_layer {
struct ggml_tensor * time_mix_first; struct ggml_tensor * time_mix_first;
struct ggml_tensor * time_mix_decay; 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_key;
struct ggml_tensor * time_mix_value; struct ggml_tensor * time_mix_value;
struct ggml_tensor * time_mix_receptance; struct ggml_tensor * time_mix_receptance;
@ -2555,6 +2583,13 @@ struct llama_layer {
struct ggml_tensor * time_mix_ln_b; struct ggml_tensor * time_mix_ln_b;
struct ggml_tensor * time_mix_output; 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 // long rope factors
struct ggml_tensor * rope_long = nullptr; struct ggml_tensor * rope_long = nullptr;
struct ggml_tensor * rope_short = nullptr; struct ggml_tensor * rope_short = nullptr;
@ -5148,6 +5183,7 @@ static const char * llama_model_vocab_type_name(enum llama_vocab_type type){
case LLAMA_VOCAB_TYPE_BPE: return "BPE"; case LLAMA_VOCAB_TYPE_BPE: return "BPE";
case LLAMA_VOCAB_TYPE_WPM: return "WPM"; case LLAMA_VOCAB_TYPE_WPM: return "WPM";
case LLAMA_VOCAB_TYPE_UGM: return "UGM"; case LLAMA_VOCAB_TYPE_UGM: return "UGM";
case LLAMA_VOCAB_TYPE_RWKV: return "RWKV";
default: return "unknown"; default: return "unknown";
} }
} }
@ -6125,7 +6161,7 @@ static void llm_load_vocab(
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT; vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
vocab.tokenizer_add_space_prefix = false; vocab.tokenizer_add_space_prefix = false;
vocab.tokenizer_clean_spaces = false; vocab.tokenizer_clean_spaces = false;
vocab.tokenizer_add_bos = true; vocab.tokenizer_add_bos = false;
vocab.tokenizer_add_eos = false; vocab.tokenizer_add_eos = false;
} else { } else {
vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT; vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
@ -6231,6 +6267,10 @@ static void llm_load_vocab(
} }
} else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) { } else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) {
vocab.linefeed_id = vocab.special_pad_id; 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 { } else {
const std::vector<int> ids = llama_tokenize_internal(vocab, "\xC4\x8A", false); // U+010A const std::vector<int> ids = llama_tokenize_internal(vocab, "\xC4\x8A", false); // U+010A
GGML_ASSERT(!ids.empty() && "model vocab missing newline token"); GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
@ -8288,7 +8328,14 @@ static bool llm_load_tensors(
// output // output
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); 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_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_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
// TODO: Parameterize this
const int time_mix_extra_dim = 32;
const int time_decay_extra_dim = 64;
const int head_size = 64;
const int attn_hidden_size = n_embd;
const int ffn_size = (int)(n_embd * 3.5 / 32) * 32;
for (int i = 0; i < n_layer; ++i) { for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i); ggml_context * ctx_layer = ctx_for_layer(i);
@ -8301,25 +8348,39 @@ static bool llm_load_tensors(
layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", 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.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), {time_mix_extra_dim * 5, n_embd});
layer.time_mix_w2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_W2, "weight", i), {n_embd, time_mix_extra_dim, 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_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_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_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_lerp_g = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_LERP_G, "weight", i), {n_embd, 1, 1});
// TODO: Parametrize hardcoded dimensions for first & decay // TODO: Parametrize hardcoded dimensions for first & decay
layer.time_mix_first = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_FIRST, "weight", i), {64, 32}); 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), {64, 32}); layer.time_mix_decay = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_DECAY, "weight", i), {n_embd});
layer.time_mix_key = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_KEY, "weight", i), {n_embd, n_embd}); layer.time_mix_decay_w1 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_DECAY_W1, "weight", i), {time_decay_extra_dim, n_embd});
layer.time_mix_value = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_VALUE, "weight", i), {n_embd, n_embd}); layer.time_mix_decay_w2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_DECAY_W2, "weight", i), {attn_hidden_size, time_decay_extra_dim});
layer.time_mix_receptance = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_RECEPTANCE, "weight", i), {n_embd, n_embd}); 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_gate = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_TIME_MIX_GATE, "weight", i), {n_embd, 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 = 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_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, 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: default:
throw std::runtime_error("unknown architecture"); throw std::runtime_error("unknown architecture");
} }