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llama : add support for BitnetForCausalLM (ggerganov#7931)
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* hf bitnet v1

* hf bitnet e2e v2

* finish bitnet e2e

* finish f16 hf bitnet e2e

* remove unsed

* finish bitnet i2 e2e

* move i2s to quantize v1

* move i2 to quantize

* clean code

* clean code 2

* fix codestyle

* fix code

* fix

* fix code

* fix merge

* remove unused

* change table name

* fix whitespace

* delete redundant

* i2_s to absmax

* finish i2_s/i8_s vec_dot x86 simd

* i2s->q22

* fix code

* remove block scale

* add dequantize

* fix seq

* update avx2

* remove q2_2

* remove q22_grid

* fix whitespace

* reuse llm_build_kv

* fix bo

---------

Co-authored-by: root <root@wangjinheng>
2 people authored and NeoZhangJianyu committed Jun 30, 2024
1 parent 6b3a6ed commit aaf3ebf
Showing 4 changed files with 307 additions and 1 deletion.
42 changes: 42 additions & 0 deletions convert-hf-to-gguf.py
Original file line number Diff line number Diff line change
@@ -1404,6 +1404,48 @@ def write_tensors(self):
raise ValueError(f"Unprocessed experts: {experts}")


@Model.register("BitnetForCausalLM")
class BitnetModel(Model):
model_arch = gguf.MODEL_ARCH.BITNET

def set_vocab(self):
self._set_vocab_sentencepiece()

def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
self.gguf_writer.add_rope_scaling_factor(1.0)

def weight_quant(self, weight):
dtype = weight.dtype
weight = weight.float()
s = 1 / weight.abs().mean().clamp(min=1e-5)
weight = (weight * s).round().clamp(-1, 1) / s
scale = weight.abs().max().unsqueeze(0)
weight = torch.where(weight.abs().less(1e-6), 0, weight).type(dtype)
weight = torch.sign(weight).type(dtype)
return weight.type(dtype), scale.type(torch.float32)

def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
new_name = self.map_tensor_name(name)

if any(self.match_model_tensor_name(new_name, key, bid) for key in [
gguf.MODEL_TENSOR.ATTN_Q,
gguf.MODEL_TENSOR.ATTN_K,
gguf.MODEL_TENSOR.ATTN_V,
gguf.MODEL_TENSOR.ATTN_OUT,
gguf.MODEL_TENSOR.FFN_UP,
gguf.MODEL_TENSOR.FFN_DOWN,
gguf.MODEL_TENSOR.FFN_GATE,
]):
# transform weight into 1/0/-1 (in fp32)
weight_torch, scale_torch = self.weight_quant(data_torch)
yield (new_name, weight_torch)
yield (new_name.removesuffix(".weight") + ".scale", scale_torch)
else:
yield (new_name, data_torch)


@Model.register("GrokForCausalLM")
class GrokModel(Model):
model_arch = gguf.MODEL_ARCH.GROK
21 changes: 21 additions & 0 deletions gguf-py/gguf/constants.py
Original file line number Diff line number Diff line change
@@ -149,6 +149,7 @@ class MODEL_ARCH(IntEnum):
OLMO = auto()
ARCTIC = auto()
DEEPSEEK2 = auto()
BITNET = auto()


class MODEL_TENSOR(IntEnum):
@@ -200,6 +201,8 @@ class MODEL_TENSOR(IntEnum):
ATTN_KV_B = auto()
ATTN_Q_A_NORM = auto()
ATTN_KV_A_NORM = auto()
FFN_SUB_NORM = auto()
ATTN_SUB_NORM = auto()


MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
@@ -237,6 +240,7 @@ class MODEL_TENSOR(IntEnum):
MODEL_ARCH.OLMO: "olmo",
MODEL_ARCH.ARCTIC: "arctic",
MODEL_ARCH.DEEPSEEK2: "deepseek2",
MODEL_ARCH.BITNET: "bitnet",
}

TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@@ -288,6 +292,8 @@ class MODEL_TENSOR(IntEnum):
MODEL_TENSOR.ATTN_KV_B: "blk.{bid}.attn_kv_b",
MODEL_TENSOR.ATTN_Q_A_NORM: "blk.{bid}.attn_q_a_norm",
MODEL_TENSOR.ATTN_KV_A_NORM: "blk.{bid}.attn_kv_a_norm",
MODEL_TENSOR.ATTN_SUB_NORM: "blk.{bid}.attn_sub_norm",
MODEL_TENSOR.FFN_SUB_NORM: "blk.{bid}.ffn_sub_norm",
}

MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@@ -808,6 +814,21 @@ class MODEL_TENSOR(IntEnum):
MODEL_TENSOR.FFN_DOWN_SHEXP,
MODEL_TENSOR.FFN_UP_SHEXP,
],
MODEL_ARCH.BITNET: [
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.ATTN_SUB_NORM,
MODEL_TENSOR.FFN_SUB_NORM,
],
# TODO
}

8 changes: 8 additions & 0 deletions gguf-py/gguf/tensor_mapping.py
Original file line number Diff line number Diff line change
@@ -413,6 +413,14 @@ class TensorNameMap:
MODEL_TENSOR.ATTN_KV_A_NORM: (
"model.layers.{bid}.self_attn.kv_a_layernorm", # deepseek2
),

MODEL_TENSOR.ATTN_SUB_NORM: (
"model.layers.{bid}.self_attn.inner_attn_ln", # bitnet
),

MODEL_TENSOR.FFN_SUB_NORM: (
"model.layers.{bid}.mlp.ffn_layernorm", # bitnet
),
}

# architecture-specific block mappings
237 changes: 236 additions & 1 deletion llama.cpp
Original file line number Diff line number Diff line change
@@ -225,6 +225,7 @@ enum llm_arch {
LLM_ARCH_OLMO,
LLM_ARCH_ARCTIC,
LLM_ARCH_DEEPSEEK2,
LLM_ARCH_BITNET,
LLM_ARCH_UNKNOWN,
};

@@ -263,6 +264,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_OLMO, "olmo" },
{ LLM_ARCH_ARCTIC, "arctic" },
{ LLM_ARCH_DEEPSEEK2, "deepseek2" },
{ LLM_ARCH_BITNET, "bitnet" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};

@@ -500,6 +502,8 @@ enum llm_tensor {
LLM_TENSOR_ATTN_KV_B,
LLM_TENSOR_ATTN_Q_A_NORM,
LLM_TENSOR_ATTN_KV_A_NORM,
LLM_TENSOR_ATTN_SUB_NORM,
LLM_TENSOR_FFN_SUB_NORM,
};

static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
@@ -1113,6 +1117,24 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
},
},
{
LLM_ARCH_BITNET,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_SUB_NORM, "blk.%d.attn_sub_norm" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_SUB_NORM, "blk.%d.ffn_sub_norm" },
},
},
{
LLM_ARCH_UNKNOWN,
{
@@ -2118,6 +2140,8 @@ struct llama_layer {
struct ggml_tensor * attn_out_norm_b;
struct ggml_tensor * attn_q_a_norm;
struct ggml_tensor * attn_kv_a_norm;
struct ggml_tensor * attn_sub_norm;
struct ggml_tensor * ffn_sub_norm;

// attention
struct ggml_tensor * wq;
@@ -2185,6 +2209,15 @@ struct llama_layer {
// long rope factors
struct ggml_tensor * rope_long = nullptr;
struct ggml_tensor * rope_short = nullptr;

// bitnet scale
struct ggml_tensor * wq_scale;
struct ggml_tensor * wk_scale;
struct ggml_tensor * wv_scale;
struct ggml_tensor * wo_scale;
struct ggml_tensor * ffn_gate_scale;
struct ggml_tensor * ffn_up_scale;
struct ggml_tensor * ffn_down_scale;
};

struct llama_kv_cell {
@@ -4711,6 +4744,15 @@ static void llm_load_hparams(
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_BITNET:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);

switch (hparams.n_layer) {
case 26: model.type = e_model::MODEL_3B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
default: (void)0;
}

@@ -6656,6 +6698,44 @@ static bool llm_load_tensors(
}
}
} break;
case LLM_ARCH_BITNET:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});

// output
{
model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
}

for (int i = 0; i < n_layer; ++i) {
ggml_context * ctx_layer = ctx_for_layer(i);
ggml_context * ctx_split = ctx_for_layer_split(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_sub_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_SUB_NORM, "weight", i), {n_embd});

layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
layer.wq_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "scale", i), {1});
layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
layer.wk_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "scale", i), {1});
layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
layer.wv_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "scale", i), {1});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.wo_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "scale", i), {1});

layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_sub_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_SUB_NORM, "weight", i), {n_ff});

layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
layer.ffn_gate_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE, "scale", i), {1});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
layer.ffn_down_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "scale", i), {1});
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
layer.ffn_up_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "scale", i), {1});
}
} break;
default:
throw std::runtime_error("unknown architecture");
}
@@ -7296,7 +7376,10 @@ static struct ggml_tensor * llm_build_kqv(

ggml_build_forward_expand(graph, cur);

cur = ggml_mul_mat(ctx, wo, cur);
if (wo) {
cur = ggml_mul_mat(ctx, wo, cur);
}

if (wo_b) {
cb(cur, "kqv_wo", il);
}
@@ -11710,6 +11793,153 @@ struct llm_build_context {
return gf;
}

struct ggml_cgraph * build_bitnet() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);

const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);

struct ggml_tensor * cur;
struct ggml_tensor * inpL;

inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);

// inp_pos - contains the positions
struct ggml_tensor * inp_pos = build_inp_pos();

// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;

cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "attn_norm", il);

// self-attention
{
// compute Q and K and RoPE them
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
Qcur = ggml_mul(ctx0, Qcur, model.layers[il].wq_scale);
cb(Qcur, "Qcur", il);
if (model.layers[il].bq) {
Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
cb(Qcur, "Qcur", il);
}

// B1.K
struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
Kcur = ggml_mul(ctx0, Kcur, model.layers[il].wk_scale);
cb(Kcur, "Kcur", il);
if (model.layers[il].bk) {
Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
cb(Kcur, "Kcur", il);
}

// B1.V
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
Vcur = ggml_mul(ctx0, Vcur, model.layers[il].wv_scale);
cb(Vcur, "Vcur", il);
if (model.layers[il].bv) {
Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
cb(Vcur, "Vcur", il);
}

Qcur = ggml_rope_ext(
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);

Kcur = ggml_rope_ext(
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);

cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
nullptr, nullptr,
Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);

cur = llm_build_norm(ctx0, cur, hparams,
model.layers[il].attn_sub_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "attn_sub_norm", il);

cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
cur = ggml_mul(ctx0, cur, model.layers[il].wo_scale);
if (model.layers[il].bo) {
cur = ggml_add(ctx0, cur, model.layers[il].bo);
}
cb(cur, "attn_o_out", il);
}

if (il == n_layer - 1) {
// skip computing output for unused tokens
struct 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);
}

struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);

// feed-forward forward
if (model.layers[il].ffn_gate_inp == nullptr) {
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);

struct ggml_tensor *tmp = ggml_mul_mat(ctx0, model.layers[il].ffn_up, cur);
tmp = ggml_mul(ctx0, tmp, model.layers[il].ffn_up_scale);
cb(tmp, "ffn_up", il);

cur = ggml_mul_mat(ctx0, model.layers[il].ffn_gate, cur);
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_gate_scale);
cb(cur, "ffn_gate", il);

cur = ggml_silu(ctx0, cur);
cb(cur, "ffn_silu", il);

cur = ggml_mul(ctx0, cur, tmp);
cb(cur, "ffn_gate_par", il);

cur = llm_build_norm(ctx0, cur, hparams,
model.layers[il].ffn_sub_norm, NULL,
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_sub_norm", il);

cur = ggml_mul_mat(ctx0, model.layers[il].ffn_down, cur);
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_down_scale);
cb(cur, "ffn_down", il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "l_out", il);

// input for next layer
inpL = cur;
}

cur = inpL;

cur = llm_build_norm(ctx0, cur, hparams,
model.output_norm, NULL,
LLM_NORM_RMS, cb, -1);
cb(cur, "result_norm", -1);

// lm_head
cur = ggml_mul_mat(ctx0, model.tok_embd, 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) {
@@ -11933,6 +12163,10 @@ static struct ggml_cgraph * llama_build_graph(
{
result = llm.build_deepseek2();
} break;
case LLM_ARCH_BITNET:
{
result = llm.build_bitnet();
} break;
default:
GGML_ASSERT(false);
}
@@ -16760,6 +16994,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
case LLM_ARCH_BERT:
case LLM_ARCH_NOMIC_BERT:
case LLM_ARCH_STABLELM:
case LLM_ARCH_BITNET:
case LLM_ARCH_QWEN:
case LLM_ARCH_QWEN2:
case LLM_ARCH_QWEN2MOE:

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