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Support StableLM2 12B #6635

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merged 25 commits into from
Apr 16, 2024
Merged

Support StableLM2 12B #6635

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d383c0d
StableLM2 12B support for huggingface -> GGUF
ashishdatta Apr 12, 2024
13387d9
StableLM12 tensormapping and constants
ashishdatta Apr 12, 2024
b89fa97
StableLM-2-12b model support
ashishdatta Apr 12, 2024
b5afc44
fix
ashishdatta Apr 12, 2024
0eb8492
Added 12B support
ashishdatta Apr 12, 2024
15a5e7d
Removed autoformatting; resolved bug where model_arch was not selecti…
ashishdatta Apr 13, 2024
91a3db9
Formatting
ashishdatta Apr 13, 2024
29d940b
Do QK norm stacking in model conversion step
ashishdatta Apr 14, 2024
0ec53cf
Converge StableLM and StableLM2 code to simplify graph construction
ashishdatta Apr 14, 2024
8dcd997
Fix accidental removal
ashishdatta Apr 14, 2024
0dc779b
Removed warnings
ashishdatta Apr 14, 2024
f7b40d7
Revert formatter
ashishdatta Apr 14, 2024
e3f7360
Move QK norm stack to private function so it's easier to read
ashishdatta Apr 14, 2024
96695fb
refactor stablelm graph builder to support 1.6, 3b and 12b more effic…
ashishdatta Apr 14, 2024
13c75c2
Proper check for None type for new_name to avoid crash; formatting; r…
ashishdatta Apr 14, 2024
412a280
Format
ashishdatta Apr 14, 2024
91728fa
Formatting
ashishdatta Apr 14, 2024
bf1a9a5
format
ashishdatta Apr 14, 2024
d2ab693
Fix incorrect check for K norm
ashishdatta Apr 14, 2024
b7f984a
space after commas; Keep indentation multiple of 4 spaces
ashishdatta Apr 14, 2024
1f6929e
Flake8 format
ashishdatta Apr 14, 2024
8a15f93
Removed unnecessary conditional branches
ashishdatta Apr 16, 2024
6ae4dad
Removed unused comment
ashishdatta Apr 16, 2024
e6ec203
Fixed incorrect tensor passing
ashishdatta Apr 16, 2024
94e8c49
Format
ashishdatta Apr 16, 2024
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82 changes: 81 additions & 1 deletion convert-hf-to-gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1186,7 +1186,6 @@ def write_tensors(self):
@Model.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM")
class StableLMModel(Model):
model_arch = gguf.MODEL_ARCH.STABLELM

def set_vocab(self):
if (self.dir_model / "tokenizer.json").is_file():
self._set_vocab_gpt2()
Expand All @@ -1206,9 +1205,90 @@ def set_gguf_parameters(self):
rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"])
self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
self.gguf_writer.add_head_count(hparams["num_attention_heads"])
self.gguf_writer.add_head_count_kv(hparams["num_key_value_heads"])
self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"]))

def write_tensors(self):
block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
n_head = self.hparams.get("num_attention_heads")
n_kv_head = self.hparams.get("num_key_value_heads")
q_norms = dict()
k_norms = dict()
for name, data_torch in self.get_tensors():
# we don't need these
if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")):
continue

old_dtype = data_torch.dtype

# convert any unsupported data types to float32
if data_torch.dtype not in (torch.float16, torch.float32):
data_torch = data_torch.to(torch.float32)

data = data_torch.squeeze().numpy()
n_dims = len(data.shape)
if name.find("q_layernorm.norms") != -1:
q_norms[name] = data
if len(q_norms) >= (block_count * n_head):
self._stack_qk_norm(block_count, name, tensor_map, n_head, q_norms, n_dims, layer_name="q_layernorm")
continue
if name.find("k_layernorm.norms") != -1:
k_norms[name] = data
if len(k_norms) >= (block_count * n_kv_head):
self._stack_qk_norm(block_count, name, tensor_map, n_kv_head, k_norms, n_dims, layer_name="k_layernorm")
continue

# map tensor names
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()

n_dims = len(data.shape)
data_dtype = data.dtype

# if f32 desired, convert any float16 to float32
if self.ftype == 0 and data_dtype == np.float16:
data = data.astype(np.float32)

# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
if self.ftype == 1 and data_dtype == np.float16 and (n_dims == 1 or new_name.endswith("_norm.weight")):
data = data.astype(np.float32)

# if f16 desired, convert any float32 2-dim weight tensors to float16
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and not new_name.endswith("_norm.weight") and n_dims == 2:
data = data.astype(np.float16)

print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")

self.gguf_writer.add_tensor(new_name, data)

def _stack_qk_norm(self, block_count, name, tensor_map, n_head, norms, n_dims, layer_name="q_layernorm"):
for bid in range(block_count):
datas = []
for xid in range(n_head):
ename = f"model.layers.{bid}.self_attn.{layer_name}.norms.{xid}.weight"
datas.append(norms[ename])
del norms[ename]
data = np.stack(datas, axis=0)
data_dtype = data.dtype
merged_name = f"model.layers.{bid}.self_attn.{layer_name}.weight"
new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias"))
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()
if self.ftype == 1 and data_dtype == np.float16 and (n_dims == 1 or new_name.endswith("_norm.weight")):
data = data.astype(np.float32)

# if f16 desired, convert any float32 2-dim weight tensors to float16
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and not new_name.endswith("_norm.weight") and n_dims == 2:
data = data.astype(np.float16)

print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}")

self.gguf_writer.add_tensor(new_name, data)

@Model.register("LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM")
class LlamaModel(Model):
Expand Down
2 changes: 2 additions & 0 deletions gguf-py/gguf/constants.py
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Original file line number Diff line number Diff line change
Expand Up @@ -440,6 +440,8 @@ class MODEL_TENSOR(IntEnum):
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K_NORM,
],
MODEL_ARCH.QWEN: [
MODEL_TENSOR.TOKEN_EMBD,
Expand Down
87 changes: 67 additions & 20 deletions llama.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -702,6 +702,8 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ 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_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
},
},
{
Expand Down Expand Up @@ -1708,6 +1710,7 @@ enum e_model {
MODEL_4B,
MODEL_7B,
MODEL_8B,
MODEL_12B,
MODEL_13B,
MODEL_14B,
MODEL_15B,
Expand Down Expand Up @@ -3564,6 +3567,7 @@ static const char * llama_model_type_name(e_model type) {
case MODEL_3B: return "3B";
case MODEL_7B: return "7B";
case MODEL_8B: return "8B";
case MODEL_12B: return "12B";
case MODEL_13B: return "13B";
case MODEL_14B: return "14B";
case MODEL_15B: return "15B";
Expand Down Expand Up @@ -3854,6 +3858,7 @@ static void llm_load_hparams(
switch (hparams.n_layer) {
case 24: model.type = e_model::MODEL_1B; break;
case 32: model.type = e_model::MODEL_3B; break;
case 40: model.type = e_model::MODEL_12B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
Expand Down Expand Up @@ -5048,8 +5053,13 @@ static bool llm_load_tensors(
layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, false);
layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, false);

layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
// optional q and k layernorms, present in StableLM 2 12B
layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM,"weight", i), {hparams.n_embd_head_k, hparams.n_head}, false);
layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM,"weight", i), {hparams.n_embd_head_k, hparams.n_head_kv}, false);

// optional FFN norm, not present in StableLM 2 12B which uses parallel residual
layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, false);
layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, false);

layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
Expand Down Expand Up @@ -8067,7 +8077,7 @@ struct llm_build_context {
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();

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


// norm
cur = llm_build_norm(ctx0, inpL, hparams,
Expand All @@ -8076,6 +8086,8 @@ struct llm_build_context {
LLM_NORM, cb, il);
cb(cur, "attn_norm", il);

struct ggml_tensor * inpSA = cur;

// self-attention
{
// compute Q and K and RoPE them
Expand All @@ -8100,15 +8112,36 @@ struct llm_build_context {
cb(Vcur, "Vcur", il);
}

Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
cb(Qcur, "Qcur", il);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
cb(Kcur, "Kcur", il);

if (model.layers[il].attn_q_norm) {
Qcur = llm_build_norm(ctx0, Qcur, hparams,
model.layers[il].attn_q_norm,
NULL,
LLM_NORM, cb, il);
cb(Qcur, "Qcur", il);
}
if (model.layers[il].attn_q_norm) {
Kcur = llm_build_norm(ctx0, Kcur, hparams,
model.layers[il].attn_k_norm,
NULL,
LLM_NORM, cb, il);
cb(Kcur, "Kcur", il);
}


Qcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
ctx0, Qcur, inp_pos,
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);

Kcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
ctx0, Kcur, inp_pos,
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
Expand All @@ -8123,30 +8156,44 @@ struct llm_build_context {
// 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);
inpL = ggml_get_rows(ctx0, inpL, 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);
struct ggml_tensor * attn_out = cur;
// only used for non-parallel residual
struct ggml_tensor * ffn_inp = ggml_add(ctx0, attn_out, inpL);
cb(cur, "ffn_inp", il);

// feed-forward network
{
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm,
model.layers[il].ffn_norm_b,
LLM_NORM, cb, il);
cb(cur, "ffn_norm", il);

if (model.layers[il].ffn_norm) {
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm,
model.layers[il].ffn_norm_b,
LLM_NORM, cb, il);
cb(cur, "ffn_norm", il);
} else {
// parallel residual
cur = inpSA;
}
cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, NULL,
model.layers[il].ffn_gate, NULL,
model.layers[il].ffn_down, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
model.layers[il].ffn_up, NULL,
model.layers[il].ffn_gate, NULL,
model.layers[il].ffn_down, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
cb(cur, "ffn_out", il);
}

cur = ggml_add(ctx0, cur, ffn_inp);
if (model.layers[il].ffn_norm) {
// non-parallel residual
cur = ggml_add(ctx0, cur, ffn_inp);
} else {
// add together residual + FFN + self-attention
cur = ggml_add(ctx0, cur, inpL);
cur = ggml_add(ctx0, cur, attn_out);
}
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Aren't these 2 branches equivalent?

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I don't believe so. One is doing parallel residual (eg 12b) and the other when the ffn norm is present (eg stablelm 1.6 and 3b) is not doing parallel residual. If I am missing something please let me know thanks !

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Since ffn_inp = attn_out + inpL I think these branches do the same and can be replaced by simply with:

cur = ggml_add(ctx0, cur, ffn_inp);

I am looking for ways to avoid the unused ffn_inp = ggml_add(...) in the parallel-residual case

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@compilade compilade Apr 16, 2024 •
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Since ffn_inp = attn_out + inpL I think these branches do the same

Reasoning from the relevant modeling code in transformers, even though they separate them for clarity, I think you're right, theses branches do the same thing.

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@ashishdatta ashishdatta Apr 16, 2024 •
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@ggerganov Thanks! I removed the branches. And re-ran on 1.6B, 3B and 12B no problems. Please let me know if there is anything else !


cb(cur, "l_out", il);

// input for next layer
Expand Down
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