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feat: support starcoder mqa #3

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Merged
merged 1 commit into from
Sep 15, 2023
Merged

feat: support starcoder mqa #3

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21 changes: 1 addition & 20 deletions convert-starcoder-hf-to-gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -109,7 +109,7 @@ def parse_args() -> argparse.Namespace:
gguf_writer.add_feed_forward_length(4 * hparams["n_embd"])
gguf_writer.add_block_count(block_count)
gguf_writer.add_head_count(hparams["n_head"])
gguf_writer.add_head_count_kv(hparams["n_head"])
gguf_writer.add_head_count_kv(1)
gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"])
gguf_writer.add_file_type(ftype)

Expand Down Expand Up @@ -209,25 +209,6 @@ def parse_args() -> argparse.Namespace:

data = data.squeeze().numpy()

# TODO: implement MQA directly, instead of duplicate into MHA.
if name.endswith(".attn.c_attn.weight") or name.endswith(".attn.c_attn.bias"):
print("Duplicate K,V heads to use MHA instead of MQA for", name)

embed_dim = hparams["n_embd"]
head_dim = embed_dim // hparams["n_head"]

# ((n_heads + 2) * head_dim, hidden_dim) -> (3 * n_heads * head_dim, hidden_dim)
q, k ,v = np.split(data, (hparams["n_head"] * head_dim, (hparams["n_head"] + 1) * head_dim), axis=0)
# duplicate k, v along the first axis (head_dim, hidden_dim) -> (n_heads * head_dim, hidden_dim)
if len(k.shape) == 2:
k = np.tile(k, (hparams["n_head"], 1))
v = np.tile(v, (hparams["n_head"], 1))
elif len(k.shape) == 1:
k = np.tile(k, (hparams["n_head"]))
v = np.tile(v, (hparams["n_head"]))
# concat q, k, v along the first axis (n_heads * head_dim, hidden_dim) -> (3 * n_heads * head_dim, hidden_dim)
data = np.concatenate((q, k, v), axis=0)

# map tensor names
new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
if new_name is None:
Expand Down
8 changes: 4 additions & 4 deletions llama.cpp
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Expand Up @@ -2265,8 +2265,8 @@ static void llm_load_tensors(
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);

layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, 3*n_embd}, backend_split);
layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {3*n_embd}, backend_split);
layer.wqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, backend_split);
layer.bqkv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, backend_split);

layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
layer.bo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, backend_split);
Expand Down Expand Up @@ -3538,8 +3538,8 @@ static struct ggml_cgraph * llm_build_starcoder(
cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wqkv, cur), model.layers[il].bqkv);

struct ggml_tensor * tmpq = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*sizeof(float)*n_embd);
struct ggml_tensor * tmpk = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*sizeof(float)*n_embd);
struct ggml_tensor * tmpv = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*sizeof(float)*n_embd);
struct ggml_tensor * tmpk = ggml_view_2d(ctx0, cur, n_embd_gqa, N, cur->nb[1], sizeof(float)*n_embd);
struct ggml_tensor * tmpv = ggml_view_2d(ctx0, cur, n_embd_gqa, N, cur->nb[1], sizeof(float)*(n_embd + n_embd_gqa));

struct ggml_tensor * Qcur = tmpq;
struct ggml_tensor * Kcur = tmpk;
Expand Down