root/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-12-24 10:24:35 +00:00
Code Issues Actions 5 Packages Projects Releases Wiki Activity

model: support arch DbrxForCausalLM (#6515)

Browse Source 
* model: dbrx convert to gguf
#6344

* llama: support dbrx
#6344

* doc: dbrx: add the model as supported

* scripts: get-wikitext-2 add unzip

* llama: increase maximum experts allowed

* llama: factorize moe graph implementation between grok, mixtral and dbrx


---------

Co-authored-by: Megha Agarwal <16129366+megha95@users.noreply.github.com>
This commit is contained in:
Pierrick Hymbert 2024-04-13 11:33:52 +02:00 committed by GitHub
parent ab9a3240a9
commit 4bd0f93e4a
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
7 changed files with 428 additions and 148 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
README.md
View File

@ -94,6 +94,7 @@ Typically finetunes of the base models below are supported as well.
- [x] LLaMA 2 🦙🦙
- [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
- [x] [DBRX](https://huggingface.co/databricks/dbrx-instruct)
- [X] Falcon
- [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)
- [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)

96
convert-hf-to-gguf.py
View File

@ -1427,6 +1427,102 @@ class GrokModel(Model):
self.gguf_writer.add_tensor(new_name, data)
@Model.register("DbrxForCausalLM")
class DbrxModel(Model):
model_arch = gguf.MODEL_ARCH.DBRX
def set_gguf_parameters(self):
ffn_config = self.hparams["ffn_config"]
attn_config = self.hparams["attn_config"]
self.gguf_writer.add_name(self.hparams["model_type"])
self.gguf_writer.add_block_count(self.hparams["n_layers"])
self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
self.gguf_writer.add_embedding_length(self.hparams["d_model"])
self.gguf_writer.add_feed_forward_length(ffn_config["ffn_hidden_size"])
self.gguf_writer.add_head_count(self.hparams["n_heads"])
self.gguf_writer.add_head_count_kv(attn_config["kv_n_heads"])
self.gguf_writer.add_rope_freq_base(attn_config["rope_theta"])
self.gguf_writer.add_clamp_kqv(attn_config["clip_qkv"])
self.gguf_writer.add_file_type(self.ftype)
self.gguf_writer.add_expert_count(ffn_config["moe_num_experts"])
self.gguf_writer.add_expert_used_count(ffn_config["moe_top_k"])
self.gguf_writer.add_layer_norm_eps(1e-5)
self.gguf_writer.add_file_type(self.ftype)
print(f"gguf: file type = {self.ftype}")
def write_tensors(self):
block_count = self.hparams.get("n_layers")
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
for name, data_torch in self.get_tensors():
n_expert = self.hparams["ffn_config"]["moe_num_experts"]
n_ff = self.hparams["ffn_config"]["ffn_hidden_size"]
n_embd = self.hparams["d_model"]
# Specific behavior for experts tensors: suffix .weight, view as 3D and transpose
# original implementation expects (n_expert, n_ff, n_embd) for all experts weights
# But llama.cpp moe graph works differently
# AND the dimensions in ggml are typically in the reverse order of the pytorch dimensions
# so (n_expert, n_ff, n_embd) in pytorch is {n_embd, n_ff, n_expert} in ggml_tensor
exp_tensor_names = {"ffn.experts.mlp.w1": None, # LLM_TENSOR_FFN_GATE_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert}
"ffn.experts.mlp.w2": (0, 2, 1), # LLM_TENSOR_FFN_DOWN_EXPS ggml_tensor->ne{n_ff, n_embd, n_expert}
"ffn.experts.mlp.v1": None} # LLM_TENSOR_FFN_UP_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert}
experts = False
for exp_tensor_name in exp_tensor_names.keys():
if name.find(exp_tensor_name) != -1 and name.find(".weight") == -1:
experts = True
data_torch = data_torch.view(n_expert, n_ff, n_embd)
if (permute_tensor := exp_tensor_names[exp_tensor_name]) is not None:
data_torch = data_torch.permute(*permute_tensor)
break
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()
# map tensor names
# In MoE models the ffn tensors are typically most of the model weights,
# and need to be quantizable. Quantize expects tensor names to be suffixed by .weight.
# Every other model has the weight names ending in .weight,
# let's assume that is the convention which is not the case for dbrx:
# https://huggingface.co/databricks/dbrx-instruct/blob/main/model.safetensors.index.json#L15
new_name = tensor_map.get_name(name if not experts else name + ".weight", try_suffixes=(".weight",))
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()
n_dims = len(data.shape)
data_dtype = data.dtype
# Most of the codebase that takes in 1D tensors only handles F32 tensors
# and most of the outputs tensors are F32.
if data_dtype != np.float32 and n_dims == 1:
print(f"Can not map tensor {name!r}: all 1D tensors must be F32")
sys.exit()
# if f32 desired, convert any float16 to float32
if self.ftype == 0 and data_dtype == np.float16:
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 n_dims > 1:
data = data.astype(np.float16)
print(f"{new_name}, n_dims = {n_dims}, shape = {data.shape}, {old_dtype} --> {data.dtype}")
self.gguf_writer.add_tensor(new_name, data)
@Model.register("MiniCPMForCausalLM")
class MiniCPMModel(Model):
model_arch = gguf.MODEL_ARCH.MINICPM

26
examples/eval-callback/eval-callback.cpp
View File

@ -28,14 +28,27 @@ static std::string ggml_ne_string(const ggml_tensor * t) {
}
static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) {
GGML_ASSERT(n > 0);
float sum = 0;
for (int64_t i3 = 0; i3 < ne[3]; i3++) {
printf(" [\n");
for (int64_t i2 = 0; i2 < ne[2] && i2 < n; i2++) {
for (int64_t i2 = 0; i2 < ne[2]; i2++) {
if (i2 == n && ne[2] > 2*n) {
printf(" ..., \n");
i2 = ne[2] - n;
}
printf(" [\n");
for (int64_t i1 = 0; i1 < ne[1] && i1 < n; i1++) {
for (int64_t i1 = 0; i1 < ne[1]; i1++) {
if (i1 == n && ne[1] > 2*n) {
printf(" ..., \n");
i1 = ne[1] - n;
}
printf(" [");
for (int64_t i0 = 0; i0 < ne[0] && i0 < n; i0++) {
for (int64_t i0 = 0; i0 < ne[0]; i0++) {
if (i0 == n && ne[0] > 2*n) {
printf("..., ");
i0 = ne[0] - n;
}
size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
float v;
if (type == GGML_TYPE_F16) {
@ -51,17 +64,14 @@ static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne
} else {
GGML_ASSERT(false);
}
printf("%8.4f", v);
printf("%12.4f", v);
sum += v;
if (i0 < ne[0] - 1 && i0 < n - 1) printf(", ");
if (i0 < ne[0] - 1) printf(", ");
}
if (ne[0] > n) printf(", ...");
printf("],\n");
}
if (ne[1] > n) printf(" ...\n");
printf(" ],\n");
}
if (ne[2] > n) printf(" ...\n");
printf(" ]\n");
printf(" sum = %f\n", sum);
}

15
gguf-py/gguf/constants.py
View File

@ -126,6 +126,7 @@ class MODEL_ARCH(IntEnum):
MAMBA = auto()
XVERSE = auto()
COMMAND_R = auto()
DBRX = auto()
class MODEL_TENSOR(IntEnum):
@ -195,6 +196,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.MAMBA: "mamba",
MODEL_ARCH.XVERSE: "xverse",
MODEL_ARCH.COMMAND_R: "command-r",
MODEL_ARCH.DBRX: "dbrx",
}
TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@ -642,6 +644,19 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.ATTN_K_NORM,
MODEL_TENSOR.ATTN_Q_NORM,
],
MODEL_ARCH.DBRX: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.ATTN_OUT_NORM,
MODEL_TENSOR.FFN_GATE_INP,
MODEL_TENSOR.FFN_GATE_EXP,
MODEL_TENSOR.FFN_DOWN_EXP,
MODEL_TENSOR.FFN_UP_EXP,
],
# TODO
}

20
gguf-py/gguf/tensor_mapping.py
View File

@ -10,7 +10,7 @@ class TensorNameMap:
# Token embeddings
MODEL_TENSOR.TOKEN_EMBD: (
"gpt_neox.embed_in", # gptneox
"transformer.wte", # gpt2 gpt-j mpt refact qwen
"transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx
"transformer.word_embeddings", # falcon
"word_embeddings", # bloom
"model.embed_tokens", # llama-hf
@ -48,7 +48,7 @@ class TensorNameMap:
# Output
MODEL_TENSOR.OUTPUT: (
"embed_out", # gptneox
"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba
"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx
"output", # llama-pth bloom internlm2
"word_embeddings_for_head", # persimmon
"lm_head.linear", # phi2
@ -60,7 +60,7 @@ class TensorNameMap:
"transformer.ln_f", # gpt2 gpt-j falcon
"model.norm", # llama-hf baichuan internlm2
"norm", # llama-pth
"transformer.norm_f", # mpt
"transformer.norm_f", # mpt dbrx
"ln_f", # refact bloom qwen gpt2
"language_model.encoder.final_layernorm", # persimmon
"model.final_layernorm", # persimmon
@ -96,6 +96,7 @@ class TensorNameMap:
"model.layers.{bid}.norm", # mamba-qbert
"backbone.layers.{bid}.norm", # mamba
"transformer.decoder_layer.{bid}.rms_norm", # Grok
"transformer.blocks.{bid}.norm_attn_norm.norm_1", # dbrx
),
# Attention norm 2
@ -108,6 +109,7 @@ class TensorNameMap:
"gpt_neox.layers.{bid}.attention.query_key_value", # gptneox
"transformer.h.{bid}.attn.c_attn", # gpt2 qwen
"transformer.blocks.{bid}.attn.Wqkv", # mpt
"transformer.blocks.{bid}.norm_attn_norm.attn.Wqkv", # dbrx
"transformer.h.{bid}.self_attention.query_key_value", # falcon
"h.{bid}.self_attention.query_key_value", # bloom
"language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon
@ -168,7 +170,8 @@ class TensorNameMap:
"model.layers.layers.{bid}.self_attn.o_proj", # plamo
"model.layers.{bid}.attention.wo", # internlm2
"encoder.layers.{bid}.attn.out_proj", # nomic-bert
"transformer.decoder_layer.{bid}.multi_head_attention.linear"# Grok
"transformer.decoder_layer.{bid}.multi_head_attention.linear", # Grok
"transformer.blocks.{bid}.norm_attn_norm.attn.out_proj", # dbrx
),
# Attention output norm
@ -176,6 +179,7 @@ class TensorNameMap:
"encoder.layer.{bid}.attention.output.LayerNorm", # bert
"encoder.layers.{bid}.norm1", # nomic-bert
"transformer.decoder_layer.{bid}.rms_norm_1", # Grok
"transformer.blocks.{bid}.norm_attn_norm.norm_2", # dbrx
),
# Rotary embeddings
@ -204,7 +208,8 @@ class TensorNameMap:
MODEL_TENSOR.FFN_GATE_INP: (
"layers.{bid}.feed_forward.gate", # mixtral
"model.layers.{bid}.block_sparse_moe.gate", # mixtral
"transformer.decoder_layer.{bid}.router" # Grok
"transformer.decoder_layer.{bid}.router", # Grok
"transformer.blocks.{bid}.ffn.router.layer", # dbrx
),
# Feed-forward up
@ -233,6 +238,7 @@ class TensorNameMap:
MODEL_TENSOR.FFN_UP_EXP: (
"layers.{bid}.feed_forward.experts.w3", # mixtral (merged)
"transformer.decoder_layer.{bid}.moe.linear_v", # Grok (merged)
"transformer.blocks.{bid}.ffn.experts.mlp.v1", # dbrx
),
# AWQ-activation gate
@ -252,7 +258,8 @@ class TensorNameMap:
MODEL_TENSOR.FFN_GATE_EXP: (
"layers.{bid}.feed_forward.experts.w1", # mixtral (merged)
"transformer.decoder_layer.{bid}.moe.linear" # Grok (merged)
"transformer.decoder_layer.{bid}.moe.linear", # Grok (merged)
"transformer.blocks.{bid}.ffn.experts.mlp.w1", # dbrx
),
# Feed-forward down
@ -280,6 +287,7 @@ class TensorNameMap:
MODEL_TENSOR.FFN_DOWN_EXP: (
"layers.{bid}.feed_forward.experts.w2", # mixtral (merged)
"transformer.decoder_layer.{bid}.moe.linear_1", # Grok (merged)
"transformer.blocks.{bid}.ffn.experts.mlp.w2", # dbrx
),
MODEL_TENSOR.ATTN_Q_NORM: (

335
llama.cpp
View File

@ -105,7 +105,7 @@
#endif
#define LLAMA_MAX_NODES 8192
#define LLAMA_MAX_EXPERTS 8
#define LLAMA_MAX_EXPERTS 16
//
@ -220,6 +220,7 @@ enum llm_arch {
LLM_ARCH_MAMBA,
LLM_ARCH_XVERSE,
LLM_ARCH_COMMAND_R,
LLM_ARCH_DBRX,
LLM_ARCH_UNKNOWN,
};
@ -252,6 +253,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_MAMBA, "mamba" },
{ LLM_ARCH_XVERSE, "xverse" },
{ LLM_ARCH_COMMAND_R, "command-r" },
{ LLM_ARCH_DBRX, "dbrx" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
@ -934,6 +936,22 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
},
},
{
LLM_ARCH_DBRX,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
},
},
{
LLM_ARCH_UNKNOWN,
{
@ -1707,6 +1725,7 @@ enum e_model {
MODEL_XL,
MODEL_8x7B,
MODEL_8x22B,
MODEL_16x12B,
};
static const size_t kiB = 1024;
@ -3562,6 +3581,7 @@ static const char * llama_model_type_name(e_model type) {
case MODEL_XL: return "1.5B";
case MODEL_8x7B: return "8x7B";
case MODEL_8x22B: return "8x22B";
case MODEL_16x12B: return "16x12B";
default: return "?B";
}
}
@ -3983,6 +4003,16 @@ static void llm_load_hparams(
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_DBRX:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv);
switch (hparams.n_layer) {
case 40: model.type = e_model::MODEL_16x12B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
default: (void)0;
}
@ -4671,6 +4701,39 @@ static bool llm_load_tensors(
layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
}
} break;
case LLM_ARCH_DBRX:
{
if (n_expert == 0) {
throw std::runtime_error("DBRX model cannot have zero experts");
}
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});
model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
}
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.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert});
layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert});
layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert});
}
} break;
case LLM_ARCH_BAICHUAN:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
@ -6433,6 +6496,40 @@ struct llm_build_context {
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
cur = build_moe_ffn(cur, n_tokens, LLM_FFN_SILU, il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "ffn_out", il);
ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
if (layer_dir != nullptr) {
cur = ggml_add(ctx0, cur, layer_dir);
}
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.output, cur);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
// REVIEW: will be replaced by https://github.com/ggerganov/llama.cpp/pull/6505
ggml_tensor * build_moe_ffn(ggml_tensor * cur, int32_t n_tokens, llm_ffn_op_type type_op, int il) {
ggml_tensor * logits = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp, cur); // [n_tokens, num_experts]
cb(logits, "ffn_moe_logits", il);
@ -6464,13 +6561,25 @@ struct llm_build_context {
ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exps, selected_experts, i, cur);
cb(cur_up, "ffn_moe_up", il);
ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exps, selected_experts, i, cur);
cb(cur_gate, "ffn_moe_gate", il);
ggml_tensor * gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exps, selected_experts, i, cur);
cb(gate, "ffn_moe_gate", il);
cur_gate = ggml_silu(ctx0, cur_gate);
cb(cur_gate, "ffn_moe_silu", il);
switch (type_op) {
case LLM_FFN_SILU:
{
gate = ggml_silu(ctx0, gate);
cb(gate, "ffn_moe_silu", il);
} break;
case LLM_FFN_GELU:
{
gate = ggml_gelu(ctx0, gate);
cb(gate, "ffn_moe_gelu", il);
} break;
default:
GGML_ASSERT(false);
}
cur_expert = ggml_mul(ctx0, cur_up, cur_gate);
cur_expert = ggml_mul(ctx0, cur_up, gate);
cb(cur_expert, "ffn_moe_gate_par", il);
cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exps, selected_experts, i, cur_expert); // [n_tokens, n_embd]
@ -6488,36 +6597,7 @@ struct llm_build_context {
}
}
cur = moe_out;
}
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "ffn_out", il);
ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
if (layer_dir != nullptr) {
cur = ggml_add(ctx0, cur, layer_dir);
}
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.output, cur);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
return moe_out;
}
struct ggml_cgraph * build_baichuan() {
@ -6967,63 +7047,7 @@ struct llm_build_context {
LLM_NORM_RMS, cb, il);
cb(cur, "ffn_norm", il);
ggml_tensor * logits = ggml_mul_mat(ctx0, model.layers[il].ffn_gate_inp, cur); // [n_tokens, num_experts]
cb(logits, "ffn_moe_logits", il);
ggml_tensor * probs = ggml_soft_max(ctx0, logits); // [n_tokens, num_experts]
cb(probs, "ffn_moe_probs", il);
// select experts
ggml_tensor * selected_experts = ggml_top_k(ctx0, probs, n_expert_used); // [n_tokens, num_experts_per_tok]
cb(selected_experts->src[0], "ffn_moe_argsort", il);
ggml_tensor * weights = ggml_get_rows(ctx0,
ggml_reshape_3d(ctx0, probs, 1, n_expert, n_tokens), selected_experts);
cb(weights, "ffn_moe_weights", il);
weights = ggml_reshape_2d(ctx0, weights, n_expert_used, n_tokens); // [n_tokens, num_experts_per_tok]
ggml_tensor * weights_sum = ggml_sum_rows(ctx0, weights);
cb(weights_sum, "ffn_moe_weights_sum", il);
weights = ggml_div(ctx0, weights, weights_sum); // [n_tokens, num_experts_per_tok]
cb(weights, "ffn_moe_weights_norm", il);
// compute expert outputs
ggml_tensor * moe_out = nullptr;
for (int i = 0; i < n_expert_used; ++i) {
ggml_tensor * cur_expert;
ggml_tensor * cur_up = ggml_mul_mat_id(ctx0, model.layers[il].ffn_up_exps, selected_experts, i, cur);
cb(cur_up, "ffn_moe_up", il);
ggml_tensor * cur_gate = ggml_mul_mat_id(ctx0, model.layers[il].ffn_gate_exps, selected_experts, i, cur);
cb(cur_gate, "ffn_moe_gate", il);
//GeLU
cur_gate = ggml_gelu(ctx0, cur_gate);
cb(cur_gate, "ffn_moe_gelu", il);
cur_expert = ggml_mul(ctx0, cur_up, cur_gate);
cb(cur_expert, "ffn_moe_gate_par", il);
cur_expert = ggml_mul_mat_id(ctx0, model.layers[il].ffn_down_exps, selected_experts, i, cur_expert); // [n_tokens, n_embd]
cb(cur_expert, "ffn_moe_down", il);
cur_expert = ggml_mul(ctx0, cur_expert,
ggml_view_2d(ctx0, weights, 1, n_tokens, weights->nb[1], i*weights->nb[0]));
cb(cur_expert, "ffn_moe_weighted", il);
if (i == 0) {
moe_out = cur_expert;
} else {
moe_out = ggml_add(ctx0, moe_out, cur_expert);
cb(moe_out, "ffn_moe_out", il);
}
}
cur = moe_out;
cur = build_moe_ffn(cur, n_tokens, LLM_FFN_GELU, il);
// Grok
// if layer_out_norm is present then apply it before adding the input
@ -7071,6 +7095,126 @@ struct llm_build_context {
return gf;
}
struct ggml_cgraph * build_dbrx() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
// mutable variable, needed during the last layer of the computation to skip unused tokens
int32_t n_tokens = this->n_tokens;
const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
GGML_ASSERT(n_embd_head == hparams.n_rot);
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;
// norm
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm, NULL,
LLM_NORM, cb, il);
cb(cur, "attn_norm", il);
// self-attention
{
struct ggml_tensor * Qcur = nullptr;
struct ggml_tensor * Kcur = nullptr;
struct ggml_tensor * Vcur = nullptr;
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
cb(cur, "wqkv", il);
cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
cb(cur, "wqkv_clamped", il);
Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
Qcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), 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,
n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, NULL,
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
}
if (il == n_layer - 1) {
// skip computing output for unused tokens
struct ggml_tensor * inp_out_ids = build_inp_out_ids();
n_tokens = n_outputs;
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 network
// MoE branch
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].attn_out_norm, NULL,
LLM_NORM, cb, il);
cb(cur, "attn_out_norm", il);
cur = build_moe_ffn(cur, n_tokens, LLM_FFN_SILU, il);
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "ffn_out", il);
ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
if (layer_dir != nullptr) {
cur = ggml_add(ctx0, cur, layer_dir);
}
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, cb, -1);
cb(cur, "result_norm", -1);
// lm_head
cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
struct ggml_cgraph * build_starcoder() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
@ -9785,6 +9929,10 @@ static struct ggml_cgraph * llama_build_graph(
{
result = llm.build_command_r();
} break;
case LLM_ARCH_DBRX:
{
result = llm.build_dbrx();
} break;
default:
GGML_ASSERT(false);
}
@ -14638,6 +14786,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
// the pairs of head values are offset by n_rot/2
case LLM_ARCH_FALCON:
case LLM_ARCH_GROK:
case LLM_ARCH_DBRX:
case LLM_ARCH_PERSIMMON:
case LLM_ARCH_BERT:
case LLM_ARCH_NOMIC_BERT:

3
scripts/get-wikitext-2.sh
View File

@ -1,10 +1,11 @@
#!/bin/bash
wget https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
unzip wikitext-2-raw-v1.zip
echo "Usage:"
echo ""
echo " ./perplexity -m model.gguf -f wiki.test.raw [other params]"
echo " ./perplexity -m model.gguf -f wikitext-2-raw/wiki.test.raw [other params]"
echo ""
exit 0