mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-09-22 21:16:20 +00:00
Implement the OLMo architecture (#6741)
* implement olmo architecture * remove unused variable * remove unused moe branch * remove check for weight * remove superfluous moe, bias and rope tensors * clarified comment * fix clamp_kqv setting * remove obsolete parameter name filter
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nopperl
GitHub
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@ -122,6 +122,7 @@ Typically finetunes of the base models below are supported as well.
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- [x] [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
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- [x] [SEA-LION](https://huggingface.co/models?search=sea-lion)
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- [x] [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) + [GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B)
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- [x] [OLMo](https://allenai.org/olmo)
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(instructions for supporting more models: [HOWTO-add-model.md](./docs/HOWTO-add-model.md))
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@ -2636,6 +2636,66 @@ class CommandR2Model(Model):
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self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
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@Model.register("OlmoForCausalLM")
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@Model.register("OLMoForCausalLM")
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class OlmoModel(Model):
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model_arch = gguf.MODEL_ARCH.OLMO
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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self.gguf_writer.add_layer_norm_eps(1e-5)
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if "clip_qkv" in self.hparams is not None:
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self.gguf_writer.add_clamp_kqv(self.hparams["clip_qkv"])
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# Same as super class, but permuting q_proj, k_proj
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# Copied from: LlamaModel
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def write_tensors(self):
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block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer")))
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tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
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n_head = self.hparams.get("num_attention_heads")
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n_kv_head = self.hparams.get("num_key_value_heads")
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for name, data_torch in self.get_tensors():
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old_dtype = data_torch.dtype
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# convert any unsupported data types to float32
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if data_torch.dtype not in (torch.float16, torch.float32):
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data_torch = data_torch.to(torch.float32)
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data = data_torch.numpy()
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if name.endswith("q_proj.weight"):
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data = permute(data, n_head, n_head)
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if name.endswith("k_proj.weight"):
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data = permute(data, n_head, n_kv_head)
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data = data.squeeze()
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# map tensor names
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new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
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if new_name is None:
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print(f"Can not map tensor {name!r}")
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sys.exit()
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n_dims = len(data.shape)
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data_dtype = data.dtype
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# if f32 desired, convert any float16 to float32
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if self.ftype == 0 and data_dtype == np.float16:
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data = data.astype(np.float32)
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# 1d tensors need to be converted to float32
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if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
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data = data.astype(np.float32)
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# if f16 desired, convert any float32 2-dim weight tensors to float16
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if self.ftype == 1 and data_dtype == np.float32 and n_dims == 2:
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data = data.astype(np.float16)
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print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
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self.gguf_writer.add_tensor(new_name, data)
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###### CONVERSION LOGIC ######
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@ -135,6 +135,7 @@ class MODEL_ARCH(IntEnum):
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XVERSE = auto()
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COMMAND_R = auto()
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DBRX = auto()
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OLMO = auto()
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class MODEL_TENSOR(IntEnum):
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@ -210,6 +211,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.XVERSE: "xverse",
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MODEL_ARCH.COMMAND_R: "command-r",
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MODEL_ARCH.DBRX: "dbrx",
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MODEL_ARCH.OLMO: "olmo",
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}
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TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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@ -695,6 +697,17 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_DOWN_EXP,
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MODEL_TENSOR.FFN_UP_EXP,
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],
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MODEL_ARCH.OLMO: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ATTN_Q,
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MODEL_TENSOR.ATTN_K,
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MODEL_TENSOR.ATTN_V,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.FFN_GATE,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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],
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# TODO
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}
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197
llama.cpp
197
llama.cpp
@ -222,6 +222,7 @@ enum llm_arch {
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LLM_ARCH_XVERSE,
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LLM_ARCH_COMMAND_R,
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LLM_ARCH_DBRX,
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LLM_ARCH_OLMO,
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LLM_ARCH_UNKNOWN,
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};
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@ -256,6 +257,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_XVERSE, "xverse" },
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{ LLM_ARCH_COMMAND_R, "command-r" },
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{ LLM_ARCH_DBRX, "dbrx" },
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{ LLM_ARCH_OLMO, "olmo" },
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{ LLM_ARCH_UNKNOWN, "(unknown)" },
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};
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@ -990,6 +992,20 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
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{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
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},
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},
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{
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LLM_ARCH_OLMO,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_OUTPUT, "output" },
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{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
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{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
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{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_UNKNOWN,
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{
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@ -4070,6 +4086,18 @@ static void llm_load_hparams(
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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case LLM_ARCH_OLMO:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
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ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv, false);
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switch (hparams.n_layer) {
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case 22: model.type = e_model::MODEL_1B; break;
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case 32: model.type = e_model::MODEL_7B; break;
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case 80: model.type = e_model::MODEL_70B; break;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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default: (void)0;
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}
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@ -5666,6 +5694,37 @@ static bool llm_load_tensors(
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layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
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layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
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layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
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layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
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layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
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}
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} break;
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case LLM_ARCH_OLMO: // adapted from LLM_ARCH_LLAMA with norm params removed
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{
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model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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// output
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{
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model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false);
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// if output is NULL, init from the input tok embed
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if (model.output == NULL) {
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model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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ml.n_created--; // artificial tensor
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ml.size_data += ggml_nbytes(model.output);
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}
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}
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for (int i = 0; i < n_layer; ++i) {
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ggml_context * ctx_split = ctx_for_layer_split(i);
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auto & layer = model.layers[i];
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layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
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layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
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layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
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layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
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layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
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layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
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layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
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@ -10096,6 +10155,139 @@ struct llm_build_context {
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return gf;
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}
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// ref: https://allenai.org/olmo
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// based on the original build_llama() function, changes:
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// * non-parametric layer norm
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// * clamp qkv
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// * removed bias
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// * removed MoE
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struct ggml_cgraph * build_olmo() {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
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// mutable variable, needed during the last layer of the computation to skip unused tokens
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int32_t n_tokens = this->n_tokens;
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const int64_t n_embd_head = hparams.n_embd_head_v;
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
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GGML_ASSERT(n_embd_head == hparams.n_rot);
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struct ggml_tensor * cur;
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struct ggml_tensor * inpL;
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inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
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// inp_pos - contains the positions
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struct ggml_tensor * inp_pos = build_inp_pos();
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// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
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struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * inpSA = inpL;
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// norm
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cur = llm_build_norm(ctx0, inpL, hparams,
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NULL, NULL,
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LLM_NORM, cb, il);
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cb(cur, "attn_norm", il);
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// self-attention
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{
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// compute Q and K and RoPE them
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struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
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cb(Qcur, "Qcur", il);
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if (hparams.f_clamp_kqv > 0.0f) {
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Qcur = ggml_clamp(ctx0, Qcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
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cb(Qcur, "Qcur", il);
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}
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struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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if (hparams.f_clamp_kqv > 0.0f) {
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Kcur = ggml_clamp(ctx0, Kcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
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cb(Kcur, "Kcur", il);
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}
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struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
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cb(Vcur, "Vcur", il);
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if (hparams.f_clamp_kqv > 0.0f) {
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Vcur = ggml_clamp(ctx0, Vcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
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cb(Vcur, "Vcur", il);
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}
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Qcur = ggml_rope_custom(
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ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
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n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(Qcur, "Qcur", il);
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Kcur = ggml_rope_custom(
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ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
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n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(Kcur, "Kcur", il);
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cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
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model.layers[il].wo, nullptr,
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Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
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}
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if (il == n_layer - 1) {
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// skip computing output for unused tokens
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struct ggml_tensor * inp_out_ids = build_inp_out_ids();
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n_tokens = n_outputs;
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cur = ggml_get_rows(ctx0, cur, inp_out_ids);
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inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
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}
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struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
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cb(ffn_inp, "ffn_inp", il);
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// feed-forward network
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cur = llm_build_norm(ctx0, ffn_inp, hparams,
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NULL, NULL,
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LLM_NORM, cb, il);
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cb(cur, "ffn_norm", il);
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cur = llm_build_ffn(ctx0, cur,
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model.layers[il].ffn_up, NULL,
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model.layers[il].ffn_gate, NULL,
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model.layers[il].ffn_down, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
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cb(cur, "ffn_out", il);
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cur = ggml_add(ctx0, cur, ffn_inp);
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cb(cur, "ffn_out", il);
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ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
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if (layer_dir != nullptr) {
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cur = ggml_add(ctx0, cur, layer_dir);
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}
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cb(cur, "l_out", il);
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// input for next layer
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inpL = cur;
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}
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cur = inpL;
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cur = llm_build_norm(ctx0, cur, hparams,
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NULL, NULL,
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LLM_NORM, cb, -1);
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cb(cur, "result_norm", -1);
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// lm_head
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cur = ggml_mul_mat(ctx0, model.output, cur);
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cb(cur, "result_output", -1);
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ggml_build_forward_expand(gf, cur);
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return gf;
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}
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};
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static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
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@ -10301,6 +10493,10 @@ static struct ggml_cgraph * llama_build_graph(
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{
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result = llm.build_dbrx();
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} break;
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case LLM_ARCH_OLMO:
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{
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result = llm.build_olmo();
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} break;
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default:
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GGML_ASSERT(false);
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}
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@ -15154,6 +15350,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
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case LLM_ARCH_MINICPM:
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case LLM_ARCH_XVERSE:
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case LLM_ARCH_COMMAND_R:
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case LLM_ARCH_OLMO:
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return LLAMA_ROPE_TYPE_NORM;
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// the pairs of head values are offset by n_rot/2
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