llama : add Command-R support (#6033)
Information about the Command-R 35B model (128k context) can be found at: https://huggingface.co/CohereForAI/c4ai-command-r-v01 Based on the llama2 model with a few changes: 1) New hyper parameter to scale output logits (logit_scale) 2) Uses LayerNorm instead of RMSNorm 3) Transfomer layers have a single shared LayerNorm that feeds into both the self-attention and FFN layers in parallel. There is no post-attention LayerNorm. 4) No support for Rotary Position Embeddings (RoPE) scaling 5) No biases used Find GGUF files here: https://huggingface.co/andrewcanis/c4ai-command-r-v01-GGUF To convert model to GGUF format yourself: 1) Download Command-R Hugging Face safetensors: git lfs install git clone https://huggingface.co/CohereForAI/c4ai-command-r-v01 2) Run: python3 convert-hf-to-gguf.py --outtype f16 ./c4ai-command-r-v01
This commit is contained in:
Andrew Canis
GitHub
parent
4e9a7f7f7f
commit
12247f4c69
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@ -112,6 +112,7 @@ Typically finetunes of the base models below are supported as well.
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- [x] [CodeShell](https://github.com/WisdomShell/codeshell)
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- [x] [Gemma](https://ai.google.dev/gemma)
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- [x] [Mamba](https://github.com/state-spaces/mamba)
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- [x] [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
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**Multimodal models:**
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@ -1965,6 +1965,23 @@ class MambaModel(Model):
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self.gguf_writer.add_tensor(new_name, data)
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@Model.register("CohereForCausalLM")
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class CommandR2Model(Model):
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model_arch = gguf.MODEL_ARCH.COMMAND_R
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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# max_position_embeddings = 8192 in config.json but model was actually
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# trained on 128k context length
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self.hparams["max_position_embeddings"] = self.hparams["model_max_length"]
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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_logit_scale(self.hparams["logit_scale"])
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self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
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###### CONVERSION LOGIC ######
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@ -42,6 +42,7 @@ class Keys:
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EXPERT_COUNT = "{arch}.expert_count"
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EXPERT_USED_COUNT = "{arch}.expert_used_count"
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POOLING_TYPE = "{arch}.pooling_type"
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LOGIT_SCALE = "{arch}.logit_scale"
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class Attention:
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HEAD_COUNT = "{arch}.attention.head_count"
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@ -121,6 +122,7 @@ class MODEL_ARCH(IntEnum):
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GEMMA = auto()
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STARCODER2 = auto()
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MAMBA = auto()
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COMMAND_R = auto()
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class MODEL_TENSOR(IntEnum):
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@ -187,6 +189,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.GEMMA: "gemma",
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MODEL_ARCH.STARCODER2: "starcoder2",
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MODEL_ARCH.MAMBA: "mamba",
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MODEL_ARCH.COMMAND_R: "command-r",
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}
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TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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@ -579,6 +582,18 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.SSM_D,
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MODEL_TENSOR.SSM_OUT,
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],
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MODEL_ARCH.COMMAND_R: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.ATTN_NORM,
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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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@ -361,6 +361,9 @@ class GGUFWriter:
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def add_clamp_kqv(self, value: float) -> None:
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self.add_float32(Keys.Attention.CLAMP_KQV.format(arch=self.arch), value)
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def add_logit_scale(self, value: float) -> None:
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self.add_float32(Keys.LLM.LOGIT_SCALE.format(arch=self.arch), value)
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def add_expert_count(self, count: int) -> None:
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self.add_uint32(Keys.LLM.EXPERT_COUNT.format(arch=self.arch), count)
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183
llama.cpp
183
llama.cpp
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@ -214,6 +214,7 @@ enum llm_arch {
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LLM_ARCH_GEMMA,
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LLM_ARCH_STARCODER2,
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LLM_ARCH_MAMBA,
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LLM_ARCH_COMMAND_R,
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LLM_ARCH_UNKNOWN,
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};
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@ -243,6 +244,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_GEMMA, "gemma" },
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{ LLM_ARCH_STARCODER2, "starcoder2" },
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{ LLM_ARCH_MAMBA, "mamba" },
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{ LLM_ARCH_COMMAND_R, "command-r" },
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{ LLM_ARCH_UNKNOWN, "(unknown)" },
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};
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@ -268,6 +270,7 @@ enum llm_kv {
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LLM_KV_EXPERT_COUNT,
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LLM_KV_EXPERT_USED_COUNT,
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LLM_KV_POOLING_TYPE,
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LLM_KV_LOGIT_SCALE,
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LLM_KV_ATTENTION_HEAD_COUNT,
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LLM_KV_ATTENTION_HEAD_COUNT_KV,
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@ -332,6 +335,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
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{ LLM_KV_EXPERT_COUNT, "%s.expert_count" },
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{ LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" },
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{ LLM_KV_POOLING_TYPE , "%s.pooling_type" },
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{ LLM_KV_LOGIT_SCALE, "%s.logit_scale" },
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{ LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" },
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{ LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" },
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@ -838,6 +842,21 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
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{ LLM_TENSOR_SSM_OUT, "blk.%d.ssm_out" },
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},
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},
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{
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LLM_ARCH_COMMAND_R,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
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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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@ -1597,6 +1616,7 @@ enum e_model {
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MODEL_20B,
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MODEL_30B,
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MODEL_34B,
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MODEL_35B,
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MODEL_40B,
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MODEL_65B,
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MODEL_70B,
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@ -1643,6 +1663,7 @@ struct llama_hparams {
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float f_clamp_kqv = 0.0f;
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float f_max_alibi_bias = 0.0f;
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float f_logit_scale = 0.0f;
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bool causal_attn = true;
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bool need_kq_pos = false;
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@ -3231,6 +3252,7 @@ static const char * llama_model_type_name(e_model type) {
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case MODEL_20B: return "20B";
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case MODEL_30B: return "30B";
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case MODEL_34B: return "34B";
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case MODEL_35B: return "35B";
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case MODEL_40B: return "40B";
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case MODEL_65B: return "65B";
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case MODEL_70B: return "70B";
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@ -3623,6 +3645,15 @@ 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_COMMAND_R:
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{
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ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
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switch (hparams.n_layer) {
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case 40: model.type = e_model::MODEL_35B; 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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@ -3944,6 +3975,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
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LLAMA_LOG_INFO("%s: f_norm_rms_eps = %.1e\n", __func__, hparams.f_norm_rms_eps);
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LLAMA_LOG_INFO("%s: f_clamp_kqv = %.1e\n", __func__, hparams.f_clamp_kqv);
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LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n", __func__, hparams.f_max_alibi_bias);
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LLAMA_LOG_INFO("%s: f_logit_scale = %.1e\n", __func__, hparams.f_logit_scale);
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LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, hparams.n_ff);
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LLAMA_LOG_INFO("%s: n_expert = %u\n", __func__, hparams.n_expert);
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LLAMA_LOG_INFO("%s: n_expert_used = %u\n", __func__, hparams.n_expert_used);
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@ -4918,6 +4950,37 @@ static bool llm_load_tensors(
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layer.ssm_out = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd});
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}
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} break;
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case LLM_ARCH_COMMAND_R:
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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_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
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// init output from the input tok embed
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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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for (int i = 0; i < n_layer; ++i) {
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ggml_context * ctx_layer = ctx_for_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.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
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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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}
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} break;
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default:
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throw std::runtime_error("unknown architecture");
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}
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@ -8315,6 +8378,121 @@ struct llm_build_context {
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return gf;
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}
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struct ggml_cgraph * build_command_r() {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
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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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const float f_logit_scale = hparams.f_logit_scale;
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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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// norm
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cur = llm_build_norm(ctx0, inpL, hparams,
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model.layers[il].attn_norm, NULL,
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LLM_NORM, cb, il);
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cb(cur, "attn_norm", il);
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struct ggml_tensor * ffn_inp = cur;
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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 (model.layers[il].bq) {
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Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
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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 (model.layers[il].bk) {
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Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
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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 (model.layers[il].bv) {
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Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
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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, model.layers[il].bo,
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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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struct ggml_tensor * attn_out = cur;
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// feed-forward network
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{
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cur = llm_build_ffn(ctx0, ffn_inp,
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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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}
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// add together residual + FFN + self-attention
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cur = ggml_add(ctx0, cur, inpL);
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cur = ggml_add(ctx0, cur, attn_out);
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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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model.output_norm, 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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if (f_logit_scale) {
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cur = ggml_scale(ctx0, cur, f_logit_scale);
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}
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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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@ -8497,6 +8675,10 @@ static struct ggml_cgraph * llama_build_graph(
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{
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result = llm.build_mamba();
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} break;
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case LLM_ARCH_COMMAND_R:
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{
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result = llm.build_command_r();
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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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@ -13147,6 +13329,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
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case LLM_ARCH_ORION:
|
||||
case LLM_ARCH_INTERNLM2:
|
||||
case LLM_ARCH_MINICPM:
|
||||
case LLM_ARCH_COMMAND_R:
|
||||
return LLAMA_ROPE_TYPE_NORM;
|
||||
|
||||
// the pairs of head values are offset by n_rot/2
|
||||
|
|
|
|||
Loading…
Reference in a new issue