mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-09-22 21:16:20 +00:00
llama : add support for BitnetForCausalLM (#7931)
* hf bitnet v1 * hf bitnet e2e v2 * finish bitnet e2e * finish f16 hf bitnet e2e * remove unsed * finish bitnet i2 e2e * move i2s to quantize v1 * move i2 to quantize * clean code * clean code 2 * fix codestyle * fix code * fix * fix code * fix merge * remove unused * change table name * fix whitespace * delete redundant * i2_s to absmax * finish i2_s/i8_s vec_dot x86 simd * i2s->q22 * fix code * remove block scale * add dequantize * fix seq * update avx2 * remove q2_2 * remove q22_grid * fix whitespace * reuse llm_build_kv * fix bo --------- Co-authored-by: root <root@wangjinheng>
This commit is contained in:
Eddie-Wang
GitHub
parent
6a2f298bd7
commit
e112b610a1
@ -1404,6 +1404,48 @@ class LlamaModel(Model):
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raise ValueError(f"Unprocessed experts: {experts}")
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@Model.register("BitnetForCausalLM")
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class BitnetModel(Model):
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model_arch = gguf.MODEL_ARCH.BITNET
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def set_vocab(self):
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self._set_vocab_sentencepiece()
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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_rope_scaling_type(gguf.RopeScalingType.LINEAR)
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self.gguf_writer.add_rope_scaling_factor(1.0)
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def weight_quant(self, weight):
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dtype = weight.dtype
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weight = weight.float()
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s = 1 / weight.abs().mean().clamp(min=1e-5)
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weight = (weight * s).round().clamp(-1, 1) / s
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scale = weight.abs().max().unsqueeze(0)
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weight = torch.where(weight.abs().less(1e-6), 0, weight).type(dtype)
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weight = torch.sign(weight).type(dtype)
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return weight.type(dtype), scale.type(torch.float32)
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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new_name = self.map_tensor_name(name)
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if any(self.match_model_tensor_name(new_name, key, bid) for key in [
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gguf.MODEL_TENSOR.ATTN_Q,
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gguf.MODEL_TENSOR.ATTN_K,
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gguf.MODEL_TENSOR.ATTN_V,
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gguf.MODEL_TENSOR.ATTN_OUT,
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gguf.MODEL_TENSOR.FFN_UP,
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gguf.MODEL_TENSOR.FFN_DOWN,
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gguf.MODEL_TENSOR.FFN_GATE,
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]):
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# transform weight into 1/0/-1 (in fp32)
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weight_torch, scale_torch = self.weight_quant(data_torch)
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yield (new_name, weight_torch)
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yield (new_name.removesuffix(".weight") + ".scale", scale_torch)
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else:
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yield (new_name, data_torch)
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@Model.register("GrokForCausalLM")
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class GrokModel(Model):
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model_arch = gguf.MODEL_ARCH.GROK
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@ -149,6 +149,7 @@ class MODEL_ARCH(IntEnum):
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OLMO = auto()
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ARCTIC = auto()
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DEEPSEEK2 = auto()
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BITNET = auto()
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class MODEL_TENSOR(IntEnum):
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@ -200,6 +201,8 @@ class MODEL_TENSOR(IntEnum):
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ATTN_KV_B = auto()
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ATTN_Q_A_NORM = auto()
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ATTN_KV_A_NORM = auto()
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FFN_SUB_NORM = auto()
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ATTN_SUB_NORM = auto()
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MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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@ -237,6 +240,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.OLMO: "olmo",
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MODEL_ARCH.ARCTIC: "arctic",
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MODEL_ARCH.DEEPSEEK2: "deepseek2",
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MODEL_ARCH.BITNET: "bitnet",
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}
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TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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@ -288,6 +292,8 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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MODEL_TENSOR.ATTN_KV_B: "blk.{bid}.attn_kv_b",
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MODEL_TENSOR.ATTN_Q_A_NORM: "blk.{bid}.attn_q_a_norm",
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MODEL_TENSOR.ATTN_KV_A_NORM: "blk.{bid}.attn_kv_a_norm",
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MODEL_TENSOR.ATTN_SUB_NORM: "blk.{bid}.attn_sub_norm",
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MODEL_TENSOR.FFN_SUB_NORM: "blk.{bid}.ffn_sub_norm",
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}
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MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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@ -808,6 +814,21 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_DOWN_SHEXP,
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MODEL_TENSOR.FFN_UP_SHEXP,
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],
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MODEL_ARCH.BITNET: [
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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.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_OUT,
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MODEL_TENSOR.FFN_NORM,
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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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MODEL_TENSOR.ATTN_SUB_NORM,
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MODEL_TENSOR.FFN_SUB_NORM,
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],
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# TODO
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}
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@ -413,6 +413,14 @@ class TensorNameMap:
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MODEL_TENSOR.ATTN_KV_A_NORM: (
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"model.layers.{bid}.self_attn.kv_a_layernorm", # deepseek2
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),
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MODEL_TENSOR.ATTN_SUB_NORM: (
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"model.layers.{bid}.self_attn.inner_attn_ln", # bitnet
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),
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MODEL_TENSOR.FFN_SUB_NORM: (
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"model.layers.{bid}.mlp.ffn_layernorm", # bitnet
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),
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}
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# architecture-specific block mappings
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237
llama.cpp
237
llama.cpp
@ -225,6 +225,7 @@ enum llm_arch {
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LLM_ARCH_OLMO,
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LLM_ARCH_ARCTIC,
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LLM_ARCH_DEEPSEEK2,
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LLM_ARCH_BITNET,
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LLM_ARCH_UNKNOWN,
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};
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@ -263,6 +264,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_OLMO, "olmo" },
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{ LLM_ARCH_ARCTIC, "arctic" },
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{ LLM_ARCH_DEEPSEEK2, "deepseek2" },
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{ LLM_ARCH_BITNET, "bitnet" },
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{ LLM_ARCH_UNKNOWN, "(unknown)" },
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};
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@ -500,6 +502,8 @@ enum llm_tensor {
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LLM_TENSOR_ATTN_KV_B,
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LLM_TENSOR_ATTN_Q_A_NORM,
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LLM_TENSOR_ATTN_KV_A_NORM,
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LLM_TENSOR_ATTN_SUB_NORM,
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LLM_TENSOR_FFN_SUB_NORM,
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};
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static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
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@ -1113,6 +1117,24 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
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{ LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
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},
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},
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{
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LLM_ARCH_BITNET,
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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_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_ATTN_NORM, "blk.%d.attn_norm" },
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{ LLM_TENSOR_ATTN_SUB_NORM, "blk.%d.attn_sub_norm" },
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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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{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
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{ LLM_TENSOR_FFN_SUB_NORM, "blk.%d.ffn_sub_norm" },
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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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@ -2118,6 +2140,8 @@ struct llama_layer {
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struct ggml_tensor * attn_out_norm_b;
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struct ggml_tensor * attn_q_a_norm;
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struct ggml_tensor * attn_kv_a_norm;
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struct ggml_tensor * attn_sub_norm;
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struct ggml_tensor * ffn_sub_norm;
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// attention
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struct ggml_tensor * wq;
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@ -2185,6 +2209,15 @@ struct llama_layer {
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// long rope factors
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struct ggml_tensor * rope_long = nullptr;
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struct ggml_tensor * rope_short = nullptr;
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// bitnet scale
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struct ggml_tensor * wq_scale;
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struct ggml_tensor * wk_scale;
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struct ggml_tensor * wv_scale;
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struct ggml_tensor * wo_scale;
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struct ggml_tensor * ffn_gate_scale;
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struct ggml_tensor * ffn_up_scale;
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struct ggml_tensor * ffn_down_scale;
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};
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struct llama_kv_cell {
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@ -4710,6 +4743,15 @@ 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_BITNET:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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switch (hparams.n_layer) {
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case 26: model.type = e_model::MODEL_3B; 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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@ -6655,6 +6697,44 @@ static bool llm_load_tensors(
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}
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}
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} break;
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case LLM_ARCH_BITNET:
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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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}
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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.attn_sub_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_SUB_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.wq_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "scale", i), {1});
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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.wk_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "scale", i), {1});
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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.wv_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "scale", i), {1});
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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.wo_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "scale", i), {1});
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layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
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layer.ffn_sub_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_SUB_NORM, "weight", i), {n_ff});
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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_gate_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE, "scale", i), {1});
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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_down_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "scale", i), {1});
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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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layer.ffn_up_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "scale", i), {1});
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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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@ -7295,7 +7375,10 @@ static struct ggml_tensor * llm_build_kqv(
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ggml_build_forward_expand(graph, cur);
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cur = ggml_mul_mat(ctx, wo, cur);
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if (wo) {
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cur = ggml_mul_mat(ctx, wo, cur);
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}
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if (wo_b) {
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cb(cur, "kqv_wo", il);
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}
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@ -11709,6 +11792,153 @@ struct llm_build_context {
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return gf;
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}
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struct ggml_cgraph * build_bitnet() {
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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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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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cur = llm_build_norm(ctx0, inpL, hparams,
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model.layers[il].attn_norm, NULL,
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LLM_NORM_RMS, 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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Qcur = ggml_mul(ctx0, Qcur, model.layers[il].wq_scale);
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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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// B1.K
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struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
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Kcur = ggml_mul(ctx0, Kcur, model.layers[il].wk_scale);
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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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// B1.V
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struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
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Vcur = ggml_mul(ctx0, Vcur, model.layers[il].wv_scale);
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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_ext(
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ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
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n_rot, rope_type, n_ctx_orig, 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_ext(
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ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
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n_rot, rope_type, n_ctx_orig, 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, cparams, kv_self, gf,
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nullptr, nullptr,
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Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
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cur = llm_build_norm(ctx0, cur, hparams,
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model.layers[il].attn_sub_norm, NULL,
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LLM_NORM_RMS, cb, il);
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cb(cur, "attn_sub_norm", il);
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cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
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cur = ggml_mul(ctx0, cur, model.layers[il].wo_scale);
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if (model.layers[il].bo) {
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cur = ggml_add(ctx0, cur, model.layers[il].bo);
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}
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cb(cur, "attn_o_out", il);
|
||||
}
|
||||
|
||||
if (il == n_layer - 1) {
|
||||
// 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);
|
||||
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 forward
|
||||
if (model.layers[il].ffn_gate_inp == nullptr) {
|
||||
cur = llm_build_norm(ctx0, ffn_inp, hparams,
|
||||
model.layers[il].ffn_norm, NULL,
|
||||
LLM_NORM_RMS, cb, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
struct ggml_tensor *tmp = ggml_mul_mat(ctx0, model.layers[il].ffn_up, cur);
|
||||
tmp = ggml_mul(ctx0, tmp, model.layers[il].ffn_up_scale);
|
||||
cb(tmp, "ffn_up", il);
|
||||
|
||||
cur = ggml_mul_mat(ctx0, model.layers[il].ffn_gate, cur);
|
||||
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_gate_scale);
|
||||
cb(cur, "ffn_gate", il);
|
||||
|
||||
cur = ggml_silu(ctx0, cur);
|
||||
cb(cur, "ffn_silu", il);
|
||||
|
||||
cur = ggml_mul(ctx0, cur, tmp);
|
||||
cb(cur, "ffn_gate_par", il);
|
||||
|
||||
cur = llm_build_norm(ctx0, cur, hparams,
|
||||
model.layers[il].ffn_sub_norm, NULL,
|
||||
LLM_NORM_RMS, cb, il);
|
||||
cb(cur, "ffn_sub_norm", il);
|
||||
|
||||
cur = ggml_mul_mat(ctx0, model.layers[il].ffn_down, cur);
|
||||
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_down_scale);
|
||||
cb(cur, "ffn_down", il);
|
||||
}
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
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.tok_embd, cur);
|
||||
cb(cur, "result_output", -1);
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
return gf;
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
|
||||
@ -11932,6 +12162,10 @@ static struct ggml_cgraph * llama_build_graph(
|
||||
{
|
||||
result = llm.build_deepseek2();
|
||||
} break;
|
||||
case LLM_ARCH_BITNET:
|
||||
{
|
||||
result = llm.build_bitnet();
|
||||
} break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
}
|
||||
@ -16760,6 +16994,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
|
||||
case LLM_ARCH_BERT:
|
||||
case LLM_ARCH_NOMIC_BERT:
|
||||
case LLM_ARCH_STABLELM:
|
||||
case LLM_ARCH_BITNET:
|
||||
case LLM_ARCH_QWEN:
|
||||
case LLM_ARCH_QWEN2:
|
||||
case LLM_ARCH_QWEN2MOE:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user