diff --git a/conversion/__init__.py b/conversion/__init__.py index 4d58bcd106..9c5d984388 100644 --- a/conversion/__init__.py +++ b/conversion/__init__.py @@ -168,6 +168,7 @@ TEXT_MODEL_MAP: dict[str, str] = { "Mamba2ForCausalLM": "mamba", "MambaForCausalLM": "mamba", "MambaLMHeadModel": "mamba", + "MapleForCausalLM": "maple", "MellumForCausalLM": "mellum", "MiMoV2FlashForCausalLM": "mimo", "MiMoV2ForCausalLM": "mimo", diff --git a/conversion/maple.py b/conversion/maple.py new file mode 100644 index 0000000000..fb0e87804d --- /dev/null +++ b/conversion/maple.py @@ -0,0 +1,87 @@ +from __future__ import annotations + +from typing import Iterable, TYPE_CHECKING, cast + +import torch + +if TYPE_CHECKING: + from torch import Tensor + +from .base import LazyTorchTensor, ModelBase, TextModel, gguf + + +@ModelBase.register("MapleForCausalLM") +@ModelBase.example("deepgrove/maple-preview") +class MapleModel(TextModel): + model_arch = gguf.MODEL_ARCH.MAPLE + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + + assert hparams["hidden_act"] == "silu" + assert hparams.get("num_shared_experts", 0) == 0 + assert hparams.get("norm_topk_prob", True) + assert hparams.get("nope_on_global_attention", False) + + head_dim = hparams.get("head_dim", hparams["hidden_size"] // hparams["num_attention_heads"]) + partial_rotary_factor = self.rope_parameters.get("partial_rotary_factor", 1.0) + + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + self.gguf_writer.add_rope_dimension_count(int(head_dim * partial_rotary_factor)) + self.gguf_writer.add_sliding_window(hparams["sliding_window"]) + self.gguf_writer.add_sliding_window_pattern([layer_type == "sliding_attention" for layer_type in hparams["layer_types"]]) + self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"]) + # the reference clamps the MoE SwiGLU gate/up at 7.0 (modeling_maple.py) + self.gguf_writer.add_swiglu_clamp_exp([7.0] * self.block_count) + + _experts: list[dict[str, Tensor]] | None = None + + @staticmethod + def _stack_experts(tensors: list[Tensor]) -> Tensor: + shape = (len(tensors), *tensors[0].shape) + dtype = tensors[0].dtype + meta = LazyTorchTensor.meta_with_dtype_and_shape(dtype, shape) + + # tensors goes through args, not the closure, so that `func` matches + # LazyBase's single-argument shape + def stack(ts: list[Tensor]) -> Tensor: + result = torch.empty(shape, dtype=dtype) + for expert_id, tensor in enumerate(ts): + result[expert_id].copy_(LazyTorchTensor.to_eager(tensor)) + ts.clear() + return result + + return cast(torch.Tensor, LazyTorchTensor(meta=meta, args=(tensors,), func=stack)) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + if "mlp.experts" in name: + n_experts = self.hparams["num_experts"] + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + for weight_name in ("down_proj", "gate_proj", "up_proj"): + tensors = [] + + for expert_id in range(n_experts): + expert_name = f"model.layers.{bid}.mlp.experts.{expert_id}.{weight_name}.weight" + tensors.append(self._experts[bid].pop(expert_name)) + + merged_name = f"model.layers.{bid}.mlp.experts.{weight_name}.weight" + yield from super().modify_tensors(self._stack_experts(tensors), merged_name, bid) + return + + yield from super().modify_tensors(data_torch, name, bid) + + def prepare_tensors(self): + super().prepare_tensors() + + if self._experts is not None: + experts = [name for layer in self._experts for name in layer] + if experts: + raise ValueError(f"Unprocessed experts: {experts}") diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index d3a639f374..e54ee5a0fd 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -541,6 +541,7 @@ class MODEL_ARCH(IntEnum): ARWKV7 = auto() MAMBA = auto() MAMBA2 = auto() + MAPLE = auto() JAMBA = auto() XVERSE = auto() COMMAND_R = auto() @@ -1295,6 +1296,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.ARWKV7: "arwkv7", MODEL_ARCH.MAMBA: "mamba", MODEL_ARCH.MAMBA2: "mamba2", + MODEL_ARCH.MAPLE: "maple", MODEL_ARCH.JAMBA: "jamba", MODEL_ARCH.XVERSE: "xverse", MODEL_ARCH.COMMAND_R: "command-r", @@ -3487,6 +3489,23 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.SSM_NORM, MODEL_TENSOR.SSM_OUT, ], + MODEL_ARCH.MAPLE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], MODEL_ARCH.JAMBA: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp index b5efb72065..0fac27efc0 100644 --- a/src/llama-arch.cpp +++ b/src/llama-arch.cpp @@ -62,6 +62,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_STARCODER2, "starcoder2" }, { LLM_ARCH_MAMBA, "mamba" }, { LLM_ARCH_MAMBA2, "mamba2" }, + { LLM_ARCH_MAPLE, "maple" }, { LLM_ARCH_JAMBA, "jamba" }, { LLM_ARCH_FALCON_H1, "falcon-h1" }, { LLM_ARCH_XVERSE, "xverse" }, diff --git a/src/llama-arch.h b/src/llama-arch.h index f1d173a575..6e67f5d659 100644 --- a/src/llama-arch.h +++ b/src/llama-arch.h @@ -67,6 +67,7 @@ enum llm_arch { LLM_ARCH_STARCODER2, LLM_ARCH_MAMBA, LLM_ARCH_MAMBA2, + LLM_ARCH_MAPLE, LLM_ARCH_JAMBA, LLM_ARCH_FALCON_H1, LLM_ARCH_XVERSE, diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index 5855393ef7..fd4290cf05 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -2225,7 +2225,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn( const float limit = hparams.swiglu_clamp_exp[il]; constexpr float eps = 1e-6f; if (limit > eps) { - if (arch == LLM_ARCH_DEEPSEEK4 || (arch == LLM_ARCH_DFLASH && hparams.dsv4_hc_mult > 0) || arch == LLM_ARCH_HY_V4) { + if (arch == LLM_ARCH_MAPLE || arch == LLM_ARCH_DEEPSEEK4 || (arch == LLM_ARCH_DFLASH && hparams.dsv4_hc_mult > 0) || arch == LLM_ARCH_HY_V4) { cur = ggml_swiglu_clamp(ctx0, cur, up, limit); } else { up = ggml_clamp(ctx0, up, -limit, limit); diff --git a/src/llama-model-saver.cpp b/src/llama-model-saver.cpp index 66f8bdec37..59a8ff84f8 100644 --- a/src/llama-model-saver.cpp +++ b/src/llama-model-saver.cpp @@ -33,6 +33,7 @@ bool llama_model_saver_supports_arch(llm_arch arch) { case LLM_ARCH_LAGUNA: case LLM_ARCH_GRANITE_SWA: case LLM_ARCH_DOTS3NOTE: // TODO: need to handle SWA pattern and MLA+SWA config + case LLM_ARCH_MAPLE: return false; default: return true; diff --git a/src/llama-model.cpp b/src/llama-model.cpp index f9e9a8bcb0..3b2536283c 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -162,6 +162,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params return new llama_model_mamba(params); case LLM_ARCH_MAMBA2: return new llama_model_mamba2(params); + case LLM_ARCH_MAPLE: + return new llama_model_maple(params); case LLM_ARCH_JAMBA: return new llama_model_jamba(params); case LLM_ARCH_XVERSE: @@ -3019,6 +3021,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) { case LLM_ARCH_SPARK2_5: case LLM_ARCH_TALKIE: case LLM_ARCH_MELLUM: + case LLM_ARCH_MAPLE: return LLAMA_ROPE_TYPE_NEOX; case LLM_ARCH_DFLASH: diff --git a/src/models/maple.cpp b/src/models/maple.cpp new file mode 100644 index 0000000000..7604b7dfee --- /dev/null +++ b/src/models/maple.cpp @@ -0,0 +1,150 @@ +#include "models.h" + +void llama_model_maple::load_arch_hparams(llama_model_loader & ml) { + hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; + + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa); + ml.get_key_or_arr(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp_arr, hparams.n_layer_all); + + ml.get_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl); + + hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train; + hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train; + ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false); + + ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP, hparams.swiglu_clamp_exp, hparams.n_layer_all); + + switch (hparams.n_layer()) { + case 24: type = LLM_TYPE_20B; break; + default: type = LLM_TYPE_UNKNOWN; + } +} + +void llama_model_maple::load_arch_tensors(llama_model_loader &) { + LLAMA_LOAD_LOCALS; + + const int64_t n_ff_exp = hparams.n_ff_exp(); + const int64_t head_dim = hparams.n_embd_head_k(); + + tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); + + output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); + output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0); + + if (n_expert == 0) { + throw std::runtime_error("n_expert must be > 0 for Maple"); + } + if (n_expert_used == 0) { + throw std::runtime_error("n_expert_used must be > 0 for Maple"); + } + + for (int i = 0; i < n_layer; ++i) { + auto & layer = layers[i]; + + layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); + + create_tensor_qkv(layer, i, n_embd, n_head * head_dim, n_head_kv * head_dim, n_head_kv * head_dim, 0); + layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * head_dim, n_embd}, 0); + + layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {head_dim}, 0); + layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {head_dim}, 0); + layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); + + layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0); + layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0); + layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0); + layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0); + } +} + +std::unique_ptr llama_model_maple::build_arch_graph(const llm_graph_params & params) const { + return std::make_unique(*this, params); +} + +llama_model_maple::graph::graph(const llama_model & model, const llm_graph_params & params) : + llm_graph_context(params) { + const int64_t n_embd_head = hparams.n_embd_head_k(); + + GGML_ASSERT(n_embd_head == hparams.n_embd_head_v()); + + ggml_tensor * inpL = build_inp_embd(model.tok_embd); + ggml_tensor * inp_pos = build_inp_pos(); + auto * inp_attn = build_attn_inp_kv_iswa(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + for (int il = 0; il < n_layer; ++il) { + ggml_tensor * inpSA = inpL; + + ggml_tensor * cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + + { + auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur, n_embd_head, n_head, n_head_kv, il); + + Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il); + Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il); + cb(Qcur, "Qcur_normed", il); + cb(Kcur, "Kcur_normed", il); + + if (hparams.is_swa(il)) { + const int64_t n_rot_l = hparams.n_rot(il); + const float freq_base_l = model.get_rope_freq_base(cparams, il); + const float freq_scale_l = model.get_rope_freq_scale(cparams, il); + + Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot_l, rope_type, n_ctx_orig, freq_base_l, + freq_scale_l, ext_factor, attn_factor, beta_fast, beta_slow); + Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot_l, rope_type, n_ctx_orig, freq_base_l, + freq_scale_l, ext_factor, attn_factor, beta_fast, beta_slow); + } + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(inp_attn, + model.layers[il].wo, nullptr, model.layers[il].wo_s, + Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il); + cb(cur, "attn_out", il); + } + + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + cur = build_norm(ffn_inp, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + cur = build_moe_ffn(cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + nullptr, + n_expert, n_expert_used, + LLM_FFN_SILU, true, + 1.0f, + LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, + il); + cb(cur, "ffn_moe_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + inpL = cur; + } + + ggml_tensor * cur = build_norm(inpL, model.output_norm, nullptr, LLM_NORM_RMS, -1); + cb(cur, "result_norm", -1); + res->t_embd = cur; + + cur = build_lora_mm(model.output, cur, model.output_s); + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); +} diff --git a/src/models/models.h b/src/models/models.h index 87195fddd1..da519dcfdc 100644 --- a/src/models/models.h +++ b/src/models/models.h @@ -945,6 +945,19 @@ struct llama_model_mamba2 : public llama_model_base { }; +struct llama_model_maple : public llama_model_base { + llama_model_maple(const struct llama_model_params & params) : llama_model_base(params) {} + void load_arch_hparams(llama_model_loader & ml) override; + void load_arch_tensors(llama_model_loader & ml) override; + + struct graph : public llm_graph_context { + graph(const llama_model & model, const llm_graph_params & params); + }; + + std::unique_ptr build_arch_graph(const llm_graph_params & params) const override; +}; + + struct llama_model_jamba : public llama_model_base { llama_model_jamba(const struct llama_model_params & params) : llama_model_base(params) {} void load_arch_hparams(llama_model_loader & ml) override; diff --git a/tests/test-llama-archs.cpp b/tests/test-llama-archs.cpp index 018ff1f42c..90a6a71623 100644 --- a/tests/test-llama-archs.cpp +++ b/tests/test-llama-archs.cpp @@ -239,7 +239,8 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) { // SWA pattern: every 5th layer is full attention (matches E2B layer_types) ms.add_kv(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, uint32_t(5)); } else if (arch == LLM_ARCH_COHERE2MOE || arch == LLM_ARCH_MIMO2 || arch == LLM_ARCH_STEP35 || arch == LLM_ARCH_SPARK2_5 || - arch == LLM_ARCH_MUSE_GLIMMER || arch == LLM_ARCH_GRANITE_SWA || arch == LLM_ARCH_DOTS3NOTE) { + arch == LLM_ARCH_MUSE_GLIMMER || arch == LLM_ARCH_GRANITE_SWA || arch == LLM_ARCH_DOTS3NOTE || + arch == LLM_ARCH_MAPLE) { std::vector pattern; pattern.reserve(n_layer); for (uint32_t il = 0; il < n_layer; il++) { @@ -323,6 +324,11 @@ static gguf_context_ptr get_gguf_ctx(const llm_arch arch, const bool moe) { ms.add_kv(LLM_KV_EXPERT_WEIGHTS_SCALE, 1.0f); ms.add_kv(LLM_KV_EXPERT_WEIGHTS_NORM, true); } + + if (arch == LLM_ARCH_MAPLE) { + ms.add_kv(LLM_KV_SWIGLU_CLAMP_EXP, 7.0f); + } + ms.add_kv(LLM_KV_TOKENIZER_MODEL, "no_vocab"); // ms.add_kv(LLM_KV_DENSE_2_FEAT_OUT, n_embd); // ms.add_kv(LLM_KV_DENSE_3_FEAT_IN, n_embd); @@ -505,6 +511,7 @@ static bool moe_mandatory(const llm_arch arch) { case LLM_ARCH_MISTRAL4: case LLM_ARCH_MELLUM: case LLM_ARCH_LAGUNA: + case LLM_ARCH_MAPLE: return true; default: return false;