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update: support Qwen2-57B-A14B #7835

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merged 4 commits into from
Jun 17, 2024
Merged

update: support Qwen2-57B-A14B #7835

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284cec4
update: convert-hf-to-gguf.py to support Qwen2-57B-A14B
legraphista Jun 8, 2024
aa8a7cd
fix: QWEN2MOE support for expert_feed_forward_length
legraphista Jun 10, 2024
06531cb
update: convert-hf-to-gguf.py cleanup for Qwen2MoeForCausalLM
legraphista Jun 12, 2024
d945226
fix: QWEN2MOE support for expert_feed_forward_length
legraphista Jun 14, 2024
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3 changes: 3 additions & 0 deletions convert-hf-to-gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1627,6 +1627,9 @@ def set_gguf_parameters(self):
super().set_gguf_parameters()
if (n_experts := self.hparams.get("num_experts")) is not None:
self.gguf_writer.add_expert_count(n_experts)
if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")

_experts: list[dict[str, Tensor]] | None = None

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15 changes: 11 additions & 4 deletions llama.cpp
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Expand Up @@ -4247,6 +4247,8 @@ static void llm_load_hparams(
} break;
case LLM_ARCH_QWEN2MOE:
{
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);

ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 24: model.type = e_model::MODEL_A2_7B; break;
Expand Down Expand Up @@ -5019,6 +5021,10 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
LLAMA_LOG_INFO("%s: rope_yarn_log_mul = %.4f\n", __func__, hparams.rope_yarn_log_mul);
}

if (model.arch == LLM_ARCH_QWEN2MOE) {
LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
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}
}

// Returns false if cancelled by progress_callback
Expand Down Expand Up @@ -5805,16 +5811,17 @@ static bool llm_load_tensors(
GGML_ASSERT(hparams.n_expert_used > 0);

// MoE branch
auto n_ff_exp = n_ff / hparams.n_expert_used;
auto n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / hparams.n_expert_used;
layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert});
layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});

// Shared expert branch
auto n_ff_shared_exp = hparams.n_ff_exp && hparams.n_expert_used ? hparams.n_ff_exp * hparams.n_expert_used : n_ff;
layer.ffn_gate_inp_shexp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd});
layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff});
layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff, n_embd});
layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff});
layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shared_exp});
layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_shared_exp, n_embd});
layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_shared_exp});
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}
} break;
case LLM_ARCH_PHI2:
Expand Down
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