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model : Granite MoE shared (#13269)
* feat: Add GGUF conversion for granitemoeshared Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: hparam and arch plumbing for granitemoeshared Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Split MoE fused tensors for shared experts in conversion Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: First WIP cut at model arch in cpp The hparam and architecture plumbing should be correct, but the implementation of the shared experts seems to still be broken. Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Cleaner (maybe more correct?) splitting for gate/up Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix the input to the shared experts I had misread that the shared experts take the inputs _before_ the standard MoE layer and was feeding the output of the MoE to the shared experts. Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Avoid architecture-specific checks for Granite MoE Shared This is a cleaner way that will allow more flexibility in architecture strings going forward. Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Split granite architectures out of llm_build_llama This helps de-clutter the llama-family graph construction and allows granite to diverge further (in preparation for Granite 4). NOTE: I removed the granite scale factors from llm_build_deci because they appear to only be there as copy-paste from llm_build_llama. The HF config does not seem to set those values: https://huggingface.co/Deci/DeciLM-7B/blob/main/config.json Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix compiler warning about uninitialized inp_pos This should not have been reachable, but it warns on some compliers Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Consoladate GraniteMoEShared into GraniteMoE for conversion Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Consolidate GraniteMoEShared into GraniteMoE on the c++ side Branch: GraniteMoEShared Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
This commit is contained in:
Gabe Goodhart
GitHub
@ -5746,11 +5746,20 @@ class GraniteModel(LlamaModel):
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logger.info("gguf: (granite) logits_scale = %s", logits_scale)
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@ModelBase.register("GraniteMoeForCausalLM")
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@ModelBase.register("GraniteMoeForCausalLM", "GraniteMoeSharedForCausalLM")
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class GraniteMoeModel(GraniteModel):
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"""Conversion for IBM's GraniteMoeForCausalLM"""
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model_arch = gguf.MODEL_ARCH.GRANITE_MOE
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def set_gguf_parameters(self):
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"""GraniteMoeShared uses GraniteMoe parameters plus the following:
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- shared_intermediate_size
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"""
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super().set_gguf_parameters()
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if shared_feed_forward_length := self.hparams.get("shared_intermediate_size"):
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self.gguf_writer.add_expert_shared_feed_forward_length(shared_feed_forward_length)
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logger.info("gguf: (granitemoeshared) shared_feed_forward_length = %s", shared_feed_forward_length)
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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"""In modeling_granitemoe, the JetMoe implementation of parallel experts
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is used. This essentially merges w1 and w3 into a single tensor with 2x
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@ -5761,12 +5770,21 @@ class GraniteMoeModel(GraniteModel):
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if name.endswith("block_sparse_moe.input_linear.weight"):
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ffn_dim = self.hparams["intermediate_size"]
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assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * intermediate_size"
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gate, up = data_torch[..., :ffn_dim, :], data_torch[..., ffn_dim:, :]
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gate, up = data_torch.split(ffn_dim, dim=-2)
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return [
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(self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid), gate),
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(self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_EXP, bid), up),
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]
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if name.endswith("shared_mlp.input_linear.weight"):
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ffn_dim = self.hparams["shared_intermediate_size"]
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assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * shared_intermediate_size"
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gate, up = data_torch.split(ffn_dim, dim=-2)
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return [
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(self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_SHEXP, bid), gate),
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(self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_SHEXP, bid), up),
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]
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return super().modify_tensors(data_torch, name, bid)
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@ -1905,6 +1905,9 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_GATE_EXP,
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MODEL_TENSOR.FFN_DOWN_EXP,
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MODEL_TENSOR.FFN_UP_EXP,
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MODEL_TENSOR.FFN_GATE_SHEXP,
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MODEL_TENSOR.FFN_UP_SHEXP,
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MODEL_TENSOR.FFN_DOWN_SHEXP,
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],
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MODEL_ARCH.CHAMELEON: [
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MODEL_TENSOR.TOKEN_EMBD,
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@ -428,6 +428,7 @@ class TensorNameMap:
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"model.layers.{bid}.mlp.shared_expert.down_proj", # qwen2moe
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"model.layers.{bid}.mlp.shared_experts.down_proj", # deepseek deepseek2
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"language_model.model.layers.{bid}.feed_forward.shared_expert.down_proj", # llama4
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"model.layers.{bid}.shared_mlp.output_linear", # granitemoe
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),
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MODEL_TENSOR.ATTN_Q_NORM: (
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@ -1481,6 +1481,9 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
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{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
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{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
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{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
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{ LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" },
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{ LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" },
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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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@ -1389,6 +1389,9 @@ void llama_model::load_hparams(llama_model_loader & ml) {
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// Add additional layer/vocab/etc checks here for other model sizes
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default: type = LLM_TYPE_UNKNOWN;
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}
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// For Granite MoE Shared
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ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, /* required */ false);
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} break;
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case LLM_ARCH_CHAMELEON:
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{
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@ -1772,6 +1775,13 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
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layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, TENSOR_NOT_REQUIRED);
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layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0);
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layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
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// For Granite MoE Shared
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if (hparams.n_ff_shexp > 0) {
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layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
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layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
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layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);
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}
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}
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}
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} break;
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@ -4385,10 +4395,13 @@ void llama_model::print_info() const {
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LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
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}
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if (arch == LLM_ARCH_MINICPM || arch == LLM_ARCH_GRANITE || arch == LLM_ARCH_GRANITE_MOE) {
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if (arch == LLM_ARCH_MINICPM ||
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arch == LLM_ARCH_GRANITE ||
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arch == LLM_ARCH_GRANITE_MOE) {
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LLAMA_LOG_INFO("%s: f_embedding_scale = %f\n", __func__, hparams.f_embedding_scale);
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LLAMA_LOG_INFO("%s: f_residual_scale = %f\n", __func__, hparams.f_residual_scale);
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LLAMA_LOG_INFO("%s: f_attention_scale = %f\n", __func__, hparams.f_attention_scale);
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LLAMA_LOG_INFO("%s: n_ff_shexp = %d\n", __func__, hparams.n_ff_shexp);
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}
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if (arch == LLM_ARCH_BAILINGMOE) {
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@ -4598,11 +4611,6 @@ struct llm_build_llama : public llm_graph_context {
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inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
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}
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// For Granite architecture
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if (hparams.f_residual_scale) {
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cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
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}
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ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
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cb(ffn_inp, "ffn_inp", il);
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@ -4674,11 +4682,6 @@ struct llm_build_llama : public llm_graph_context {
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cb(cur, "ffn_moe_out", il);
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}
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// For Granite architecture
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if (hparams.f_residual_scale) {
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cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
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}
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cur = ggml_add(ctx0, cur, ffn_inp);
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cb(cur, "ffn_out", il);
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@ -4701,11 +4704,6 @@ struct llm_build_llama : public llm_graph_context {
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// lm_head
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cur = build_lora_mm(model.output, cur);
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// For Granite architecture
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if (hparams.f_logit_scale) {
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cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
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}
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cb(cur, "result_output", -1);
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res->t_logits = cur;
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@ -4816,11 +4814,6 @@ struct llm_build_deci : public llm_graph_context {
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continue;
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}
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// For Granite architecture
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if (hparams.f_residual_scale) {
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cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
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}
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// modified to support attention-free layer of Llama-3_1-Nemotron-51B
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ggml_tensor * ffn_inp = cur;
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if (n_head > 0) {
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@ -4844,11 +4837,6 @@ struct llm_build_deci : public llm_graph_context {
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cb(cur, "ffn_out", il);
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}
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// For Granite architecture
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if (hparams.f_residual_scale) {
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cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
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}
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cur = ggml_add(ctx0, cur, ffn_inp);
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cb(cur, "ffn_out", il);
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@ -4871,11 +4859,6 @@ struct llm_build_deci : public llm_graph_context {
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// lm_head
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cur = build_lora_mm(model.output, cur);
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// For Granite architecture
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if (hparams.f_logit_scale) {
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cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
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}
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cb(cur, "result_output", -1);
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res->t_logits = cur;
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@ -12214,6 +12197,195 @@ struct llm_build_arwkv7 : public llm_build_rwkv7_base {
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}
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};
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struct llm_build_granite : public llm_graph_context {
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llm_build_granite(
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const llama_model & model,
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const llm_graph_params & params,
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ggml_cgraph * gf,
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const bool use_rope = true)
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: llm_graph_context(params) {
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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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ggml_tensor * cur;
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ggml_tensor * inpL;
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inpL = build_inp_embd(model.tok_embd);
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// inp_pos - built only if rope enabled
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ggml_tensor * inp_pos = nullptr;
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auto * inp_attn = build_attn_inp_kv_unified();
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const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
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for (int il = 0; il < n_layer; ++il) {
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ggml_tensor * inpSA = inpL;
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// norm
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cur = build_norm(inpL,
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model.layers[il].attn_norm, NULL,
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LLM_NORM_RMS, 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 (optionally) RoPE them
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ggml_tensor * Qcur = build_lora_mm(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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ggml_tensor * Kcur = build_lora_mm(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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ggml_tensor * Vcur = build_lora_mm(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_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
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Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
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if (use_rope) {
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if (!inp_pos) {
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inp_pos = build_inp_pos();
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}
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ggml_tensor * rope_factors = model.get_rope_factors(n_ctx_per_seq, il);
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Qcur = ggml_rope_ext(
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ctx0, Qcur, inp_pos, rope_factors,
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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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Kcur = ggml_rope_ext(
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ctx0, Kcur, inp_pos, rope_factors,
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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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}
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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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cur = build_attn(inp_attn, gf,
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model.layers[il].wo, model.layers[il].bo,
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Qcur, Kcur, Vcur, nullptr, nullptr, kq_scale, il);
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cb(cur, "attn_out", 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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ggml_tensor * inp_out_ids = build_inp_out_ids();
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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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// For Granite architectures - scale residual
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cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
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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 (non-MoE)
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if (model.layers[il].ffn_gate_inp == nullptr) {
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cur = build_norm(ffn_inp,
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model.layers[il].ffn_norm, NULL,
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LLM_NORM_RMS, il);
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cb(cur, "ffn_norm", il);
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cur = build_ffn(cur,
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model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
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model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
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model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, il);
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cb(cur, "ffn_out", il);
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} else {
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// MoE branch
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cur = build_norm(ffn_inp,
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model.layers[il].ffn_norm, NULL,
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LLM_NORM_RMS, il);
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cb(cur, "ffn_norm", il);
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ggml_tensor * moe_out = build_moe_ffn(cur,
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model.layers[il].ffn_gate_inp,
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model.layers[il].ffn_up_exps,
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model.layers[il].ffn_gate_exps,
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model.layers[il].ffn_down_exps,
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nullptr,
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n_expert, n_expert_used,
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LLM_FFN_SILU, true,
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false, 0.0,
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LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
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il);
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cb(moe_out, "ffn_moe_out", il);
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// For Granite MoE Shared
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if (hparams.n_ff_shexp > 0) {
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ggml_tensor * ffn_shexp = build_ffn(cur,
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model.layers[il].ffn_up_shexp, NULL, NULL,
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model.layers[il].ffn_gate_shexp, NULL, NULL,
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model.layers[il].ffn_down_shexp, NULL, NULL,
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NULL,
|
||||
LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(ffn_shexp, "ffn_shexp", il);
|
||||
|
||||
cur = ggml_add(ctx0, moe_out, ffn_shexp);
|
||||
cb(cur, "ffn_out", il);
|
||||
} else {
|
||||
cur = moe_out;
|
||||
}
|
||||
}
|
||||
|
||||
// For Granite architectures - scale residual
|
||||
cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
cb(cur, "ffn_out", il);
|
||||
|
||||
cur = build_cvec(cur, il);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
cur = build_norm(cur,
|
||||
model.output_norm, NULL,
|
||||
LLM_NORM_RMS, -1);
|
||||
|
||||
cb(cur, "result_norm", -1);
|
||||
res->t_embd = cur;
|
||||
|
||||
// lm_head
|
||||
cur = build_lora_mm(model.output, cur);
|
||||
|
||||
// For Granite architectures - scale logits
|
||||
cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
|
||||
cb(cur, "result_output", -1);
|
||||
res->t_logits = cur;
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
};
|
||||
|
||||
// ref: https://github.com/facebookresearch/chameleon
|
||||
// based on the original build_llama() function, changes:
|
||||
// * qk-norm
|
||||
@ -12921,8 +13093,6 @@ llm_graph_result_ptr llama_model::build_graph(
|
||||
case LLM_ARCH_LLAMA:
|
||||
case LLM_ARCH_LLAMA4:
|
||||
case LLM_ARCH_MINICPM:
|
||||
case LLM_ARCH_GRANITE:
|
||||
case LLM_ARCH_GRANITE_MOE:
|
||||
{
|
||||
llm = std::make_unique<llm_build_llama>(*this, params, gf);
|
||||
} break;
|
||||
@ -13153,6 +13323,11 @@ llm_graph_result_ptr llama_model::build_graph(
|
||||
{
|
||||
llm = std::make_unique<llm_build_arwkv7>(*this, params, gf);
|
||||
} break;
|
||||
case LLM_ARCH_GRANITE:
|
||||
case LLM_ARCH_GRANITE_MOE:
|
||||
{
|
||||
llm = std::make_unique<llm_build_granite>(*this, params, gf);
|
||||
} break;
|
||||
case LLM_ARCH_CHAMELEON:
|
||||
{
|
||||
llm = std::make_unique<llm_build_chameleon>(*this, params, gf);
|
||||
|
||||
Reference in New Issue
Block a user