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IBM Granite MoE Architecture #9438

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merged 10 commits into from
Sep 25, 2024
Merged

IBM Granite MoE Architecture #9438

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8a4ca23
feat(gguf-py): Add granitemoe architecture
gabe-l-hart Sep 10, 2024
e0b7229
feat(convert_hf_to_gguf): Add GraniteMoeModel
gabe-l-hart Sep 10, 2024
014e59d
fix(granitemoe convert): Split the double-sized input layer into gate…
gabe-l-hart Sep 11, 2024
eca37cd
feat(granitemoe): Implement granitemoe
gabe-l-hart Sep 10, 2024
71bc4c1
Typo fix in docstring
gabe-l-hart Sep 23, 2024
5eb28c4
fix(conversion): Simplify tensor name mapping in conversion
gabe-l-hart Sep 23, 2024
f236099
fix(convert): Remove unused tensor name mappings
gabe-l-hart Sep 23, 2024
317b15b
fix(convert): Sanity check on merged FFN tensor sizes
gabe-l-hart Sep 23, 2024
1c8b3e4
fix: Allow "output" layer in granite moe architecture (convert and cpp)
gabe-l-hart Sep 23, 2024
a843f1f
fix(granite): Add missing 'output' tensor for Granite
gabe-l-hart Sep 24, 2024
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33 changes: 31 additions & 2 deletions convert_hf_to_gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4102,16 +4102,45 @@ def set_gguf_parameters(self):
# consistency
if attention_scale := self.hparams.get("attention_multiplier"):
self.gguf_writer.add_attention_scale(attention_scale)
logger.info("gguf: (granite) attention_scale = %s", attention_scale)
if embedding_scale := self.hparams.get("embedding_multiplier"):
self.gguf_writer.add_embedding_scale(embedding_scale)
logger.info("gguf: (granite) embedding_scale = %s", embedding_scale)
if residual_scale := self.hparams.get("residual_multiplier"):
self.gguf_writer.add_residual_scale(residual_scale)
if logits_scaling := self.hparams.get("logits_scaling"):
self.gguf_writer.add_logit_scale(logits_scaling)
logger.info("gguf: (granite) residual_scale = %s", residual_scale)
if logits_scale := self.hparams.get("logits_scaling"):
self.gguf_writer.add_logit_scale(logits_scale)
logger.info("gguf: (granite) logits_scale = %s", logits_scale)


@Model.register("GraniteMoeForCausalLM")
class GraniteMoeModel(GraniteModel):
"""Conversion for IBM's GraniteMoeForCausalLM"""
model_arch = gguf.MODEL_ARCH.GRANITE_MOE

def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
"""In modeling_granitemoe, the JetMoe implementation of parallel experts
is used. This essentially merges w1 and w3 into a single tensor with 2x
the hidden size that is then split during forward. To keep compatibility
with existing mixtral support, we pull them apart here.
"""

if name.endswith("block_sparse_moe.input_linear.weight"):
ffn_dim = self.hparams["intermediate_size"]
assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * intermediate_size"
gate, up = data_torch[..., :ffn_dim, :], data_torch[..., ffn_dim:, :]
return [
(self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid), gate),
(self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_EXP, bid), up),
]

return super().modify_tensors(data_torch, name, bid)


###### CONVERSION LOGIC ######


# tree of lazy tensors
class LazyTorchTensor(gguf.LazyBase):
_tensor_type = torch.Tensor
Expand Down
18 changes: 18 additions & 0 deletions gguf-py/gguf/constants.py
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Expand Up @@ -235,6 +235,7 @@ class MODEL_ARCH(IntEnum):
NEMOTRON = auto()
EXAONE = auto()
GRANITE = auto()
GRANITE_MOE = auto()


class MODEL_TENSOR(IntEnum):
Expand Down Expand Up @@ -392,6 +393,7 @@ class MODEL_TENSOR(IntEnum):
MODEL_ARCH.NEMOTRON: "nemotron",
MODEL_ARCH.EXAONE: "exaone",
MODEL_ARCH.GRANITE: "granite",
MODEL_ARCH.GRANITE_MOE: "granitemoe",
}

TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
Expand Down Expand Up @@ -1232,6 +1234,7 @@ class MODEL_TENSOR(IntEnum):
MODEL_ARCH.GRANITE: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
Expand All @@ -1242,6 +1245,21 @@ class MODEL_TENSOR(IntEnum):
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
MODEL_ARCH.GRANITE_MOE: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
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,
],
# TODO
}

Expand Down
20 changes: 11 additions & 9 deletions gguf-py/gguf/tensor_mapping.py
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Original file line number Diff line number Diff line change
Expand Up @@ -251,11 +251,12 @@ class TensorNameMap:
),

MODEL_TENSOR.FFN_GATE_INP: (
"layers.{bid}.feed_forward.gate", # mixtral
"model.layers.{bid}.block_sparse_moe.gate", # mixtral
"model.layers.{bid}.mlp.gate", # qwen2moe olmoe
"transformer.decoder_layer.{bid}.router", # Grok
"transformer.blocks.{bid}.ffn.router.layer", # dbrx
"layers.{bid}.feed_forward.gate", # mixtral
"model.layers.{bid}.block_sparse_moe.gate", # mixtral
"model.layers.{bid}.mlp.gate", # qwen2moe olmoe
"transformer.decoder_layer.{bid}.router", # Grok
"transformer.blocks.{bid}.ffn.router.layer", # dbrx
"model.layers.{bid}.block_sparse_moe.router.layer", # granitemoe
),

MODEL_TENSOR.FFN_GATE_INP_SHEXP: (
Expand Down Expand Up @@ -364,10 +365,11 @@ class TensorNameMap:
),

MODEL_TENSOR.FFN_DOWN_EXP: (
"layers.{bid}.feed_forward.experts.w2", # mixtral (merged)
"transformer.decoder_layer.{bid}.moe.linear_1", # Grok (merged)
"transformer.blocks.{bid}.ffn.experts.mlp.w2", # dbrx
"model.layers.{bid}.mlp.experts.down_proj", # qwen2moe olmoe (merged)
"layers.{bid}.feed_forward.experts.w2", # mixtral (merged)
"transformer.decoder_layer.{bid}.moe.linear_1", # Grok (merged)
"transformer.blocks.{bid}.ffn.experts.mlp.w2", # dbrx
"model.layers.{bid}.mlp.experts.down_proj", # qwen2moe olmoe (merged)
"model.layers.{bid}.block_sparse_moe.output_linear", # granitemoe
),

MODEL_TENSOR.FFN_DOWN_SHEXP: (
Expand Down
30 changes: 28 additions & 2 deletions src/llama.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -215,6 +215,7 @@ enum llm_arch {
LLM_ARCH_EXAONE,
LLM_ARCH_RWKV6,
LLM_ARCH_GRANITE,
LLM_ARCH_GRANITE_MOE,
LLM_ARCH_UNKNOWN,
};

Expand Down Expand Up @@ -266,6 +267,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_EXAONE, "exaone" },
{ LLM_ARCH_RWKV6, "rwkv6" },
{ LLM_ARCH_GRANITE, "granite" },
{ LLM_ARCH_GRANITE_MOE, "granitemoe" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};

Expand Down Expand Up @@ -1467,6 +1469,7 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
Expand All @@ -1478,6 +1481,24 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_GRANITE_MOE,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
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{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
},
},
{
LLM_ARCH_UNKNOWN,
{
Expand Down Expand Up @@ -2396,7 +2417,7 @@ struct llama_hparams {
float f_max_alibi_bias = 0.0f;
float f_logit_scale = 0.0f;

// Additional scale factors (Granite)
// Additional scale factors (Granite/Granite MoE)
float f_residual_scale = 0.0f;
float f_embedding_scale = 0.0f;
float f_attention_scale = 0.0f;
Expand Down Expand Up @@ -6048,6 +6069,7 @@ static void llm_load_hparams(
}
} break;
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
Expand All @@ -6056,6 +6078,7 @@ static void llm_load_hparams(
ml.get_key(LLM_KV_ATTENTION_SCALE, hparams.f_attention_scale);

switch (hparams.n_layer) {
case 32: model.type = e_model::MODEL_3B; break;
case 40: model.type = e_model::MODEL_3B; break;
// Add additional layer/vocab/etc checks here for other model sizes
default: model.type = e_model::MODEL_UNKNOWN;
Expand Down Expand Up @@ -6810,7 +6833,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
LLAMA_LOG_INFO("%s: n_ff_shexp = %d\n", __func__, hparams.n_ff_shexp);
}

if (model.arch == LLM_ARCH_GRANITE) {
if (model.arch == LLM_ARCH_GRANITE || model.arch == LLM_ARCH_GRANITE_MOE) {
LLAMA_LOG_INFO("%s: f_embedding_scale = %f\n", __func__, hparams.f_embedding_scale);
LLAMA_LOG_INFO("%s: f_residual_scale = %f\n", __func__, hparams.f_residual_scale);
LLAMA_LOG_INFO("%s: f_attention_scale = %f\n", __func__, hparams.f_attention_scale);
Expand Down Expand Up @@ -6984,6 +7007,7 @@ static bool llm_load_tensors(
case LLM_ARCH_REFACT:
case LLM_ARCH_MINICPM:
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
{
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});

Expand Down Expand Up @@ -15930,6 +15954,7 @@ static struct ggml_cgraph * llama_build_graph(
switch (model.arch) {
case LLM_ARCH_LLAMA:
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
{
result = llm.build_llama();
} break;
Expand Down Expand Up @@ -19231,6 +19256,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
case LLM_ARCH_DEEPSEEK2:
case LLM_ARCH_CHATGLM:
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
return LLAMA_ROPE_TYPE_NORM;

// the pairs of head values are offset by n_rot/2
Expand Down
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