fix(granitemoe convert): Split the double-sized input layer into gate…

… and up After a lot of staring and squinting, it's clear that the standard mixtral expert implementation is equivalent to the vectorized parallel experts in granite. The difference is that in granite, the w1 and w3 are concatenated into a single tensor "input_linear." Rather than reimplementing all of the math on the llama.cpp side, the much simpler route is to just split this tensor during conversion and follow the standard mixtral route. Branch: GraniteMoE Co-Authored-By: [email protected] Signed-off-by: Gabe Goodhart <[email protected]>
ggerganov · Sep 23, 2024 · 4f07702 · 4f07702
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Showing 3 changed files with 29 additions and 9 deletions.
diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
@@ -4119,8 +4119,26 @@ 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 compativility
+        with existing mixtral support, we pull them apart here.
+        """
+
+        if name.endswith("block_sparse_moe.input_linear.weight"):
+            gate, up = data_torch.chunk(2, dim=-2)
+            return [
+                (self.map_tensor_name(f"model.layers.{bid}.block_sparse_moe.input_linear.gate.weight"), gate),
+                (self.map_tensor_name(f"model.layers.{bid}.block_sparse_moe.input_linear.up.weight"), up),
+            ]
+
+        return super().modify_tensors(data_torch, name, bid)
+
+
 ###### CONVERSION LOGIC ######
 
+
 # tree of lazy tensors
 class LazyTorchTensor(gguf.LazyBase):
     _tensor_type = torch.Tensor

diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
@@ -1254,6 +1254,7 @@ class MODEL_TENSOR(IntEnum):
         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,
     ],

diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
@@ -293,11 +293,11 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.FFN_UP_EXP: (
-            "layers.{bid}.feed_forward.experts.w3",             # mixtral (merged)
-            "transformer.decoder_layer.{bid}.moe.linear_v",     # Grok (merged)
-            "transformer.blocks.{bid}.ffn.experts.mlp.v1",      # dbrx
-            "model.layers.{bid}.mlp.experts.up_proj",           # qwen2moe olmoe (merged)
-            "model.layers.{bid}.block_sparse_moe.input_linear", # granitemoe
+            "layers.{bid}.feed_forward.experts.w3",                # mixtral (merged)
+            "transformer.decoder_layer.{bid}.moe.linear_v",        # Grok (merged)
+            "transformer.blocks.{bid}.ffn.experts.mlp.v1",         # dbrx
+            "model.layers.{bid}.mlp.experts.up_proj",              # qwen2moe olmoe (merged)
+            "model.layers.{bid}.block_sparse_moe.input_linear.up", # granitemoe
         ),
 
         MODEL_TENSOR.FFN_UP_SHEXP: (
@@ -326,10 +326,11 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.FFN_GATE_EXP: (
-            "layers.{bid}.feed_forward.experts.w1",             # mixtral (merged)
-            "transformer.decoder_layer.{bid}.moe.linear",       # Grok (merged)
-            "transformer.blocks.{bid}.ffn.experts.mlp.w1",      # dbrx
-            "model.layers.{bid}.mlp.experts.gate_proj",         # qwen2moe olmoe (merged)
+            "layers.{bid}.feed_forward.experts.w1",                  # mixtral (merged)
+            "transformer.decoder_layer.{bid}.moe.linear",            # Grok (merged)
+            "transformer.blocks.{bid}.ffn.experts.mlp.w1",           # dbrx
+            "model.layers.{bid}.mlp.experts.gate_proj",              # qwen2moe olmoe (merged)
+            "model.layers.{bid}.block_sparse_moe.input_linear.gate", # granitemoe
         ),
 
         MODEL_TENSOR.FFN_GATE_SHEXP: (