add support granite and granitemoe models

docs/source/openvino/models.mdx

@@ -58,6 +58,8 @@ Here is the list of the supported architectures :
 - GPT-NeoX-Japanese
 - Gemma
 - Gemma2
+- Granite
+- GraniteMoE
 - Hubert
 - IBert
 - InternLM

optimum/exporters/openvino/model_configs.py

@@ -72,6 +72,7 @@
     GptNeoModelPatcher,
     GptNeoxJapaneseModelPatcher,
     GptNeoxModelPatcher,
+    GraniteMoEModelPatcher,
     IBertModelPatcher,
     InputEmbeddingPatcher,
     InternLM2Patcher,
@@ -2554,3 +2555,30 @@ def outputs(self) -> Dict[str, Dict[int, str]]:
 )
 class GLMOpenVINOConfig(LlamaOpenVINOConfig):
     MIN_TRANSFORMERS_VERSION = "4.46.0"
+
+
+@register_in_tasks_manager(
+    "granite",
+    *[
+        "feature-extraction",
+        "feature-extraction-with-past",
+        "text-generation",
+        "text-generation-with-past",
+        "text-classification",
+    ],
+    library_name="transformers",
+)
+class GraniteOpenVINOConfig(LlamaOpenVINOConfig):
+    MIN_TRANSFORMERS_VERSION = "4.45.0"
+
+
+@register_in_tasks_manager(
+    "granitemoe", *["text-generation", "text-generation-with-past"], library_name="transformers"
+)
+class GraniteMoEOpenVINOConfig(LlamaOpenVINOConfig):
+    MIN_TRANSFORMERS_VERSION = "4.45.0"
+
+    def patch_model_for_export(
+        self, model: Union["PreTrainedModel", "TFPreTrainedModel"], model_kwargs: Optional[Dict[str, Any]] = None
+    ) -> ModelPatcher:
+        return GraniteMoEModelPatcher(self, model, model_kwargs=model_kwargs)

optimum/exporters/openvino/model_patcher.py

@@ -3601,3 +3601,72 @@ def __exit__(self, exc_type, exc_value, traceback):
         for block in self._model.blocks:
             block.forward = block._orig_forward
             block.attn.forward = block.attn._orig_forward
+
+
+# copied from https://github.com/huggingface/transformers/blob/v4.47.1/src/transformers/models/granitemoe/modeling_granitemoe.py#L321
+def _granite_moe_topk_gating_forward(self, hidden_states):
+    # compute the top_k routing decision
+    logits = self.layer(hidden_states).float()  # [batch_size x seq_len, num_experts]
+    top_k_logits, top_k_indices = logits.topk(self.top_k, dim=1)  # [num_tokens, top_k]
+    top_k_gates = torch.softmax(top_k_logits, dim=1).type_as(hidden_states)  # [num_tokens, top_k]
+
+    # compute number of input given to each expert
+    zeros = torch.zeros(
+        [top_k_gates.size(0), self.num_experts], dtype=top_k_gates.dtype, device=top_k_gates.device
+    )  # [num_tokens, num_experts]
+    gates = zeros.scatter(1, top_k_indices, 1)  # [num_tokens, num_experts]
+    expert_size = gates.long().sum(0)  # [num_experts,]
+    # difference with original, removed expert_size = expert_size.tolist() due to incorrect tracing
+
+    # sort and group input tokens according to expert assignment
+    top_k_experts = top_k_indices.flatten()  # [num_tokens * top_k]
+    _, index_sorted_experts = top_k_experts.sort(0)  # [num_tokens * top_k]
+    batch_index = index_sorted_experts.div(self.top_k, rounding_mode="trunc")  # [num_tokens * top_k]
+
+    # gather the gate values for grouped input tokens
+    top_k_gates = top_k_gates.flatten()  # [num_tokens * top_k]
+    batch_gates = top_k_gates[index_sorted_experts]  # [num_tokens * top_k]
+
+    return index_sorted_experts, batch_index, batch_gates, expert_size, logits
+
+
+# copied from https://github.com/huggingface/transformers/blob/v4.47.1/src/transformers/models/granitemoe/modeling_granitemoe.py#L281
+def _granite_moe_parallel_experts_forward(self, inputs, expert_size):
+    output_list = []
+    # difference with original
+    # 1) expert_size is tensor instead of list of ints after gating patching, that does not allow use original inputs.split(expert_size)
+    # 2) use index_start:next_index for obtaining expert inputs splits one by one instead of precomputed splits once before cycle
+    index_start = torch.tensor(0, dtype=torch.int64)
+    for i in range(self.num_experts):
+        next_index = index_start + expert_size[i]
+        output_list.append(F.linear(inputs[index_start:next_index], self.weight[i]))
+        index_start = next_index
+    results = torch.cat(output_list, dim=0)
+    return results
+
+
+class GraniteMoEModelPatcher(LlamaModelPatcher):
+    def __enter__(self):
+        super().__enter__()
+        for layer in self._model.model.layers:
+            block_sparse_moe = layer.block_sparse_moe
+            block_sparse_moe.router._orig_forward = block_sparse_moe.router.forward
+            block_sparse_moe.router.forward = types.MethodType(
+                _granite_moe_topk_gating_forward, block_sparse_moe.router
+            )
+            block_sparse_moe.input_linear._orig_forward = block_sparse_moe.input_linear.forward
+            block_sparse_moe.input_linear.forward = types.MethodType(
+                _granite_moe_parallel_experts_forward, block_sparse_moe.input_linear
+            )
+            block_sparse_moe.output_linear._orig_forward = block_sparse_moe.output_linear.forward
+            block_sparse_moe.output_linear.forward = types.MethodType(
+                _granite_moe_parallel_experts_forward, block_sparse_moe.output_linear
+            )
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        super().__exit__(exc_type, exc_value, traceback)
+        for layer in self._model.model.layers:
+            block_sparse_moe = layer.block_sparse_moe
+            block_sparse_moe.router.forward = block_sparse_moe.router._orig_forward
+            block_sparse_moe.input_linear.forward = block_sparse_moe.input_linear._orig_forward
+            block_sparse_moe.output_linear.forward = block_sparse_moe.output_linear._orig_forward

tests/openvino/utils_tests.py

@@ -72,6 +72,8 @@
     "gpt_neox": "hf-internal-testing/tiny-random-GPTNeoXForCausalLM",
     "gpt_neox_japanese": "hf-internal-testing/tiny-random-GPTNeoXJapaneseForCausalLM",
     "gptj": "hf-internal-testing/tiny-random-GPTJModel",
+    "granite": "katuni4ka/tiny-random-granite",
+    "granite-moe": "katuni4ka/tiny-random-granite-moe",
     "hubert": "hf-internal-testing/tiny-random-HubertModel",
     "ibert": "hf-internal-testing/tiny-random-ibert",
     "internlm": "katuni4ka/tiny-random-internlm",