add basic llama 3.2 vision support (#12163)

Author: MeouSker77

python/llm/src/ipex_llm/transformers/convert.py

@@ -1279,6 +1279,16 @@ def _optimize_post(model, lightweight_bmm=False):
         convert_forward(model, module.LlamaMLP, mlp_silu_forward)
         convert_forward(model, module.LlamaModel, llama_model_forward)
         convert_forward(model, module.LlamaAttention, llama_attention_forward)
+    elif model.config.model_type == "mllama":
+        # llama 3.2 vision
+        modeling_module_name = model.__class__.__module__
+        module = importlib.import_module(modeling_module_name)
+        from ipex_llm.transformers.models.common import rms_norm_forward
+        from ipex_llm.transformers.models.common import mlp_silu_forward
+        from ipex_llm.transformers.models.mllama import mllama_vision_attention_forward
+        convert_forward(model, module.MllamaVisionAttention, mllama_vision_attention_forward)
+        convert_forward(model, module.MllamaTextRMSNorm, rms_norm_forward)
+        convert_forward(model, module.MllamaTextMLP, mlp_silu_forward)
     elif model.config.model_type == "llama":
         from transformers.models.llama.modeling_llama import LlamaRMSNorm
         from transformers.models.llama.modeling_llama import LlamaMLP

python/llm/src/ipex_llm/transformers/models/mllama.py

@@ -0,0 +1,78 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Some parts of this file is adapted from
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/mllama/modeling_mllama.py
+# which is licensed under Apache License 2.0:
+#
+# Copyright 2024 the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import math
+import torch
+
+from typing import Optional
+
+
+def mllama_vision_attention_forward(
+    self,
+    hidden_state: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    output_attentions: bool = None,
+):
+    query = self.q_proj(hidden_state)
+    key = self.k_proj(hidden_state)
+    value = self.v_proj(hidden_state)
+
+    batch_size, q_seq_len, _ = query.shape
+    _, kv_seq_len, _ = key.shape
+
+    query = query.view(batch_size, q_seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+    key = key.view(batch_size, kv_seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+    value = value.view(batch_size, kv_seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+
+    attn_weights = torch.matmul(query, key.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+    if attention_mask is not None:  # no matter the length, we just slice it
+        causal_mask = attention_mask[:, :, :, : key.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+
+    # upcast attention to fp32
+    from ipex_llm.transformers.models.common import attention_softmax
+    attn_weights = attention_softmax(attn_weights, self.training)
+
+    attn_output = torch.matmul(attn_weights, value)
+
+    attn_output = attn_output.transpose(1, 2).contiguous()
+    attn_output = attn_output.reshape(batch_size, q_seq_len, -1)
+
+    output = self.o_proj(attn_output)
+
+    if not output_attentions:
+        attn_weights = None
+
+    return output, attn_weights