add support and optimization for minicpmo audio part (#12716)

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

@@ -1030,9 +1030,9 @@ def _optimize_pre(model, qtype=None):
         model.llm.config.model_type = "minicpmv"
     elif model.config.model_type == "minicpmo":
         # vpm opt
-        from ipex_llm.transformers.models.minicpmv import merge_qkv
-        model.vpm.apply(merge_qkv)
-
+        if hasattr(model, "vpm"):
+            from ipex_llm.transformers.models.minicpmv import merge_qkv
+            model.vpm.apply(merge_qkv)
         # llm opt
         model.llm.config.model_type = "qwen2"
         _optimize_pre(model.llm, qtype=qtype)
@@ -1955,12 +1955,18 @@ def _optimize_post(model):
             model.chat = MethodType(minicpmv_chat, model)
     elif model.config.model_type == "minicpmo":
         # vpm opt
-        vpm_modeling_module_name = model.vpm.__class__.__module__
-        vpm_module = importlib.import_module(vpm_modeling_module_name)
-
-        from ipex_llm.transformers.models.minicpmv import siglip_attention_forward
-        convert_forward(model.vpm, vpm_module.SiglipAttention, siglip_attention_forward)
-
+        if hasattr(model, "vpm"):
+            vpm_modeling_module_name = model.vpm.__class__.__module__
+            vpm_module = importlib.import_module(vpm_modeling_module_name)
+            from ipex_llm.transformers.models.minicpmv import siglip_attention_forward
+            convert_forward(model.vpm, vpm_module.SiglipAttention, siglip_attention_forward)
+        # apm opt
+        if hasattr(model, "apm"):
+            apm_modeling_module_name = model.apm.__class__.__module__
+            apm_module = importlib.import_module(apm_modeling_module_name)
+            from transformers.models.whisper.modeling_whisper import WhisperSdpaAttention
+            from ipex_llm.transformers.models.whisper import whisper_attention_forward
+            convert_forward(model.apm, WhisperSdpaAttention, whisper_attention_forward)
         # llm opt
         model.llm.config.model_type = "qwen2"
         _optimize_post(model.llm)

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

@@ -0,0 +1,103 @@
+#
+# 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/whisper/modeling_whisper.py
+# which is licensed under Apache License 2.0:
+#
+# Copyright 2022 The OpenAI Authors and 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 torch
+
+from typing import Optional, Tuple
+from transformers.cache_utils import EncoderDecoderCache
+
+from ipex_llm.transformers.utils import invalidInputError
+from ipex_llm.transformers.models.common import scaled_dot_product_attention
+
+
+def whisper_attention_forward(
+    self,
+    hidden_states: torch.Tensor,
+    key_value_states: Optional[torch.Tensor] = None,
+    past_key_value: Optional[EncoderDecoderCache] = None,
+    attention_mask: Optional[torch.Tensor] = None,
+    layer_head_mask: Optional[torch.Tensor] = None,
+    output_attentions: bool = False,
+    cache_position: Optional[torch.LongTensor] = None,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    invalidInputError(not output_attentions and layer_head_mask is None,
+                      "`output_attentions` and `layer_head_mask` are not supported")
+
+    # if key_value_states are provided this layer is used as a cross-attention layer
+    # for the decoder
+    is_cross_attention = key_value_states is not None
+    bsz, tgt_len, _ = hidden_states.size()
+
+    # get query proj
+    query_states = self._shape(self.q_proj(hidden_states), tgt_len, bsz)
+
+    if past_key_value is not None:
+        is_updated = past_key_value.is_updated.get(self.layer_idx)
+        if is_cross_attention:
+            past_key_value.is_updated[self.layer_idx] = True
+            past_key_value = past_key_value.cross_attention_cache
+        else:
+            past_key_value = past_key_value.self_attention_cache
+
+    # use key_value_states if cross attention
+    current_states = key_value_states if key_value_states is not None else hidden_states
+    if is_cross_attention and past_key_value and is_updated:
+        # reuse k,v, cross_attentions
+        key_states = past_key_value.key_cache[self.layer_idx]
+        value_states = past_key_value.value_cache[self.layer_idx]
+    else:
+        key_states = self._shape(self.k_proj(current_states), -1, bsz)
+        value_states = self._shape(self.v_proj(current_states), -1, bsz)
+        if past_key_value is not None:
+            cache_position = cache_position if not is_cross_attention else None
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, {"cache_position": cache_position}
+            )
+
+    # IPEX-LLM OPT: sdpa
+    is_causal = True if self.is_causal and attention_mask is None and tgt_len > 1 else False
+    attn_output = scaled_dot_product_attention(
+        query_states,
+        key_states.contiguous(),
+        value_states.contiguous(),
+        attention_mask,
+        is_causal
+    )
+
+    attn_output = attn_output.transpose(1, 2)
+    attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
+
+    attn_output = self.out_proj(attn_output)
+
+    return attn_output, None, past_key_value