fix bauchan-13b

Author: eaidova

optimum/exporters/openvino/convert.py

@@ -347,6 +347,7 @@ def ts_patched_forward(*args, **kwargs):
 
             with patcher:
                 check_dummy_inputs_are_allowed(model, dummy_inputs)
+                sig = inspect.signature(model.forward) if hasattr(model, "forward") else inspect.signature(model.call)
                 inputs = config.ordered_inputs(model)
                 input_names = list(inputs.keys())
                 output_names = list(config.outputs.keys())
@@ -376,7 +377,6 @@ def ts_patched_forward(*args, **kwargs):
                 ov_config=ov_config,
             )
 
-        sig = inspect.signature(model.forward) if hasattr(model, "forward") else inspect.signature(model.call)
         ordered_dummy_inputs = {param: dummy_inputs[param] for param in sig.parameters if param in dummy_inputs}
         if not ordered_dummy_inputs:
             ordered_dummy_inputs = dummy_inputs
@@ -388,15 +388,16 @@ def ts_patched_forward(*args, **kwargs):
                 out_tensor.get_tensor().set_names({output_names[idx]})
 
         for idx, inp_tensor in enumerate(ov_model.inputs):
-            input_name = ordered_input_names[idx]
-            inp_tensor.get_tensor().set_names({input_name})
-            inp_data = flatten_inputs[idx]
-            static_shape = PartialShape(inp_data.shape)
-            dims = inputs[input_name]
-            for dim in dims:
-                static_shape[dim] = -1
-            inp_tensor.get_node().set_partial_shape(static_shape)
-            inp_tensor.get_node().set_element_type(get_element_type(inp_data.cpu().numpy().dtype))
+            if idx < len(ordered_input_names):
+                input_name = ordered_input_names[idx]
+                inp_tensor.get_tensor().set_names({input_name})
+                inp_data = flatten_inputs[idx]
+                static_shape = PartialShape(inp_data.shape)
+                dims = inputs.get(input_name, [])
+                for dim in dims:
+                    static_shape[dim] = -1
+                inp_tensor.get_node().set_partial_shape(static_shape)
+                inp_tensor.get_node().set_element_type(get_element_type(inp_data.cpu().numpy().dtype))
         ov_model.validate_nodes_and_infer_types()
 
         if stateful:

optimum/exporters/openvino/model_configs.py

@@ -34,11 +34,11 @@
     BaichuanModelPatcher,
     ChatGLMModelPatcher,
     GemmaModelPatcher,
+    InternLMPatcher,
     LlamaModelPatcher,
     MixtralModelPatcher,
-    QwenModelPatcher,
     MPTModelPatcher,
-    InternLMPatcher,
+    QwenModelPatcher,
 )
 
 

optimum/exporters/openvino/model_patcher.py

@@ -12,6 +12,7 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
+import inspect
 import logging as log
 import math
 import types
@@ -601,6 +602,46 @@ def __exit__(self, exc_type, exc_value, traceback):
         self._model.config.fp16 = self.original_fp16
 
 
+def _baichuan13b_atten_forward(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    output_attentions: bool = False,
+    use_cache: bool = True,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    bsz, q_len, _ = hidden_states.size()
+
+    proj = self.W_pack(hidden_states)
+    proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
+    query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+    key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+    value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+
+    kv_seq_len = key_states.shape[-2]
+    if past_key_value is not None:
+        kv_seq_len += past_key_value[0].shape[-2]
+
+    if past_key_value is not None:
+        # reuse k, v, self_attention
+        if attention_mask is not None:
+            attention_mask = attention_mask[:, :, -key_states.shape[-2] :, :]
+        key_states = torch.cat([past_key_value[0], key_states], dim=2)
+        value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+    past_key_value = (key_states, value_states) if use_cache else None
+    attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=attention_mask)
+    attn_output = attn_output.transpose(1, 2)
+    attn_weights = None
+    attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+    attn_output = self.o_proj(attn_output)
+
+    if not output_attentions:
+        attn_weights = None
+
+    return attn_output, attn_weights, past_key_value
+
+
 class BaichuanModelPatcher(DecoderModelPatcher):
     def __init__(
         self,
@@ -613,6 +654,50 @@ def __init__(
         if hasattr(self._model.lm_head, "first_flag"):
             self._model(torch.ones((1, 10), dtype=torch.int64), torch.ones((1, 10), dtype=torch.int64))
 
+    def __enter__(self):
+        super().__enter__()
+        # override signature to have position_ids
+        if "position_ids" not in inspect.signature(self._model.forward).parameters:
+            self._model._orig_forward = self._model.forward
+
+            def forward(
+                self,
+                input_ids: torch.LongTensor = None,
+                attention_mask: Optional[torch.Tensor] = None,
+                past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
+                inputs_embeds: Optional[torch.FloatTensor] = None,
+                labels: Optional[torch.LongTensor] = None,
+                use_cache: Optional[bool] = None,
+                output_attentions: Optional[bool] = False,
+                output_hidden_states: Optional[bool] = False,
+                return_dict: Optional[bool] = True,
+                position_ids: Optional[torch.LongTensor] = None,
+            ):
+                return self._orig_forward(
+                    input_ids=input_ids,
+                    attention_mask=attention_mask,
+                    past_key_values=past_key_values,
+                    inputs_embeds=inputs_embeds,
+                    labels=labels,
+                    use_cache=past_key_values is not None,
+                    output_attentions=output_attentions,
+                    output_hidden_states=output_hidden_states,
+                    return_dict=self.config.return_dict,
+                )
+
+            self._model.forward = types.MethodType(forward, self._model)
+            for layer in self._model.model.layers:
+                layer.self_attn._orig_forward = layer.self_attn.forward
+                layer.self_attn.forward = types.MethodType(_baichuan13b_atten_forward, layer.self_attn)
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        super().__exit__(exc_type, exc_value, traceback)
+        if hasattr(self._model, "_orig_forward"):
+            self._model.forward = self._model._orig_forward
+
+            for layer in self._model.model.layers:
+                layer.self_attn.forward = layer.self_attn._orig_forward
+
 
 def _mpt_attention_forward(
     self,
@@ -680,7 +765,7 @@ def _internlm_attention_forward(
     **kwargs,
 ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
 
-    from transformers.models.llama.modeling_llama import repeat_kv, apply_rotary_pos_emb
+    from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv
 
     bsz, q_len, _ = hidden_states.size()
 

optimum/intel/openvino/quantization.py

@@ -484,9 +484,9 @@ def _quantize_torchmodel(
                 subset_size=quantization_config.num_samples,
                 ignored_scope=quantization_config.get_ignored_scope_instance(),
                 model_type=nncf.ModelType(quantization_config.model_type),
-                preset=nncf.QuantizationPreset.PERFORMANCE
-                if quantization_config.sym
-                else nncf.QuantizationPreset.MIXED,
+                preset=(
+                    nncf.QuantizationPreset.PERFORMANCE if quantization_config.sym else nncf.QuantizationPreset.MIXED
+                ),
                 fast_bias_correction=quantization_config.fast_bias_correction,
                 advanced_parameters=nncf.AdvancedQuantizationParameters(
                     overflow_fix=OverflowFix(quantization_config.overflow_fix)