Add Support for Mistral Model in Llama-Adapter Method

src/peft/tuners/adaption_prompt/config.py

@@ -54,6 +54,13 @@ def is_adaption_prompt(self) -> bool:
         v_proj_layer="v_proj",
         o_proj_layer="o_proj",
     ),
+    "mistral": ModelTypeConfig(  # same as llama,
+        compute_query_states=llama_compute_query_states,
+        target_modules="self_attn",
+        k_proj_layer="k_proj",
+        v_proj_layer="v_proj",
+        o_proj_layer="o_proj",
+    ),
 }
 
 

src/peft/tuners/adaption_prompt/layer.py

@@ -74,31 +74,38 @@ def forward(self, **kwargs):
         k_proj_layer = TRANSFORMERS_MODEL_CONFIG[self.model_type].k_proj_layer
         v_proj_layer = TRANSFORMERS_MODEL_CONFIG[self.model_type].v_proj_layer
         o_proj_layer = TRANSFORMERS_MODEL_CONFIG[self.model_type].o_proj_layer
+        factor = (
+            self.model.k_proj.in_features // self.model.k_proj.out_features
+        )  # Mistral has different input and output dimension for k_proj and v_proj layers
 
         if k_proj_layer == v_proj_layer:
             _, key, value = getattr(self.model, k_proj_layer)(self.adaption_prompt).split(embed_dim, dim=2)
         else:
             key = getattr(self.model, k_proj_layer)(self.adaption_prompt)
             value = getattr(self.model, v_proj_layer)(self.adaption_prompt)
-        # (bsz, num_heads, adapter_len, head_dim)
+
+        # (bsz, num_key_value_heads, adapter_len, head_dim)
         adapter_k = (
-            key.view(1, self.adapter_len, self.model.num_heads, self.model.head_dim)
+            key.view(1, self.adapter_len, (self.model.num_heads // factor), self.model.head_dim)
             .repeat(bsz, 1, 1, 1)
             .transpose(1, 2)
         )
-        # (bsz, num_heads, adapter_len, head_dim)
         adapter_v = (
-            value.view(1, self.adapter_len, self.model.num_heads, self.model.head_dim)
+            value.view(1, self.adapter_len, (self.model.num_heads // factor), self.model.head_dim)
             .repeat(bsz, 1, 1, 1)
             .transpose(1, 2)
         )
-
+        # Below is taken from https://github.com/huggingface/transformers/blob/e547458c43dfdbbb8f6a7757237e234c44e20a8f/src/transformers/models/mistral/modeling_mistral.py#L181
+        # (bsz, num_heads, adapter_len, head_dim)
+        adapter_k = torch.repeat_interleave(adapter_k, repeats=factor, dim=1)
+        adapter_v = torch.repeat_interleave(adapter_v, repeats=factor, dim=1)
         # Recompute query states.
         compute_query_states = TRANSFORMERS_MODEL_CONFIG[self.model_type].compute_query_states
         # (bsz, num_heads, q_len, head_dim)
         query_states = compute_query_states(model=self.model, **kwargs)
 
         previous_dtype = query_states.dtype
+
         # (bsz, num_heads, q_len, adapter_len)
         scores = torch.matmul(query_states, adapter_k.transpose(2, 3).to(previous_dtype)) / math.sqrt(
             self.model.head_dim
@@ -108,6 +115,7 @@ def forward(self, **kwargs):
         scores = self.adaption_gate * F.softmax(scores, dim=-1, dtype=torch.float32).to(previous_dtype)
         # (bsz, q_len, num_heads * head_dim)
         adapter_output = torch.matmul(scores, adapter_v).transpose(1, 2).reshape(bsz, q_len, -1)
+
         # (bsz, q_len, hidden_size)
         if o_proj_layer is not None:
             adapter_output = getattr(self.model, o_proj_layer)(adapter_output)

src/peft/tuners/adaption_prompt/utils.py

@@ -68,7 +68,12 @@ def llama_compute_query_states(model: nn.Module, **kwargs) -> torch.Tensor:
     past_key_value = kwargs.get("past_key_value")
     bsz, q_len, _ = hidden_states.size()
     query_states = model.q_proj(hidden_states).view(bsz, q_len, model.num_heads, model.head_dim).transpose(1, 2)
-    value_states = model.v_proj(hidden_states).view(bsz, q_len, model.num_heads, model.head_dim).transpose(1, 2)
+
+    factor = model.k_proj.in_features // model.k_proj.out_features
+    value_states = (
+        model.v_proj(hidden_states).view(bsz, q_len, (model.num_heads // factor), model.head_dim).transpose(1, 2)
+    )
+
     seq_len = q_len
 
     if past_key_value is not None: