fix mistral for transformers>=4.39 (#11191)

* fix mistral for transformers>=4.39

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

@@ -1400,11 +1400,19 @@ def _optimize_post(model, lightweight_bmm=False):
                             module.MistralRMSNorm,
                             llama_rms_norm_forward)
         else:
-            if version.parse(trans_version) >= version.parse("4.36.0"):
             modeling_module_name = model.__class__.__module__
             module = importlib.import_module(modeling_module_name)
-                from ipex_llm.transformers.models.mistral import mistral_attention_forward_4_36
+            if version.parse(trans_version) >= version.parse("4.36.0"):
                 from ipex_llm.transformers.models.mistral import mistral_model_forward_4_36
+                if version.parse(trans_version) >= version.parse("4.39.0"):
+                    from ipex_llm.transformers.models.mistral import mistral_attention_forward_4_39
+                    convert_forward(model,
+                                    module.MistralAttention,
+                                    mistral_attention_forward_4_39
+                                    )
+                else:
+                    from ipex_llm.transformers.models.mistral import mistral_attention_forward_4_36
+
                     convert_forward(model,
                                     module.MistralAttention,
                                     mistral_attention_forward_4_36
@@ -1420,8 +1428,6 @@ def _optimize_post(model, lightweight_bmm=False):
                                 module.MistralMLP,
                                 llama_mlp_forward)
             else:
-                modeling_module_name = model.__class__.__module__
-                module = importlib.import_module(modeling_module_name)
                 from ipex_llm.transformers.models.mistral import mistral_attention_forward
                 convert_forward(model,
                                 module.MistralAttention,

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

@@ -1074,3 +1074,232 @@ def mistral_attention_forward_4_36_original(
         attn_weights = None
 
     return attn_output.to(original_dtype), attn_weights, past_key_value
+
+
+def mistral_attention_forward_4_39(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor]=None,
+    position_ids: Optional[torch.LongTensor]=None,
+    past_key_value: Optional[Cache]=None,
+    output_attentions: bool=False,
+    use_cache: bool=False,
+    **kwargs
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
+    if use_quantize_kv_cache(self.q_proj, hidden_states):
+        forward_function = mistral_attention_forward_4_36_quantized
+    else:
+        forward_function = mistral_attention_forward_4_39_original
+    return forward_function(
+        self=self,
+        hidden_states=hidden_states,
+        attention_mask=attention_mask,
+        position_ids=position_ids,
+        past_key_value=past_key_value,
+        output_attentions=output_attentions,
+        use_cache=use_cache,
+        kwargs=kwargs
+    )
+
+
+def mistral_attention_forward_4_39_original(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor]=None,
+    position_ids: Optional[torch.LongTensor]=None,
+    past_key_value: Optional[Cache]=None,
+    output_attentions: bool=False,
+    use_cache: bool=False,
+    **kwargs
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
+    bsz, q_len, hidden_size = hidden_states.size()
+    device = hidden_states.device
+    # for flash attention
+    original_dtype = hidden_states.dtype
+
+    use_fuse_rope = should_use_fuse_rope(self, hidden_states, position_ids)
+    enough_kv_room = is_enough_kv_cache_room_4_36(past_key_value, self.layer_idx)
+    decoding_fast_path = use_decoding_fast_path(self.q_proj,
+                                                use_fuse_rope,
+                                                enough_kv_room,
+                                                bsz * q_len)
+    decoding_fast_path = decoding_fast_path and not self.q_proj.enable_xetla
+
+    if decoding_fast_path:
+        hidden_states = hidden_states.view(1, -1)
+
+        cache_k = past_key_value.key_cache[self.layer_idx]
+        cache_v = past_key_value.value_cache[self.layer_idx]
+
+        kv_seq_len = cache_k.shape[-2]
+
+        import xe_linear
+        query_states, key_states, value_states = xe_linear.forward_qkv(hidden_states,
+                                                                       self.q_proj.weight,
+                                                                       self.k_proj.weight,
+                                                                       self.v_proj.weight,
+                                                                       position_ids,
+                                                                       cache_k, cache_v,
+                                                                       self.q_proj.weight.qtype,
+                                                                       self.v_proj.weight.qtype,
+                                                                       kv_seq_len,
+                                                                       self.head_dim)
+        kv_seq_len += 1
+
+        # update past_key_value's seem_tokens and kv caches.
+        if self.layer_idx == 0:
+            past_key_value._seen_tokens = kv_seq_len
+        past_key_value.key_cache[self.layer_idx] = key_states
+        past_key_value.value_cache[self.layer_idx] = value_states
+
+    else:
+        if should_use_xetla_mm_qkv(self, device):
+            if not hasattr(self, "qkv_proj_qweight"):
+                self.qkv_proj_qweight = fuse_qkv_weight_xetla(self.q_proj,
+                                                              self.k_proj,
+                                                              self.v_proj,
+                                                              self.q_proj.qtype)
+            import xe_linear
+            q_out_len = self.q_proj.out_len
+            k_out_len = self.k_proj.out_len
+            v_out_len = self.v_proj.out_len
+            qkv_states = xe_linear.mm_xetla(hidden_states,
+                                            self.qkv_proj_qweight,
+                                            self.q_proj.qtype)
+            query_states = qkv_states[:, :, :q_out_len]
+            key_states = qkv_states[:, :, q_out_len:q_out_len + k_out_len]
+            value_states = qkv_states[:, :, q_out_len + k_out_len:]
+        else:
+            query_states = self.q_proj(hidden_states)
+            key_states = self.k_proj(hidden_states)
+            value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len,
+                                     self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len,
+                                         self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                invalidInputError(False,
+                                  "The cache structure has changed since version v4.36. "
+                                  f"If you are using {self.__class__.__name__} for "
+                                  "auto-regressive decodingwith k/v caching, please make sure "
+                                  "to initialize the attention class with a layer index.")
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+
+        if use_fuse_rope:
+            query_states, key_states = apply_rotary_pos_emb_no_cache_xpu(query_states,
+                                                                         key_states,
+                                                                         position_ids,
+                                                                         "mistral")
+        else:
+            cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+            query_states, key_states = apply_rotary_pos_emb(query_states, key_states,
+                                                            cos, sin, position_ids, "mistral")
+
+        if past_key_value is not None:
+            # update the number of seen tokens
+            if self.layer_idx == 0:
+                past_key_value._seen_tokens += key_states.shape[-2]
+
+            # reuse k, v, self_attention
+            # update `past_key_value` with `key_states` and `value_states` for layer `layer_idx`
+            if len(past_key_value.key_cache) <= self.layer_idx:
+                past_key_value.key_cache.append(key_states)
+                past_key_value.value_cache.append(value_states)
+            else:
+                cache_k = past_key_value.key_cache[self.layer_idx]
+                cache_v = past_key_value.value_cache[self.layer_idx]
+
+                if not enough_kv_room:
+                    # allocate new
+                    new_c_k, new_c_v = extend_kv_cache(bsz,
+                                                       self.num_key_value_heads,  # Support GQA
+                                                       self.head_dim,
+                                                       cache_k.size(2),
+                                                       kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
+                                                       dtype=cache_k.dtype,
+                                                       device=device)
+
+                    new_c_k[:] = cache_k
+                    new_c_v[:] = cache_v
+                    cache_k = new_c_k
+                    cache_v = new_c_v
+
+                key_states, value_states = append_kv_cache(cache_k, cache_v,
+                                                           key_states, value_states)
+
+                # update past_key_value
+                past_key_value.key_cache[self.layer_idx] = key_states
+                past_key_value.value_cache[self.layer_idx] = value_states
+
+    if not self.training and not hidden_states.requires_grad:
+        fsdp_flag = use_flash_attention(query_states, key_states)
+    else:
+        fsdp_flag = False
+    if fsdp_flag:
+        attention_dtype = torch.float16  # use fp16 for flash attention
+    else:
+        attention_dtype = original_dtype
+
+    if fsdp_flag:
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups).to(device,
+                                                                         dtype=attention_dtype)
+        value_states = repeat_kv(value_states, self.num_key_value_groups).to(device,
+                                                                             dtype=attention_dtype)
+        attn_output = F.scaled_dot_product_attention(query_states.to(dtype=attention_dtype),
+                                                     key_states,
+                                                     value_states,
+                                                     is_causal=True)
+        attn_weights = None
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+    elif use_sdp(q_len, key_states.shape[2], self.head_dim, query_states):
+        # new fp16 sdp doesn't require repeat_kv
+        import xe_addons
+        attn_output = xe_addons.sdp(query_states, key_states, value_states, attention_mask)
+        attn_output = attn_output.view(query_states.shape)
+        attn_weights = None
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+    else:
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups).to(device,
+                                                                         dtype=attention_dtype)
+        value_states = repeat_kv(value_states, self.num_key_value_groups).to(device,
+                                                                             dtype=attention_dtype)
+        if should_split_qkv_tensor(query_states, bsz, self.num_heads,
+                                   q_len, kv_seq_len, output_attentions):
+            attn_output, attn_weights = compute_attn_outputs_weights_split_tensor(query_states,
+                                                                                  key_states,
+                                                                                  value_states,
+                                                                                  bsz,
+                                                                                  q_len,
+                                                                                  kv_seq_len,
+                                                                                  self.num_heads,
+                                                                                  self.head_dim,
+                                                                                  self.hidden_size,
+                                                                                  attention_mask)
+        else:
+            attn_output, attn_weights = compute_attn_outputs_weights(query_states,
+                                                                     key_states,
+                                                                     value_states,
+                                                                     bsz,
+                                                                     q_len,
+                                                                     kv_seq_len,
+                                                                     self.num_heads,
+                                                                     self.head_dim,
+                                                                     self.hidden_size,
+                                                                     attention_mask)
+
+    attn_output = self.o_proj(attn_output)
+
+    if not output_attentions:
+        attn_weights = None
+
+    return attn_output.to(original_dtype), attn_weights, past_key_value