LLM: support kv_cache optimization for Qwen-VL-Chat (#9193)

* dupport qwen_vl_chat

* fix style

python/llm/example/GPU/HF-Transformers-AutoModels/Model/qwen/generate.py

@@ -42,7 +42,7 @@ if __name__ == '__main__':
     # which convert the relevant layers in the model into INT4 format
     model = AutoModelForCausalLM.from_pretrained(model_path,
                                                  load_in_4bit=True,
-                                                 optimize_model=False,
+                                                 optimize_model=True,
                                                  trust_remote_code=True,
                                                  use_cache=True)
     model = model.to('xpu')

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

@@ -331,6 +331,17 @@ def optimize(model):
                         llama_rms_norm_forward
                         )
     elif model.config.model_type == "qwen":
+        if hasattr(model.config, "visual"):
+            # for Qwen-VL-Chat
+            modeling_module_name = model.__class__.__module__
+            module = importlib.import_module(modeling_module_name)
+            from bigdl.llm.transformers.models.qwen_vl import qwen_attention_forward_vl
+            convert_forward(model,
+                            module.QWenAttention,
+                            qwen_attention_forward_vl
+                            )
+        else:
+            # for Qwen-7B and Qwen-14B
             modeling_module_name = model.__class__.__module__
             module = importlib.import_module(modeling_module_name)
             from bigdl.llm.transformers.models.qwen import qwen_attention_forward

python/llm/src/bigdl/llm/transformers/models/qwen_vl.py

@@ -0,0 +1,153 @@
+#
+# 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://huggingface.co/Qwen/Qwen-VL-Chat/blob/bbe5a805de49a41b7343d240ab84d4c305caa265/modeling_qwen.py
+#
+# Copyright (c) Alibaba Cloud.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+
+import importlib
+import math
+from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from transformers.utils import logging
+from bigdl.llm.transformers.models.utils import extend_kv_cache, init_kv_cache, append_kv_cache
+from bigdl.llm.transformers.models.utils import rotate_half
+
+
+KV_CACHE_ALLOC_BLOCK_LENGTH = 256
+
+
+def apply_rotary_pos_emb(t, freqs):
+    cos, sin = freqs
+    rot_dim = freqs[0].shape[-1]
+    t_, t_pass_ = t[..., :rot_dim], t[..., rot_dim:]
+    t_ = t_.float()
+    t_pass_ = t_pass_.float()
+    t_ = (t_ * cos) + (rotate_half(t_) * sin)
+    return torch.cat((t_, t_pass_), dim=-1).type_as(t)
+
+
+def qwen_attention_forward_vl(
+    self,
+    hidden_states: Optional[Tuple[torch.FloatTensor]],
+    rotary_pos_emb: Optional[List[torch.Tensor]] = None,
+    registered_causal_mask: Optional[torch.Tensor] = None,
+    layer_past: Optional[Tuple[torch.Tensor]] = None,
+    attention_mask: Optional[torch.FloatTensor] = None,
+    head_mask: Optional[torch.FloatTensor] = None,
+    encoder_hidden_states: Optional[torch.Tensor] = None,
+    encoder_attention_mask: Optional[torch.FloatTensor] = None,
+    output_attentions: Optional[bool] = False,
+    use_cache: Optional[bool] = False,
+):
+
+    mixed_x_layer = self.c_attn(hidden_states)
+
+    query, key, value = mixed_x_layer.split(self.split_size, dim=2)
+
+    query = self._split_heads(query, self.num_heads, self.head_dim)
+    key = self._split_heads(key, self.num_heads, self.head_dim)
+    value = self._split_heads(value, self.num_heads, self.head_dim)
+
+    kv_seq_len = hidden_states.size()[1]
+
+    if rotary_pos_emb is not None:
+        cur_len = query.shape[1]
+        rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb]
+        rotary_pos_emb = (rotary_pos_emb,) * 2
+        q_pos_emb, k_pos_emb = rotary_pos_emb
+        # Slice the pos emb for current inference
+        query = apply_rotary_pos_emb(query, q_pos_emb)
+        key = apply_rotary_pos_emb(key, k_pos_emb)
+
+    bsz, _, n_heads, head_dim = key.size()
+
+    if layer_past is not None:
+        kv_seq_len += layer_past[0].shape[1]
+        # past_key, past_value = layer_past[0], layer_past[1]
+        # key = torch.cat((past_key, key), dim=1)
+        # value = torch.cat((past_value, value), dim=1)
+        cache_k = layer_past[0].transpose(1, 2)
+        cache_v = layer_past[1].transpose(1, 2)
+        if cache_k.stride()[1] <= cache_k.size(2) * cache_k.size(3):
+            # allocate new
+            new_cache_k, new_cache_v = extend_kv_cache(bsz,
+                                                       self.num_heads,
+                                                       self.head_dim,
+                                                       cache_k.size(2),
+                                                       kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
+                                                       dtype=cache_k.dtype,
+                                                       device=hidden_states.device)
+            new_cache_k[:] = cache_k
+            new_cache_v[:] = cache_v
+            cache_k = new_cache_k
+            cache_v = new_cache_v
+
+        key_states, value_states = append_kv_cache(cache_k, cache_v,
+                                                   key.transpose(1, 2), value.transpose(1, 2))
+        key = key_states.transpose(1, 2)
+        value = value_states.transpose(1, 2)
+    elif use_cache:
+        max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
+        new_key_states, new_value_states = init_kv_cache(bsz,
+                                                         self.num_heads,
+                                                         self.head_dim,
+                                                         kv_seq_len,
+                                                         max_cache_length,
+                                                         dtype=key.dtype,
+                                                         device=hidden_states.device)
+        new_key_states[:] = key.transpose(1, 2)
+        new_value_states[:] = value.transpose(1, 2)
+        key = new_key_states.transpose(1, 2)
+        value = new_value_states.transpose(1, 2)
+
+    if use_cache:
+        present = (key, value)
+    else:
+        present = None
+
+    if self.use_logn_attn and not self.training:
+        if self.logn_tensor.device != query.device or self.logn_tensor.dtype != query.dtype:
+            self.logn_tensor = self.logn_tensor.to(query.device).type_as(query)
+        seq_start = key.size(1) - query.size(1)
+        seq_end = key.size(1)
+        logn_tensor = self.logn_tensor[:, seq_start:seq_end, :, :]
+        query = query * logn_tensor.expand_as(query)
+
+    query = query.permute(0, 2, 1, 3)
+    key = key.permute(0, 2, 1, 3)
+    value = value.permute(0, 2, 1, 3)
+    attn_output, attn_weight = self._attn(
+        query, key, value, registered_causal_mask, attention_mask, head_mask
+    )
+    context_layer = self._merge_heads(
+        attn_output, self.num_heads, self.head_dim
+    )
+
+    attn_output = self.c_proj(context_layer)
+
+    outputs = (attn_output, present)
+    if output_attentions:
+        outputs += (attn_weight,)
+
+    return outputs