[LLM] Add basic optimization framework for Yuan2 (#10227)

* Add basic optimization framework for Yuan2

* Small fix

* Python style fix

* Small fix

* Small fix

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

@@ -819,8 +819,9 @@ def _optimize_post(model, lightweight_bmm=False):
                 if model.config.hidden_size != 4544:
                     # falcon-180b and new falcon-40b
                     if version.parse(trans_version) >= version.parse("4.36.0"):
-                    # transformers version >= 4.36.0
+                        # transformers version >= 4.36.0
-                        from bigdl.llm.transformers.models.falcon import falcon_attention_forward_4_36
+                        from bigdl.llm.transformers.models.falcon import \
+                            falcon_attention_forward_4_36
                         convert_forward(model,
                                         module.FalconAttention,
                                         falcon_attention_forward_4_36
@@ -1147,4 +1148,12 @@ def _optimize_post(model, lightweight_bmm=False):
                      module.GPTBigCodeAttention,
                      "_attn",
                      _attn)
+    elif model.config.model_type == 'yuan':
+        modeling_module_name = model.__class__.__module__
+        module = importlib.import_module(modeling_module_name)
+        from bigdl.llm.transformers.models.yuan import yuan_attention_forward
+        convert_forward(model,
+                        module.YuanAttention,
+                        yuan_attention_forward
+                        )
     return model

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

@@ -143,7 +143,7 @@ def rotate_every_two(x):
 
 def apply_rotary_pos_emb(q, k, cos, sin, position_ids, model_family):
     if model_family in ["llama", "baichuan", "internlm", "aquila", "gpt_neox", "mistral",
-                        "mixtral", "qwen2"]:
+                        "mixtral", "qwen2", "yuan"]:
         # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
         cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
         sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]

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

@@ -0,0 +1,169 @@
+#
+# 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/IEITYuan/Yuan2-2B-hf/blob/7ab7b3c18eb8e5232ce2a3f720d4e6f4b53a2806/yuan_hf_model.py
+# which is licensed under Apache License 2.0:
+#
+# https://huggingface.co/IEITYuan/Yuan2-2B-hf/blob/7ab7b3c18eb8e5232ce2a3f720d4e6f4b53a2806/README.md#%E5%A3%B0%E6%98%8E%E4%B8%8E%E5%8D%8F%E8%AE%AEterms-and-conditions
+#
+
+import torch
+import copy
+import math
+from typing import Optional, Tuple
+
+from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb
+from bigdl.llm.utils.common import invalidInputError
+
+
+def yuan_attention_forward(
+    self,
+    hidden_states: torch.Tensor,
+    attention_mask: Optional[torch.Tensor] = None,
+    position_ids: Optional[torch.LongTensor] = None,
+    past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    output_attentions: bool = False,
+    use_cache: bool = False,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    bsz, q_len, _ = hidden_states.size()
+    before_hidden_states = None
+    is_first_step = False
+    if use_cache:
+        if past_key_value is None:
+            inference_hidden_states_memory = torch.empty(bsz, 2,
+                                                         hidden_states.shape[2],
+                                                         dtype=hidden_states.dtype)
+            is_first_step = True
+        else:
+            before_hidden_states = past_key_value[2]
+
+    if use_cache:
+        if is_first_step:
+            if q_len >= 2:
+                inference_hidden_states_memory = hidden_states[ :, -2:, :]
+            else:
+                inference_hidden_states_memory[:, :, :] = 0
+                inference_hidden_states_memory[:, -1:, :] = hidden_states[:, -1:, :]
+        else:
+            hidden_states_tmp = before_hidden_states[:, -1:, :]
+            inference_hidden_states_memory = copy.deepcopy(torch.cat((hidden_states_tmp, hidden_states),
+                                                                     dim=1))
+
+    value_states = \
+        self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+    if self.use_shareqk:
+        qk_states = self.qk_proj(hidden_states).view(bsz, q_len, self.num_heads*self.head_dim)
+        query_key = qk_states.unsqueeze(2) * self.qk_weight + self.qk_bias
+        query_states, key_states = torch.unbind(query_key, dim=2)
+
+        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_heads, self.head_dim).transpose(1, 2)
+    else:
+        hidden_states = self.lf_gate(hidden_states,before_hidden_states)
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        qk_states = torch.cat([query_states, key_states], dim=-1)
+        qk_states = qk_states.view(bsz,q_len,self.num_heads,int(qk_states.shape[-1]//self.num_heads))
+        (query_states,key_states) =  torch.chunk(qk_states, 2, dim=-1)
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.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]
+    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,
+                                                    "yuan")
+
+    if past_key_value is not None:
+        # reuse k, v, self_attention
+        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,inference_hidden_states_memory) if use_cache else None
+    
+    if self.use_flash_attention:
+        attn_weights = None
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        batch_size, seqlen_q = query_states.shape[0], query_states.shape[1]
+        seqlen_k = key_states.shape[1]
+
+        q, k, v = [rearrange(x, 'b s ... -> (b s) ...') for x in [query_states, key_states, value_states]]
+
+        cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int,
+                                device=q.device)
+                                
+        if self.training:
+            invalidInputError(seqlen_k == seqlen_q,
+                "`seqlen_k` should be equal to `seqlen_q`, but is not")
+            cu_seqlens_k = cu_seqlens_q
+            is_causal = self.causal_mask
+        else:
+            is_causal = seqlen_q == seqlen_k
+            cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, 
+                                        step=seqlen_k, 
+                                        dtype=torch.int,
+                                        device=q.device)
+            self.dropout = 0
+
+        output = flash_attn_unpadded_func(
+            q, k, v, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k, self.dropout, causal=is_causal
+        )
+
+        attn_output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
+    else:
+        attn_weights = \
+            torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        invalidInputError(attn_weights.size() == (bsz, self.num_heads, q_len, kv_seq_len),
+                          "Attention weights should be of size "
+                          f"{(bsz, self.num_heads, q_len, kv_seq_len)}, "
+                          f"but is {attn_weights.size()}")
+
+        if attention_mask is not None:
+            invalidInputError(attention_mask.size() == (bsz, 1, q_len, kv_seq_len),
+                              f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, "
+                              f"but is {attention_mask.size()}")
+            attn_weights = attn_weights + attention_mask
+            attn_weights = torch.max(attn_weights,
+                                     torch.tensor(torch.finfo(attn_weights.dtype).min))
+
+        # upcast attention to fp32
+        attn_weights = \
+            torch.nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        invalidInputError(attn_output.size() == (bsz, self.num_heads, q_len, self.head_dim),
+                          "`attn_output` should be of size "
+                          f"{(bsz, self.num_heads, q_len, self.head_dim)}, "
+                          f"but is {attn_output.size()}")
+
+        attn_output = attn_output.transpose(1, 2)
+
+    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