LLM: add fuse optimization for Mistral. (#9184)

* add fuse optimization for mistral.

* fix.

* fix

* fix style.

* fix.

* fix error.

* fix style.

* fix style.

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

@@ -348,4 +348,15 @@ def optimize(model):
         convert_forward(model,
                         module.AquilaRMSNorm,
                         llama_rms_norm_forward)
+    elif model.config.model_type == "mistral":
+        modeling_module_name = model.__class__.__module__
+        module = importlib.import_module(modeling_module_name)
+        from bigdl.llm.transformers.models.mistral import mistral_attention_forward
+        convert_forward(model,
+                        module.MistralAttention,
+                        mistral_attention_forward
+                        )
+        convert_forward(model,
+                        module.MistralRMSNorm,
+                        llama_rms_norm_forward)
     return model

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

@@ -0,0 +1,148 @@
+#
+# 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://github.com/huggingface/transformers/blob/main/src/transformers/models/mistral/modeling_mistral.py
+#
+# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+""" PyTorch Mistral model."""
+import math
+from typing import Optional, Tuple
+
+import torch
+from torch import nn
+from bigdl.llm.utils.common import invalidInputError
+from bigdl.llm.transformers.models.utils import apply_rotary_pos_emb,\
+    apply_rotary_pos_emb_no_cache_xpu
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep).
+    The hidden states go from (batch, num_key_value_heads, seqlen, head_dim)
+    to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads,
+                                                           n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def mistral_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,
+    padding_mask: Optional[torch.Tensor]=None,
+) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+    bsz, q_len, _ = hidden_states.size()
+
+    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:
+        kv_seq_len += past_key_value[0].shape[-2]
+    if query_states.device.type == "xpu" and not (self.training and query_states.requires_grad):
+        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:
+        # 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) if use_cache else None
+
+    # repeat k/v heads if n_kv_heads < n_heads
+    key_states = repeat_kv(key_states, self.num_key_value_groups)
+    value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+    attn_weights = torch.matmul(
+        query_states,
+        key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+    if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+        invalidInputError(
+            False,
+            f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)},"
+            f" but is {attn_weights.size()}"
+        )
+
+    if attention_mask is not None:
+        if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+            invalidInputError(
+                False,
+                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
+
+    # upcast attention to fp32
+    attn_weights = nn.functional.\
+        softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+    attn_output = torch.matmul(attn_weights, value_states)
+
+    if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+        invalidInputError(
+            f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)},"
+            f" but is {attn_output.size()}"
+        )
+
+    attn_output = attn_output.transpose(1, 2).contiguous()
+    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

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

@@ -98,7 +98,7 @@ def apply_rotary_pos_emb_no_cache_xpu(q, k, position_ids, model_family):
     import linear_q4_0
     q_embed = torch.empty(q.shape, dtype=q.dtype, device=q.device)
     k_embed = torch.empty(k.shape, dtype=k.dtype, device=k.device)
-    if model_family in ["llama", "baichuan", "internlm", "aquila", "gpt_neox"]:
+    if model_family in ["llama", "baichuan", "internlm", "aquila", "gpt_neox", "mistral"]:
         linear_q4_0.apply_rotary_embedding_half_qk(q, k, position_ids, q_embed, k_embed)
         return q_embed, k_embed
     else: