optimize Chatglm4 (#11239)

* chatglm4

* update

* update

* add rms norm

* chatglm4

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

@@ -1033,6 +1033,24 @@ def _optimize_post(model, lightweight_bmm=False):
                             module.SelfAttention,
                             chatglm_attention_forward
                             )
+        elif (model.config.num_layers == 40 and hasattr(model.config, 'rope_ratio')
+                and model.config.rope_ratio == 500):
+            # glm-4-9b-chat
+            modeling_module_name = model.__class__.__module__
+            module = importlib.import_module(modeling_module_name)
+            from ipex_llm.transformers.models.chatglm4 import chatglm4_attention_forward
+            from ipex_llm.transformers.models.chatglm4 import chatglm4_model_forward
+            from ipex_llm.transformers.models.chatglm2 import chatglm_rms_norm_forward
+            convert_forward(model,
+                            module.SelfAttention,
+                            chatglm4_attention_forward)
+            convert_forward(model,
+                            module.ChatGLMModel,
+                            chatglm4_model_forward)
+            convert_forward(model,
+                            module.RMSNorm,
+                            chatglm_rms_norm_forward)
+
     elif "mpt" in model.config.model_type:
         if model.config.architectures is not None:
             modeling_module_name = model.__class__.__module__

python/llm/src/ipex_llm/transformers/models/chatglm4.py

@@ -0,0 +1,321 @@
+#
+# 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.
+#
+# This file is adapted from
+# https://huggingface.co/THUDM/chatglm2-6b-32k/blob/main/configuration_chatglm.py
+#
+
+import torch
+from typing import Optional, Tuple, Union, List, Callable, Dict, Any
+import torch.nn.functional as F
+from ipex_llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
+from ipex_llm.transformers.models.utils import use_quantize_kv_cache, apply_ipex_rotate_every_two
+from transformers.modeling_outputs import BaseModelOutputWithPast
+
+
+import os
+
+KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
+KV_CACHE_ALLOC_MIN_LENGTH = 512
+
+
+def split_tensor_along_last_dim(
+        tensor: torch.Tensor,
+        num_partitions: int,
+        contiguous_split_chunks: bool = False,
+) -> List[torch.Tensor]:
+    """Split a tensor along its last dimension.
+    Arguments:
+        tensor: input tensor.
+        num_partitions: number of partitions to split the tensor
+        contiguous_split_chunks: If True, make each chunk contiguous
+                                 in memory.
+    Returns:
+        A list of Tensors
+    """
+    # Get the size and dimension.
+    last_dim = tensor.dim() - 1
+    last_dim_size = tensor.size()[last_dim] // num_partitions
+    # Split.
+    tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
+    # Note: torch.split does not create contiguous tensors by default.
+    if contiguous_split_chunks:
+        return tuple(chunk.contiguous() for chunk in tensor_list)
+
+    return tensor_list
+
+
+def chatglm4_model_forward(
+    self,
+    input_ids,
+    position_ids: Optional[torch.Tensor] = None,
+    attention_mask: Optional[torch.BoolTensor] = None,
+    full_attention_mask: Optional[torch.BoolTensor] = None,
+    past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]]=None,
+    inputs_embeds: Optional[torch.Tensor] = None,
+    use_cache: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    return_dict: Optional[bool] = None,
+) -> Union[Tuple, BaseModelOutputWithPast]:
+    from ipex_llm.transformers.kv import DynamicFp8Cache
+    use_cache = use_cache if use_cache is not None else self.config.use_cache
+    # if use_cache and use_quantize_kv_cache(
+    #         self.encoder.layers[0].self_attention.query_key_value, input_ids):
+    #     if not isinstance(past_key_values, DynamicFp8Cache):
+    #         past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values)
+    return chatglm4_model_forward_internal(
+        self=self,
+        input_ids=input_ids,
+        position_ids=position_ids,
+        attention_mask=attention_mask,
+        full_attention_mask=full_attention_mask,
+        past_key_values=past_key_values,
+        inputs_embeds=inputs_embeds,
+        use_cache=use_cache,
+        output_hidden_states=output_hidden_states,
+        return_dict=return_dict,
+    )
+
+
+def chatglm4_model_forward_internal(
+        self,
+        input_ids,
+        position_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.BoolTensor] = None,
+        full_attention_mask: Optional[torch.BoolTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]]=None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        use_cache: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+):
+    output_hidden_states = (
+        output_hidden_states if output_hidden_states is not None else
+        self.config.output_hidden_states
+    )
+    use_cache = use_cache if use_cache is not None else self.config.use_cache
+    return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+    batch_size, seq_length = input_ids.shape
+
+    if inputs_embeds is None:
+        inputs_embeds = self.embedding(input_ids)
+
+    if full_attention_mask is None:
+        if (attention_mask is not None and not attention_mask.all()) or\
+                (past_key_values and seq_length != 1):
+            full_attention_mask = self.get_masks(input_ids,
+                                                 past_key_values,
+                                                 padding_mask=attention_mask)
+
+    use_fuse_rope = input_ids.device.type == "xpu"
+    use_fuse_rope = use_fuse_rope and not self.training
+
+    # Rotary positional embeddings
+    rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
+    if position_ids is not None:
+        rotary_pos_emb = rotary_pos_emb[position_ids]
+    else:
+        rotary_pos_emb = rotary_pos_emb[None, :seq_length]
+    if use_fuse_rope:
+        # Repeat cos sin here, call only once for each token.
+        # Chatglm2's rotary embedding is similar to gptj's, is rotate_every_two.
+        # If put this to attension forward, it will generate too many times.
+        cos, sin = rotary_pos_emb.split(rotary_pos_emb.shape[-1] // 2, dim=-1)
+        cos = cos.squeeze(-1)
+        sin = sin.squeeze(-1)
+        cos = torch.repeat_interleave(cos[:, :, None, :], 2, 3)
+        sin = torch.repeat_interleave(sin[:, :, None, :], 2, 3)
+        rotary_pos_emb = (cos, sin)
+
+    # Run encoder.
+    hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder(
+        inputs_embeds, full_attention_mask, rotary_pos_emb=rotary_pos_emb,
+        kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states
+    )
+    if presents is not None and type(presents) is torch.Tensor:
+        presents = presents.split(1, dim=0)
+        presents = list(presents)
+        presents = [list(x.squeeze(0).split(1, dim=0)) for x in presents]
+        presents = [tuple([x.squeeze(0) for x in y]) for y in presents]
+        presents = tuple(presents)
+
+    if not return_dict:
+        return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions]
+                     if v is not None)
+
+    return BaseModelOutputWithPast(
+        last_hidden_state=hidden_states,
+        past_key_values=presents,
+        hidden_states=all_hidden_states,
+        attentions=all_self_attentions,
+    )
+
+
+@torch.jit.script
+def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor:
+    # x: [b, np, sq, hn]
+    b, np, sq, hn = x.size(0), x.size(1), x.size(2), x.size(3)
+    rot_dim = rope_cache.shape[-2] * 2
+    x, x_pass = x[..., :rot_dim], x[..., rot_dim:]
+    # truncate to support variable sizes
+    rope_cache = rope_cache[:, :sq]
+    xshaped = x.reshape(b, np, sq, rot_dim // 2, 2)
+    rope_cache = rope_cache.view(-1, 1, sq, xshaped.size(3), 2)
+    x_out2 = torch.stack(
+        [
+            xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1],
+            xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1],
+        ],
+        -1,
+    )
+    x_out2 = x_out2.flatten(3)
+    return torch.cat((x_out2, x_pass), dim=-1)
+
+
+def chatglm4_attention_forward(
+    self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True
+):
+    # hidden_states: [sq, b, h]
+
+    # =================================================
+    # Pre-allocate memory for key-values for inference.
+    # =================================================
+    # =====================
+    # Query, Key, and Value
+    # =====================
+
+    # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)]
+    device = hidden_states.device
+    mixed_x_layer = self.query_key_value(hidden_states)
+
+    if self.multi_query_attention:
+        (query_layer, key_layer, value_layer) = mixed_x_layer.split(
+            [
+                self.num_attention_heads_per_partition * self.hidden_size_per_attention_head,
+                self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
+                self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
+            ],
+            dim=-1,
+        )
+        query_layer = query_layer.view(
+            query_layer.size()[:-1] + (self.num_attention_heads_per_partition,
+                                       self.hidden_size_per_attention_head)
+        )
+        key_layer = key_layer.view(
+            key_layer.size()[:-1] + (self.num_multi_query_groups_per_partition,
+                                     self.hidden_size_per_attention_head)
+        )
+        value_layer = value_layer.view(
+            value_layer.size()[:-1]
+            + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head)
+        )
+    else:
+        new_tensor_shape = mixed_x_layer.size()[:-1] + (self.num_attention_heads_per_partition,
+                                                        3 * self.hidden_size_per_attention_head)
+        mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)
+
+        # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn]
+        (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3)
+
+    # [b, sq, np, hn] -> [b, np, sq, hn]
+    query_layer, key_layer, value_layer = [k.transpose(1, 2)
+                                           for k in [query_layer, key_layer, value_layer]]
+
+    # apply relative positional encoding (rotary embedding)
+    if isinstance(rotary_pos_emb, tuple) and len(rotary_pos_emb) == 2:
+        # use_fuse_rope, see chatglm2_model_forward
+        cos, sin = rotary_pos_emb
+        rot_dim = cos.shape[-1]
+        query_layer = query_layer.transpose(1, 2)
+        key_layer = key_layer.transpose(1, 2)
+        query_layer_cur = query_layer[..., :rot_dim]
+        key_layer_cur = key_layer[..., :rot_dim]
+        # ipex_llm's apply_rotary_embedding can change the origin storage,
+        # so query_layer will get the result directly.
+        torch.ops.torch_ipex.apply_rotary_embedding(query_layer_cur, sin, cos, query_layer_cur)
+        torch.ops.torch_ipex.apply_rotary_embedding(key_layer_cur, sin, cos, key_layer_cur)
+        query_layer = query_layer.transpose(1, 2)
+        key_layer = key_layer.transpose(1, 2)
+    elif rotary_pos_emb is not None:
+        query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb)
+        key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb)
+
+    cur_length, batch_size = query_layer.shape[2], query_layer.shape[0]
+
+    # adjust key and value for inference
+    if kv_cache is not None and use_cache:
+        cache_k, cache_v = kv_cache
+        past_length = cache_k.size(2)
+
+        if cache_k.stride()[1] < (past_length + cur_length) * cache_k.size(3):
+            max_cache_length = past_length + cur_length + KV_CACHE_ALLOC_BLOCK_LENGTH
+            new_cache_k, new_cache_v = extend_kv_cache(batch_size,
+                                                       key_layer.size(1),
+                                                       self.hidden_size_per_attention_head,
+                                                       past_length,
+                                                       max_cache_length,
+                                                       dtype=query_layer.dtype,
+                                                       device=device)
+            new_cache_k[:] = cache_k
+            new_cache_v[:] = cache_v
+            cache_k = new_cache_k
+            cache_v = new_cache_v
+
+        key_layer, value_layer = append_kv_cache(cache_k, cache_v, key_layer, value_layer)
+
+    if use_cache:
+        if kv_cache is None:
+            kv_cache = torch.cat((key_layer.unsqueeze(0).unsqueeze(0),
+                                  value_layer.unsqueeze(0).unsqueeze(0)), dim=1)
+        else:
+            kv_cache = (key_layer, value_layer)
+    else:
+        kv_cache = None
+
+    if self.multi_query_attention:
+        key_layer = key_layer.unsqueeze(2)
+        key_layer = key_layer.expand(
+            -1, -1,
+            self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition,
+            -1, -1
+        )
+        key_layer = key_layer.contiguous().view(
+            key_layer.size()[:1] + (self.num_attention_heads_per_partition,) + key_layer.size()[3:]
+        )
+        value_layer = value_layer.unsqueeze(2)
+        value_layer = value_layer.expand(
+            -1, -1,
+            self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition,
+            -1, -1
+        )
+        value_layer = value_layer.contiguous().view(
+            value_layer.size()[:1] +
+            (self.num_attention_heads_per_partition,) + value_layer.size()[3:]
+        )
+
+    # ==================================
+    # core attention computation
+    # ==================================
+
+    context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask)
+
+    # =================
+    # Output. [sq, b, h]
+    # =================
+
+    output = self.dense(context_layer)
+
+    return output, kv_cache