diff --git a/python/llm/src/ipex_llm/transformers/convert.py b/python/llm/src/ipex_llm/transformers/convert.py index 05ac94a4..828ea556 100644 --- a/python/llm/src/ipex_llm/transformers/convert.py +++ b/python/llm/src/ipex_llm/transformers/convert.py @@ -1070,6 +1070,10 @@ def _optimize_pre(model, qtype=None): model.apply(split_mlp) elif model.config.num_layers in [40, 28]: model.apply(split_mlp) + elif model.config.model_type == "glm": + from ipex_llm.transformers.models.glm import merge_qkv, split_mlp + model.apply(merge_qkv) + model.apply(split_mlp) return model @@ -1487,7 +1491,19 @@ def _optimize_post(model, lightweight_bmm=False): # workaround glm4-9b fp16 overflow from ipex_llm.transformers.models.chatglm4 import chatglm4_block_forward convert_forward(model, module.GLMBlock, chatglm4_block_forward) - + elif model.config.model_type == "glm": + # glm-edge series + modeling_module_name = model.__class__.__module__ + module = importlib.import_module(modeling_module_name) + from ipex_llm.transformers.models.common import rms_norm_forward + from ipex_llm.transformers.models.common import mlp_silu_forward + from ipex_llm.transformers.models.glm import glm_attention_forward + from ipex_llm.transformers.models.glm import glm_model_forward_wrapper + convert_forward(model, module.GlmRMSNorm, rms_norm_forward) + convert_forward(model, module.GlmMLP, mlp_silu_forward) + convert_forward(model, module.GlmAttention, glm_attention_forward) + glm_model_forward = glm_model_forward_wrapper(module.GlmModel.forward) + convert_forward(model, module.GlmModel, glm_model_forward) elif "mpt" in model.config.model_type: if model.config.architectures is not None: modeling_module_name = model.__class__.__module__ diff --git a/python/llm/src/ipex_llm/transformers/models/glm.py b/python/llm/src/ipex_llm/transformers/models/glm.py new file mode 100644 index 00000000..c82ebc32 --- /dev/null +++ b/python/llm/src/ipex_llm/transformers/models/glm.py @@ -0,0 +1,202 @@ +# +# 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://github.com/huggingface/transformers/blob/main/src/transformers/models/glm/modeling_glm.py +# +# which is licensed under Apache License 2.0: +# +# Copyright 2024 The GLM & ZhipuAI team and HuggingFace Inc. team. All rights reserved. +# +# +# 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. + +import torch + +from typing import Optional, Tuple +from transformers.cache_utils import Cache +from transformers.models.glm.modeling_glm import GlmAttention, GlmMLP +from transformers.models.glm.modeling_glm import repeat_kv, apply_rotary_pos_emb +from ipex_llm.transformers.kv import DynamicNormalCache, DynamicFp8Cache +from ipex_llm.transformers.models.common import merge_qkv_base +from ipex_llm.transformers.models.utils import use_sdp, use_sdp_causal +from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache + + +def merge_qkv(module: torch.nn.Module): + merge_qkv_base(module, GlmAttention) + + +def split_mlp(module: torch.nn.Module): + if isinstance(module, GlmMLP): + gate_weight, up_weight = module.gate_up_proj.weight.data.chunk(2, dim=0) + + gate_proj = torch.nn.Linear(0, 0, bias=False) + gate_proj.weight = torch.nn.Parameter(gate_weight, requires_grad=False) + gate_proj.in_features = gate_weight.size(1) + gate_proj.out_features = gate_weight.size(0) + + up_proj = torch.nn.Linear(0, 0, bias=False) + up_proj.weight = torch.nn.Parameter(up_weight, requires_grad=False) + up_proj.in_features = up_weight.size(1) + up_proj.out_features = up_weight.size(0) + + module.gate_proj = gate_proj + module.up_proj = up_proj + + del module.gate_up_proj + + # rename activation function + module.act_fn = module.activation_fn + + +def glm_attention_forward( + 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, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]]=None, + **kwargs, +): + bsz, q_len, _ = hidden_states.size() + + qkv = self.qkv_proj(hidden_states) + qkv = qkv.view(bsz, q_len, self.num_heads + 2 * self.num_key_value_heads, self.head_dim) + qkv = qkv.transpose(1, 2) + query_states, key_states, value_states = qkv.split([self.num_heads, + self.num_key_value_heads, + self.num_key_value_heads], dim=1) + + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + use_quantizekv = isinstance(past_key_value, DynamicFp8Cache) + # sin and cos are specific to RoPE models; cache_position needed for the static cache + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} + key_states, value_states = past_key_value.update(key_states, value_states, + self.layer_idx, cache_kwargs) + + kv_seq_len = key_states.size(-2) + if attention_mask is not None: # no matter the length, we just slice it + attention_mask = attention_mask[:, :, :, : kv_seq_len] + + if use_sdp(q_len, kv_seq_len, self.head_dim, query_states): + import xe_addons + if use_quantizekv: + attn_output = xe_addons.sdp_fp8(query_states, key_states, value_states, + attention_mask) + else: + attn_output = xe_addons.sdp(query_states, key_states, value_states, + attention_mask) + elif use_sdp_causal(q_len, kv_seq_len, self.head_dim, query_states, self.training): + import xe_addons + if use_quantizekv: + attn_output = xe_addons.sdp_fp8_causal(query_states, key_states, + value_states, attention_mask) + else: + attn_output = xe_addons.sdp_causal(query_states, key_states, + value_states, attention_mask) + else: + if use_quantizekv: + key_states, value_states = restore_fp8_kv_cache(key_states, value_states, + query_states.dtype) + # 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)) * self.scaling + + if attention_mask is not None: + attn_weights = attn_weights + attention_mask + + # upcast attention to fp32 + attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1, + dtype=torch.float32).to(query_states.dtype) + attn_weights = torch.nn.functional.dropout(attn_weights, p=self.attention_dropout, + training=self.training) + attn_output = torch.matmul(attn_weights, value_states) + + 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 + + +def glm_model_forward_wrapper(origin_forward): + def glm_model_forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Cache] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + **flash_attn_kwargs, + ): + # ipex-llm changes start + # IPEX-LLM OPT: kv cache and quantize kv cache + inputs = input_ids if input_ids is not None else inputs_embeds + use_cache = use_cache if use_cache is not None else self.config.use_cache + use_cache = use_cache or inputs.device.type == 'xpu' + use_quantize_kv = use_quantize_kv_cache(self.layers[0].mlp.down_proj, inputs, + self.config.num_attention_heads // + self.config.num_key_value_heads) + + if use_cache: + if use_quantize_kv and not isinstance(past_key_values, DynamicFp8Cache): + past_key_values = DynamicFp8Cache.from_legacy_cache(past_key_values) + elif not use_quantize_kv and not isinstance(past_key_values, DynamicNormalCache): + past_key_values = DynamicNormalCache.from_legacy_cache(past_key_values) + # ipex-llm changes end + + return origin_forward( + self=self, + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + cache_position=cache_position, + **flash_attn_kwargs, + ) + + return glm_model_forward