137 lines
5.1 KiB
Python
137 lines
5.1 KiB
Python
# coding=utf-8
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# Copyright 2025 The GLM4 & ZhipuAI team and HuggingFace Inc. team. All rights reserved.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Optional, Union
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import torch
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from ...cache_utils import Cache
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from ...modeling_flash_attention_utils import FlashAttentionKwargs
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import CausalLMOutputWithPast
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, logging
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from ..glm.modeling_glm import GlmAttention, GlmForCausalLM, GlmForSequenceClassification, GlmForTokenClassification
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from ..phi3.modeling_phi3 import Phi3MLP
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from .configuration_glm4 import Glm4Config
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from .modeling_glm4 import Glm4RMSNorm
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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "THUDM/GLM-4-9B-0414"
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class Glm4MLP(Phi3MLP):
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pass
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class Glm4DecoderLayer(GradientCheckpointingLayer):
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def __init__(self, config: Glm4Config, layer_idx: int):
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super().__init__()
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self.hidden_size = config.hidden_size
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self.self_attn = Glm4Attention(config=config, layer_idx=layer_idx)
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self.mlp = Glm4MLP(config)
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self.input_layernorm = Glm4RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = Glm4RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_self_attn_layernorm = Glm4RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_mlp_layernorm = Glm4RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Cache] = None,
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use_cache: Optional[bool] = False,
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cache_position: Optional[torch.LongTensor] = None,
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position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
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residual = hidden_states
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hidden_states = self.input_layernorm(hidden_states)
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hidden_states, _ = self.self_attn(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_value,
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use_cache=use_cache,
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cache_position=cache_position,
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position_embeddings=position_embeddings,
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**kwargs,
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)
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hidden_states = self.post_self_attn_layernorm(hidden_states)
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hidden_states = residual + hidden_states
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residual = hidden_states
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = self.post_mlp_layernorm(hidden_states)
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hidden_states = residual + hidden_states
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return hidden_states
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class Glm4Attention(GlmAttention):
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pass
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class Glm4ForCausalLM(GlmForCausalLM):
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def forward(
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self,
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**super_kwargs: Unpack[TransformersKwargs],
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) -> Union[tuple, CausalLMOutputWithPast]:
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r"""
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
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(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
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Example:
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```python
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>>> from transformers import AutoTokenizer, Glm4ForCausalLM
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>>> model = Glm4ForCausalLM.from_pretrained("THUDM/GLM-4-9B-0414")
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>>> tokenizer = AutoTokenizer.from_pretrained("THUDM/GLM-4-9B-0414")
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>>> prompt = "Hey, are you conscious? Can you talk to me?"
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>>> inputs = tokenizer(prompt, return_tensors="pt")
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>>> # Generate
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>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
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>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
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```"""
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return super().forward(**super_kwargs)
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class Glm4ForSequenceClassification(GlmForSequenceClassification):
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pass
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class Glm4ForTokenClassification(GlmForTokenClassification):
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pass
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__all__ = [
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"Glm4PreTrainedModel", # noqa: F822
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"Glm4Model", # noqa: F822
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"Glm4ForCausalLM",
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"Glm4ForSequenceClassification",
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"Glm4ForTokenClassification",
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]
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