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ModelHub XC
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modular_qwen3.py
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191
modular_qwen3.py
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# coding=utf-8
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# Copyright 2025 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
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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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"""PyTorch Qwen3 model."""
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from typing import Callable, Optional, Tuple
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import torch
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import torch.utils.checkpoint
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from transformers.cache_utils import Cache
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from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
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from transformers.modeling_outputs import CausalLMOutputWithPast
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from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
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from transformers.processing_utils import Unpack
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from transformers.utils import LossKwargs, logging
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from ..gemma.modeling_gemma import GemmaMLP
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from ..llama.modeling_llama import (
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LlamaAttention,
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LlamaDecoderLayer,
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LlamaForCausalLM,
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LlamaForQuestionAnswering,
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LlamaForSequenceClassification,
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LlamaForTokenClassification,
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LlamaRMSNorm,
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apply_rotary_pos_emb,
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eager_attention_forward,
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)
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from ..mistral.modeling_mistral import MistralModel
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from .configuration_qwen3 import Qwen3Config
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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "Qwen/Qwen3-8B"
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class Qwen3RMSNorm(LlamaRMSNorm):
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pass
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class Qwen3MLP(GemmaMLP):
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pass
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class Qwen3Attention(LlamaAttention):
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def __init__(self, config: Qwen3Config, layer_idx: int):
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super().__init__(config, layer_idx)
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self.q_norm = Qwen3RMSNorm(self.head_dim, eps=config.rms_norm_eps) # unlike olmo, only on the head dim!
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self.k_norm = Qwen3RMSNorm(self.head_dim, eps=config.rms_norm_eps) # thus post q_norm does not need reshape
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self.sliding_window = config.sliding_window
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if not (
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self.config.use_sliding_window
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and getattr(self.config, "sliding_window", None) is not None
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and self.layer_idx >= self.config.max_window_layers
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):
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self.sliding_window = None
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: Tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor],
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
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key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
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logger.warning_once(
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"`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
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'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
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)
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else:
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=0.0 if not self.training else self.attention_dropout,
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scaling=self.scaling,
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sliding_window=self.sliding_window, # diff with Llama
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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attn_output = self.o_proj(attn_output)
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return attn_output, attn_weights
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class Qwen3DecoderLayer(LlamaDecoderLayer):
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def __init__(self, config: Qwen3Config, layer_idx: int):
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super().__init__()
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self.self_attn = Qwen3Attention(config=config, layer_idx=layer_idx)
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self.mlp = Qwen3MLP(config)
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if (
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config.sliding_window and config._attn_implementation != "flash_attention_2"
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): # diff with Llama is this warning
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logger.warning_once(
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f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; "
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"unexpected results may be encountered."
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)
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class Qwen3Model(MistralModel): # mistral model creates sliding window
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pass
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class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
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class Qwen3ForCausalLM(LlamaForCausalLM):
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def forward(
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self,
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**super_kwargs: Unpack[KwargsForCausalLM],
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) -> 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, Qwen3ForCausalLM
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>>> model = Qwen3ForCausalLM.from_pretrained("Qwen/Qwen3-8B")
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>>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
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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 Qwen3ForSequenceClassification(LlamaForSequenceClassification):
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pass
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class Qwen3ForTokenClassification(LlamaForTokenClassification):
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pass
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class Qwen3ForQuestionAnswering(LlamaForQuestionAnswering):
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pass
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__all__ = [
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"Qwen3ForCausalLM",
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"Qwen3ForQuestionAnswering",
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"Qwen3Model",
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"Qwen3PreTrainedModel", # noqa: F822
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"Qwen3ForSequenceClassification",
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"Qwen3ForTokenClassification",
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]
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