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Addiing ByteDance Seed Seed-OSS (#40272)

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add seed oss
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Fazzie
2025-08-22 20:54:28 +08:00
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parent 8a6908c10d
commit 56d68c6706
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2
docs/source/en/_toctree.yml
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@@ -675,6 +675,8 @@
title: RoFormer
- local: model_doc/rwkv
title: RWKV
- local: model_doc/seed_oss
title: Seed-Oss
- local: model_doc/splinter
title: Splinter
- local: model_doc/squeezebert

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docs/source/en/model_doc/seed_oss.md Normal file
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@@ -0,0 +1,57 @@
<!--
# Copyright 2025 Bytedance-Seed Ltd and the 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. -->
# SeedOss
## Overview
To be released with the official model launch.
### Model Details
To be released with the official model launch.
## Usage tips
To be released with the official model launch.
## SeedOssConfig
[[autodoc]] SeedOssConfig
## SeedOssModel
[[autodoc]] SeedOssModel
- forward
## SeedOssForCausalLM
[[autodoc]] SeedOssForCausalLM
- forward
## SeedOssForSequenceClassification
[[autodoc]] SeedOssForSequenceClassification
- forward
## SeedOssForTokenClassification
[[autodoc]] SeedOssForTokenClassification
- forward
## SeedOssForQuestionAnswering
[[autodoc]] SeedOssForQuestionAnswering
- forward

1
src/transformers/models/__init__.py
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@@ -295,6 +295,7 @@ if TYPE_CHECKING:
from .sam_hq import *
from .seamless_m4t import *
from .seamless_m4t_v2 import *
from .seed_oss import *
from .segformer import *
from .seggpt import *
from .sew import *

2
src/transformers/models/auto/configuration_auto.py
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@@ -348,6 +348,7 @@ CONFIG_MAPPING_NAMES = OrderedDict[str, str](
("sam_vision_model", "SamVisionConfig"),
("seamless_m4t", "SeamlessM4TConfig"),
("seamless_m4t_v2", "SeamlessM4Tv2Config"),
("seed_oss", "SeedOssConfig"),
("segformer", "SegformerConfig"),
("seggpt", "SegGptConfig"),
("sew", "SEWConfig"),
@@ -782,6 +783,7 @@ MODEL_NAMES_MAPPING = OrderedDict[str, str](
("sam_vision_model", "SamVisionModel"),
("seamless_m4t", "SeamlessM4T"),
("seamless_m4t_v2", "SeamlessM4Tv2"),
("seed_oss", "SeedOss"),
("segformer", "SegFormer"),
("seggpt", "SegGPT"),
("sew", "SEW"),

5
src/transformers/models/auto/modeling_auto.py
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@@ -339,6 +339,7 @@ MODEL_MAPPING_NAMES = OrderedDict(
("sam_vision_model", "SamVisionModel"),
("seamless_m4t", "SeamlessM4TModel"),
("seamless_m4t_v2", "SeamlessM4Tv2Model"),
("seed_oss", "SeedOssModel"),
("segformer", "SegformerModel"),
("seggpt", "SegGptModel"),
("sew", "SEWModel"),
@@ -718,6 +719,7 @@ MODEL_FOR_CAUSAL_LM_MAPPING_NAMES = OrderedDict(
("roc_bert", "RoCBertForCausalLM"),
("roformer", "RoFormerForCausalLM"),
("rwkv", "RwkvForCausalLM"),
("seed_oss", "SeedOssForCausalLM"),
("smollm3", "SmolLM3ForCausalLM"),
("speech_to_text_2", "Speech2Text2ForCausalLM"),
("stablelm", "StableLmForCausalLM"),
@@ -1264,6 +1266,7 @@ MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
("roberta-prelayernorm", "RobertaPreLayerNormForSequenceClassification"),
("roc_bert", "RoCBertForSequenceClassification"),
("roformer", "RoFormerForSequenceClassification"),
("seed_oss", "SeedOssForSequenceClassification"),
("smollm3", "SmolLM3ForSequenceClassification"),
("squeezebert", "SqueezeBertForSequenceClassification"),
("stablelm", "StableLmForSequenceClassification"),
@@ -1352,6 +1355,7 @@ MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict(
("roberta-prelayernorm", "RobertaPreLayerNormForQuestionAnswering"),
("roc_bert", "RoCBertForQuestionAnswering"),
("roformer", "RoFormerForQuestionAnswering"),
("seed_oss", "SeedOssForQuestionAnswering"),
("smollm3", "SmolLM3ForQuestionAnswering"),
("splinter", "SplinterForQuestionAnswering"),
("squeezebert", "SqueezeBertForQuestionAnswering"),
@@ -1462,6 +1466,7 @@ MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
("roberta-prelayernorm", "RobertaPreLayerNormForTokenClassification"),
("roc_bert", "RoCBertForTokenClassification"),
("roformer", "RoFormerForTokenClassification"),
("seed_oss", "SeedOssForTokenClassification"),
("smollm3", "SmolLM3ForTokenClassification"),
("squeezebert", "SqueezeBertForTokenClassification"),
("stablelm", "StableLmForTokenClassification"),

27
src/transformers/models/seed_oss/__init__.py Normal file
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@@ -0,0 +1,27 @@
# Copyright 2025 Bytedance-Seed Ltd and the 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.
from typing import TYPE_CHECKING
from ...utils import _LazyModule
from ...utils.import_utils import define_import_structure
if TYPE_CHECKING:
from .configuration_seed_oss import *
from .modeling_seed_oss import *
else:
import sys
_file = globals()["__file__"]
sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)

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src/transformers/models/seed_oss/configuration_seed_oss.py Normal file
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@@ -0,0 +1,224 @@
# Copyright 2025 Bytedance-Seed Ltd and the 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.
"""SeedOss model configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
class SeedOssConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`SeedOssModel`]. It is used to instantiate an SeedOss
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the SeedOss-36B.
e.g. [ByteDance-Seed/SeedOss-36B](https://huggingface.co/ByteDance-Seed/SeedOss-36B)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 155136):
Vocabulary size of the SeedOss model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`SeedOssModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 27648):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 64):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 80):
Number of attention heads for each attention layer in the Transformer decoder.
num_key_value_heads (`int`, *optional*, defaults to 8):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details, check out [this
paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `8`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 524288):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*, defaults to 1):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 0):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2):
End of stream token id.
pretraining_tp (`int`, *optional*, defaults to 1):
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to
understand more about it. This value is necessary to ensure exact reproducibility of the pretraining
results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232).
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
`factor` (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
original maximum pre-trained length.
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested value.
`beta_fast` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
ramp function. If unspecified, it defaults to 32.
`beta_slow` (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
ramp function. If unspecified, it defaults to 1.
`short_factor` (`list[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`long_factor` (`list[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`low_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
`high_freq_factor` (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
attention_bias (`bool`, *optional*, defaults to `True`):
Whether to use a bias in the query, key, value layers during self-attention.
attention_out_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in the output projection layer during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
residual_dropout (`float`, *optional*, defaults to 0.1):
Residual connection dropout value.
mlp_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
head_dim (`int`, *optional*, defaults to 128):
The attention head dimension.
```python
>>> from transformers import SeedOssModel, SeedOssConfig
>>> # Initializing a SeedOss-36b style configuration
>>> configuration = SeedOssConfig()
>>> # Initializing a model from the SeedOss-36b style configuration
>>> model = SeedOssModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "seed_oss"
keys_to_ignore_at_inference = ["past_key_values"]
# Default tensor parallel plan for base model `SeedOssModel`
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
vocab_size=155136,
hidden_size=4096,
intermediate_size=27648,
num_hidden_layers=64,
num_attention_heads=80,
num_key_value_heads=8,
hidden_act="silu",
max_position_embeddings=524288,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
pretraining_tp=1,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=True,
attention_out_bias=False,
attention_dropout=0.1,
residual_dropout=0.1,
mlp_bias=False,
head_dim=128,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_out_bias = attention_out_bias
self.attention_dropout = attention_dropout
self.residual_dropout = residual_dropout
self.mlp_bias = mlp_bias
self.head_dim = head_dim if head_dim is not None else self.hidden_size // self.num_attention_heads
# Validate the correctness of rotary position embeddings parameters
# BC: if there is a 'type' field, copy it it to 'rope_type'.
if self.rope_scaling is not None and "type" in self.rope_scaling:
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
rope_config_validation(self)
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
__all__ = ["SeedOssConfig"]

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src/transformers/models/seed_oss/modeling_seed_oss.py Normal file
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# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# This file was automatically generated from src/transformers/models/seed_oss/modular_seed_oss.py.
# Do NOT edit this file manually as any edits will be overwritten by the generation of
# the file from the modular. If any change should be done, please apply the change to the
# modular_seed_oss.py file directly. One of our CI enforces this.
# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
# Copyright 2025 Bytedance-Seed Ltd and the 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.
from typing import Callable, Optional, Union
import torch
import torch.nn as nn
from ...activations import ACT2FN
from ...cache_utils import Cache, DynamicCache
from ...generation import GenerationMixin
from ...integrations import use_kernel_forward_from_hub
from ...masking_utils import create_causal_mask
from ...modeling_layers import (
GenericForQuestionAnswering,
GenericForSequenceClassification,
GenericForTokenClassification,
GradientCheckpointingLayer,
)
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
from ...utils.deprecation import deprecate_kwarg
from ...utils.generic import check_model_inputs
from .configuration_seed_oss import SeedOssConfig
@use_kernel_forward_from_hub("RMSNorm")
class SeedOssRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
SeedOssRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
def extra_repr(self):
return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
class SeedOssMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
self.act_fn = ACT2FN[config.hidden_act]
self.residual_dropout = config.residual_dropout
def forward(self, x):
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
down_proj = nn.functional.dropout(down_proj, p=self.residual_dropout, training=self.training)
return down_proj
def rotate_half(x):
"""Rotates half the hidden dims of the input."""
x1 = x[..., : x.shape[-1] // 2]
x2 = x[..., x.shape[-1] // 2 :]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
"""Applies Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`): The query tensor.
k (`torch.Tensor`): The key tensor.
cos (`torch.Tensor`): The cosine part of the rotary embedding.
sin (`torch.Tensor`): The sine part of the rotary embedding.
position_ids (`torch.Tensor`, *optional*):
Deprecated and unused.
unsqueeze_dim (`int`, *optional*, defaults to 1):
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
Returns:
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
"""
cos = cos.unsqueeze(unsqueeze_dim)
sin = sin.unsqueeze(unsqueeze_dim)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
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 eager_attention_forward(
module: nn.Module,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
attention_mask: Optional[torch.Tensor],
scaling: float,
dropout: float = 0.0,
**kwargs: Unpack[TransformersKwargs],
):
key_states = repeat_kv(key, module.num_key_value_groups)
value_states = repeat_kv(value, module.num_key_value_groups)
attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
if attention_mask is not None:
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
attn_output = torch.matmul(attn_weights, value_states)
attn_output = attn_output.transpose(1, 2).contiguous()
return attn_output, attn_weights
class SeedOssAttention(nn.Module):
def __init__(self, config: SeedOssConfig, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = config.head_dim
self.num_key_value_heads = config.num_key_value_heads
self.num_attention_heads = config.num_attention_heads
self.num_key_value_groups = self.num_attention_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.attention_dropout = config.attention_dropout
self.is_causal = True
self.q_proj = nn.Linear(
config.hidden_size, self.num_attention_heads * self.head_dim, bias=config.attention_bias
)
self.k_proj = nn.Linear(
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
)
self.v_proj = nn.Linear(
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
)
self.o_proj = nn.Linear(
self.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_out_bias
)
self.residual_dropout = config.residual_dropout
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor],
past_key_values: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[TransformersKwargs],
) -> tuple[torch.Tensor, torch.Tensor]:
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, self.head_dim)
query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_values is not None:
# 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_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
**kwargs,
)
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
attn_output = self.o_proj(attn_output)
attn_output = nn.functional.dropout(attn_output, p=self.residual_dropout, training=self.training)
return attn_output, attn_weights
class SeedOssDecoderLayer(GradientCheckpointingLayer):
def __init__(self, config: SeedOssConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = SeedOssAttention(config=config, layer_idx=layer_idx)
self.mlp = SeedOssMLP(config)
self.input_layernorm = SeedOssRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_attention_layernorm = SeedOssRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
**kwargs: Unpack[TransformersKwargs],
) -> torch.Tensor:
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
use_cache=use_cache,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(hidden_states)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
return hidden_states
@auto_docstring
class SeedOssPreTrainedModel(PreTrainedModel):
config: SeedOssConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["SeedOssDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn = True
_supports_sdpa = True
_supports_flex_attn = True
_can_compile_fullgraph = True
_supports_attention_backend = True
_can_record_outputs = {
"hidden_states": SeedOssDecoderLayer,
"attentions": SeedOssAttention,
}
class SeedOssRotaryEmbedding(nn.Module):
inv_freq: torch.Tensor # fix linting for `register_buffer`
def __init__(self, config: SeedOssConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
else:
self.rope_type = "default"
self.max_seq_len_cached = config.max_position_embeddings
self.original_max_seq_len = config.max_position_embeddings
self.config = config
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
self.register_buffer("inv_freq", inv_freq, persistent=False)
self.original_inv_freq = self.inv_freq
@torch.no_grad()
@dynamic_rope_update # power user: used with advanced RoPE types (e.g. dynamic rope)
def forward(self, x, position_ids):
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
position_ids_expanded = position_ids[:, None, :].float()
device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
with torch.autocast(device_type=device_type, enabled=False): # Force float32
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
emb = torch.cat((freqs, freqs), dim=-1)
cos = emb.cos() * self.attention_scaling
sin = emb.sin() * self.attention_scaling
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
@auto_docstring
class SeedOssModel(SeedOssPreTrainedModel):
def __init__(self, config: SeedOssConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
self.layers = nn.ModuleList(
[SeedOssDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.norm = SeedOssRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.rotary_emb = SeedOssRotaryEmbedding(config=config)
self.gradient_checkpointing = False
# Initialize weights and apply final processing
self.post_init()
@check_model_inputs
@auto_docstring
def forward(
self,
input_ids: Optional[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,
cache_position: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
**kwargs: Unpack[TransformersKwargs],
) -> BaseModelOutputWithPast:
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds: torch.Tensor = self.embed_tokens(input_ids)
if use_cache and past_key_values is None:
past_key_values = DynamicCache(config=self.config)
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position: torch.Tensor = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = create_causal_mask(
config=self.config,
input_embeds=inputs_embeds,
attention_mask=attention_mask,
cache_position=cache_position,
past_key_values=past_key_values,
position_ids=position_ids,
)
hidden_states = inputs_embeds
position_embeddings = self.rotary_emb(hidden_states, position_ids)
for decoder_layer in self.layers[: self.config.num_hidden_layers]:
hidden_states = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_values=past_key_values,
cache_position=cache_position,
position_embeddings=position_embeddings,
**kwargs,
)
hidden_states = self.norm(hidden_states)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values,
)
@auto_docstring
class SeedOssForCausalLM(SeedOssPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
_pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
def __init__(self, config):
super().__init__(config)
self.model = SeedOssModel(config)
self.vocab_size = config.vocab_size
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids: Optional[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,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
logits_to_keep: Union[int, torch.Tensor] = 0,
**kwargs: Unpack[TransformersKwargs],
) -> CausalLMOutputWithPast:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Example:
```python
>>> from transformers import AutoTokenizer, SeedOssForCausalLM
>>> model = SeedOssForCausalLM.from_pretrained("ByteDance-Seed/SeedOss-36B")
>>> tokenizer = AutoTokenizer.from_pretrained("ByteDance-Seed/SeedOss-36B")
>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
outputs: BaseModelOutputWithPast = self.model(
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,
cache_position=cache_position,
**kwargs,
)
hidden_states = outputs.last_hidden_state
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
logits = self.lm_head(hidden_states[:, slice_indices, :])
loss = None
if labels is not None:
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class SeedOssForSequenceClassification(GenericForSequenceClassification, SeedOssPreTrainedModel):
pass
class SeedOssForTokenClassification(GenericForTokenClassification, SeedOssPreTrainedModel):
pass
class SeedOssForQuestionAnswering(GenericForQuestionAnswering, SeedOssPreTrainedModel):
base_model_prefix = "transformer" # For BC, where `transformer` was used instead of `model`
__all__ = [
"SeedOssForCausalLM",
"SeedOssForQuestionAnswering",
"SeedOssPreTrainedModel",
"SeedOssModel",
"SeedOssForSequenceClassification",
"SeedOssForTokenClassification",
]

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# Copyright 2025 Bytedance-Seed Ltd and the 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.
"""PyTorch SeedOss model."""
from typing import Callable, Optional
import torch
import torch.nn as nn
from ...activations import ACT2FN
from ...cache_utils import Cache
from ...modeling_outputs import CausalLMOutputWithPast
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, logging
from ...utils.deprecation import deprecate_kwarg
from ..llama.modeling_llama import (
LlamaDecoderLayer,
LlamaForCausalLM,
LlamaForQuestionAnswering,
LlamaForSequenceClassification,
LlamaForTokenClassification,
LlamaModel,
LlamaPreTrainedModel,
LlamaRMSNorm,
apply_rotary_pos_emb,
eager_attention_forward,
)
from .configuration_seed_oss import SeedOssConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "ByteDance-Seed/SeedOss-36B"
class SeedOssRMSNorm(LlamaRMSNorm):
pass
class SeedOssMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
self.act_fn = ACT2FN[config.hidden_act]
self.residual_dropout = config.residual_dropout
def forward(self, x):
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
down_proj = nn.functional.dropout(down_proj, p=self.residual_dropout, training=self.training)
return down_proj
class SeedOssAttention(nn.Module):
def __init__(self, config: SeedOssConfig, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = config.head_dim
self.num_key_value_heads = config.num_key_value_heads
self.num_attention_heads = config.num_attention_heads
self.num_key_value_groups = self.num_attention_heads // self.num_key_value_heads
self.scaling = self.head_dim**-0.5
self.attention_dropout = config.attention_dropout
self.is_causal = True
self.q_proj = nn.Linear(
config.hidden_size, self.num_attention_heads * self.head_dim, bias=config.attention_bias
)
self.k_proj = nn.Linear(
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
)
self.v_proj = nn.Linear(
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
)
self.o_proj = nn.Linear(
self.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_out_bias
)
self.residual_dropout = config.residual_dropout
@deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor],
past_key_values: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[TransformersKwargs],
) -> tuple[torch.Tensor, torch.Tensor]:
input_shape = hidden_states.shape[:-1]
hidden_shape = (*input_shape, -1, self.head_dim)
query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_values is not None:
# 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_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
**kwargs,
)
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
attn_output = self.o_proj(attn_output)
attn_output = nn.functional.dropout(attn_output, p=self.residual_dropout, training=self.training)
return attn_output, attn_weights
class SeedOssDecoderLayer(LlamaDecoderLayer):
pass
class SeedOssPreTrainedModel(LlamaPreTrainedModel):
pass
class SeedOssModel(LlamaModel):
pass
class SeedOssForCausalLM(LlamaForCausalLM):
def forward(
self,
**super_kwargs: Unpack[TransformersKwargs],
) -> CausalLMOutputWithPast:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
Example:
```python
>>> from transformers import AutoTokenizer, SeedOssForCausalLM
>>> model = SeedOssForCausalLM.from_pretrained("ByteDance-Seed/SeedOss-36B")
>>> tokenizer = AutoTokenizer.from_pretrained("ByteDance-Seed/SeedOss-36B")
>>> prompt = "Hey, are you conscious? Can you talk to me?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
return super().forward(**super_kwargs)
class SeedOssForSequenceClassification(LlamaForSequenceClassification):
pass
class SeedOssForTokenClassification(LlamaForTokenClassification):
pass
class SeedOssForQuestionAnswering(LlamaForQuestionAnswering):
pass
__all__ = [
"SeedOssForCausalLM",
"SeedOssForQuestionAnswering",
"SeedOssPreTrainedModel",
"SeedOssModel",
"SeedOssForSequenceClassification",
"SeedOssForTokenClassification",
]

0
tests/models/seed_oss/__init__.py Normal file
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tests/models/seed_oss/test_modeling_seed_oss.py Normal file
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# Copyright 2025 Bytedance-Seed Ltd and the 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.
"""Testing suite for the PyTorch SeedOss model."""
import unittest
import pytest
from transformers import AutoModelForCausalLM, AutoTokenizer, SeedOssConfig, is_torch_available
from transformers.testing_utils import (
cleanup,
require_flash_attn,
require_torch,
require_torch_large_accelerator,
require_torch_large_gpu,
slow,
torch_device,
)
from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
if is_torch_available():
import torch
from transformers import (
SeedOssForCausalLM,
SeedOssForQuestionAnswering,
SeedOssForSequenceClassification,
SeedOssForTokenClassification,
SeedOssModel,
)
class SeedOssModelTester(CausalLMModelTester):
if is_torch_available():
config_class = SeedOssConfig
base_model_class = SeedOssModel
causal_lm_class = SeedOssForCausalLM
sequence_classification_class = SeedOssForSequenceClassification
token_classification_class = SeedOssForTokenClassification
question_answering_class = SeedOssForQuestionAnswering
@require_torch
class SeedOssModelTest(CausalLMModelTest, unittest.TestCase):
model_tester_class = SeedOssModelTester
all_model_classes = (
(
SeedOssModel,
SeedOssForCausalLM,
SeedOssForSequenceClassification,
SeedOssForTokenClassification,
SeedOssForQuestionAnswering,
)
if is_torch_available()
else ()
)
pipeline_model_mapping = (
{
"feature-extraction": SeedOssModel,
"text-classification": SeedOssForSequenceClassification,
"token-classification": SeedOssForTokenClassification,
"text-generation": SeedOssForCausalLM,
"zero-shot": SeedOssForSequenceClassification,
}
if is_torch_available()
else {}
)
test_headmasking = False
test_pruning = False
_is_stateful = True
model_split_percents = [0.5, 0.6]
@slow
@require_torch_large_accelerator
class SeedOssIntegrationTest(unittest.TestCase):
input_text = ["How to make pasta?", "Hi ByteDance-Seed"]
model_id = "ByteDance-Seed/Seed-OSS-36B-Base"
def tearDown(self):
cleanup(torch_device, gc_collect=True)
def test_model_36b_fp16(self):
EXPECTED_TEXTS = [
"How to make pasta?\nHow to make pasta?\nPasta is a popular dish that is enjoyed by people all over",
"Hi ByteDance-Seed team,\nI am trying to run the code on my local machine. I have installed all the",
]
model = AutoModelForCausalLM.from_pretrained(self.model_id, torch_dtype=torch.float16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(self.model_id)
inputs = tokenizer(self.input_text, return_tensors="pt", padding=True, return_token_type_ids=False).to(
model.model.embed_tokens.weight.device
)
output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
self.assertEqual(output_text, EXPECTED_TEXTS)
def test_model_36b_bf16(self):
EXPECTED_TEXTS = [
"How to make pasta?\nHow to make pasta?\nPasta is a popular dish that is enjoyed by people all over",
"Hi ByteDance-Seed team,\nI am trying to run the code on my local machine. I have installed all the",
]
model = AutoModelForCausalLM.from_pretrained(self.model_id, torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(self.model_id)
inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(
model.model.embed_tokens.weight.device
)
output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
self.assertEqual(output_text, EXPECTED_TEXTS)
def test_model_36b_eager(self):
EXPECTED_TEXTS = ""
model = AutoModelForCausalLM.from_pretrained(
self.model_id, torch_dtype=torch.bfloat16, attn_implementation="eager", device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(self.model_id)
inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(
model.model.embed_tokens.weight.device
)
output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
self.assertEqual(output_text, EXPECTED_TEXTS)
def test_model_36b_sdpa(self):
EXPECTED_TEXTS = [
"How to make pasta?\nHow to make pasta?\nPasta is a popular dish that is enjoyed by people all over",
"Hi ByteDance-Seed team,\nI am trying to run the code on my local machine. I have installed all the",
]
model = AutoModelForCausalLM.from_pretrained(
self.model_id, torch_dtype=torch.bfloat16, attn_implementation="sdpa", device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(self.model_id)
inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(
model.model.embed_tokens.weight.device
)
output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
self.assertEqual(output_text, EXPECTED_TEXTS)
@require_flash_attn
@require_torch_large_gpu
@pytest.mark.flash_attn_test
def test_model_36b_flash_attn(self):
EXPECTED_TEXTS = ""
model = AutoModelForCausalLM.from_pretrained(
self.model_id, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2", device_map="auto"
)
model.to(torch_device)
tokenizer = AutoTokenizer.from_pretrained(self.model_id)
inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(
model.model.embed_tokens.weight.device
)
output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
self.assertEqual(output_text, EXPECTED_TEXTS)

1
tests/repo_utils/modular/test_conversion_order.py
View File

@@ -39,6 +39,7 @@ FILES_TO_PARSE = [
os.path.join(MODEL_ROOT, "phi3", "modular_phi3.py"),
os.path.join(MODEL_ROOT, "cohere", "modular_cohere.py"),
os.path.join(MODEL_ROOT, "glm4", "modular_glm4.py"),
os.path.join(MODEL_ROOT, "seed_oss", "modular_seed_oss.py"),
]