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[modular] Do not track imports in functions (#36279)

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* Add check

* just check for function

* Update examples
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Cyril Vallez 2025-02-25 10:29:47 +01:00 committed by GitHub
parent 4b5cf5496d
commit bc65f3fc1c
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10 changed files with 82 additions and 33 deletions
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5
examples/modular-transformers/configuration_my_new_model.py
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@ -140,6 +140,11 @@ class MyNewModelConfig(PretrainedConfig):
"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,

5
examples/modular-transformers/configuration_my_new_model2.py
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@ -43,6 +43,11 @@ class MyNewModel2Config(PretrainedConfig):
"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,

14
examples/modular-transformers/configuration_new_model.py
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@ -79,6 +79,20 @@ class NewModelConfig(PretrainedConfig):
model_type = "new_model"
keys_to_ignore_at_inference = ["past_key_values"]
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,

5
examples/modular-transformers/image_processing_new_imgproc_model.py
View File

@ -19,7 +19,7 @@ from ...image_utils import (
PILImageResampling,
infer_channel_dimension_format,
is_scaled_image,
make_list_of_images,
make_flat_list_of_images,
to_numpy_array,
valid_images,
validate_preprocess_arguments,
@ -221,8 +221,7 @@ class ImgprocModelImageProcessor(BaseImageProcessor):
size = size if size is not None else self.size
size = get_size_dict(size, default_to_square=False)
images = make_list_of_images(images)
images = make_flat_list_of_images(images)
if not valid_images(images):
raise ValueError(

5
examples/modular-transformers/modeling_dummy.py
View File

@ -356,6 +356,7 @@ class DummyPreTrainedModel(PreTrainedModel):
_supports_cache_class = True
_supports_quantized_cache = True
_supports_static_cache = True
_supports_attention_backend = True
def _init_weights(self, module):
std = self.config.initializer_range
@ -698,7 +699,9 @@ class DummyModel(DummyPreTrainedModel):
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype

5
examples/modular-transformers/modeling_multimodal1.py
View File

@ -356,6 +356,7 @@ class Multimodal1TextPreTrainedModel(PreTrainedModel):
_supports_cache_class = True
_supports_quantized_cache = True
_supports_static_cache = True
_supports_attention_backend = True
def _init_weights(self, module):
std = self.config.initializer_range
@ -698,7 +699,9 @@ class Multimodal1TextModel(Multimodal1TextPreTrainedModel):
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype

27
examples/modular-transformers/modeling_my_new_model2.py
View File

@ -356,6 +356,7 @@ class MyNewModel2PreTrainedModel(PreTrainedModel):
_supports_cache_class = True
_supports_quantized_cache = True
_supports_static_cache = True
_supports_attention_backend = True
def _init_weights(self, module):
std = self.config.initializer_range
@ -491,6 +492,7 @@ class MyNewModel2Model(MyNewModel2PreTrainedModel):
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs, # NOOP kwarg for now
) -> Union[Tuple, BaseModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
@ -703,7 +705,9 @@ class MyNewModel2Model(MyNewModel2PreTrainedModel):
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
@ -787,17 +791,20 @@ class MyNewModel2ForSequenceClassification(MyNewModel2PreTrainedModel):
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
sequence_lengths = -1
last_non_pad_token = -1
elif input_ids is not None:
# To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
token_indices = torch.arange(input_ids.shape[-1], device=logits.device)
last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
else:
if input_ids is not None:
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
sequence_lengths = sequence_lengths % input_ids.shape[-1]
sequence_lengths = sequence_lengths.to(logits.device)
else:
sequence_lengths = -1
last_non_pad_token = -1
logger.warning_once(
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
)
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
loss = None
if labels is not None:

36
examples/modular-transformers/modeling_new_task_model.py
View File

@ -19,6 +19,7 @@ from ...utils import (
add_start_docstrings_to_model_forward,
replace_return_docstrings,
)
from ...utils.deprecation import deprecate_kwarg
from ..auto import AutoModel, AutoModelForCausalLM
from .configuration_new_task_model import NewTaskModelConfig
@ -254,8 +255,7 @@ class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
token_type_ids,
past_key_values,
cache_position,
input_ids=None,
inputs_embeds=None,
input_tensor,
is_training: bool = False,
):
if self.config.text_config._attn_implementation == "flash_attention_2":
@ -265,8 +265,7 @@ class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
using_static_cache = isinstance(past_key_values, StaticCache)
min_dtype = torch.finfo(self.dtype).min
inputs_lead_dim = input_ids.shape[0] if input_ids is not None else inputs_embeds.shape[0]
sequence_length = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
inputs_lead_dim, sequence_length = input_tensor.shape[:2]
if using_static_cache:
target_length = past_key_values.get_max_cache_shape()
elif isinstance(past_key_values, HybridCache):
@ -297,16 +296,20 @@ class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
# First unmask prefix tokens during training
if is_training:
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
token_type_ids[:, None, None, :].to(causal_mask.device) == 0, 0
)
# Then apply padding mask (will mask pad tokens)
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(causal_mask.device)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
# we are training thus we need to create a full mask on the image + prefix but causal on suffix
if is_training:
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
token_type_ids[:, None, None, :].to(causal_mask.device) == 0, 0
)
return causal_mask
def get_image_features(self, pixel_values: torch.FloatTensor):
@ -325,6 +328,7 @@ class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
image_features = image_features / (self.config.text_config.hidden_size**0.5)
return image_features
@deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep")
@add_start_docstrings_to_model_forward(NEW_TASK_MODEL_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=NewTaskModelCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
@ -351,10 +355,12 @@ class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
config.text_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.text_config.vocab_size]`.
num_logits_to_keep (`int`, *optional*):
Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
logits_to_keep (`int` or `torch.Tensor`, *optional*):
If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
This is useful when using packed tensor format (single dimension for batch and sequence length).
Returns:
@ -418,7 +424,7 @@ class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
attention_mask=None,
token_type_ids=None,
use_cache=True,
num_logits_to_keep=None,
logits_to_keep=None,
labels=None,
**kwargs,
):
@ -431,7 +437,7 @@ class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
position_ids=position_ids,
cache_position=cache_position,
use_cache=use_cache,
num_logits_to_keep=num_logits_to_keep,
logits_to_keep=logits_to_keep,
token_type_ids=token_type_ids,
**kwargs,
)
@ -445,10 +451,12 @@ class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
model_inputs["pixel_values"] = pixel_values
is_training = token_type_ids is not None and labels is not None
if cache_position[0] == 0 and isinstance(past_key_values, HybridCache):
input_tensor = inputs_embeds if inputs_embeds is not None else input_ids
causal_mask = self._update_causal_mask(
attention_mask, token_type_ids, past_key_values, cache_position, input_ids, inputs_embeds, is_training
attention_mask, token_type_ids, past_key_values, cache_position, input_tensor, is_training
)
model_inputs["attention_mask"] = causal_mask
return model_inputs
def resize_token_embeddings(

5
examples/modular-transformers/modeling_super.py
View File

@ -356,6 +356,7 @@ class SuperPreTrainedModel(PreTrainedModel):
_supports_cache_class = True
_supports_quantized_cache = True
_supports_static_cache = True
_supports_attention_backend = True
def _init_weights(self, module):
std = self.config.initializer_range
@ -620,7 +621,9 @@ class SuperModel(SuperPreTrainedModel):
if attention_mask is not None:
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
mask_length = attention_mask.shape[-1]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
causal_mask.device
)
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype

2
utils/modular_model_converter.py
View File

@ -649,6 +649,8 @@ class ModuleMapper(CSTVisitor, ABC):
self.current_function = None
def visit_If(self, node):
# If we are inside a function, do not add the import to the list of imports
if self.current_function is None:
for stmt in node.body.body:
if m.matches(stmt, m.SimpleStatementLine(body=[m.ImportFrom() | m.Import()])):
self.imports.append(node)