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[FX] _generate_dummy_input supports audio-classification models for labels #18580

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merged 2 commits into from
Aug 11, 2022
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[FX] _generate_dummy_input supports audio-classification models for labels #18580

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1457ac6
Support audio classification architectures for labels generation, as …
michaelbenayoun Aug 11, 2022
2a07ed0
Use ENV_VARS_TRUE_VALUES
michaelbenayoun Aug 11, 2022
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21 changes: 13 additions & 8 deletions src/transformers/utils/fx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@
import inspect
import math
import operator
import os
import random
import warnings
from typing import Any, Callable, Dict, List, Optional, Type, Union
Expand Down Expand Up @@ -53,6 +54,7 @@


logger = logging.get_logger(__name__)
_IS_IN_DEBUG_MODE = os.environ.get("FX_DEBUG_MODE", False) in ["true", "True", "1"]
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Let's use ENV_VARS_TRUE_VALUES here from the utils:

Suggested change
_IS_IN_DEBUG_MODE = os.environ.get("FX_DEBUG_MODE", False) in ["true", "True", "1"]
_IS_IN_DEBUG_MODE = os.environ.get("FX_DEBUG_MODE", "").upper() in ENV_VARS_TRUE_VALUES



def _generate_supported_model_class_names(
Expand Down Expand Up @@ -678,7 +680,12 @@ def _generate_dummy_input(
if input_name in ["labels", "start_positions", "end_positions"]:

batch_size = shape[0]
if model_class_name in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES):
if model_class_name in [
*get_values(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES),
*get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES),
*get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES),
*get_values(MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES),
]:
inputs_dict["labels"] = torch.zeros(batch_size, dtype=torch.long, device=device)
elif model_class_name in [
*get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES),
Expand Down Expand Up @@ -710,11 +717,6 @@ def _generate_dummy_input(
)
inputs_dict["labels"] = torch.zeros(*labels_shape, dtype=labels_dtype, device=device)

elif model_class_name in [
*get_values(MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES),
*get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES),
]:
inputs_dict["labels"] = torch.zeros(batch_size, dtype=torch.long, device=device)
elif model_class_name in [
*get_values(MODEL_FOR_PRETRAINING_MAPPING_NAMES),
*get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES),
Expand All @@ -725,7 +727,9 @@ def _generate_dummy_input(
]:
inputs_dict["labels"] = torch.zeros(shape, dtype=torch.long, device=device)
else:
raise NotImplementedError(f"{model_class_name} not supported yet.")
raise NotImplementedError(
f"Generating the dummy input named {input_name} for {model_class_name} is not supported yet."
)
elif "pixel_values" in input_name:
batch_size = shape[0]
image_size = getattr(model.config, "image_size", None)
Expand Down Expand Up @@ -846,7 +850,8 @@ def create_proxy(self, kind, target, args, kwargs, name=None, type_expr=None, pr
raise ValueError("Don't support composite output yet")
rv.install_metadata(meta_out)
except Exception as e:
warnings.warn(f"Could not compute metadata for {kind} target {target}: {e}")
if _IS_IN_DEBUG_MODE:
warnings.warn(f"Could not compute metadata for {kind} target {target}: {e}")

return rv

Expand Down