[RFC] Laying down building stone for more flexible ONNX export capabilities (#11786)
* Laying down building stone for more flexible ONNX export capabilities * Ability to provide a map of config key to override before exporting. * Makes it possible to export BART with/without past keys. * Supports simple mathematical syntax for OnnxVariable.repeated * Effectively apply value override from onnx config for model * Supports export with additional features such as with-past for seq2seq * Store the output path directly in the args for uniform usage across. * Make BART_ONNX_CONFIG_* constants and fix imports. * Support BERT model. * Use tokenizer for more flexibility in defining the inputs of a model. * Add TODO as remainder to provide the batch/sequence_length as CLI args * Enable optimizations to be done on the model. * Enable GPT2 + past * Improve model validation with outputs containing nested structures * Enable Roberta * Enable Albert * Albert requires opset >= 12 * BERT-like models requires opset >= 12 * Remove double printing. * Enable XLM-Roberta * Enable DistilBERT * Disable optimization by default * Fix missing setattr when applying optimizer_features * Add value field to OnnxVariable to define constant input (not from tokenizers) * Add T5 support. * Simplify model type retrieval * Example exporting token_classification pipeline for DistilBERT. * Refactoring to package `transformers.onnx` * Solve circular dependency & __main__ * Remove unnecessary imports in `__init__` * Licences * Use @Narsil's suggestion to forward the model's configuration to the ONNXConfig to avoid interpolation. * Onnx export v2 fixes (#12388) * Tiny fixes Remove `convert_pytorch` from onnxruntime-less runtimes Correct reference to model * Style * Fix Copied from * LongFormer ONNX config. * Removed optimizations * Remvoe bad merge relicas. * Remove unused constants. * Remove some deleted constants from imports. * Fix unittest to remove usage of PyTorch model for onnx.utils. * Fix distilbert export * Enable ONNX export test for supported model. * Style. * Fix lint. * Enable all supported default models. * GPT2 only has one output * Fix bad property name when overriding config. * Added unittests and docstrings. * Disable with_past tests for now. * Enable outputs validation for default export. * Remove graph opt lvls. * Last commit with on-going past commented. * Style. * Disabled `with_past` for now * Remove unused imports. * Remove framework argument * Remove TFPreTrainedModel reference * Add documentation * Add onnxruntime tests to CircleCI * Add test * Rename `convert_pytorch` to `export` * Use OrderedDict for dummy inputs * WIP Wav2Vec2 * Revert "WIP Wav2Vec2" This reverts commit f665efb04c92525c3530e589029f0ae7afdf603e. * Style * Use OrderedDict for I/O * Style. * Specify OrderedDict documentation. * Style :) Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr> Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
This commit is contained in:
Funtowicz Morgan
@@ -35,7 +35,7 @@ from transformers.testing_utils import (
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_tf_gpu_memory_limit,
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is_pt_tf_cross_test,
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is_staging_test,
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require_onnx,
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require_keras2onnx,
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require_tf,
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slow,
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tooslow,
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@@ -325,7 +325,7 @@ class TFModelTesterMixin:
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self.assertEqual(len(incompatible_ops), 0, incompatible_ops)
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@require_onnx
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@require_keras2onnx
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@slow
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def test_onnx_runtime_optimize(self):
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if not self.test_onnx:
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251
tests/test_onnx_v2.py
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251
tests/test_onnx_v2.py
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@@ -0,0 +1,251 @@
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from pathlib import Path
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from tempfile import NamedTemporaryFile
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from unittest import TestCase
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from unittest.mock import patch
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from transformers import ( # LongformerConfig,
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AlbertConfig,
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AutoTokenizer,
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BartConfig,
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DistilBertConfig,
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GPT2Config,
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RobertaConfig,
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T5Config,
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XLMRobertaConfig,
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is_torch_available,
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)
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from transformers.models.albert import AlbertOnnxConfig
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from transformers.models.bart import BartOnnxConfig
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from transformers.models.bert.configuration_bert import BertConfig, BertOnnxConfig
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from transformers.models.distilbert import DistilBertOnnxConfig
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# from transformers.models.longformer import LongformerOnnxConfig
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from transformers.models.gpt2 import GPT2OnnxConfig
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from transformers.models.roberta import RobertaOnnxConfig
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from transformers.models.t5 import T5OnnxConfig
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from transformers.models.xlm_roberta import XLMRobertaOnnxConfig
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from transformers.onnx import EXTERNAL_DATA_FORMAT_SIZE_LIMIT, OnnxConfig, ParameterFormat, validate_model_outputs
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from transformers.onnx.config import DEFAULT_ONNX_OPSET, OnnxConfigWithPast
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from transformers.onnx.utils import (
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compute_effective_axis_dimension,
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compute_serialized_parameters_size,
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flatten_output_collection_property,
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)
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from transformers.testing_utils import require_onnx, require_torch, slow
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@require_onnx
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class OnnxUtilsTestCaseV2(TestCase):
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"""
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Cover all the utilities involved to export ONNX models
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"""
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def test_compute_effective_axis_dimension(self):
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"""
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When exporting ONNX model with dynamic axis (batch or sequence) we set batch_size and/or sequence_length = -1.
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We cannot generate an effective tensor with axis dim == -1, so we trick by using some "fixed" values
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(> 1 to avoid ONNX squeezing the axis).
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This test ensure we are correctly replacing generated batch / sequence tensor with axis > 1
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"""
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# Dynamic axis (batch, no token added by the tokenizer)
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self.assertEqual(compute_effective_axis_dimension(-1, fixed_dimension=2, num_token_to_add=0), 2)
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# Static axis (batch, no token added by the tokenizer)
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self.assertEqual(compute_effective_axis_dimension(0, fixed_dimension=2, num_token_to_add=0), 2)
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# Dynamic axis (sequence, token added by the tokenizer 2 (no pair))
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self.assertEqual(compute_effective_axis_dimension(0, fixed_dimension=8, num_token_to_add=2), 6)
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self.assertEqual(compute_effective_axis_dimension(0, fixed_dimension=8, num_token_to_add=2), 6)
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# Dynamic axis (sequence, token added by the tokenizer 3 (pair))
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self.assertEqual(compute_effective_axis_dimension(0, fixed_dimension=8, num_token_to_add=3), 5)
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self.assertEqual(compute_effective_axis_dimension(0, fixed_dimension=8, num_token_to_add=3), 5)
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def test_compute_parameters_serialized_size(self):
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"""
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This test ensures we compute a "correct" approximation of the underlying storage requirement (size) for all the
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parameters for the specified parameter's dtype.
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"""
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self.assertEqual(compute_serialized_parameters_size(2, ParameterFormat.Float), 2 * ParameterFormat.Float.size)
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def test_flatten_output_collection_property(self):
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"""
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This test ensures we correctly flatten nested collection such as the one we use when returning past_keys.
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past_keys = Tuple[Tuple]
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ONNX exporter will export nested collections as ${collection_name}.${level_idx_0}.${level_idx_1}...${idx_n}
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"""
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self.assertEqual(
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flatten_output_collection_property("past_key", [[0], [1], [2]]),
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{
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"past_key.0": 0,
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"past_key.1": 1,
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"past_key.2": 2,
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},
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)
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class OnnxConfigTestCaseV2(TestCase):
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"""
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Cover the test for models default.
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Default means no specific features is being enabled on the model.
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"""
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@patch.multiple(OnnxConfig, __abstractmethods__=set())
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def test_use_external_data_format(self):
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"""
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External data format is required only if the serialized size of the parameters if bigger than 2Gb
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"""
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TWO_GB_LIMIT = EXTERNAL_DATA_FORMAT_SIZE_LIMIT
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# No parameters
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self.assertFalse(OnnxConfig.use_external_data_format(0))
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# Some parameters
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self.assertFalse(OnnxConfig.use_external_data_format(1))
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# Almost 2Gb parameters
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self.assertFalse(OnnxConfig.use_external_data_format((TWO_GB_LIMIT - 1) // ParameterFormat.Float.size))
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# Exactly 2Gb parameters
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self.assertTrue(OnnxConfig.use_external_data_format(TWO_GB_LIMIT))
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# More than 2Gb parameters
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self.assertTrue(OnnxConfig.use_external_data_format((TWO_GB_LIMIT + 1) // ParameterFormat.Float.size))
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class OnnxConfigWithPastTestCaseV2(TestCase):
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"""
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Cover the tests for model which have use_cache feature (i.e. "with_past" for ONNX)
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"""
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SUPPORTED_WITH_PAST_CONFIGS = {("BART", BartConfig), ("GPT2", GPT2Config), ("T5", T5Config)}
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@patch.multiple(OnnxConfigWithPast, __abstractmethods__=set())
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def test_use_past(self):
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"""
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Ensure the use_past variable is correctly being set
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"""
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for name, config in OnnxConfigWithPastTestCaseV2.SUPPORTED_WITH_PAST_CONFIGS:
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with self.subTest(name):
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self.assertFalse(
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OnnxConfigWithPast.default(config()).use_past, "OnnxConfigWithPast.default() should not use_past"
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)
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self.assertTrue(
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OnnxConfigWithPast.with_past(config()).use_past, "OnnxConfigWithPast.default() should use_past"
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)
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@patch.multiple(OnnxConfigWithPast, __abstractmethods__=set())
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def test_values_override(self):
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"""
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Ensure the use_past variable correctly set the `use_cache` value in model's configuration
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"""
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for name, config in OnnxConfigWithPastTestCaseV2.SUPPORTED_WITH_PAST_CONFIGS:
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with self.subTest(name):
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# without past
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onnx_config_default = OnnxConfigWithPast.default(config())
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self.assertIsNotNone(onnx_config_default.values_override, "values_override should not be None")
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self.assertIn("use_cache", onnx_config_default.values_override, "use_cache should be present")
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self.assertFalse(
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onnx_config_default.values_override["use_cache"], "use_cache should be False if not using past"
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)
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# with past
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onnx_config_default = OnnxConfigWithPast.with_past(config())
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self.assertIsNotNone(onnx_config_default.values_override, "values_override should not be None")
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self.assertIn("use_cache", onnx_config_default.values_override, "use_cache should be present")
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self.assertTrue(
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onnx_config_default.values_override["use_cache"], "use_cache should be False if not using past"
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)
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if is_torch_available():
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from transformers import (
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AlbertModel,
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BartModel,
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BertModel,
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DistilBertModel,
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GPT2Model,
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RobertaModel,
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T5Model,
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XLMRobertaModel,
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)
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PYTORCH_EXPORT_DEFAULT_MODELS = {
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("ALBERT", "albert-base-v2", AlbertModel, AlbertConfig, AlbertOnnxConfig),
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("BART", "facebook/bart-base", BartModel, BartConfig, BartOnnxConfig),
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("BERT", "bert-base-cased", BertModel, BertConfig, BertOnnxConfig),
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("DistilBERT", "distilbert-base-cased", DistilBertModel, DistilBertConfig, DistilBertOnnxConfig),
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("GPT2", "gpt2", GPT2Model, GPT2Config, GPT2OnnxConfig),
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# ("LongFormer", "longformer-base-4096", LongformerModel, LongformerConfig, LongformerOnnxConfig),
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("Roberta", "roberta-base", RobertaModel, RobertaConfig, RobertaOnnxConfig),
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("XLM-Roberta", "roberta-base", XLMRobertaModel, XLMRobertaConfig, XLMRobertaOnnxConfig),
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("T5", "t5-small", T5Model, T5Config, T5OnnxConfig),
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}
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PYTORCH_EXPORT_WITH_PAST_MODELS = {
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# ("BART", "facebook/bart-base", BartModel, BartConfig, BartOnnxConfig),
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# ("GPT2", "gpt2", GPT2Model, GPT2Config, GPT2OnnxConfig),
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# ("T5", "t5-small", T5Model, T5Config, T5OnnxConfig)
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}
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class OnnxExportTestCaseV2(TestCase):
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"""
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Integration tests ensuring supported models are correctly exported
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"""
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@slow
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@require_torch
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def test_pytorch_export_default(self):
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from transformers.onnx import export
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for name, model, model_class, config_class, onnx_config_class in PYTORCH_EXPORT_DEFAULT_MODELS:
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with self.subTest(name):
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self.assertTrue(hasattr(onnx_config_class, "default"))
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tokenizer = AutoTokenizer.from_pretrained(model)
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model = model_class(config_class())
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onnx_config = onnx_config_class.default(model.config)
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with NamedTemporaryFile("w") as output:
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onnx_inputs, onnx_outputs = export(
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tokenizer, model, onnx_config, DEFAULT_ONNX_OPSET, Path(output.name)
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)
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try:
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validate_model_outputs(onnx_config, tokenizer, model, Path(output.name), onnx_outputs, 1e-5)
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except ValueError as ve:
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self.fail(f"{name} -> {ve}")
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@slow
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@require_torch
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def test_pytorch_export_with_past(self):
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from transformers.onnx import export
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for name, model, model_class, config_class, onnx_config_class in PYTORCH_EXPORT_WITH_PAST_MODELS:
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with self.subTest(name):
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self.assertTrue(hasattr(onnx_config_class, "with_past"), "OnnxConfigWithPast should have with_past()")
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tokenizer = AutoTokenizer.from_pretrained(model)
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model = model_class(config_class())
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onnx_config = onnx_config_class.with_past(model.config)
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self.assertTrue(hasattr(onnx_config, "use_past"), "OnnxConfigWithPast should have use_past attribute.")
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self.assertTrue(
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onnx_config.use_past, "OnnxConfigWithPast.use_past should be if called with with_past()"
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)
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with NamedTemporaryFile("w") as output:
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output = Path(output.name)
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onnx_inputs, onnx_outputs = export(tokenizer, model, onnx_config, DEFAULT_ONNX_OPSET, output)
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try:
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validate_model_outputs(onnx_config, tokenizer, model, output, onnx_outputs, 1e-5)
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except ValueError as ve:
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self.fail(f"{name} -> {ve}")
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