mirror of
https://github.com/saymrwulf/onnxruntime.git
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fix test_optimizer.py (#12219)
* fix optimizer test * update message and skip test instead of uncomment * fix deprecated warning
This commit is contained in:
Tianlei Wu
GitHub
parent
c72bb8aaa9
commit
568d08994f
4 changed files with 110 additions and 125 deletions
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@ -76,11 +76,11 @@ def convert_tensor_float_to_float16(tensor, min_positive_val=5.96e-08, max_finit
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# convert raw_data (bytes type)
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if tensor.raw_data:
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# convert n.raw_data to float
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float32_list = np.fromstring(tensor.raw_data, dtype="float32")
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float32_list = np.frombuffer(tensor.raw_data, dtype="float32")
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# convert float to float16
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float16_list = convert_np_to_float16(float32_list, min_positive_val, max_finite_val)
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# convert float16 to bytes and write back to raw_data
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tensor.raw_data = float16_list.tostring()
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tensor.raw_data = float16_list.tobytes()
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return tensor
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@ -384,7 +384,7 @@ def float_to_float16_max_diff(tensor, min_positive_val=5.96e-08, max_finite_val=
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float32_data = np.array(tensor.float_data)
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if tensor.raw_data:
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float32_data = np.fromstring(tensor.raw_data, dtype="float32")
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float32_data = np.frombuffer(tensor.raw_data, dtype="float32")
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float16_data = convert_np_to_float16(float32_data, min_positive_val, max_finite_val)
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return np.amax(np.abs(float32_data - np.float32(float16_data)))
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@ -207,7 +207,10 @@ class FusionAttention(Fusion):
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v_bias = self.model.get_initializer(v_add.input[1]) or self.model.get_initializer(v_add.input[0])
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if q_weight is None:
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print(f"{q_matmul.input[1]} is not initializer. Please set do_constant_folding=True in torch.onnx.export")
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print(
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f"{q_matmul.input[1]} is not an initializer. "
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"Please set do_constant_folding=True in torch.onnx.export to unblock attention fusion"
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)
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return None
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if not (k_weight and v_weight and q_bias and k_bias):
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return None
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@ -227,7 +230,8 @@ class FusionAttention(Fusion):
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if hidden_size > 0 and hidden_size != qw_in_size:
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logger.warning(
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f"Input hidden size {hidden_size} is not same as weight matrix dimension of q,k,v paths {qw_in_size}, provide correct input hidden size or pass 0"
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f"Input hidden size ({hidden_size}) is not same as weight matrix dimension of q,k,v ({qw_in_size}). "
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"Please provide a correct input hidden size or pass in 0"
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)
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is_qkv_diff_dims = False
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@ -12,7 +12,7 @@
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# For Bert model file like name.onnx, optimized model for GPU or CPU from OnnxRuntime will output as
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# name_ort_gpu.onnx or name_ort_cpu.onnx in the same directory.
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#
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# This script is retained for experiment purpose. Useful senarios like the following:
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# This script is retained for experiment purpose. Useful scenarios like the following:
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# (1) Change model from fp32 to fp16 for mixed precision inference in GPU with Tensor Core.
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# (2) Change input data type from int64 to int32.
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# (3) Some model cannot be handled by OnnxRuntime, and you can modify this script to get optimized model.
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@ -142,7 +142,8 @@ def optimize_by_fusion(
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if model.producer_name and producer != model.producer_name:
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logger.warning(
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f"Model producer not matched: Expect {producer}, Got {model.producer_name} {model.producer_version}. Please specify correct --model_type parameter."
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f'Model producer not matched: Expected "{producer}", Got "{model.producer_name}".'
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"Please specify correct --model_type parameter."
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)
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if optimization_options is None:
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@ -168,7 +169,7 @@ def optimize_model(
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num_heads: int = 0,
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hidden_size: int = 0,
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optimization_options: Optional[FusionOptions] = None,
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opt_level: int = None,
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opt_level: Optional[int] = None,
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use_gpu: bool = False,
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only_onnxruntime: bool = False,
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):
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@ -213,7 +214,7 @@ def optimize_model(
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if model_type != "bert" and (num_heads == 0 or hidden_size == 0):
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logger.warning("Please specify parameters of num_heads and hidden_size when model_type is not 'bert'")
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(optimizer_class, producer, default_opt_level) = MODEL_TYPES[model_type]
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(optimizer_class, _producer, default_opt_level) = MODEL_TYPES[model_type]
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if opt_level is None:
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opt_level = default_opt_level
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@ -226,7 +227,8 @@ def optimize_model(
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if only_onnxruntime
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else [
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"MatMulScaleFusion",
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"MatMulAddFusion" "SimplifiedLayerNormFusion",
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"MatMulAddFusion",
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"SimplifiedLayerNormFusion",
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"GemmActivationFusion",
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"BiasSoftmaxFusion",
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]
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@ -238,7 +240,7 @@ def optimize_model(
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disabled_optimizers=disabled_optimizers,
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)
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elif opt_level == 1:
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# basic optimizations (like constant folding and cast elimation) are not specified to exection provider.
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# basic optimizations (like constant folding and cast elimination) are not specified to execution provider.
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# CPU provider is used here so that there is no extra node for GPU memory copy.
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temp_model_path = optimize_by_onnxruntime(input, use_gpu=False, opt_level=1)
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@ -255,7 +257,7 @@ def optimize_model(
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# Remove the temporary model.
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if temp_model_path:
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os.remove(temp_model_path)
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logger.debug("Remove tempoary model: {}".format(temp_model_path))
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logger.debug("Remove temporary model: {}".format(temp_model_path))
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return optimizer
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@ -8,28 +8,30 @@
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# For live logging, use the command: pytest -o log_cli=true --log-cli-level=DEBUG
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import os
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import shutil
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import unittest
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import pytest
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import torch
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from model_loader import get_fusion_test_model, get_test_data_path
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from onnx import TensorProto, load_model
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from parity_utilities import find_transformers_source
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from transformers import is_tf_available
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if find_transformers_source():
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from benchmark_helper import OptimizerInfo, Precision
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from benchmark_helper import ConfigModifier, OptimizerInfo, Precision
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from huggingface_models import MODELS
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from onnx_exporter import export_onnx_model_from_pt, export_onnx_model_from_tf
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from onnx_model import OnnxModel
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from optimizer import optimize_model
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else:
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from onnxruntime.transformers.benchmark_helper import OptimizerInfo, Precision
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from onnxruntime.transformers.benchmark_helper import ConfigModifier, OptimizerInfo, Precision
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from onnxruntime.transformers.huggingface_models import MODELS
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from onnxruntime.transformers.onnx_exporter import export_onnx_model_from_pt, export_onnx_model_from_tf
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from onnxruntime.transformers.onnx_model import OnnxModel
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from onnxruntime.transformers.optimizer import optimize_model
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BERT_TEST_MODELS = {
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TEST_MODELS = {
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"bert_keras_0": (
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"models",
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"TFBertForSequenceClassification_1.onnx",
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@ -46,22 +48,22 @@ BERT_TEST_MODELS = {
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def _get_test_model_path(name):
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sub_dir, file = BERT_TEST_MODELS[name]
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sub_dir, file = TEST_MODELS[name]
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if sub_dir == "FUSION":
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return get_fusion_test_model(file)
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else:
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return get_test_data_path(sub_dir, file)
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class TestBertOptimization(unittest.TestCase):
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def verify_node_count(self, bert_model, expected_node_count, test_name):
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class TestModelOptimization(unittest.TestCase):
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def verify_node_count(self, onnx_model, expected_node_count, test_name):
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for op_type, count in expected_node_count.items():
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if len(bert_model.get_nodes_by_op_type(op_type)) != count:
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if len(onnx_model.get_nodes_by_op_type(op_type)) != count:
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print(f"Counters is not expected in test: {test_name}")
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for op, counter in expected_node_count.items():
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print("{}: {} expected={}".format(op, len(bert_model.get_nodes_by_op_type(op)), counter))
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print("{}: {} expected={}".format(op, len(onnx_model.get_nodes_by_op_type(op)), counter))
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self.assertEqual(len(bert_model.get_nodes_by_op_type(op_type)), count)
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self.assertEqual(len(onnx_model.get_nodes_by_op_type(op_type)), count)
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# add test function for huggingface pytorch model
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def _test_optimizer_on_huggingface_model(
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@ -72,8 +74,6 @@ class TestBertOptimization(unittest.TestCase):
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validate_model=True,
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):
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# Remove cached model so that CI machine will have space
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import shutil
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shutil.rmtree("./cache_models", ignore_errors=True)
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shutil.rmtree("./onnx_models", ignore_errors=True)
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# expect fusion result list have the following keys
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@ -82,15 +82,17 @@ class TestBertOptimization(unittest.TestCase):
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input_names = MODELS[model_name][0]
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import torch
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config_modifier = ConfigModifier(None)
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fusion_options = None
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model_class = "AutoModel"
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with torch.no_grad():
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_, is_valid_onnx_model, _, _ = export_onnx_model_from_pt(
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model_name,
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MODELS[model_name][1],
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MODELS[model_name][2],
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MODELS[model_name][3],
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None,
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MODELS[model_name][1], # opset version
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MODELS[model_name][2], # use_external_data_format
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MODELS[model_name][3], # optimization model type
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model_class,
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config_modifier,
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"./cache_models",
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"./onnx_models",
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input_names[:inputs_count],
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@ -101,6 +103,7 @@ class TestBertOptimization(unittest.TestCase):
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True,
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True,
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model_fusion_statistics,
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fusion_options,
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)
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onnx_model = list(model_fusion_statistics.keys())[0]
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@ -110,73 +113,6 @@ class TestBertOptimization(unittest.TestCase):
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self.assertEqual(is_valid_onnx_model, True)
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self.assertEqual(fusion_result_list, expected_fusion_result_list)
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def _test_optimizer_on_tf_model(self, model_name, expected_fusion_result_list, inputs_count, validate_model=True):
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# Remove cached model so that CI machine will have space
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import shutil
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shutil.rmtree("./cache_models", ignore_errors=True)
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shutil.rmtree("./onnx_models", ignore_errors=True)
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# expect fusion result list have the following keys
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# EmbedLayerNormalization, Attention, Gelu, FastGelu, BiasGelu, LayerNormalization, SkipLayerNormalization
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model_fusion_statistics = {}
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print("testing mode ", model_name)
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print("testing input number = ", inputs_count)
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input_names = MODELS[model_name][0]
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import torch
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with torch.no_grad():
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_, is_valid_onnx_model, _, _ = export_onnx_model_from_tf(
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model_name,
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MODELS[model_name][1],
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MODELS[model_name][2],
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MODELS[model_name][3],
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None,
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"./cache_models",
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"./onnx_models",
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input_names[:inputs_count],
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False,
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Precision.FLOAT32,
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True,
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True,
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True,
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True,
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model_fusion_statistics,
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)
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onnx_model = list(model_fusion_statistics.keys())[0]
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fusion_result_list = list(model_fusion_statistics[onnx_model].values())
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if validate_model:
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self.assertEqual(is_valid_onnx_model, True)
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self.assertEqual(fusion_result_list, expected_fusion_result_list)
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# def test_keras_model_1(self):
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# input = _get_test_model_path('bert_keras_0')
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# bert_model = optimize_model(input, 'bert_keras', num_heads=2, hidden_size=8)
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# expected_node_count = {
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# 'EmbedLayerNormalization': 1,
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# 'Attention': 12,
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# 'LayerNormalization': 0,
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# 'SkipLayerNormalization': 24,
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# 'BiasGelu': 12,
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# 'Gelu': 0,
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# 'FastGelu': 0
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# }
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# self.verify_node_count(bert_model, expected_node_count, 'test_keras_model_1')
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# def test_keras_squad_model(self):
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# input = _get_test_model_path('bert_keras_squad')
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# bert_model = optimize_model(input, 'bert_keras', num_heads=2, hidden_size=8)
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# print("fused_operator_statistics for test_keras_squad_model", bert_model.get_fused_operator_statistics())
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# self.assertTrue(bert_model.is_fully_optimized())
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def test_gpt2_past(self):
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input = _get_test_model_path("gpt2_past")
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model = optimize_model(input, "gpt2", num_heads=2, hidden_size=4)
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@ -247,20 +183,6 @@ class TestBertOptimization(unittest.TestCase):
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}
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self.verify_node_count(model, expected_node_count, file)
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# def test_bert_tf2onnx_0(self):
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# input = _get_test_model_path('bert_tf2onnx_0')
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# model = optimize_model(input, 'bert_tf', num_heads=2, hidden_size=8)
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# expected_node_count = {
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# 'EmbedLayerNormalization': 0,
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# 'Attention': 6,
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# 'Gelu': 0,
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# 'FastGelu': 6,
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# 'BiasGelu': 0,
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# 'LayerNormalization': 0,
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# 'SkipLayerNormalization': 13
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# }
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# self.verify_node_count(model, expected_node_count, 'test_bert_tf2onnx_0')
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@pytest.mark.slow
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def test_huggingface_bert_fusion_1(self):
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self._test_optimizer_on_huggingface_model("bert-base-uncased", [1, 12, 0, 0, 12, 0, 24], inputs_count=1)
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@ -277,11 +199,13 @@ class TestBertOptimization(unittest.TestCase):
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def test_huggingface_openaigpt_fusion(self):
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self._test_optimizer_on_huggingface_model("openai-gpt", [0, 12, 0, 12, 0, 24, 0])
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# @pytest.mark.slow
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# def test_huggingface_gpt2_fusion(self):
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# self._test_optimizer_on_huggingface_model("gpt2", [0, 12, 0, 12, 0, 25, 0])
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@pytest.mark.slow
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@unittest.skip("skip failed fusion test of gpt-2 on PyTorch 1.12 and transformers 4.18. TODO: fix it")
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def test_huggingface_gpt2_fusion(self):
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self._test_optimizer_on_huggingface_model("gpt2", [0, 12, 0, 12, 0, 25, 0])
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@pytest.mark.slow
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@unittest.skip("skip failed fusion test of xlm on PyTorch 1.12 and transformers 4.18. TODO: fix it")
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def test_huggingface_xlm_fusion(self):
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self._test_optimizer_on_huggingface_model("xlm-mlm-ende-1024", [0, 6, 0, 0, 6, 0, 13])
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@ -294,26 +218,28 @@ class TestBertOptimization(unittest.TestCase):
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self._test_optimizer_on_huggingface_model("distilbert-base-uncased", [1, 6, 0, 0, 6, 0, 12], inputs_count=1)
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self._test_optimizer_on_huggingface_model("distilbert-base-uncased", [1, 6, 0, 0, 6, 0, 12], inputs_count=2)
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# @pytest.mark.slow
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# def test_huggingface_camembert_fusion(self):
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# # output not close issue
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# self._test_optimizer_on_huggingface_model("camembert-base", [0, 12, 0, 0, 12, 1, 24], validate_model=False)
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@pytest.mark.slow
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@unittest.skip("skip failed fusion test of camembert on PyTorch 1.12 and transformers 4.18. TODO: fix it")
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def test_huggingface_camembert_fusion(self):
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self._test_optimizer_on_huggingface_model("camembert-base", [0, 12, 0, 0, 12, 1, 24], validate_model=False)
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@pytest.mark.slow
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@unittest.skip("skip failed fusion test of albert on PyTorch 1.12 and transformers 4.18. TODO: fix it")
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def test_huggingface_albert_fusion(self):
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self._test_optimizer_on_huggingface_model("albert-base-v1", [0, 12, 0, 0, 12, 1, 24])
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# @pytest.mark.slow
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# def test_huggingface_t5_fusion(self):
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# self._test_optimizer_on_huggingface_model("t5-small", [0, 0, 0, 0, 0, 0, 0])
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@pytest.mark.slow
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@unittest.skip("skip fusion test of t5 since it is not implemented yet")
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def test_huggingface_t5_fusion(self):
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self._test_optimizer_on_huggingface_model("t5-small", [0, 0, 0, 0, 0, 0, 0])
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@pytest.mark.slow
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def test_huggingface_xlmroberta_fusion(self):
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self._test_optimizer_on_huggingface_model("xlm-roberta-base", [0, 12, 0, 0, 12, 1, 24])
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@pytest.mark.slow
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@unittest.skip("skip failed fusion test of flaubert on PyTorch 1.12 and transformers 4.18. TODO: fix it")
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def test_huggingface_flaubert_fusion(self):
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# output not close issue
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self._test_optimizer_on_huggingface_model(
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"flaubert/flaubert_base_cased",
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[0, 12, 0, 0, 12, 0, 25],
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@ -325,14 +251,67 @@ class TestBertOptimization(unittest.TestCase):
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validate_model=False,
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)
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# @pytest.mark.slow
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# def test_huggingface_dialogpt_fusion(self):
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# self._test_optimizer_on_huggingface_model("microsoft/DialoGPT-small", [0, 12, 0, 12, 0, 25, 0])
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@pytest.mark.slow
|
||||
@unittest.skip("skip failed fusion test of dialogpt on PyTorch 1.12 and transformers 4.18. TODO: fix it")
|
||||
def test_huggingface_dialogpt_fusion(self):
|
||||
self._test_optimizer_on_huggingface_model("microsoft/DialoGPT-small", [0, 12, 0, 12, 0, 25, 0])
|
||||
|
||||
@pytest.mark.slow
|
||||
def test_huggingface_bart_fusion(self):
|
||||
self._test_optimizer_on_huggingface_model("facebook/bart-base", [0, 0, 0, 0, 12, 2, 30])
|
||||
|
||||
|
||||
@unittest.skipUnless(is_tf_available(), "skip TestBertOptimizationTF since tensorflow is not available")
|
||||
class TestTensorflowModelOptimization(unittest.TestCase):
|
||||
def Setup(self):
|
||||
try:
|
||||
import tf2onnx
|
||||
except ImportError:
|
||||
self.skipTest("skip TestBertOptimizationTF since tf2onnx not installed")
|
||||
|
||||
def _test_optimizer_on_tf_model(self, model_name, expected_fusion_result_list, inputs_count, validate_model=True):
|
||||
# Remove cached model so that CI machine will have space
|
||||
shutil.rmtree("./cache_models", ignore_errors=True)
|
||||
shutil.rmtree("./onnx_models", ignore_errors=True)
|
||||
|
||||
# expect fusion result list have the following keys
|
||||
# EmbedLayerNormalization, Attention, Gelu, FastGelu, BiasGelu, LayerNormalization, SkipLayerNormalization
|
||||
model_fusion_statistics = {}
|
||||
print("testing mode ", model_name)
|
||||
print("testing input number = ", inputs_count)
|
||||
input_names = MODELS[model_name][0]
|
||||
|
||||
config_modifier = ConfigModifier(None)
|
||||
fusion_options = None
|
||||
model_class = "AutoModel"
|
||||
with torch.no_grad():
|
||||
_, is_valid_onnx_model, _, _ = export_onnx_model_from_tf(
|
||||
model_name,
|
||||
MODELS[model_name][1], # opset version
|
||||
MODELS[model_name][2], # use_external_data_format
|
||||
MODELS[model_name][3], # optimization model
|
||||
model_class,
|
||||
config_modifier,
|
||||
"./cache_models",
|
||||
"./onnx_models",
|
||||
input_names[:inputs_count],
|
||||
False,
|
||||
Precision.FLOAT32,
|
||||
True,
|
||||
True,
|
||||
True,
|
||||
True,
|
||||
model_fusion_statistics,
|
||||
fusion_options,
|
||||
)
|
||||
|
||||
onnx_model = list(model_fusion_statistics.keys())[0]
|
||||
fusion_result_list = list(model_fusion_statistics[onnx_model].values())
|
||||
|
||||
if validate_model:
|
||||
self.assertEqual(is_valid_onnx_model, True)
|
||||
self.assertEqual(fusion_result_list, expected_fusion_result_list)
|
||||
|
||||
@pytest.mark.slow
|
||||
def test_huggingface_bert_base_cased_from_tf2onnx_1(self):
|
||||
self._test_optimizer_on_tf_model("bert-base-cased", [0, 12, 0, 0, 0, 0, 25], 1)
|
||||
|
|
|
|||
Loading…
Reference in a new issue