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Move gpt2 script to models\gpt2 sub-directory (#11256)

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* move gpt-2 scripts to models\gpt2
* change gpt2 beam search helper to make test_gpt2 passes
This commit is contained in:
Tianlei Wu 2022-04-20 11:09:26 -07:00 committed by GitHub
parent cb46d79108
commit 1d96cbec73
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GPG key ID: 4AEE18F83AFDEB23
22 changed files with 293 additions and 256 deletions
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7
cmake/onnxruntime_python.cmake
View file

@ -364,6 +364,9 @@ file(GLOB onnxruntime_python_quantization_cal_table_flatbuffers_src CONFIGURE_DE
file(GLOB onnxruntime_python_transformers_src CONFIGURE_DEPENDS
"${ONNXRUNTIME_ROOT}/python/tools/transformers/*.py"
)
file(GLOB onnxruntime_python_transformers_models_gpt2_src CONFIGURE_DEPENDS
"${ONNXRUNTIME_ROOT}/python/tools/transformers/models/gpt2/*.py"
)
file(GLOB onnxruntime_python_transformers_models_longformer_src CONFIGURE_DEPENDS
"${ONNXRUNTIME_ROOT}/python/tools/transformers/models/longformer/*.py"
)
@ -420,6 +423,7 @@ add_custom_command(
COMMAND ${CMAKE_COMMAND} -E make_directory $<TARGET_FILE_DIR:${build_output_target}>/onnxruntime/tools/ort_format_model/ort_flatbuffers_py
COMMAND ${CMAKE_COMMAND} -E make_directory $<TARGET_FILE_DIR:${build_output_target}>/onnxruntime/transformers
COMMAND ${CMAKE_COMMAND} -E make_directory $<TARGET_FILE_DIR:${build_output_target}>/onnxruntime/transformers/models
COMMAND ${CMAKE_COMMAND} -E make_directory $<TARGET_FILE_DIR:${build_output_target}>/onnxruntime/transformers/models/gpt2
COMMAND ${CMAKE_COMMAND} -E make_directory $<TARGET_FILE_DIR:${build_output_target}>/onnxruntime/transformers/models/longformer
COMMAND ${CMAKE_COMMAND} -E make_directory $<TARGET_FILE_DIR:${build_output_target}>/onnxruntime/transformers/models/t5
COMMAND ${CMAKE_COMMAND} -E make_directory $<TARGET_FILE_DIR:${build_output_target}>/onnxruntime/quantization
@ -501,6 +505,9 @@ add_custom_command(
COMMAND ${CMAKE_COMMAND} -E copy
${onnxruntime_python_transformers_src}
$<TARGET_FILE_DIR:${build_output_target}>/onnxruntime/transformers/
COMMAND ${CMAKE_COMMAND} -E copy
${onnxruntime_python_transformers_models_gpt2_src}
$<TARGET_FILE_DIR:${build_output_target}>/onnxruntime/transformers/models/gpt2/
COMMAND ${CMAKE_COMMAND} -E copy
${onnxruntime_python_transformers_models_longformer_src}
$<TARGET_FILE_DIR:${build_output_target}>/onnxruntime/transformers/models/longformer/

12
onnxruntime/python/tools/transformers/README.md
View file

@ -66,9 +66,9 @@ In your python code, you can use the optimizer like the following:
```python
from onnxruntime.transformers import optimizer
optimized_model = optimizer.optimize_model("gpt2.onnx", model_type='gpt2', num_heads=12, hidden_size=768)
optimized_model = optimizer.optimize_model("bert.onnx", model_type='bert', num_heads=12, hidden_size=768)
optimized_model.convert_float_to_float16()
optimized_model.save_model_to_file("gpt2_fp16.onnx")
optimized_model.save_model_to_file("bert_fp16.onnx")
```
You can also use command line. Example of optimizing a BERT-large model to use mixed precision (float16):
@ -78,7 +78,7 @@ python -m onnxruntime.transformers.optimizer --input bert_large.onnx --output be
You can also download the latest script files from [here](https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/python/tools/transformers/). Then run it like the following:
```console
python optimizer.py --input gpt2.onnx --output gpt2_opt.onnx --model_type gpt2
python optimizer.py --input bert.onnx --output bert_opt.onnx --model_type bert
```
### Optimizer Options
@ -164,11 +164,11 @@ The model has 12 layers and 768 hidden, with input_ids, position_ids, attention_
Since past state is used, sequence length in input_ids is 1. For example, s=4 means the past sequence length is 4 and the total sequence length is 5.
[benchmark_gpt2.py](https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/python/tools/transformers/benchmark_gpt2.py) is used to get the results like the following commands:
[benchmark_gpt2.py](https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/python/tools/transformers/models/gpt2/benchmark_gpt2.py) is used to get the results like the following commands:
```console
python -m onnxruntime.transformers.benchmark_gpt2 --use_gpu -m gpt2 -o -v -b 1 8 32 128 -s 4 8 32 128 -p fp32
python -m onnxruntime.transformers.benchmark_gpt2 --use_gpu -m gpt2 -o -v -b 1 8 32 128 -s 4 8 32 128 -p fp16
python -m onnxruntime.transformers.models.gpt2.benchmark_gpt2 --use_gpu -m gpt2 -o -v -b 1 8 32 128 -s 4 8 32 128 -p fp32
python -m onnxruntime.transformers.models.gpt2.benchmark_gpt2 --use_gpu -m gpt2 -o -v -b 1 8 32 128 -s 4 8 32 128 -p fp16
```
### Benchmark.py

15
onnxruntime/python/tools/transformers/__init__.py
View file

@ -1,4 +1,13 @@
import os
import sys
#-------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
#--------------------------------------------------------------------------
sys.path.append(os.path.dirname(__file__))
import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), 'models', 'gpt2'))
# added for backward compatible
import gpt2_helper
import convert_to_onnx

13
onnxruntime/python/tools/transformers/convert_beam_search.py
View file

@ -2,7 +2,6 @@
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
#-------------------------------------------------------------------------
"""
This converts GPT2 or T5 model to onnx with beam search operator.
@ -26,11 +25,15 @@ from typing import List, Union
import torch
from packaging import version
from transformers import GPT2Config, T5Config
from gpt2_helper import PRETRAINED_GPT2_MODELS
from convert_to_onnx import main as convert_gpt2_to_onnx
from benchmark_helper import Precision
from onnx import onnx_pb as onnx_proto
import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), 'models', 'gpt2'))
from gpt2_helper import PRETRAINED_GPT2_MODELS
from convert_to_onnx import main as convert_gpt2_to_onnx
config: Union[GPT2Config, T5Config] = None
@ -295,11 +298,11 @@ def convert_model(args):
# TODO: fix shape inference for T5. Currently symbolic shape inference on T5 is broken.
enable_shape_inference = args.model_type == "gpt2"
if enable_shape_inference:
print(f"Run symbolic shape inference on {args.decoder_onnx}. The file will be overwritten.")
shape_inference(args.decoder_onnx)
global config
if args.model_type == "gpt2":
config = GPT2Config.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)

9
onnxruntime/python/tools/transformers/io_binding_helper.py
View file

@ -28,9 +28,10 @@ class TypeHelper:
def ort_type_to_numpy_type(ort_type: str):
ort_type_to_numpy_type_map = {
"tensor(int64)": numpy.longlong,
"tensor(int32)": numpy.int32, #numpy.intc?
"tensor(int32)": numpy.intc,
"tensor(float)": numpy.float32,
"tensor(float16)": numpy.float16,
"tensor(bool)": numpy.bool,
}
if ort_type not in ort_type_to_numpy_type_map:
raise ValueError(f"{ort_type} not found in map")
@ -44,6 +45,7 @@ class TypeHelper:
"tensor(int32)": torch.int32,
"tensor(float)": torch.float32,
"tensor(float16)": torch.float16,
"tensor(bool)": torch.bool,
}
if ort_type not in ort_type_to_torch_type_map:
raise ValueError(f"{ort_type} not found in map")
@ -54,9 +56,11 @@ class TypeHelper:
def numpy_type_to_torch_type(numpy_type: numpy.dtype):
numpy_type_to_torch_type_map = {
numpy.longlong: torch.int64,
numpy.intc: torch.int32,
numpy.int32: torch.int32,
numpy.float32: torch.float32,
numpy.float16: torch.float16,
numpy.bool: torch.bool,
}
if numpy_type not in numpy_type_to_torch_type_map:
raise ValueError(f"{numpy_type} not found in map")
@ -67,9 +71,10 @@ class TypeHelper:
def torch_type_to_numpy_type(torch_type: torch.dtype):
torch_type_to_numpy_type_map = {
torch.int64: numpy.longlong,
torch.int32: numpy.int32,
torch.int32: numpy.intc,
torch.float32: numpy.float32,
torch.float16: numpy.float16,
torch.bool: numpy.bool,
}
if torch_type not in torch_type_to_numpy_type_map:
raise ValueError(f"{torch_type} not found in map")

8
onnxruntime/python/tools/transformers/models/__init__.py
View file

@ -1,8 +1,4 @@
#-------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
#-------------------------------------------------------------------------
import os
import sys
sys.path.append(os.path.dirname(__file__))
#--------------------------------------------------------------------------

4
onnxruntime/python/tools/transformers/models/gpt2/__init__.py Normal file
View file

@ -0,0 +1,4 @@
#-------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
#--------------------------------------------------------------------------

54
onnxruntime/python/tools/transformers/benchmark_gpt2.py → onnxruntime/python/tools/transformers/models/gpt2/benchmark_gpt2.py
View file

@ -6,20 +6,23 @@
# This script benchmarks gpt2 model with past state.
# For gpt2 model without past state, use benchmark.py to measure performance.
import os
import sys
import numpy
import csv
from datetime import datetime
import psutil
import argparse
import logging
import torch
import onnx
from packaging import version
from transformers import AutoConfig
from gpt2_helper import Gpt2Helper, DEFAULT_TOLERANCE, PRETRAINED_GPT2_MODELS
from gpt2_beamsearch_helper import Gpt2HelperFactory, MODEL_CLASSES
import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..'))
from quantize_helper import QuantizeHelper
from benchmark_helper import create_onnxruntime_session, setup_logger, prepare_environment, Precision
@ -227,8 +230,13 @@ def main(args):
if args.optimize_onnx or args.precision != Precision.FLOAT32:
onnx_model_path = onnx_model_paths[str(args.precision) if args.precision != Precision.INT8 else 'fp32']
gpt2helper.optimize_onnx(onnx_model_paths["raw"], onnx_model_path, args.precision == Precision.FLOAT16,
model.config.num_attention_heads, model.config.hidden_size, use_external_data_format)
gpt2helper.optimize_onnx(onnx_model_paths["raw"],
onnx_model_path,
args.precision == Precision.FLOAT16,
model.config.num_attention_heads,
model.config.hidden_size,
use_external_data_format,
auto_mixed_precision=True)
if args.precision == Precision.INT8:
logger.info("quantizing model...")
@ -254,9 +262,15 @@ def main(args):
# Allocate output buffers for IO Binding
if model_type == 'beam_search_step' or model_type == 'configurable_one_step_search':
max_output_shapes = gpt2helper.get_output_shapes(max(args.batch_sizes), max(args.past_sequence_lengths),
max(args.past_sequence_lengths), max(args.sequence_lengths), 4,
0, config, args.model_class)
max_output_shapes = gpt2helper.get_output_shapes(max(args.batch_sizes),
context_len=max(args.past_sequence_lengths),
past_sequence_length=max(args.past_sequence_lengths),
sequence_length=max(args.sequence_lengths),
beam_size=args.beam_size,
step=0,
config=config,
model_class=args.model_class)
output_buffers = gpt2helper.get_output_buffers(max_output_shapes, device, args.precision == Precision.FLOAT16)
else:
@ -294,8 +308,8 @@ def main(args):
has_position_ids=use_padding,
has_attention_mask=use_padding)
output_shapes = gpt2helper.get_output_shapes(batch_size, past_sequence_length,
past_sequence_length, sequence_length, 4, 0,
config, args.model_class)
past_sequence_length, sequence_length,
args.beam_size, 0, config, args.model_class)
else:
dummy_inputs = gpt2helper.get_dummy_inputs(batch_size,
past_sequence_length,
@ -312,9 +326,18 @@ def main(args):
config, args.model_class)
try:
outputs, torch_latency = Gpt2Helper.pytorch_inference(model, dummy_inputs, args.test_times)
ort_outputs, ort_latency = Gpt2Helper.onnxruntime_inference(session, dummy_inputs,
outputs, torch_latency = gpt2helper.pytorch_inference(model, dummy_inputs, args.test_times)
# Dump Torch output shape
for i, value in enumerate(outputs):
if isinstance(value, tuple):
logger.debug(f"torch output {i} is tuple of size {len(value)}, shape {value[0].shape}")
else:
logger.debug(f"torch output {i} shape {value.shape}")
ort_outputs, ort_latency = gpt2helper.onnxruntime_inference(session, dummy_inputs,
args.test_times)
ort_io_outputs, ort_io_latency = gpt2helper.onnxruntime_inference_with_binded_io(
session,
dummy_inputs,
@ -327,7 +350,7 @@ def main(args):
if args.validate_onnx:
if gpt2helper.compare_outputs(outputs,
ort_outputs,
model_class,
model_class=args.model_class,
rtol=DEFAULT_TOLERANCE[args.precision],
atol=DEFAULT_TOLERANCE[args.precision]):
logger.info(
@ -341,7 +364,7 @@ def main(args):
if gpt2helper.compare_outputs(outputs,
copy_outputs,
model_class,
model_class=args.model_class,
rtol=DEFAULT_TOLERANCE[args.precision],
atol=DEFAULT_TOLERANCE[args.precision]):
logger.info(
@ -369,6 +392,7 @@ def main(args):
csv_writer.writerow(row)
except:
logger.error(f"Exception", exc_info=True)
return None
logger.info(f"Results are saved to file {csv_filename}")
return csv_filename

7
onnxruntime/python/tools/transformers/convert_to_onnx.py → onnxruntime/python/tools/transformers/models/gpt2/convert_to_onnx.py
View file

@ -15,7 +15,6 @@ This converts GPT2 model to onnx. Examples:
"""
import os
import argparse
import logging
import torch
@ -27,6 +26,12 @@ from transformers import AutoConfig
from gpt2_helper import DEFAULT_TOLERANCE, PRETRAINED_GPT2_MODELS
from gpt2_beamsearch_helper import Gpt2HelperFactory, MODEL_CLASSES
from gpt2_beamsearch_tester import Gpt2TesterFactory
import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..'))
from quantize_helper import QuantizeHelper
from benchmark_helper import create_onnxruntime_session, setup_logger, prepare_environment, Precision

272
onnxruntime/python/tools/transformers/gpt2_beamsearch_helper.py → onnxruntime/python/tools/transformers/models/gpt2/gpt2_beamsearch_helper.py
View file

@ -4,19 +4,21 @@
# license information.
# --------------------------------------------------------------------------
# This script helps onnx conversion and validation for GPT2 model with past state.
import os
import logging
import torch
import onnx
import random
import numpy
import time
import re
from pathlib import Path
from typing import List, Dict, Tuple, Union
from typing import List, Dict, Union
from transformers import GPT2LMHeadModel, GPT2Config
from benchmark_helper import Precision
from gpt2_helper import Gpt2Helper, Gpt2Inputs, GPT2ModelNoPastState, MyGPT2Model, MyGPT2LMHeadModel, MyGPT2LMHeadModel_NoPadding
from gpt2_helper import Gpt2Helper, Gpt2Inputs, MyGPT2Model, MyGPT2LMHeadModel, MyGPT2LMHeadModel_NoPadding
import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..'))
from io_binding_helper import TypeHelper
from torch_onnx_export_helper import torch_onnx_export
logger = logging.getLogger(__name__)
@ -25,6 +27,7 @@ BIG_NEG = -1e4
class Gpt2HelperFactory:
@staticmethod
def create_helper(helper_type="default"):
helpers = {
@ -39,6 +42,7 @@ class Gpt2HelperFactory:
class GPT2LMHeadModel_BeamSearchStep(GPT2LMHeadModel):
"""Here we wrap a class for Onnx model conversion for GPT2LMHeadModel with past state and one
step beam search."""
def __init__(self, config, batch_size, beam_size):
super().__init__(config)
self.config.batch_size = batch_size
@ -89,7 +93,9 @@ class GPT2LMHeadModel_BeamSearchStep(GPT2LMHeadModel):
sorted=True) # output shape=(batch, beam_size)
# select the correspondent sentences/next tokens
selected_input_seq = selected_index_flat // self.config.beam_size
selected_input_seq = torch.div(selected_index_flat, self.config.beam_size,
rounding_mode='trunc') # selected_index_flat // self.config.beam_size
next_token_ids = next_token_ids.view(self.config.batch_size, -1).gather(-1, selected_index_flat)
prev_step_results = prev_step_results.view(self.config.batch_size, -1, prev_step_results.size(-1))
@ -98,7 +104,9 @@ class GPT2LMHeadModel_BeamSearchStep(GPT2LMHeadModel):
selected_input_seq.unsqueeze(-1).repeat(1, 1, prev_step_results.size(-1)))
output_unfinished_sents = input_unfinished_sents.gather(1, selected_input_seq)
output_unfinished_sents = (output_unfinished_sents & next_token_ids.ne(self.config.eos_token_id))
# Add ones_like to walkaround error like Shape mismatch attempting to re-use buffer. {1,1} != {1,4}
output_unfinished_sents = (output_unfinished_sents & next_token_ids.ne(
torch.ones_like(next_token_ids, dtype=torch.int) * self.config.eos_token_id))
# get the next full input_ids
current_step_results = torch.cat([prev_step_results, next_token_ids.unsqueeze(-1)], dim=-1).contiguous()
@ -123,6 +131,7 @@ class GPT2LMHeadModel_BeamSearchStep(GPT2LMHeadModel):
class GPT2LMHeadModel_ConfigurableOneStepSearch(GPT2LMHeadModel):
"""Here we wrap a class for Onnx model conversion for GPT2LMHeadModel with past state and one
step beam search with configuration support."""
def __init__(self,
config,
batch_size,
@ -190,6 +199,7 @@ class GPT2LMHeadModel_ConfigurableOneStepSearch(GPT2LMHeadModel):
if past:
last_seq_len = past[0].size(-2)
# input_ids and position_ids contains past sequence
input_ids_unfinished_flat = input_ids_unfinished_flat[:, last_seq_len:]
position_ids = position_ids[:, last_seq_len:]
@ -264,7 +274,9 @@ class GPT2LMHeadModel_ConfigurableOneStepSearch(GPT2LMHeadModel):
sorted=True) # output shape=(batch, beam_size)
# select the correspondent sentences/next tokens
selected_input_seq = selected_index_flat // self.config.beam_size
selected_input_seq = torch.div(selected_index_flat, self.config.beam_size,
rounding_mode='trunc') # selected_index_flat // self.config.beam_size
next_token_ids = next_token_ids.view(self.config.batch_size, -1).gather(-1, selected_index_flat)
prev_step_results = input_ids.view(self.config.batch_size, -1, input_ids.size(-1)).contiguous()
@ -302,14 +314,13 @@ MODEL_CLASSES = {
'GPT2LMHeadModel': (MyGPT2LMHeadModel, 'logits', True),
'GPT2LMHeadModel_NoPadding': (MyGPT2LMHeadModel_NoPadding, 'logits', False),
'GPT2Model': (MyGPT2Model, 'last_state', True),
"GPT2LMHeadModel_BeamSearchStep":
(GPT2LMHeadModel_BeamSearchStep, "last_state", True), # defined in gpt2_beamsearch_helper.py
"GPT2LMHeadModel_ConfigurableOneStepSearch":
(GPT2LMHeadModel_ConfigurableOneStepSearch, "last_state", False), # defined in gpt2_beamsearch_helper.py
"GPT2LMHeadModel_BeamSearchStep": (GPT2LMHeadModel_BeamSearchStep, "last_state", True),
"GPT2LMHeadModel_ConfigurableOneStepSearch": (GPT2LMHeadModel_ConfigurableOneStepSearch, "last_state", False),
}
class Gpt2BeamSearchInputs(Gpt2Inputs):
def __init__(
self,
input_ids,
@ -362,6 +373,7 @@ class Gpt2BeamSearchInputs(Gpt2Inputs):
class Gpt2BeamSearchHelper(Gpt2Helper):
"""A helper class for Gpt2 model conversion, inference and verification."""
@staticmethod
def get_dummy_inputs(batch_size: int,
past_sequence_length: int,
@ -373,13 +385,26 @@ class Gpt2BeamSearchHelper(Gpt2Helper):
device: torch.device,
float16: bool = False,
has_position_ids: bool = True,
has_attention_mask: bool = True) -> Gpt2BeamSearchInputs:
"""Create random inputs for GPT2 model.
Returns torch tensors of input_ids, position_ids, attention_mask and a list of past state tensors.
has_attention_mask: bool = True,
input_ids_dtype: torch.dtype = torch.int64,
position_ids_dtype: torch.dtype = torch.int64,
attention_mask_dtype: torch.dtype = torch.int64) -> Gpt2BeamSearchInputs:
"""Create random inputs for GPT2 beam search.
"""
gpt2_dummy_inputs = Gpt2Helper.get_dummy_inputs(batch_size, past_sequence_length, sequence_length,
num_attention_heads, hidden_size, num_layer, vocab_size, device,
float16, has_position_ids, has_attention_mask)
gpt2_dummy_inputs = Gpt2Helper.get_dummy_inputs(batch_size,
past_sequence_length,
sequence_length,
num_attention_heads,
hidden_size,
num_layer,
vocab_size,
device,
float16,
has_position_ids,
has_attention_mask,
input_ids_dtype=input_ids_dtype,
position_ids_dtype=position_ids_dtype,
attention_mask_dtype=attention_mask_dtype)
float_type = torch.float16 if float16 else torch.float32
beam_select_idx = torch.zeros([1, batch_size], device=device).long()
@ -418,7 +443,7 @@ class Gpt2BeamSearchHelper(Gpt2Helper):
beam_size: int,
step: int,
config: GPT2Config,
model_class: str = "GPT2LMHeadModel",
model_class: str = "GPT2LMHeadModel_BeamSearchStep",
num_seq: int = 0) -> Dict[str, List[int]]:
"""Returns a dictionary with output name as key, and shape as value."""
num_attention_heads = config.num_attention_heads
@ -431,25 +456,34 @@ class Gpt2BeamSearchHelper(Gpt2Helper):
if model_class == "GPT2LMHeadModel_BeamSearchStep":
last_state_shape = [batch_size, beam_size]
else:
last_state_shape = [batch_size * beam_size, past_sequence_length + sequence_length + 1]
last_state_shape = [batch_size * beam_size, past_sequence_length - context_len + sequence_length + 1]
if step == 0:
if model_class == "GPT2LMHeadModel_BeamSearchStep":
if step == 0:
present_state_shape = [
2,
batch_size,
num_attention_heads,
past_sequence_length + sequence_length,
int(hidden_size / num_attention_heads),
]
else:
if num_seq == 0:
num_seq = beam_size
present_state_shape = [
2,
batch_size * num_seq,
num_attention_heads,
past_sequence_length + sequence_length,
int(hidden_size / num_attention_heads),
]
else:
present_state_shape = [
2,
batch_size,
num_attention_heads,
past_sequence_length + sequence_length,
int(hidden_size / num_attention_heads),
]
else:
if num_seq == 0:
num_seq = beam_size
present_state_shape = [
2,
batch_size * num_seq,
num_attention_heads,
past_sequence_length + sequence_length,
past_sequence_length - context_len + sequence_length,
int(hidden_size / num_attention_heads),
]
@ -457,14 +491,16 @@ class Gpt2BeamSearchHelper(Gpt2Helper):
for i in range(num_layer):
output_shapes["present_" + str(i)] = present_state_shape
# TODO: reshape output_selected_indices as [batch_size, beam_size]
output_shapes["output_selected_indices"] = [1, batch_size * beam_size]
output_shapes["output_log_probs"] = [batch_size, beam_size]
output_shapes["output_unfinished_sents"] = [batch_size, beam_size]
if model_class == "GPT2LMHeadModel_BeamSearchStep":
output_shapes["current_step_results"] = [batch_size * beam_size, past_sequence_length + sequence_length + 1]
output_shapes["current_step_scores"] = [
batch_size * beam_size, past_sequence_length + sequence_length - context_len + 2
]
output_shapes["current_step_results"] = [
batch_size * beam_size, past_sequence_length - context_len + sequence_length + 1
]
output_shapes["current_step_scores"] = [batch_size * beam_size, past_sequence_length - context_len + 2]
print("output_shapes", output_shapes)
return output_shapes
@staticmethod
@ -528,6 +564,8 @@ class Gpt2BeamSearchHelper(Gpt2Helper):
has_position_ids: bool = True,
has_attention_mask: bool = True):
"""Export GPT-2 model with past state to ONNX model."""
assert isinstance(model, (GPT2LMHeadModel_BeamSearchStep, GPT2LMHeadModel_ConfigurableOneStepSearch))
config: GPT2Config = model.config
num_layer = config.n_layer
dummy_inputs = Gpt2BeamSearchHelper.get_dummy_inputs(batch_size=1,
@ -544,7 +582,6 @@ class Gpt2BeamSearchHelper(Gpt2Helper):
input_list = dummy_inputs.to_list()
with torch.no_grad():
# outputs = model(input_ids, position_id, attention_mask, beam_select_idx, past)
outputs = model(*input_list)
past_names = [f"past_{i}" for i in range(num_layer)]
@ -568,14 +605,6 @@ class Gpt2BeamSearchHelper(Gpt2Helper):
"current_step_scores",
]
# Shape of input tensors:
# input_ids: (batch_size, seq_len)
# past_{i}: (2, batch_size, num_heads, past_seq_len, hidden_size/num_heads)
# attention_mask: (batch_size, past_seq_len + seq_len)
# Shape of output tensors:
# last_state: (batch_size, seq_len, hidden_size)
# or logits: (batch_size, seq_len, vocab_size)
# present_{i}: (2, batch_size, num_heads, past_seq_len + seq_len, hidden_size/num_heads)
dynamic_axes = {
"input_ids": {
0: "batch_size",
@ -613,15 +642,23 @@ class Gpt2BeamSearchHelper(Gpt2Helper):
# add dynamic output axes
present_axes = {1: 'batch_size', 3: 'cur_seq_len'}
dynamic_axes["last_state"] = {0: 'batch_size', 1: 'beam_size'}
if isinstance(model, GPT2LMHeadModel_BeamSearchStep):
dynamic_axes["last_state"] = {0: "batch_size", 1: "beam_size"}
else:
dynamic_axes["last_state"] = {0: "batch_size * beam_size", 1: "total_seq_len"}
for i in range(num_layer):
dynamic_axes["present_" + str(i)] = present_axes
dynamic_axes["output_selected_indices"] = {0: "batch_size", 1: "'beam_size_or_1'"}
dynamic_axes["output_log_probs"] = {0: "batch_size", 1: "'beam_size'"}
dynamic_axes["output_unfinished_sents"] = {0: "batch_size", 1: "'beam_size'"}
dynamic_axes["current_step_results"] = {0: "beam_size_or_1", 1: "total_seq_len"}
dynamic_axes["current_step_scores"] = {0: "beam_size_or_1", 1: "total_seq_len"}
dynamic_axes["output_selected_indices"] = {1: "batch_size * beam_size"}
dynamic_axes["output_log_probs"] = {0: "batch_size", 1: "beam_size"}
dynamic_axes["output_unfinished_sents"] = {0: "batch_size", 1: "beam_size"}
if "current_step_results" in output_names:
dynamic_axes["current_step_results"] = {0: "batch_size * beam_size", 1: "total_seq_len"}
dynamic_axes["current_step_scores"] = {0: "batch_size * beam_size"}
logger.info(
f"Shapes: input_ids={dummy_inputs.input_ids.shape} past={dummy_inputs.past[0].shape} output={outputs[0].shape} present={outputs[1][0].shape}"
@ -636,7 +673,7 @@ class Gpt2BeamSearchHelper(Gpt2Helper):
input_names=input_names,
output_names=output_names,
dynamic_axes=dynamic_axes,
opset_version=12,
opset_version=14,
do_constant_folding=True,
use_external_data_format=use_external_data_format,
verbose=verbose,
@ -697,7 +734,7 @@ class Gpt2BeamSearchHelper(Gpt2Helper):
prev_step_scores=None):
"""Returnas IO binding object for a session."""
# Bind inputs and outputs to onnxruntime session
# Bind (input_ids, position_ids, attention_mask and past_*) and all outputs
io_binding = Gpt2Helper.prepare_io_binding(ort_session,
input_ids,
position_ids,
@ -706,114 +743,25 @@ class Gpt2BeamSearchHelper(Gpt2Helper):
output_buffers=output_buffers,
output_shapes=output_shapes)
# Bind inputs
data_type = output_buffers[ort_session.get_outputs()[1].name].dtype
float_type = numpy.float16 if data_type == torch.float16 else numpy.float32
if past is not None:
for i, past_i in enumerate(past):
assert past_i.is_contiguous()
data_ptr = past_i.data_ptr()
if data_ptr == 0:
# When past_sequence_length is 0, its data_ptr will be zero. IO Binding asserts that data_ptr shall not be zero.
# Here we workaround and pass data pointer of input_ids. Actual data is not used for past so it does not matter.
data_ptr = input_ids.data_ptr()
io_binding.bind_input(f'past_{i}', past_i.device.type, 0, float_type, list(past_i.size()), data_ptr)
if attention_mask is not None:
assert attention_mask.is_contiguous()
io_binding.bind_input('attention_mask', attention_mask.device.type, 0, float_type,
list(attention_mask.size()), attention_mask.data_ptr())
if beam_select_idx is not None:
assert beam_select_idx.is_contiguous()
io_binding.bind_input(
"beam_select_idx",
beam_select_idx.device.type,
0,
numpy.longlong,
list(beam_select_idx.size()),
beam_select_idx.data_ptr(),
)
if input_log_probs is not None:
assert input_log_probs.is_contiguous()
io_binding.bind_input(
"input_log_probs",
input_log_probs.device.type,
0,
float_type,
list(input_log_probs.size()),
input_log_probs.data_ptr(),
)
if input_unfinished_sents is not None:
assert input_unfinished_sents.is_contiguous()
io_binding.bind_input(
"input_unfinished_sents",
input_unfinished_sents.device.type,
0,
numpy.bool,
list(input_unfinished_sents.size()),
input_unfinished_sents.data_ptr(),
)
if prev_step_results is not None:
assert prev_step_results.is_contiguous()
io_binding.bind_input(
"prev_step_results",
prev_step_results.device.type,
0,
numpy.longlong,
list(prev_step_results.size()),
prev_step_results.data_ptr(),
)
if prev_step_scores is not None:
assert prev_step_scores.is_contiguous()
io_binding.bind_input(
"prev_step_scores",
prev_step_scores.device.type,
0,
float_type,
list(prev_step_scores.size()),
prev_step_scores.data_ptr(),
)
# Bind outputs
for output in ort_session.get_outputs():
output_name = output.name
output_buffer = output_buffers[output_name]
logger.debug(f"{output_name} device type={output_buffer.device.type} shape={list(output_buffer.size())}")
if (output_name == "output_selected_indices" or output_name == "last_state"
or output_name == "current_step_results"):
io_binding.bind_output(
output_name,
output_buffer.device.type,
# Bind the remaining inputs
other_inputs = {
"beam_select_idx": beam_select_idx,
"input_log_probs": input_log_probs,
"input_unfinished_sents": input_unfinished_sents,
"prev_step_results": prev_step_results,
"prev_step_scores": prev_step_scores,
}
name_to_np_type = TypeHelper.get_io_numpy_type_map(ort_session)
for name, tensor in other_inputs.items():
if tensor is not None:
assert tensor.is_contiguous()
io_binding.bind_input(
name,
tensor.device.type,
0,
numpy.longlong,
output_shapes[output_name],
output_buffer.data_ptr(),
)
elif output_name == "output_unfinished_sents":
io_binding.bind_output(
output_name,
output_buffer.device.type,
0,
numpy.bool,
output_shapes[output_name],
output_buffer.data_ptr(),
)
else:
io_binding.bind_output(
output_name,
output_buffer.device.type,
0,
float_type,
output_shapes[output_name],
output_buffer.data_ptr(),
name_to_np_type[name],
list(tensor.size()),
tensor.data_ptr(),
)
return io_binding

14
onnxruntime/python/tools/transformers/gpt2_beamsearch_tester.py → onnxruntime/python/tools/transformers/models/gpt2/gpt2_beamsearch_tester.py
View file

@ -4,24 +4,25 @@
# license information.
# --------------------------------------------------------------------------
# This script helps evaluation of GPT-2 model.
import os
import logging
import torch
import random
import numpy
import time
import timeit
import math
import statistics
from pathlib import Path
from gpt2_tester import Gpt2Tester, Gpt2Metric
from gpt2_beamsearch_helper import Gpt2BeamSearchHelper, Gpt2BeamSearchInputs
import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..'))
from benchmark_helper import Precision
logger = logging.getLogger(__name__)
class Gpt2TesterFactory:
@staticmethod
def create_tester(tester_type="default"):
testers = {
@ -34,6 +35,7 @@ class Gpt2TesterFactory:
class Gpt2BeamSearchTester(Gpt2Tester):
def __init__(
self,
input_ids,

20
onnxruntime/python/tools/transformers/gpt2_helper.py → onnxruntime/python/tools/transformers/models/gpt2/gpt2_helper.py
View file

@ -4,18 +4,22 @@
# license information.
# --------------------------------------------------------------------------
# This script helps onnx conversion and validation for GPT2 model with past state.
import os
import logging
import torch
import shutil
import random
import numpy
import time
import re
import pickle
from pathlib import Path
from typing import List, Dict, Tuple, Union
from transformers import GPT2Model, GPT2LMHeadModel, GPT2Config, TFGPT2Model
import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..'))
from float16 import float_to_float16_max_diff
from onnx_model import OnnxModel
from fusion_utils import FusionUtils
@ -261,8 +265,9 @@ class Gpt2Helper:
return numpy.amax(diff)
@staticmethod
def compare_outputs(torch_outputs, ort_outputs, rtol=1e-03, atol=1e-03):
def compare_outputs(torch_outputs, ort_outputs, rtol=1e-03, atol=1e-03, **kwargs):
""" Returns True if torch and ORT outputs are close for given thresholds, and False otherwise.
Note: need kwargs since Gpt2BeamSearchHelper.compare_outputs has an extra parameter model_class
"""
is_close = numpy.allclose(ort_outputs[0], torch_outputs[0].cpu().numpy(), rtol=rtol, atol=atol)
logger.debug(f'PyTorch and OnnxRuntime output 0 (last_state) are close: {is_close}')
@ -444,6 +449,8 @@ class Gpt2Helper:
if auto_mixed_precision:
Gpt2Helper.auto_mixed_precision(m)
else:
if "keep_io_types" not in kwargs:
kwargs["keep_io_types"] = False
m.convert_float_to_float16(use_symbolic_shape_infer=True, **kwargs)
m.save_model_to_file(optimized_model_path, use_external_data_format)
@ -490,10 +497,9 @@ class Gpt2Helper:
else:
logger.warning(f"Failed to find MatMul node for logits. Found {node.op_type} of node {node.name}")
if is_weight_fp16_precision:
keep_io_types = []
node_block_list = []
else:
keep_io_types = []
node_block_list = []
if (not is_weight_fp16_precision) and (last_matmul_node is not None):
# When original weight is float32 precision, keep logits and last MatMul in float32 could get better precision.
keep_io_types = [logits_output_name]
node_block_list = [last_matmul_node.name]

15
onnxruntime/python/tools/transformers/gpt2_parity.py → onnxruntime/python/tools/transformers/models/gpt2/gpt2_parity.py
View file

@ -10,12 +10,8 @@
# pvalue < 0.05 means two experiments have significant difference on top 1 match rate.
# User could use this script to select the best mixed precision model according to these metrics.
from convert_to_onnx import main, get_latency_name
import os
import argparse
import logging
from gpt2_helper import PRETRAINED_GPT2_MODELS, Gpt2Helper
from benchmark_helper import setup_logger
from onnx_model import OnnxModel
import onnx
import csv
@ -23,6 +19,16 @@ import datetime
import scipy.stats
import torch
from gpt2_helper import PRETRAINED_GPT2_MODELS, Gpt2Helper
from convert_to_onnx import main, get_latency_name
import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..'))
from benchmark_helper import setup_logger
logger = logging.getLogger('')
@ -69,6 +75,7 @@ def parse_arguments(argv=None):
class ParityTask:
def __init__(self, test_cases, total_runs, csv_path):
self.total_runs = total_runs
self.test_cases = test_cases

11
onnxruntime/python/tools/transformers/gpt2_tester.py → onnxruntime/python/tools/transformers/models/gpt2/gpt2_tester.py
View file

@ -4,22 +4,26 @@
# license information.
# --------------------------------------------------------------------------
# This script helps evaluation of GPT-2 model.
import os
import logging
import torch
import random
import numpy
import time
import timeit
import math
import statistics
from gpt2_helper import Gpt2Helper, Gpt2Inputs
import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..'))
from benchmark_helper import Precision
logger = logging.getLogger(__name__)
class Gpt2Metric:
def __init__(self, treatment_name, baseline_name='Torch', top_k=20):
assert top_k > 1 and top_k <= 100
self.baseline = baseline_name
@ -112,6 +116,7 @@ class Gpt2Metric:
class Gpt2Tester:
def __init__(self,
input_ids,
position_ids,

6
onnxruntime/python/tools/transformers/parity_check_helper.py → onnxruntime/python/tools/transformers/models/gpt2/parity_check_helper.py
View file

@ -14,6 +14,12 @@ import torch
from pathlib import Path
from onnx import numpy_helper, TensorProto
from gpt2_helper import Gpt2Helper
import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..'))
from benchmark_helper import create_onnxruntime_session
NON_ZERO_VALUE = str(1)

12
onnxruntime/python/tools/transformers/models/longformer/__init__.py
View file

@ -1,10 +1,4 @@
# -------------------------------------------------------------------------
#-------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
import os
import sys
sys.path.append(os.path.dirname(__file__))
# Licensed under the MIT License.
#--------------------------------------------------------------------------

8
onnxruntime/python/tools/transformers/models/t5/__init__.py
View file

@ -1,8 +1,4 @@
#-------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
#-------------------------------------------------------------------------
import os
import sys
sys.path.append(os.path.dirname(__file__))
#--------------------------------------------------------------------------

8
onnxruntime/python/tools/transformers/notebooks/Inference_GPT2-OneStepSearch_OnnxRuntime_CPU.ipynb
View file

@ -128,7 +128,13 @@
}
],
"source": [
"from onnxruntime.transformers.gpt2_beamsearch_helper import Gpt2BeamSearchHelper, GPT2LMHeadModel_BeamSearchStep\n",
"from packaging import version\n",
"from onnxruntime import __version__ as ort_verison\n",
"if version.parse(ort_verison) >= version.parse('1.12.0'):\n",
" from onnxruntime.transformers.models.gpt2.gpt2_beamsearch_helper import Gpt2BeamSearchHelper, GPT2LMHeadModel_BeamSearchStep\n",
"else:\n",
" from onnxruntime.transformers.gpt2_beamsearch_helper import Gpt2BeamSearchHelper, GPT2LMHeadModel_BeamSearchStep\n",
"\n",
"from transformers import AutoConfig\n",
"import torch\n",
"\n",

8
onnxruntime/python/tools/transformers/notebooks/Inference_GPT2_with_OnnxRuntime_on_CPU.ipynb
View file

@ -183,7 +183,13 @@
}
],
"source": [
"from onnxruntime.transformers.gpt2_helper import Gpt2Helper, MyGPT2LMHeadModel\n",
"from packaging import version\n",
"from onnxruntime import __version__ as ort_verison\n",
"if version.parse(ort_verison) >= version.parse('1.12.0'):\n",
" from onnxruntime.transformers.models.gpt2.gpt2_helper import Gpt2Helper, MyGPT2LMHeadModel\n",
"else:\n",
" from onnxruntime.transformers.gpt2_helper import Gpt2Helper, MyGPT2LMHeadModel\n",
"\n",
"from transformers import AutoConfig\n",
"import torch\n",
"\n",

6
onnxruntime/test/python/transformers/parity_utilities.py
View file

@ -10,13 +10,13 @@ import numpy
import torch
def find_transformers_source():
source_dir = os.path.join(os.path.dirname(__file__), '..', '..', '..', 'python', 'tools', 'transformers')
def find_transformers_source(sub_dir_paths=[]):
source_dir = os.path.join(os.path.dirname(__file__), '..', '..', '..', 'python', 'tools', 'transformers', *sub_dir_paths)
if (os.path.exists(source_dir)):
if source_dir not in sys.path:
sys.path.append(source_dir)
return True
return False
return False
def create_inputs(batch_size=1, sequence_length=1, hidden_size=768, float16=False, device=torch.device('cuda')):

37
onnxruntime/test/python/transformers/test_gpt2.py
View file

@ -13,13 +13,15 @@ import coloredlogs
import pytest
from parity_utilities import find_transformers_source
if find_transformers_source():
if find_transformers_source(sub_dir_paths=['models', 'gpt2']):
from benchmark_gpt2 import parse_arguments, main
else:
from onnxruntime.transformers.benchmark_gpt2 import parse_arguments, main
from onnxruntime.transformers.models.gpt2.benchmark_gpt2 import parse_arguments, main
class TestGpt2(unittest.TestCase):
def setUp(self):
from onnxruntime import get_available_providers
self.test_cuda = 'CUDAExecutionProvider' in get_available_providers()
@ -27,51 +29,56 @@ class TestGpt2(unittest.TestCase):
def run_benchmark_gpt2(self, arguments: str):
args = parse_arguments(arguments.split())
csv_filename = main(args)
self.assertIsNotNone(csv_filename)
self.assertTrue(os.path.exists(csv_filename))
@pytest.mark.slow
def test_gpt2_fp32(self):
self.run_benchmark_gpt2('-m gpt2 --precision fp32 -v -b 1 -s 128')
self.run_benchmark_gpt2('-m gpt2 --precision fp32 -v -b 1 --sequence_lengths 2 -s 3')
@pytest.mark.slow
def test_gpt2_fp16(self):
if self.test_cuda:
self.run_benchmark_gpt2('-m gpt2 --precision fp16 -o -b 1 -s 128 --use_gpu')
self.run_benchmark_gpt2('-m gpt2 --precision fp16 -o -b 1 --sequence_lengths 2 -s 3 --use_gpu')
@pytest.mark.slow
def test_gpt2_int8(self):
self.run_benchmark_gpt2('-m gpt2 --precision int8 -o -b 1 -s 128')
self.run_benchmark_gpt2('-m gpt2 --precision int8 -o -b 1 --sequence_lengths 2 -s 3')
@pytest.mark.slow
def test_gpt2_beam_search_step_fp32(self):
self.run_benchmark_gpt2('-m gpt2 --model_class=GPT2LMHeadModel_BeamSearchStep --precision fp32 -v -b 1 -s 128')
self.run_benchmark_gpt2(
'-m gpt2 --model_class=GPT2LMHeadModel_BeamSearchStep --precision fp32 -v -b 1 --sequence_lengths 5 -s 3')
@pytest.mark.slow
def test_gpt2_beam_search_step_fp16(self):
if self.test_cuda:
self.run_benchmark_gpt2(
'-m gpt2 --model_class=GPT2LMHeadModel_BeamSearchStep --precision fp16 -o -b 1 -s 128 --use_gpu')
# @pytest.mark.slow
# def test_gpt2_beam_search_step_fp16(self):
# if self.test_cuda:
# self.run_benchmark_gpt2(
# '-m gpt2 --model_class=GPT2LMHeadModel_BeamSearchStep --precision fp16 -o -b 1 --sequence_lengths 5 -s 3 --use_gpu')
@pytest.mark.slow
def test_gpt2_beam_search_step_int8(self):
self.run_benchmark_gpt2('-m gpt2 --model_class=GPT2LMHeadModel_BeamSearchStep --precision int8 -o -b 1 -s 128')
self.run_benchmark_gpt2(
'-m gpt2 --model_class=GPT2LMHeadModel_BeamSearchStep --precision int8 -o -b 1 --sequence_lengths 5 -s 3')
@pytest.mark.slow
def test_gpt2_configurable_one_step_search_fp32(self):
self.run_benchmark_gpt2(
'-m gpt2 --model_class=GPT2LMHeadModel_ConfigurableOneStepSearch --precision fp32 -v -b 1 -s 128')
'-m gpt2 --model_class=GPT2LMHeadModel_ConfigurableOneStepSearch --precision fp32 -v -b 1 --sequence_lengths 5 --past_sequence_lengths 3 --use_gpu'
)
@pytest.mark.slow
def test_gpt2_configurable_one_step_search_fp16(self):
if self.test_cuda:
self.run_benchmark_gpt2(
'-m gpt2 --model_class=GPT2LMHeadModel_ConfigurableOneStepSearch --precision fp16 -o -b 1 -s 128 --use_gpu'
'-m gpt2 --model_class=GPT2LMHeadModel_ConfigurableOneStepSearch --precision fp16 -o -b 1 --sequence_lengths 5 -s 3 --use_gpu'
)
@pytest.mark.slow
def test_gpt2_configurable_one_step_search_int8(self):
self.run_benchmark_gpt2(
'-m gpt2 --model_class=GPT2LMHeadModel_ConfigurableOneStepSearch --precision int8 -o -b 1 -s 128')
'-m gpt2 --model_class=GPT2LMHeadModel_ConfigurableOneStepSearch --precision int8 -o -b 1 --sequence_lengths 5 -s 3'
)
if __name__ == '__main__':

3
setup.py
View file

@ -321,8 +321,9 @@ packages = [
'onnxruntime.quantization.operators',
'onnxruntime.quantization.CalTableFlatBuffers',
'onnxruntime.transformers',
'onnxruntime.transformers.models.t5',
'onnxruntime.transformers.models.gpt2',
'onnxruntime.transformers.models.longformer',
'onnxruntime.transformers.models.t5',
]
requirements_file = "requirements.txt"