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# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# -------------------------------------------------------------------------
import logging
import os
import tempfile
from pathlib import Path
import numpy
import onnx
import torch
from onnx_model import OnnxModel
from past_helper import PastKeyValuesHelper
from t5_decoder import T5DecoderInit
from t5_encoder import T5Encoder, T5EncoderInputs
from torch_onnx_export_helper import torch_onnx_export
from transformers import MT5Config, T5Config
from onnxruntime import InferenceSession
logger = logging.getLogger(__name__)
class T5EncoderDecoderInit(torch.nn.Module):
"""A combination of T5Encoder and T5DecoderInit."""
def __init__(
self,
encoder: torch.nn.Module,
decoder: torch.nn.Module,
lm_head: torch.nn.Linear,
config: T5Config | MT5Config,
decoder_start_token_id: int | None = None,
output_cross_only: bool = False,
):
super().__init__()
self.config: T5Config | MT5Config = config
self.t5_encoder = T5Encoder(encoder, config)
self.t5_decoder_init = T5DecoderInit(decoder, lm_head, config, decoder_start_token_id)
self.output_cross_only = output_cross_only
def forward(
self,
encoder_input_ids: torch.Tensor,
encoder_attention_mask: torch.Tensor,
decoder_input_ids: torch.Tensor | None = None,
):
encoder_hidden_states: torch.FloatTensor = self.t5_encoder(encoder_input_ids, encoder_attention_mask)
lm_logits, past_self, past_cross = self.t5_decoder_init(
decoder_input_ids, encoder_attention_mask, encoder_hidden_states
)
if self.output_cross_only:
return past_cross
else:
return lm_logits, encoder_hidden_states, past_self, past_cross
class T5EncoderDecoderInitInputs:
def __init__(self, encoder_input_ids, encoder_attention_mask, decoder_input_ids=None):
self.encoder_input_ids: torch.LongTensor = encoder_input_ids
self.encoder_attention_mask: torch.LongTensor = encoder_attention_mask
self.decoder_input_ids: torch.LongTensor | None = decoder_input_ids
@staticmethod
def create_dummy(
config: T5Config | MT5Config,
batch_size: int,
encode_sequence_length: int,
use_decoder_input_ids: int,
device: torch.device,
use_int32_inputs: bool = False,
): # -> T5EncoderDecoderInitInputs:
encoder_inputs: T5EncoderInputs = T5EncoderInputs.create_dummy(
batch_size,
encode_sequence_length,
config.vocab_size,
device,
use_int32_inputs=use_int32_inputs,
)
decoder_input_ids = None
if use_decoder_input_ids:
dtype = torch.int32 if use_int32_inputs else torch.int64
decoder_input_ids = torch.ones((batch_size, 1), dtype=dtype, device=device) * config.decoder_start_token_id
return T5EncoderDecoderInitInputs(encoder_inputs.input_ids, encoder_inputs.attention_mask, decoder_input_ids)
def to_list(self) -> list:
input_list = [self.encoder_input_ids, self.encoder_attention_mask]
if self.decoder_input_ids is not None:
input_list.append(self.decoder_input_ids)
return input_list
class T5EncoderDecoderInitHelper:
@staticmethod
def export_onnx(
model: T5EncoderDecoderInit,
device: torch.device,
onnx_model_path: str,
use_decoder_input_ids: bool = True,
verbose: bool = True,
use_external_data_format: bool = False,
use_int32_inputs: bool = False,
):
"""Export decoder to ONNX
Args:
model (T5EncoderDecoderInit): the model to export
device (torch.device): device of decoder object
onnx_model_path (str): onnx path
verbose (bool, optional): print verbose information. Defaults to True.
use_external_data_format (bool, optional): use external data format or not. Defaults to False.
use_int32_inputs (bool, optional): use int32 instead of int64 for integer inputs. Defaults to False.
"""
assert isinstance(model, T5EncoderDecoderInit)
# Do not exclude decoder in torch onnx export so that cross can show up.
output_cross_only = model.output_cross_only
model.output_cross_only = False
inputs = T5EncoderDecoderInitInputs.create_dummy(
model.config,
batch_size=2,
encode_sequence_length=3,
use_decoder_input_ids=use_decoder_input_ids,
device=device,
use_int32_inputs=use_int32_inputs,
)
input_list = inputs.to_list()
present_names = PastKeyValuesHelper.get_past_names(model.config.num_decoder_layers, present=True)
output_names = ["logits", "encoder_hidden_states", *present_names]
# Shape of input tensors (sequence_length==1):
# input_ids: (batch_size, sequence_length)
# encoder_attention_mask: (batch_size, encode_sequence_length)
# encoder_hidden_states: (batch_size, encode_sequence_length, hidden_size)
# past_self_*: (batch_size, num_heads, past_decode_sequence_length, head_size)
# past_cross_*: (batch_size, num_heads, encode_sequence_length, head_size)
# Shape of output tensors:
# logits: (batch_size, sequence_length, vocab_size)
# past_self_*: (batch_size, num_heads, past_decode_sequence_length + sequence_length, head_size)
# past_cross_*: (batch_size, num_heads, encode_sequence_length, head_size)
input_names = ["encoder_input_ids", "encoder_attention_mask"]
# ONNX exporter might mark dimension like 'present_value_self_1_dim_2' in shape inference.
# We use a workaround here: first use dim_param "1" for sequence_length, and later change to dim_value.
sequence_length = "1"
num_heads = str(model.config.num_heads)
hidden_size = str(model.config.d_model)
head_size = str(model.config.d_kv)
dynamic_axes = {
"encoder_input_ids": {0: "batch_size", 1: "encode_sequence_length"},
"encoder_attention_mask": {0: "batch_size", 1: "encode_sequence_length"},
"encoder_hidden_states": {
0: "batch_size",
1: "encode_sequence_length",
2: hidden_size,
},
"logits": {
0: "batch_size",
1: sequence_length,
},
}
if use_decoder_input_ids:
input_names.append("decoder_input_ids")
dynamic_axes["decoder_input_ids"] = {
0: "batch_size",
1: sequence_length,
}
for name in present_names:
if "cross" in name:
dynamic_axes[name] = {
0: "batch_size",
1: num_heads,
2: "encode_sequence_length",
3: head_size,
}
else: # self attention past state
dynamic_axes[name] = {
0: "batch_size",
1: num_heads,
2: sequence_length,
3: head_size,
}
with tempfile.TemporaryDirectory() as tmp_dir_name:
temp_onnx_model_path = os.path.join(tmp_dir_name, "encoder_decoder_init.onnx")
Path(temp_onnx_model_path).parent.mkdir(parents=True, exist_ok=True)
torch_onnx_export(
model,
args=tuple(input_list),
f=temp_onnx_model_path,
export_params=True,
input_names=input_names,
output_names=output_names,
dynamic_axes=dynamic_axes,
opset_version=12,
do_constant_folding=True,
use_external_data_format=use_external_data_format,
verbose=verbose,
)
# Restore output_cross_only setting.
model.output_cross_only = output_cross_only
# Workaround as mentioned earlier: change numeric dim_param to dim_value
exported_model: onnx.ModelProto = onnx.load(temp_onnx_model_path)
for tensor in exported_model.graph.output:
for dim_proto in tensor.type.tensor_type.shape.dim:
if dim_proto.HasField("dim_param") and dim_proto.dim_param in [
sequence_length,
num_heads,
hidden_size,
head_size,
]:
dim_value = int(dim_proto.dim_param)
dim_proto.Clear()
dim_proto.dim_value = dim_value
if output_cross_only:
# Rewrite onnx graph to only keep present_[key|value]_cross_* outputs.
onnx_model = OnnxModel(exported_model)
output_name_to_node = onnx_model.output_name_to_node()
for output in exported_model.graph.output:
if "cross" in output.name:
assert output.name in output_name_to_node
transpose_node = output_name_to_node[output.name]
assert transpose_node and transpose_node.op_type == "Transpose"
permutation = OnnxModel.get_node_attribute(transpose_node, "perm")
assert isinstance(permutation, list)
assert permutation == [0, 2, 1, 3]
matched_nodes = onnx_model.match_parent_path(
transpose_node,
["Reshape", "MatMul"],
[0, 0],
output_name_to_node,
)
assert matched_nodes is not None
reshape_node, matmul_node = matched_nodes
assert "encoder_hidden_states" in matmul_node.input
if not onnx_model.get_initializer("cross_reshape_shape"):
shape_tensor = onnx.helper.make_tensor(
name="cross_reshape_shape",
data_type=onnx.TensorProto.INT64,
dims=[4],
vals=[0, 0, int(num_heads), int(head_size)],
raw=False,
)
onnx_model.add_initializer(shape_tensor)
reshape_node.input[1] = "cross_reshape_shape"
cross_outputs = [output.name for output in exported_model.graph.output if "cross" in output.name]
onnx_model.prune_graph(cross_outputs, allow_remove_graph_inputs=True)
OnnxModel.save(
exported_model,
onnx_model_path,
save_as_external_data=use_external_data_format,
all_tensors_to_one_file=True,
)
@staticmethod
def onnxruntime_inference(ort_session, inputs: T5EncoderDecoderInitInputs):
"""Run inference of ONNX model."""
logger.debug("start onnxruntime_inference")
ort_inputs = {
"encoder_input_ids": numpy.ascontiguousarray(inputs.encoder_input_ids.cpu().numpy()),
"encoder_attention_mask": numpy.ascontiguousarray(inputs.encoder_attention_mask.cpu().numpy()),
}
if inputs.decoder_input_ids is not None:
ort_inputs["decoder_input_ids"] = numpy.ascontiguousarray(inputs.decoder_input_ids.cpu().numpy())
ort_outputs = ort_session.run(None, ort_inputs)
return ort_outputs
@staticmethod
def verify_onnx(
model: T5EncoderDecoderInit,
ort_session: InferenceSession,
device: torch.device,
use_int32_inputs: bool,
max_cases: int = 4,
):
"""Compare the result from PyTorch and OnnxRuntime to verify the ONNX model is good."""
ort_inputs = ort_session.get_inputs()
use_decoder_input_ids = len(ort_inputs) == 3
test_cases = [(4, 11), (1, 2), (3, 1), (8, 5)]
test_cases_max_diff = []
for batch_size, encode_sequence_length in test_cases[:max_cases]:
inputs = T5EncoderDecoderInitInputs.create_dummy(
model.config,
batch_size,
encode_sequence_length,
use_decoder_input_ids=use_decoder_input_ids,
device=device,
use_int32_inputs=use_int32_inputs,
)
ort_outputs = T5EncoderDecoderInitHelper.onnxruntime_inference(ort_session, inputs)
# Run inference of PyTorch model
input_list = inputs.to_list()
torch_outputs = model(*input_list)
num_decoder_layers = model.config.num_decoder_layers
if not model.output_cross_only:
assert torch_outputs[0].cpu().numpy().shape == ort_outputs[0].shape
max_diff = numpy.amax(numpy.abs(torch_outputs[0].cpu().numpy() - ort_outputs[0]))
logger.debug(f"logits max_diff={max_diff}")
max_diff_all = max_diff
assert torch_outputs[1].cpu().numpy().shape == ort_outputs[1].shape
max_diff = numpy.amax(numpy.abs(torch_outputs[1].cpu().numpy() - ort_outputs[1]))
logger.debug(f"encoder_hidden_states max_diff={max_diff}")
max_diff_all = max(max_diff_all, max_diff)
for i in range(2 * num_decoder_layers):
max_diff = numpy.amax(numpy.abs(torch_outputs[2][i].cpu().numpy() - ort_outputs[2 + i]))
logger.debug(f"self attention past state {i} max_diff={max_diff}")
for i in range(2 * num_decoder_layers):
max_diff = numpy.amax(
numpy.abs(torch_outputs[3][i].cpu().numpy() - ort_outputs[2 + 2 * num_decoder_layers + i])
)
logger.debug(f"cross attention past state {i} max_diff={max_diff}")
max_diff_all = max(max_diff_all, max_diff)
else:
max_diff_all = -float("inf")
for i in range(2 * num_decoder_layers):
max_diff = numpy.amax(numpy.abs(torch_outputs[i].cpu().numpy() - ort_outputs[i]))
logger.debug(f"cross attention past state {i} max_diff={max_diff}")
max_diff_all = max(max_diff_all, max_diff)
test_cases_max_diff.append(max_diff_all)
logger.info(
f"batch_size={batch_size} encode_sequence_length={encode_sequence_length}, max_diff={max_diff_all}"
)
return max(test_cases_max_diff)
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