File size: 16,461 Bytes
610d9c2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 | # -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
import logging
import os
import tempfile
from itertools import chain
from pathlib import Path
import numpy as np
import onnx
import torch
from float16 import convert_float_to_float16
from onnx import ModelProto, ValueInfoProto
from onnx_model import OnnxModel
from transformers import WhisperConfig
from whisper_decoder import WhisperDecoder
from whisper_encoder import WhisperEncoder
from whisper_inputs import (
convert_inputs_for_ort,
get_model_dynamic_axes,
get_sample_encoder_decoder_init_inputs,
group_past_key_values,
)
from onnxruntime import InferenceSession
logger = logging.getLogger(__name__)
class WhisperEncoderDecoderInit(torch.nn.Module):
"""Whisper encoder component + first pass through Whisper decoder component to initialize KV caches"""
def __init__(self, config: WhisperConfig, model: torch.nn.Module, model_impl: str, no_beam_search_op: bool = False):
super().__init__()
self.config = config
self.device = model.device
self.model_impl = model_impl
self.no_beam_search_op = no_beam_search_op
self.encoder = WhisperEncoder(config, model, model_impl)
self.decoder = WhisperDecoder(config, model, model_impl, no_beam_search_op)
self.max_source_positions = self.config.max_source_positions
self.num_heads = self.config.decoder_attention_heads
self.head_size = self.config.d_model // self.num_heads
def hf_forward_for_beam_search_op(self, audio_features: torch.Tensor, decoder_input_ids: torch.Tensor):
encoder_hidden_states = self.encoder(audio_features)
logits, present_key_values = self.decoder(decoder_input_ids, encoder_hidden_states)
return logits, encoder_hidden_states, present_key_values
def hf_forward_for_no_beam_search_op(self, audio_features: torch.Tensor):
encoder_hidden_states = self.encoder(audio_features)
# Get cross attention KV caches and return them for this model
# We do this because these MatMuls are only run once before their outputs are being re-used in the decoder
present_cross_attention_key_value_caches = []
for layer in self.decoder.decoder.layers:
cross_attn_key_cache = (
layer.encoder_attn.k_proj(encoder_hidden_states)
.view(-1, self.max_source_positions, self.num_heads, self.head_size)
.transpose(1, 2)
)
cross_attn_value_cache = (
layer.encoder_attn.v_proj(encoder_hidden_states)
.view(-1, self.max_source_positions, self.num_heads, self.head_size)
.transpose(1, 2)
)
present_cross_attention_key_value_caches.append(cross_attn_key_cache)
present_cross_attention_key_value_caches.append(cross_attn_value_cache)
return encoder_hidden_states, present_cross_attention_key_value_caches
def oai_forward_for_beam_search_op(self, audio_features: torch.Tensor, decoder_input_ids: torch.Tensor):
encoder_hidden_states = self.encoder(audio_features)
logits, present_key_values = self.decoder(decoder_input_ids, encoder_hidden_states)
return logits, encoder_hidden_states, present_key_values
def oai_forward_for_no_beam_search_op(self, audio_features: torch.Tensor):
encoder_hidden_states = self.encoder(audio_features)
# Get cross attention KV caches and return them for this model
# We do this because these MatMuls are only run once before their outputs are being re-used in the decoder
present_cross_attention_key_value_caches = []
for block in self.decoder.model.decoder.blocks:
cross_attn_key_cache = (
block.cross_attn.key(encoder_hidden_states)
.view(-1, self.max_source_positions, self.num_heads, self.head_size)
.transpose(1, 2)
)
cross_attn_value_cache = (
block.cross_attn.value(encoder_hidden_states)
.view(-1, self.max_source_positions, self.num_heads, self.head_size)
.transpose(1, 2)
)
present_cross_attention_key_value_caches.append(cross_attn_key_cache)
present_cross_attention_key_value_caches.append(cross_attn_value_cache)
return encoder_hidden_states, present_cross_attention_key_value_caches
def forward(self, audio_features: torch.Tensor, decoder_input_ids: torch.Tensor | None = None):
if self.model_impl == "openai":
if self.no_beam_search_op:
return self.oai_forward_for_no_beam_search_op(audio_features)
return self.oai_forward_for_beam_search_op(audio_features, decoder_input_ids)
# Hugging Face implementation
if self.no_beam_search_op:
return self.hf_forward_for_no_beam_search_op(audio_features)
return self.hf_forward_for_beam_search_op(audio_features, decoder_input_ids)
def input_names(self):
if self.no_beam_search_op:
input_names = ["audio_features"]
else:
input_names = ["encoder_input_ids", "decoder_input_ids"]
return input_names
def output_names(self):
if self.no_beam_search_op:
output_names = [
"encoder_hidden_states",
*list(
chain.from_iterable(
(f"present_key_cross_{i}", f"present_value_cross_{i}")
for i in range(self.config.decoder_layers)
)
),
]
else:
output_names = [
"logits",
"encoder_hidden_states",
*list(
chain.from_iterable(
(
f"present_key_self_{i}",
f"present_value_self_{i}",
f"present_key_cross_{i}",
f"present_value_cross_{i}",
)
for i in range(self.config.decoder_layers)
)
),
]
return output_names
def dynamic_axes(self, input_names, output_names):
dynamic_axes = get_model_dynamic_axes(self.config, input_names, output_names)
return dynamic_axes
def inputs(self, use_fp16_inputs: bool, use_int32_inputs: bool, return_dict: bool = False):
inputs = get_sample_encoder_decoder_init_inputs(
self.config,
self.device,
batch_size=2,
decoder_sequence_length=6,
use_fp16=use_fp16_inputs,
use_int32=use_int32_inputs,
)
if return_dict:
if self.no_beam_search_op:
del inputs["decoder_input_ids"]
return inputs
if self.no_beam_search_op:
return (inputs["audio_features"],)
return (
inputs["audio_features"],
inputs["decoder_input_ids"],
)
def fix_key_value_cache_dims(self, output: ValueInfoProto, is_cross: bool = False):
# Shape should be (batch_size, num_heads, sequence_length, head_size) for self attention KV caches
# and (batch_size, num_heads, num_frames // 2, head_size) for cross attention KV caches
num_heads = output.type.tensor_type.shape.dim[1]
if "_dim_" in num_heads.dim_param:
num_heads.Clear()
num_heads.dim_value = self.num_heads
sequence_length = output.type.tensor_type.shape.dim[2]
if "_dim_" in sequence_length.dim_param:
sequence_length.Clear()
if is_cross:
sequence_length.dim_value = self.max_source_positions
else:
sequence_length.dim_param = "total_sequence_length"
head_size = output.type.tensor_type.shape.dim[3]
if "_dim_" in head_size.dim_param:
head_size.Clear()
head_size.dim_value = self.head_size
return output
def fix_outputs(self, model: ModelProto):
# ONNX exporter might mark dimensions like 'Transposepresent_value_self_1_dim_2' in shape inference.
# We now change the dim_values to the correct one.
reordered_outputs = []
self_attn_kv_caches = []
cross_attn_kv_caches = []
for output in model.graph.output:
if "present" not in output.name:
reordered_outputs.append(output)
elif "self" in output.name:
# Self attention KV caches
new_output = self.fix_key_value_cache_dims(output, is_cross=False)
if self.no_beam_search_op:
reordered_outputs.append(new_output)
else:
self_attn_kv_caches.append(new_output)
else:
# Cross attention KV caches
new_output = self.fix_key_value_cache_dims(output, is_cross=True)
if self.no_beam_search_op:
reordered_outputs.append(new_output)
else:
cross_attn_kv_caches.append(new_output)
if not self.no_beam_search_op:
reordered_outputs += self_attn_kv_caches + cross_attn_kv_caches
while len(model.graph.output) > 0:
model.graph.output.pop()
model.graph.output.extend(reordered_outputs)
return model
def fix_layernorm_weights(self, model: ModelProto, use_fp16_inputs: bool):
if self.model_impl == "openai" and use_fp16_inputs:
# Cast ONNX model to float16 to ensure LayerNorm weights are converted from
# float32 to float16 since exported model already has float16 weights everywhere
# except for LayerNorm ops. This happens because OpenAI always upcasts to float32
# when computing LayerNorm.
#
# Reference:
# https://github.com/openai/whisper/blob/90db0de1896c23cbfaf0c58bc2d30665f709f170/whisper/model.py#L41
model = convert_float_to_float16(model)
return model
def export_onnx(
self,
onnx_model_path: str,
provider: str,
verbose: bool = True,
use_external_data_format: bool = False,
use_fp16_inputs: bool = False,
use_int32_inputs: bool = True,
):
"""Export encoder-decoder-init to ONNX
Args:
onnx_model_path (str): path to save ONNX model
provider (str): provider to use for verifying parity on ONNX model
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_fp16_inputs (bool, optional): use float16 inputs for the audio_features. Defaults to False.
use_int32_inputs (bool, optional): use int32 inputs for the decoder_input_ids. Defaults to True.
"""
# Shape of encoder's tensors:
# Inputs:
# audio_features: (batch_size, num_mels, num_frames)
# Outputs:
# encoder_hidden_states: (batch_size, num_frames // 2, hidden_size)
# Shape of decoder's tensors:
# Inputs:
# decoder_input_ids: (batch_size, sequence_length)
# encoder_hidden_states (comes from encoder's outputs): (batch_size, num_frames // 2, hidden_size)
# Outputs:
# logits: (batch_size, sequence_length, vocab_size)
# present_{key/value}_self_* (present self attention KV caches): (batch_size, num_heads, past_sequence_length + sequence_length, head_size)
# present_{key/value}_cross_* (present cross attention KV caches): (batch_size, num_heads, num_frames // 2, head_size)
inputs = self.inputs(use_fp16_inputs=use_fp16_inputs, use_int32_inputs=use_int32_inputs)
input_names = self.input_names()
output_names = self.output_names()
dynamic_axes = self.dynamic_axes(input_names, output_names)
Path(onnx_model_path).parent.mkdir(parents=True, exist_ok=True)
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)
out_path = temp_onnx_model_path if use_external_data_format else onnx_model_path
torch.onnx.export(
self,
args=inputs,
f=out_path,
export_params=True,
input_names=input_names,
output_names=output_names,
dynamic_axes=dynamic_axes,
opset_version=18,
do_constant_folding=True,
dynamo=False,
verbose=verbose,
)
model = onnx.load_model(out_path, load_external_data=use_external_data_format)
model = self.fix_outputs(model)
model = self.fix_layernorm_weights(model, use_fp16_inputs)
OnnxModel.save(
model,
onnx_model_path,
save_as_external_data=use_external_data_format,
all_tensors_to_one_file=True,
)
self.verify_onnx(onnx_model_path, provider, use_fp16_inputs, use_int32_inputs)
def verify_onnx(
self,
onnx_model_path: str,
provider: str,
use_fp16_inputs: bool,
use_int32_inputs: bool,
):
"""Verify ONNX model outputs and PyTorch model outputs match
Args:
onnx_model_path (str): path to save ONNX model
provider (str): execution provider for ONNX model
use_fp16_inputs (bool, optional): use float16 inputs for the audio_features
use_int32_inputs (bool, optional): use int32 inputs for the decoder_input_ids
"""
# Shape of encoder's tensors:
# Inputs:
# audio_features: (batch_size, num_mels, num_frames)
# Outputs:
# encoder_hidden_states: (batch_size, num_frames // 2, hidden_size)
# Shape of decoder's tensors:
# Inputs:
# decoder_input_ids: (batch_size, sequence_length)
# encoder_hidden_states (comes from encoder's outputs): (batch_size, num_frames // 2, hidden_size)
# Outputs:
# logits: (batch_size, sequence_length, vocab_size)
# present_{key/value}_self_* (present self attention KV caches): (batch_size, num_heads, past_sequence_length + sequence_length, head_size)
# present_{key/value}_cross_* (present cross attention KV caches): (batch_size, num_heads, num_frames // 2, head_size)
inputs = self.inputs(use_fp16_inputs=use_fp16_inputs, use_int32_inputs=use_int32_inputs, return_dict=True)
# Run PyTorch model
pt_outputs = []
if self.no_beam_search_op:
out = self.forward(**inputs)
pt_outputs.append(out[0].detach().cpu().numpy())
for present_cross_attn_cache in out[1]:
pt_outputs.append(present_cross_attn_cache.detach().cpu().numpy())
else:
out = self.forward(**inputs)
pt_outputs.append(out[0].detach().cpu().numpy())
pt_outputs.append(out[1].detach().cpu().numpy())
(self_attn_kv_caches, cross_attn_kv_caches) = group_past_key_values(out[2])
pt_outputs.extend([self_attn_kv_cache.detach().cpu().numpy() for self_attn_kv_cache in self_attn_kv_caches])
pt_outputs.extend(
[cross_attn_kv_cache.detach().cpu().numpy() for cross_attn_kv_cache in cross_attn_kv_caches]
)
# Run ONNX model
sess = InferenceSession(onnx_model_path, providers=[provider])
ort_outputs = sess.run(None, convert_inputs_for_ort(inputs, sess))
# Calculate output difference
for i, output_name in enumerate(self.output_names()):
diff = np.abs(pt_outputs[i] - ort_outputs[i])
logger.warning(f"Comparing {output_name}...")
logger.warning(f"Max diff: {np.max(diff)}")
|