text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
info["gpu"] = "N/A"
info["gpu_ram_mb"] = "N/A"
info["gpu_power_watts"] = "N/A"
info["gpu_performance_state"] = "N/A" | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
info["use_tpu"] = self.args.is_tpu
# TODO(PVP): See if we can add more information about TPU
# see: https://github.com/pytorch/xla/issues/2180
self._environment_info = info
return self._environment_info | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
def print_results(self, result_dict, type_label):
self.print_fn(80 * "-")
self.print_fn(
"Model Name".center(30) + "Batch Size".center(15) + "Seq Length".center(15) + type_label.center(15)
)
self.print_fn(80 * "-")
for model_name in self.args.model_names:
... | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
)
self.print_fn(80 * "-") | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
def print_memory_trace_statistics(self, summary: MemorySummary):
self.print_fn(
"\nLine by line memory consumption:\n"
+ "\n".join(
f"{state.frame.filename}:{state.frame.line_number}: mem {state.cpu_gpu}: {state.frame.line_text}"
for state in summary.seque... | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
def save_to_csv(self, result_dict, filename):
if not self.args.save_to_csv:
return
self.print_fn("Saving results to csv.")
with open(filename, mode="w") as csv_file:
if len(self.args.model_names) <= 0:
raise ValueError(f"At least 1 model should be defined,... | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
for model_name in self.args.model_names:
result_dict_model = result_dict[model_name]["result"]
for bs in result_dict_model:
for ss in result_dict_model[bs]:
result_model = result_dict_model[bs][ss]
writer.writerow(
... | 324 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py |
class TensorFlowBenchmarkArguments(BenchmarkArguments):
deprecated_args = [
"no_inference",
"no_cuda",
"no_tpu",
"no_speed",
"no_memory",
"no_env_print",
"no_multi_process",
] | 325 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_args_tf.py |
def __init__(self, **kwargs):
"""
This __init__ is there for legacy code. When removing deprecated args completely, the class can simply be
deleted
"""
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
positive_arg = deprecated_a... | 325 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_args_tf.py |
tpu_name: str = field(
default=None,
metadata={"help": "Name of TPU"},
)
device_idx: int = field(
default=0,
metadata={"help": "CPU / GPU device index. Defaults to 0."},
)
eager_mode: bool = field(default=False, metadata={"help": "Benchmark models in eager model."})
u... | 325 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_args_tf.py |
@cached_property
def _setup_tpu(self) -> Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
requires_backends(self, ["tf"])
tpu = None
if self.tpu:
try:
if self.tpu_name:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu... | 325 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_args_tf.py |
strategy = tf.distribute.TPUStrategy(self._setup_tpu)
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx], "GPU")
strategy = t... | 325 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_args_tf.py |
@property
def n_gpu(self) -> int:
requires_backends(self, ["tf"])
if self.cuda:
return len(self.gpu_list)
return 0
@property
def is_gpu(self) -> bool:
return self.n_gpu > 0 | 325 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_args_tf.py |
class SelectiveScanFn(torch.autograd.Function):
@staticmethod
def forward(
ctx, u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False, return_last_state=False
):
if u.stride(-1) != 1:
u = u.contiguous()
if delta.stride(-1) != 1:
delta = delt... | 326 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py |
last_state = x[:, :, -1, 1::2] # (batch, dim, dstate)
if not ctx.has_z:
ctx.save_for_backward(u, delta, A, B, C, D, delta_bias, x)
return out if not return_last_state else (out, last_state)
else:
ctx.save_for_backward(u, delta, A, B, C, D, z, delta_bias, x, out)
... | 326 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py |
@staticmethod
def backward(ctx, dout, *args):
if not ctx.has_z:
u, delta, A, B, C, D, delta_bias, x = ctx.saved_tensors
z = None
out = None
else:
u, delta, A, B, C, D, z, delta_bias, x, out = ctx.saved_tensors
if dout.stride(-1) != 1:
... | 326 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py |
dz = rest[0] if ctx.has_z else None
dB = dB.squeeze(1) if getattr(ctx, "squeeze_B", False) else dB
dC = dC.squeeze(1) if getattr(ctx, "squeeze_C", False) else dC
return (
du,
ddelta,
dA,
dB,
dC,
dD if D is not None else None... | 326 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py |
class MambaInnerFn(torch.autograd.Function):
@staticmethod
@custom_fwd
def forward(
ctx,
xz,
conv1d_weight,
conv1d_bias,
x_proj_weight,
delta_proj_weight,
out_proj_weight,
out_proj_bias,
A,
B=None,
C=None,
D=None... | 327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py |
x_proj_weight = x_proj_weight.to(dtype=torch.get_autocast_gpu_dtype())
delta_proj_weight = delta_proj_weight.to(dtype=torch.get_autocast_gpu_dtype())
out_proj_weight = out_proj_weight.to(dtype=torch.get_autocast_gpu_dtype())
out_proj_bias = (
out_proj_bias.to(dtype=to... | 327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py |
# and L as the fastest moving dimension, since those are what the ssm_scan kernel expects.
x_dbl = F.linear(rearrange(conv1d_out, "b d l -> (b l) d"), x_proj_weight) # (bl d)
delta = rearrange(delta_proj_weight @ x_dbl[:, :delta_rank].t(), "d (b l) -> b d l", l=L)
ctx.is_variable_B = B is None
... | 327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py |
if B.stride(-1) != 1:
B = B.contiguous()
if C is None: # variable C
C = x_dbl[:, -d_state:] # (bl dstate)
if C_proj_bias is not None:
C = C + C_proj_bias.to(dtype=C.dtype)
if not A.is_complex():
# C = rearrange(C, "(b l) dstat... | 327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py |
if b_rms_weight is not None:
B = rearrange(B, "b 1 dstate l -> (b l) dstate", l=L).contiguous()
B = rms_norm_forward(B, b_rms_weight, bias=None, eps=b_c_dt_rms_eps)
B = rearrange(B, "(b l) dstate -> b 1 dstate l", l=L).contiguous()
if c_rms_weight is not None:
C =... | 327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py |
out, scan_intermediates, out_z = selective_scan_cuda.fwd(
conv1d_out, delta, A, B, C, D, z, delta_bias, delta_softplus
)
ctx.delta_softplus = delta_softplus
ctx.out_proj_bias_is_None = out_proj_bias is None
ctx.checkpoint_lvl = checkpoint_lvl
ctx.b_rms_weight = b_rms_... | 327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py |
dt_rms_weight,
out,
)
return F.linear(rearrange(out_z, "b d l -> b l d"), out_proj_weight, out_proj_bias) | 327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py |
@staticmethod
@custom_bwd
def backward(ctx, dout):
# dout: (batch, seqlen, dim)
assert causal_conv1d_cuda is not None, "causal_conv1d_cuda is not available. Please install causal-conv1d."
(
xz,
conv1d_weight,
conv1d_bias,
x_dbl,
... | 327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py |
conv1d_out = causal_conv1d_cuda.causal_conv1d_fwd(x, conv1d_weight, conv1d_bias, None, None, None, True)
delta = rearrange(delta_proj_weight @ x_dbl[:, :delta_rank].t(), "d (b l) -> b d l", l=L)
if dt_rms_weight is not None:
delta = rearrange(delta, "b d l -> (b l) d", l=L).conti... | 327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py |
C = rms_norm_forward(C, ctx.c_rms_weight, None, ctx.b_c_dt_rms_eps)
C = rearrange(C, "(b l) dstate -> b 1 dstate l", l=L).contiguous() | 327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py |
# The kernel supports passing in a pre-allocated dz (e.g., in case we want to fuse the
# backward of selective_scan_cuda with the backward of chunk).
dxz = torch.empty_like(xz) # (batch, dim, seqlen)
dx, dz = dxz.chunk(2, dim=1)
dout = rearrange(dout, "b l e -> e (b l)")
dout_y ... | 327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py |
dD = dD if D is not None else None
dx_dbl = torch.empty_like(x_dbl)
dB_proj_bias = None
if ctx.is_variable_B:
if not A.is_complex():
dB = rearrange(dB, "b 1 dstate l -> (b l) dstate").contiguous()
else:
dB = rearrange(dB, "b 1 dstate (l two... | 327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py |
ddelta = rearrange(ddelta, "b d l -> d (b l)")
ddelta_proj_weight = torch.einsum("dB,Br->dr", ddelta, x_dbl[:, :delta_rank])
dx_dbl[:, :delta_rank] = torch.einsum("dB,dr->Br", ddelta, delta_proj_weight)
dconv1d_out = rearrange(dconv1d_out, "b d l -> d (b l)")
dx_proj_weight = torch.einsu... | 327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py |
dconv1d_weight = rearrange(dconv1d_weight, "d w -> d 1 w")
return (
dxz,
dconv1d_weight,
dconv1d_bias,
dx_proj_weight,
ddelta_proj_weight,
dout_proj_weight,
dout_proj_bias,
dA,
dB,
dC,
... | 327 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py |
class PatchingSpec:
"""
Data class that holds patching specifications.
Args:
o: Module / object where the op to patch is located
name: Name of the op to monkey patch
custom_op: Custom op that patches the original op
orig_op: Original op that is being patched
op_wrapp... | 328 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
class OnnxConfig(ABC):
"""
Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format.
""" | 329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
default_fixed_batch = 2
default_fixed_sequence = 8
default_fixed_num_choices = 4
torch_onnx_minimum_version = version.parse("1.8")
_tasks_to_common_outputs = {
"causal-lm": OrderedDict({"logits": {0: "batch", 1: "sequence"}}),
"default": OrderedDict({"last_hidden_state": {0: "batch", 1: ... | 329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
"pred_boxes": {0: "batch", 1: "sequence"},
}
),
"question-answering": OrderedDict(
{
"start_logits": {0: "batch", 1: "sequence"},
"end_logits": {0: "batch", 1: "sequence"},
}
),
"semantic-segmentation": OrderedDict({"log... | 329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
def __init__(self, config: "PretrainedConfig", task: str = "default", patching_specs: List[PatchingSpec] = None):
self._config = config
if task not in self._tasks_to_common_outputs:
raise ValueError(
f"{task} is not a supported task, supported tasks: {self._tasks_to_common_o... | 329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
Returns:
OnnxConfig for this model
"""
return cls(config, task=task)
@property
@abstractmethod
def inputs(self) -> Mapping[str, Mapping[int, str]]:
"""
Mapping containing the axis definition of the input tensors to provide to the model
Returns:
... | 329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
@property
def values_override(self) -> Optional[Mapping[str, Any]]:
"""
Dictionary of keys to override in the model's config before exporting
Returns:
Dictionary with the keys (and their corresponding values) to override
"""
if hasattr(self._config, "use_cache"):... | 329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
@property
def default_num_choices(self) -> int:
"""
The default number of choices to use if no other indication
Returns:
Integer > 0
"""
return OnnxConfig.default_fixed_num_choices
@property
def default_onnx_opset(self) -> int:
"""
Which ... | 329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
Returns:
`bool`: Whether the installed version of PyTorch is compatible with the model.
"""
if is_torch_available():
from transformers.utils import get_torch_version
return version.parse(get_torch_version()) >= self.torch_onnx_minimum_version
else:
... | 329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
def _generate_dummy_images(
self, batch_size: int = 2, num_channels: int = 3, image_height: int = 40, image_width: int = 40
):
images = []
for _ in range(batch_size):
data = np.random.rand(image_height, image_width, num_channels) * 255
images.append(Image.fromarray(da... | 329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
def generate_dummy_inputs(
self,
preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin", "ImageProcessingMixin"],
batch_size: int = -1,
seq_length: int = -1,
num_choices: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
... | 329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
Args:
preprocessor: ([`PreTrainedTokenizerBase`], [`FeatureExtractionMixin`], or [`ImageProcessingMixin`]):
The preprocessor associated with this model configuration.
batch_size (`int`, *optional*, defaults to -1):
The batch size to export the model for (-1 means ... | 329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
The number of channels of the generated images.
image_width (`int`, *optional*, defaults to 40):
The width of the generated images.
image_height (`int`, *optional*, defaults to 40):
The height of the generated images.
sampling_rate (`int`, *optional* d... | 329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
Returns:
Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
"""
from ..feature_extraction_utils import FeatureExtractionMixin
from ..image_processing_utils import ImageProcessingMixin
from ..tokenization_utils_base import PreTrainedTokenizerBas... | 329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
if isinstance(preprocessor, PreTrainedTokenizerBase) and tokenizer is not None:
raise ValueError("You cannot provide both a tokenizer and a preprocessor to generate dummy inputs.")
if tokenizer is not None:
warnings.warn(
"The `tokenizer` argument is deprecated and will b... | 329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
token_to_add = preprocessor.num_special_tokens_to_add(is_pair)
seq_length = compute_effective_axis_dimension(
seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, nu... | 329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
num_choices, fixed_dimension=OnnxConfig.default_fixed_num_choices, num_token_to_add=0
)
dummy_input = dummy_input * num_choices
# The shape of the tokenized inputs values is [batch_size * num_choices, seq_length]
tokenized_input = preprocessor(dummy_input,... | 329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
f"The `preprocessor` is an image processor ({preprocessor.__class__.__name__}) and expects"
f' `model_input_names[0]` to be "pixel_values", but got {preprocessor.model_input_names[0]}'
)
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimi... | 329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
dummy_input = self._generate_dummy_images(batch_size, num_channels, image_height, image_width)
return dict(preprocessor(images=dummy_input, return_tensors=framework))
elif (
... | 329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
"Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."
) | 329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
def generate_dummy_inputs_onnxruntime(self, reference_model_inputs: Mapping[str, Any]) -> Mapping[str, Any]:
"""
Generate inputs for ONNX Runtime using the reference model inputs. Override this to run inference with seq2seq
models which have the encoder and decoder exported as separate ONNX file... | 329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
def restore_ops(self):
for spec in self._patching_specs:
orig_op = spec.orig_op if spec.op_wrapper is None else spec.op_wrapper(spec.orig_op)
setattr(spec.o, spec.name, orig_op)
@classmethod
def flatten_output_collection_property(cls, name: str, field: Iterable[Any]) -> Dict[str... | 329 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
class OnnxConfigWithPast(OnnxConfig, ABC):
def __init__(
self,
config: "PretrainedConfig",
task: str = "default",
patching_specs: List[PatchingSpec] = None,
use_past: bool = False,
):
super().__init__(config, task=task, patching_specs=patching_specs)
self.... | 330 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
@property
def values_override(self) -> Optional[Mapping[str, Any]]:
if hasattr(self._config, "use_cache"):
return {"use_cache": self.use_past}
return None
@property
def num_layers(self) -> int:
"""
The number of layers attribute retrieved from the model config. ... | 330 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
@property
def num_attention_heads(self) -> int:
"""
The number of attention heads attribute retrieved from the model config. Override this for model configs where
the number of attention heads attribute is not called `num_attention_heads`.
"""
if not hasattr(self._config, "nu... | 330 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
def generate_dummy_inputs(
self,
tokenizer: "PreTrainedTokenizerBase",
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
# TODO: should we set seq_length = 1 when self.use_past = ... | 330 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
batch, seqlen = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
past_key_values_length = seqlen + 2
shape = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_... | 330 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
def fill_with_past_key_values_(
self, inputs_or_outputs: Mapping[str, Mapping[int, str]], direction: str, inverted_values_shape: bool = False
):
"""
Fill the input_or_outputs mapping with past_key_values dynamic axes considering.
Args:
inputs_or_outputs: The mapping to f... | 330 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
name = "past_key_values" if direction == "inputs" else "present"
for i in range(self.num_layers):
inputs_or_outputs[f"{name}.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"}
if inverted_values_shape:
inputs_or_outputs[f"{name}.{i}.value"] = {0: "batch", 1: "past_se... | 330 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
def flatten_output_collection_property(self, name: str, field: Iterable[Any]) -> Dict[str, Any]:
flattened_output = {}
if name in ["present", "past_key_values"]:
for idx, t in enumerate(field):
self._flatten_past_key_values_(flattened_output, name, idx, t)
else:
... | 330 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
class OnnxSeq2SeqConfigWithPast(OnnxConfigWithPast):
@property
def outputs(self) -> Mapping[str, Mapping[int, str]]:
common_outputs = super(OnnxConfigWithPast, self).outputs
# Renaming the outputs axes properly.
for name, axes_names in common_outputs.items():
sequence_name = ... | 331 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
@property
def num_layers(self) -> Tuple[int]:
try:
num_layers = super().num_layers
num_layers = (num_layers, num_layers)
except AttributeError:
if hasattr(self._config, "encoder_layers") and hasattr(self._config, "decoder_layers"):
num_layers = (se... | 331 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
@property
def num_attention_heads(self) -> Tuple[int]:
try:
num_attention_heads = super().num_attention_heads
num_attention_heads = (num_attention_heads, num_attention_heads)
except AttributeError:
if hasattr(self._config, "encoder_attention_heads") and hasattr(se... | 331 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
def generate_dummy_inputs(
self,
tokenizer: "PreTrainedTokenizerBase",
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional[TensorType] = None,
) -> Mapping[str, Any]:
encoder_inputs = super(OnnxConfigWithPast, self).generate_... | 331 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
else:
import torch
batch = common_inputs["input_ids"].shape[0]
encoder_seq_length = common_inputs["input_id... | 331 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
) | 331 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
common_inputs["past_key_values"] = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
num_encoder_layers, num_decoder_layers = self.num_layers
min_num_layers = min(num_encoder_layers, num_decoder_layers)
max_num_la... | 331 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
for _ in range(min_num_layers):
# For encoder-decoder models, past_key_values contains pre-computed values for both the encoder and the
# decoder layers, hence a tuple of 4 tensors instead of 2
common_inputs["past_key_values"].append(
(
... | 331 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
def fill_with_past_key_values_(self, inputs_or_outputs: Mapping[str, Mapping[int, str]], direction: str):
if direction not in ["inputs", "outputs"]:
raise ValueError(f'direction must either be "inputs" or "outputs", but {direction} was given')
name = "past_key_values" if direction == "input... | 331 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
for i in range(min_num_layers):
inputs_or_outputs[f"{name}.{i}.decoder.key"] = {0: "batch", 2: decoder_sequence}
inputs_or_outputs[f"{name}.{i}.decoder.value"] = {0: "batch", 2: decoder_sequence}
inputs_or_outputs[f"{name}.{i}.encoder.key"] = {0: "batch", 2: encoder_sequence}
... | 331 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
def _flatten_past_key_values_(self, flattened_output, name, idx, t):
flattened_output[f"{name}.{idx}.decoder.key"] = t[0]
flattened_output[f"{name}.{idx}.decoder.value"] = t[1]
flattened_output[f"{name}.{idx}.encoder.key"] = t[2]
flattened_output[f"{name}.{idx}.encoder.value"] = t[3] | 331 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py |
class FeaturesManager:
_TASKS_TO_AUTOMODELS = {}
_TASKS_TO_TF_AUTOMODELS = {}
if is_torch_available():
_TASKS_TO_AUTOMODELS = {
"default": AutoModel,
"masked-lm": AutoModelForMaskedLM,
"causal-lm": AutoModelForCausalLM,
"seq2seq-lm": AutoModelForSeq2Se... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
"speech2seq-lm": AutoModelForSpeechSeq2Seq,
}
if is_tf_available():
_TASKS_TO_TF_AUTOMODELS = {
"default": TFAutoModel,
"masked-lm": TFAutoModelForMaskedLM,
"causal-lm": TFAutoModelForCausalLM,
"seq2seq-lm": TFAutoModelForSeq2SeqLM,
"sequen... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
# Set of model topologies we support associated to the features supported by each topology and the factory
_SUPPORTED_MODEL_TYPE = {
"albert": supported_features_mapping(
"default",
"masked-lm",
"sequence-classification",
"multiple-choice",
"token-... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
"default", "image-classification", onnx_config_cls="models.beit.BeitOnnxConfig"
),
"bert": supported_features_mapping(
"default",
"masked-lm",
"causal-lm",
"sequence-classification",
"multiple-choice",
"token-classification",
... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
"sequence-classification",
"question-answering",
onnx_config_cls="models.bigbird_pegasus.BigBirdPegasusOnnxConfig",
),
"blenderbot": supported_features_mapping(
"default",
"default-with-past",
"causal-lm",
"causal-lm-with-past",
... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
"token-classification",
onnx_config_cls="models.bloom.BloomOnnxConfig",
),
"camembert": supported_features_mapping(
"default",
"masked-lm",
"causal-lm",
"sequence-classification",
"multiple-choice",
"token-classification... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
onnx_config_cls="models.convbert.ConvBertOnnxConfig",
),
"convnext": supported_features_mapping(
"default",
"image-classification",
onnx_config_cls="models.convnext.ConvNextOnnxConfig",
),
"data2vec-text": supported_features_mapping(
"defau... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
"token-classification",
"question-answering",
onnx_config_cls="models.deberta.DebertaOnnxConfig",
),
"deberta-v2": supported_features_mapping(
"default",
"masked-lm",
"sequence-classification",
"multiple-choice",
"token-... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
"question-answering",
onnx_config_cls="models.distilbert.DistilBertOnnxConfig",
),
"electra": supported_features_mapping(
"default",
"masked-lm",
"causal-lm",
"sequence-classification",
"multiple-choice",
"token-classifi... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
onnx_config_cls="models.gpt2.GPT2OnnxConfig",
),
"gptj": supported_features_mapping(
"default",
"default-with-past",
"causal-lm",
"causal-lm-with-past",
"question-answering",
"sequence-classification",
onnx_config_cls="m... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
"question-answering",
onnx_config_cls="models.ibert.IBertOnnxConfig",
),
"imagegpt": supported_features_mapping(
"default", "image-classification", onnx_config_cls="models.imagegpt.ImageGPTOnnxConfig"
),
"layoutlm": supported_features_mapping(
"default... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
"default-with-past",
"seq2seq-lm",
"seq2seq-lm-with-past",
onnx_config_cls="models.longt5.LongT5OnnxConfig",
),
"longformer": supported_features_mapping(
"default",
"masked-lm",
"multiple-choice",
"question-answering",
... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
"sequence-classification",
"question-answering",
onnx_config_cls="models.mbart.MBartOnnxConfig",
),
"mobilebert": supported_features_mapping(
"default",
"masked-lm",
"sequence-classification",
"multiple-choice",
"token-c... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
onnx_config_cls="models.mobilevit.MobileViTOnnxConfig",
),
"mt5": supported_features_mapping(
"default",
"default-with-past",
"seq2seq-lm",
"seq2seq-lm-with-past",
onnx_config_cls="models.mt5.MT5OnnxConfig",
),
"m2m-100": suppor... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
"default", "image-classification", onnx_config_cls="models.poolformer.PoolFormerOnnxConfig"
),
"rembert": supported_features_mapping(
"default",
"masked-lm",
"causal-lm",
"sequence-classification",
"multiple-choice",
"token-classifi... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
"default",
"masked-lm",
"causal-lm",
"sequence-classification",
"token-classification",
"multiple-choice",
"question-answering",
"token-classification",
onnx_config_cls="models.roformer.RoFormerOnnxConfig",
),
... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
"t5": supported_features_mapping(
"default",
"default-with-past",
"seq2seq-lm",
"seq2seq-lm-with-past",
onnx_config_cls="models.t5.T5OnnxConfig",
),
"vision-encoder-decoder": supported_features_mapping(
"vision2seq-lm", onnx_config_... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
"question-answering",
onnx_config_cls="models.xlm.XLMOnnxConfig",
),
"xlm-roberta": supported_features_mapping(
"default",
"masked-lm",
"causal-lm",
"sequence-classification",
"multiple-choice",
"token-classification",
... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
AVAILABLE_FEATURES = sorted(reduce(lambda s1, s2: s1 | s2, (v.keys() for v in _SUPPORTED_MODEL_TYPE.values())))
@staticmethod
def get_supported_features_for_model_type(
model_type: str, model_name: Optional[str] = None
) -> Dict[str, Callable[[PretrainedConfig], OnnxConfig]]:
"""
Tr... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
Returns:
The dictionary mapping each feature to a corresponding OnnxConfig constructor.
"""
model_type = model_type.lower()
if model_type not in FeaturesManager._SUPPORTED_MODEL_TYPE:
model_type_and_model_name = f"{model_type} ({model_name})" if model_name else model_type... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
@staticmethod
def _validate_framework_choice(framework: str):
"""
Validates if the framework requested for the export is both correct and available, otherwise throws an
exception.
"""
if framework not in ["pt", "tf"]:
raise ValueError(
f"Only two f... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
Args:
feature (`str`):
The feature required.
framework (`str`, *optional*, defaults to `"pt"`):
The framework to use for the export.
Returns:
The AutoModel class corresponding to the feature.
"""
task = FeaturesManager.feature_... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
The priority is in the following order:
1. User input via `framework`.
2. If local checkpoint is provided, use the same framework as the checkpoint.
3. Available framework in environment, with priority given to PyTorch
Args:
model (`str`):
The nam... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
if os.path.isdir(model):
if os.path.isfile(os.path.join(model, WEIGHTS_NAME)):
framework = "pt"
elif os.path.isfile(os.path.join(model, TF2_WEIGHTS_NAME)):
framework = "tf"
else:
raise FileNotFoundError(
"Cannot dete... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
@staticmethod
def get_model_from_feature(
feature: str, model: str, framework: str = None, cache_dir: str = None
) -> Union["PreTrainedModel", "TFPreTrainedModel"]:
"""
Attempts to retrieve a model from a model's name and the feature to be enabled.
Args:
feature (`st... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
"""
framework = FeaturesManager.determine_framework(model, framework)
model_class = FeaturesManager.get_model_class_for_feature(feature, framework)
try:
model = model_class.from_pretrained(model, cache_dir=cache_dir)
except OSError:
if framework == "pt":
... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
Args:
model: The model to export.
feature: The name of the feature to check if it is available.
Returns:
(str) The type of the model (OnnxConfig) The OnnxConfig instance holding the model export properties.
"""
model_type = model.config.model_type.replace("_... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
Args:
model_type (`str`):
The model type to retrieve the config for.
feature (`str`):
The feature to retrieve the config for.
Returns:
`OnnxConfig`: config for the combination
"""
return FeaturesManager._SUPPORTED_MODEL_TYPE[mo... | 332 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py |
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