text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
class ParameterFormat(Enum):
Float = c_float
@property
def size(self) -> int:
"""
Number of byte required for this data type
Returns:
Integer > 0
"""
return sizeof(self.value) | 333 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/utils.py |
class ImageLoss(nn.Module):
"""
This class computes the losses for DetrForObjectDetection/DetrForSegmentation. The process happens in two steps: 1)
we compute hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair
of matched ground-truth / prediction (supe... | 334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_for_object_detection.py |
Args:
matcher (`DetrHungarianMatcher`):
Module able to compute a matching between targets and proposals.
num_classes (`int`):
Number of object categories, omitting the special no-object category.
eos_coef (`float`):
Relative classification weight applied to th... | 334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_for_object_detection.py |
# removed logging parameter, which was part of the original implementation
def loss_labels(self, outputs, targets, indices, num_boxes):
"""
Classification loss (NLL) targets dicts must contain the key "class_labels" containing a tensor of dim
[nb_target_boxes]
"""
if "logits"... | 334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_for_object_detection.py |
@torch.no_grad()
def loss_cardinality(self, outputs, targets, indices, num_boxes):
"""
Compute the cardinality error, i.e. the absolute error in the number of predicted non-empty boxes.
This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients.
... | 334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_for_object_detection.py |
Targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]. The target boxes
are expected in format (center_x, center_y, w, h), normalized by the image size.
"""
if "pred_boxes" not in outputs:
raise KeyError("No predicted boxes found in outputs")
... | 334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_for_object_detection.py |
def loss_masks(self, outputs, targets, indices, num_boxes):
"""
Compute the losses related to the masks: the focal loss and the dice loss.
Targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w].
"""
if "pred_masks" not in outputs:
... | 334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_for_object_detection.py |
# upsample predictions to the target size
source_masks = nn.functional.interpolate(
source_masks[:, None], size=target_masks.shape[-2:], mode="bilinear", align_corners=False
)
source_masks = source_masks[:, 0].flatten(1)
target_masks = target_masks.flatten(1)
target_... | 334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_for_object_detection.py |
def _get_target_permutation_idx(self, indices):
# permute targets following indices
batch_idx = torch.cat([torch.full_like(target, i) for i, (_, target) in enumerate(indices)])
target_idx = torch.cat([target for (_, target) in indices])
return batch_idx, target_idx
def get_loss(self... | 334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_for_object_detection.py |
Args:
outputs (`dict`, *optional*):
Dictionary of tensors, see the output specification of the model for the format.
targets (`List[dict]`, *optional*):
List of dicts, such that `len(targets) == batch_size`. The expected keys in each dict depends on the
... | 334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_for_object_detection.py |
# Compute the average number of target boxes across all nodes, for normalization purposes
num_boxes = sum(len(t["class_labels"]) for t in targets)
num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device)
world_size = 1
if is_accelerate_avail... | 334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_for_object_detection.py |
# In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
if "auxiliary_outputs" in outputs:
for i, auxiliary_outputs in enumerate(outputs["auxiliary_outputs"]):
indices = self.matcher(auxiliary_outputs, targets)
for loss in sel... | 334 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_for_object_detection.py |
class HungarianMatcher(nn.Module):
"""
This class computes an assignment between the targets and the predictions of the network.
For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more
predictions than targets. In this case, we do a 1-to-1 matching o... | 335 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_for_object_detection.py |
self.class_cost = class_cost
self.bbox_cost = bbox_cost
self.giou_cost = giou_cost
if class_cost == 0 and bbox_cost == 0 and giou_cost == 0:
raise ValueError("All costs of the Matcher can't be 0") | 335 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_for_object_detection.py |
@torch.no_grad()
def forward(self, outputs, targets):
"""
Args:
outputs (`dict`):
A dictionary that contains at least these entries:
* "logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
* "pred_box... | 335 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_for_object_detection.py |
Returns:
`List[Tuple]`: A list of size `batch_size`, containing tuples of (index_i, index_j) where:
- index_i is the indices of the selected predictions (in order)
- index_j is the indices of the corresponding selected targets (in order)
For each batch element, it holds: ... | 335 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_for_object_detection.py |
# Compute the classification cost. Contrary to the loss, we don't use the NLL,
# but approximate it in 1 - proba[target class].
# The 1 is a constant that doesn't change the matching, it can be ommitted.
class_cost = -out_prob[:, target_ids]
# Compute the L1 cost between boxes
b... | 335 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_for_object_detection.py |
class NestedTensor:
def __init__(self, tensors, mask: Optional[Tensor]):
self.tensors = tensors
self.mask = mask
def to(self, device):
cast_tensor = self.tensors.to(device)
mask = self.mask
if mask is not None:
cast_mask = mask.to(device)
else:
... | 336 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_for_object_detection.py |
class DeformableDetrHungarianMatcher(HungarianMatcher):
@torch.no_grad()
def forward(self, outputs, targets):
"""
Differences:
- out_prob = outputs["logits"].flatten(0, 1).sigmoid() instead of softmax
- class_cost uses alpha and gamma
"""
batch_size, num_queries =... | 337 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_deformable_detr.py |
# Compute the classification cost.
alpha = 0.25
gamma = 2.0
neg_cost_class = (1 - alpha) * (out_prob**gamma) * (-(1 - out_prob + 1e-8).log())
pos_cost_class = alpha * ((1 - out_prob) ** gamma) * (-(out_prob + 1e-8).log())
class_cost = pos_cost_class[:, target_ids] - neg_cost_clas... | 337 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_deformable_detr.py |
sizes = [len(v["boxes"]) for v in targets]
indices = [linear_sum_assignment(c[i]) for i, c in enumerate(cost_matrix.split(sizes, -1))]
return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices] | 337 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_deformable_detr.py |
class DeformableDetrImageLoss(ImageLoss):
def __init__(self, matcher, num_classes, focal_alpha, losses):
nn.Module.__init__(self)
self.matcher = matcher
self.num_classes = num_classes
self.focal_alpha = focal_alpha
self.losses = losses
# removed logging parameter, which ... | 338 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_deformable_detr.py |
idx = self._get_source_permutation_idx(indices)
target_classes_o = torch.cat([t["class_labels"][J] for t, (_, J) in zip(targets, indices)])
target_classes = torch.full(
source_logits.shape[:2], self.num_classes, dtype=torch.int64, device=source_logits.device
)
target_classes[... | 338 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_deformable_detr.py |
class RTDetrHungarianMatcher(nn.Module):
"""This class computes an assignment between the targets and the predictions of the network
For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more
predictions than targets. In this case, we do a 1-to-1 matching o... | 339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
@torch.no_grad()
def forward(self, outputs, targets):
"""Performs the matching
Params:
outputs: This is a dict that contains at least these entries:
"logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
"pred_boxe... | 339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
Returns:
A list of size batch_size, containing tuples of (index_i, index_j) where:
- index_i is the indices of the selected predictions (in order)
- index_j is the indices of the corresponding selected targets (in order)
For each batch element, it holds:
... | 339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
# We flatten to compute the cost matrices in a batch
out_bbox = outputs["pred_boxes"].flatten(0, 1) # [batch_size * num_queries, 4]
# Also concat the target labels and boxes
target_ids = torch.cat([v["class_labels"] for v in targets])
target_bbox = torch.cat([v["boxes"] for v in targets... | 339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
out_prob = outputs["logits"].flatten(0, 1).softmax(-1) # [batch_size * num_queries, num_classes]
class_cost = -out_prob[:, target_ids] | 339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
# Compute the L1 cost between boxes
bbox_cost = torch.cdist(out_bbox, target_bbox, p=1)
# Compute the giou cost betwen boxes
giou_cost = -generalized_box_iou(center_to_corners_format(out_bbox), center_to_corners_format(target_bbox))
# Compute the final cost matrix
cost_matrix = s... | 339 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
class RTDetrLoss(nn.Module):
"""
This class computes the losses for RTDetr. The process happens in two steps: 1) we compute hungarian assignment
between ground truth boxes and the outputs of the model 2) we supervise each pair of matched ground-truth /
prediction (supervise class and box). | 340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
Args:
matcher (`DetrHungarianMatcher`):
Module able to compute a matching between targets and proposals.
weight_dict (`Dict`):
Dictionary relating each loss with its weights. These losses are configured in RTDetrConf as
`weight_loss_vfl`, `weight_loss_bbox`, `weight_l... | 340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
self.matcher = RTDetrHungarianMatcher(config)
self.num_classes = config.num_labels
self.weight_dict = {
"loss_vfl": config.weight_loss_vfl,
"loss_bbox": config.weight_loss_bbox,
"loss_giou": config.weight_loss_giou,
}
self.losses = ["vfl", "boxes"]
... | 340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
src_boxes = outputs["pred_boxes"][idx]
target_boxes = torch.cat([_target["boxes"][i] for _target, (_, i) in zip(targets, indices)], dim=0)
ious, _ = box_iou(center_to_corners_format(src_boxes), center_to_corners_format(target_boxes))
ious = torch.diag(ious).detach()
src_logits = outputs... | 340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
pred_score = F.sigmoid(src_logits).detach()
weight = self.alpha * pred_score.pow(self.gamma) * (1 - target) + target_score
loss = F.binary_cross_entropy_with_logits(src_logits, target_score, weight=weight, reduction="none")
loss = loss.mean(1).sum() * src_logits.shape[1] / num_boxes
ret... | 340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
idx = self._get_source_permutation_idx(indices)
target_classes_original = torch.cat([_target["class_labels"][i] for _target, (_, i) in zip(targets, indices)])
target_classes = torch.full(
src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device
)
ta... | 340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
@torch.no_grad()
def loss_cardinality(self, outputs, targets, indices, num_boxes):
"""
Compute the cardinality error, i.e. the absolute error in the number of predicted non-empty boxes. This is not
really a loss, it is intended for logging purposes only. It doesn't propagate gradients.
... | 340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
def loss_boxes(self, outputs, targets, indices, num_boxes):
"""
Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss. Targets dicts must
contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]. The target boxes are expected in
format ... | 340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
loss_giou = 1 - torch.diag(
generalized_box_iou(center_to_corners_format(src_boxes), center_to_corners_format(target_boxes))
)
losses["loss_giou"] = loss_giou.sum() / num_boxes
return losses
def loss_masks(self, outputs, targets, indices, num_boxes):
"""
Compute ... | 340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
source_idx = self._get_source_permutation_idx(indices)
target_idx = self._get_target_permutation_idx(indices)
source_masks = outputs["pred_masks"]
source_masks = source_masks[source_idx]
masks = [t["masks"] for t in targets]
target_masks, valid = nested_tensor_from_tensor_list(ma... | 340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
target_masks = target_masks.flatten(1)
target_masks = target_masks.view(source_masks.shape)
losses = {
"loss_mask": sigmoid_focal_loss(source_masks, target_masks, num_boxes),
"loss_dice": dice_loss(source_masks, target_masks, num_boxes),
}
return losses
def l... | 340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
target = F.one_hot(target_classes, num_classes=self.num_classes + 1)[..., :-1]
loss = F.binary_cross_entropy_with_logits(src_logits, target * 1.0, reduction="none")
loss = loss.mean(1).sum() * src_logits.shape[1] / num_boxes
return {"loss_bce": loss}
def _get_source_permutation_idx(self, in... | 340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
def loss_labels_focal(self, outputs, targets, indices, num_boxes, log=True):
if "logits" not in outputs:
raise KeyError("No logits found in outputs")
src_logits = outputs["logits"]
idx = self._get_source_permutation_idx(indices)
target_classes_original = torch.cat([_target[... | 340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
def get_loss(self, loss, outputs, targets, indices, num_boxes):
loss_map = {
"labels": self.loss_labels,
"cardinality": self.loss_cardinality,
"boxes": self.loss_boxes,
"masks": self.loss_masks,
"bce": self.loss_labels_bce,
"focal": self.lo... | 340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
dn_match_indices = []
for i, num_gt in enumerate(num_gts):
if num_gt > 0:
gt_idx = torch.arange(num_gt, dtype=torch.int64, device=device)
gt_idx = gt_idx.tile(dn_num_group)
assert len(dn_positive_idx[i]) == len(gt_idx)
dn_match_indices.... | 340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
Args:
outputs (`dict`, *optional*):
Dictionary of tensors, see the output specification of the model for the format.
targets (`List[dict]`, *optional*):
List of dicts, such that `len(targets) == batch_size`. The expected keys in each dict depends on the
... | 340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
# Compute all the requested losses
losses = {}
for loss in self.losses:
l_dict = self.get_loss(loss, outputs, targets, indices, num_boxes)
l_dict = {k: l_dict[k] * self.weight_dict[k] for k in l_dict if k in self.weight_dict}
losses.update(l_dict) | 340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
# In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
if "auxiliary_outputs" in outputs:
for i, auxiliary_outputs in enumerate(outputs["auxiliary_outputs"]):
indices = self.matcher(auxiliary_outputs, targets)
for loss in sel... | 340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
# In case of cdn auxiliary losses. For rtdetr
if "dn_auxiliary_outputs" in outputs:
if "denoising_meta_values" not in outputs:
raise ValueError(
"The output must have the 'denoising_meta_values` key. Please, ensure that 'outputs' includes a 'denoising_meta_values'... | 340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
for i, auxiliary_outputs in enumerate(outputs["dn_auxiliary_outputs"]):
# indices = self.matcher(auxiliary_outputs, targets)
for loss in self.losses:
if loss == "masks":
# Intermediate masks losses are too costly to compute, we ignore them.
... | 340 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py |
class CompressedTensorsHfQuantizer(HfQuantizer):
"""
Quantizer for the compressed_tensors package. Loads and restores models to
quantized state with compressed_tensors
"""
requires_calibration = True
required_packages = ["compressed_tensors"]
def __init__(self, quantization_config: Compre... | 341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_compressed_tensors.py |
def validate_environment(self, *args, **kwargs):
if not is_compressed_tensors_available():
raise ImportError(
"Using `compressed_tensors` quantized models requires the compressed-tensors library: "
"`pip install compressed-tensors`"
)
if not is_tor... | 341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_compressed_tensors.py |
def _process_model_before_weight_loading(self, model, **kwargs):
from compressed_tensors.quantization import apply_quantization_config
ct_quantization_config = self.compressor.quantization_config
if self.run_compressed and self.is_quantization_compressed:
apply_quantization_config(... | 341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_compressed_tensors.py |
config_file_path = cached_file(cache_path, "config.json")
cache_path = os.path.sep.join(config_file_path.split(os.path.sep)[:-1])
if self.is_quantization_compressed and not self.run_compressed:
from compressed_tensors.quantization import QuantizationStatus
s... | 341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_compressed_tensors.py |
return (
self.quantization_config.sparsity_config is not None
and self.quantization_config.sparsity_config.format != CompressionFormat.dense.value
)
@property
def is_trainable(self):
return True
def is_qat_trainable(self) -> bool:
"""Loaded Models can carry ... | 341 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_compressed_tensors.py |
class FbgemmFp8HfQuantizer(HfQuantizer):
"""
FP8 quantization using fbgemm kernels
"""
requires_parameters_quantization = True
requires_calibration = False
required_packages = ["fbgemm-gpu", "accelerate"]
def __init__(self, quantization_config, **kwargs):
super().__init__(quantiza... | 342 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_fbgemm_fp8.py |
def validate_environment(self, *args, **kwargs):
if not is_torch_available() or version.parse(importlib.metadata.version("torch")) < version.parse("2.1.0"):
raise ImportError(
"Using fbgemm fp8 quantization requires torch > 2.1.0"
"Please install the latest version of... | 342 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_fbgemm_fp8.py |
if not torch.cuda.is_available():
raise RuntimeError("Using FP8 quantized models with fbgemm kernels requires a GPU")
compute_capability = torch.cuda.get_device_capability()
major, minor = compute_capability
if major < 9:
raise ValueError(
"FP8 quantized ... | 342 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_fbgemm_fp8.py |
device_map = kwargs.get("device_map", None)
if device_map is None:
logger.warning_once(
"You have loaded an FP8 model on CPU and have a CUDA device available, make sure to set "
"your model on a GPU device in order to run your model. To remove this warning, pass devic... | 342 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_fbgemm_fp8.py |
def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
if torch_dtype is None:
torch_dtype = torch.bfloat16
logger.info(
"Overriding torch_dtype=%s with `torch_dtype=torch.bloat16` due to "
"requirements of `fbgemm-gpu` to enable model ... | 342 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_fbgemm_fp8.py |
def check_quantized_param(
self,
model: "PreTrainedModel",
param_value: "torch.Tensor",
param_name: str,
state_dict: Dict[str, Any],
**kwargs,
):
from ..integrations import FbgemmFp8Linear
module, tensor_name = get_module_from_name(model, param_name)
... | 342 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_fbgemm_fp8.py |
def create_quantized_param(
self,
model: "PreTrainedModel",
param_value: "torch.Tensor",
param_name: str,
target_device: "torch.device",
state_dict: Dict[str, Any],
unexpected_keys: Optional[List[str]] = None,
):
"""
Quantizes weights into weig... | 342 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_fbgemm_fp8.py |
def _process_model_before_weight_loading(
self,
model: "PreTrainedModel",
device_map,
keep_in_fp32_modules: List[str] = [],
**kwargs,
):
from ..integrations import get_keys_to_not_convert, replace_with_fbgemm_fp8_linear
self.modules_to_not_convert = get_keys_... | 342 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_fbgemm_fp8.py |
not_missing_keys = []
for name, module in model.named_modules():
if isinstance(module, FbgemmFp8Linear):
for missing in missing_keys:
if (
(name in missing or name in f"{prefix}.{missing}")
and not missing.endswith("... | 342 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_fbgemm_fp8.py |
class AwqQuantizer(HfQuantizer):
"""
4-bit quantization for Activation-aware Weight Quantization(AWQ) (https://arxiv.org/abs/2306.00978)
"""
# AWQ requires data callibration - we support only inference
requires_calibration = True
required_packages = ["awq", "accelerate"]
def __init__(self... | 343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_awq.py |
if self.quantization_config.version == AWQLinearVersion.GEMM and not torch.cuda.is_available():
logger.warning_once("No CUDA found, replace GEMM with IPEX version to support non-cuda AWQ model.")
self.quantization_config.version = AWQLinearVersion.IPEX | 343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_awq.py |
if self.quantization_config.version == AWQLinearVersion.IPEX:
if version.parse(importlib.metadata.version("autoawq")) < version.parse("0.2.6"):
raise RuntimeError(
"To use IPEX backend, you need autoawq>0.6.2. Please install the latest version or from source."
... | 343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_awq.py |
"GPU is required to run AWQ quantized model. You can use IPEX version AWQ if you have an Intel CPU"
) | 343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_awq.py |
if device_map is None:
logger.warning_once(
"You have loaded an AWQ model on CPU and have a CUDA device available, make sure to set "
"your model on a GPU device in order to run your model."
)
elif device_map is not None:
... | 343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_awq.py |
def update_torch_dtype(self, torch_dtype):
if torch_dtype is None:
torch_dtype = torch.float16
logger.info("Loading the model in `torch.float16`. To overwrite it, set `torch_dtype` manually.")
elif torch_dtype != torch.float16:
logger.warning("We suggest you to set `t... | 343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_awq.py |
model, has_been_replaced = replace_with_awq_linear(
model, quantization_config=self.quantization_config, modules_to_not_convert=self.modules_to_not_convert
)
model = replace_quantization_scales(model, model.config.model_type)
if not has_been_replaced:
logger.warning(
... | 343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_awq.py |
if self.quantization_config.version == AWQLinearVersion.EXLLAMA:
from ..integrations import post_init_awq_exllama_modules
model = post_init_awq_exllama_modules(model, self.quantization_config.exllama_config)
if self.quantization_config.version == AWQLinearVersion.IPEX:
from... | 343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_awq.py |
@property
def is_trainable(self):
# AWQ supports PEFT fine-tuning from version 0.2.0
MIN_AWQ_VERSION_FOR_PEFT = "0.2.0"
return version.parse(importlib.metadata.version("autoawq")) >= version.parse(MIN_AWQ_VERSION_FOR_PEFT) | 343 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_awq.py |
class Bnb4BitHfQuantizer(HfQuantizer):
"""
4-bit quantization from bitsandbytes.py quantization method:
before loading: converts transformer layers into Linear4bit during loading: load 16bit weight and pass to the
layer object after: quantizes individual weights in Linear4bit into 4bit at the fi... | 344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py |
def validate_environment(self, *args, **kwargs):
if not is_accelerate_available():
raise ImportError(
f"Using `bitsandbytes` 4-bit quantization requires Accelerate: `pip install 'accelerate>={ACCELERATE_MIN_VERSION}'`"
)
if not is_bitsandbytes_available():
... | 344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py |
if kwargs.get("from_tf", False) or kwargs.get("from_flax", False):
raise ValueError(
"Converting into 4-bit or 8-bit weights from tf/flax weights is currently not supported, please make"
" sure the weights are in PyTorch format."
) | 344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py |
device_map = kwargs.get("device_map", None)
if (
device_map is not None
and isinstance(device_map, dict)
and not self.quantization_config.llm_int8_enable_fp32_cpu_offload
):
device_map_without_lm_head = {
key: device_map[key] for key in dev... | 344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py |
"`from_pretrained`. Check "
"https://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu "
"for more details. "
) | 344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py |
if version.parse(importlib.metadata.version("bitsandbytes")) < version.parse("0.39.0"):
raise ValueError(
"You have a version of `bitsandbytes` that is not compatible with 4bit inference and training"
" make sure you have the latest version of `bitsandbytes` installed"
... | 344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py |
if target_dtype != torch.int8:
logger.info("target_dtype {target_dtype} is replaced by `CustomDtype.INT4` for 4-bit BnB quantization")
return CustomDtype.INT4
else:
raise ValueError(
"You are using `device_map='auto'` on a 4bit loaded version of the model.... | 344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py |
module, tensor_name = get_module_from_name(model, param_name)
if isinstance(module._parameters.get(tensor_name, None), bnb.nn.Params4bit):
# Add here check for loaded components' dtypes once serialization is implemented
return True
elif isinstance(module, bnb.nn.Linear4bit) and t... | 344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py |
def create_quantized_param(
self,
model: "PreTrainedModel",
param_value: "torch.Tensor",
param_name: str,
target_device: "torch.device",
state_dict: Dict[str, Any],
unexpected_keys: Optional[List[str]] = None,
):
"""
combines logic from _load_s... | 344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py |
# `torch.Tensor.to(<int num>)` is not supported by `torch_npu` (see this [issue](https://github.com/Ascend/pytorch/issues/16)).
if isinstance(target_device, int) and is_torch_npu_available():
target_device = f"npu:{target_device}"
if tensor_name == "bias":
if param_value is None:... | 344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py |
if not isinstance(module._parameters[tensor_name], bnb.nn.Params4bit):
raise ValueError("this function only loads `Linear4bit components`")
if (
old_value.device == torch.device("meta")
and target_device not in ["meta", torch.device("meta")]
and param_value is Non... | 344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py |
if not self.is_serializable:
raise ValueError(
"Detected int4 weights but the version of bitsandbytes is not compatible with int4 serialization. "
"Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`."
)
... | 344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py |
param_kwargs = {}
if self.is_bnb_supports_quant_storage_module:
param_kwargs["module"] = module
new_value = bnb.nn.Params4bit.from_prequantized(
data=param_value,
quantized_stats=quantized_stats,
requires_grad=False,
... | 344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py |
# Copied from transformers.quantizers.quantizer_bnb_8bit.Bnb8BitHfQuantizer.adjust_max_memory
def adjust_max_memory(self, max_memory: Dict[str, Union[int, str]]) -> Dict[str, Union[int, str]]:
# need more space for buffers that are created during quantization
max_memory = {key: val * 0.90 for key, v... | 344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py |
# Copied from transformers.quantizers.quantizer_bnb_8bit.Bnb8BitHfQuantizer.update_torch_dtype
def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
if torch_dtype is None:
# We force the `dtype` to be float16, this is a requirement from `bitsandbytes`
logger.inf... | 344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py |
def update_device_map(self, device_map):
if device_map is None:
if torch.cuda.is_available():
device_map = {"": torch.cuda.current_device()}
elif is_torch_npu_available():
device_map = {"": f"npu:{torch.npu.current_device()}"}
elif is_torch_xpu... | 344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py |
# Copied from transformers.quantizers.quantizer_bnb_8bit.Bnb8BitHfQuantizer._process_model_before_weight_loading
def _process_model_before_weight_loading(
self,
model: "PreTrainedModel",
device_map,
keep_in_fp32_modules: List[str] = [],
**kwargs,
):
from ..integra... | 344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py |
self.modules_to_not_convert.extend(keep_in_fp32_modules)
# Extend `self.modules_to_not_convert` to keys that are supposed to be offloaded to `cpu` or `disk`
if isinstance(device_map, dict) and len(device_map.keys()) > 1:
keys_on_cpu = [key for key, value in device_map.items() if value in ["... | 344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py |
model = replace_with_bnb_linear(
model, modules_to_not_convert=self.modules_to_not_convert, quantization_config=self.quantization_config
)
# TODO: consider bringing replace_with_bnb_linear() code from ..integrations/bitsandbyter.py to here
model.config.quantization_config = self.qua... | 344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py |
if not _is_4bit_serializable:
logger.warning(
"You are calling `save_pretrained` to a 4-bit converted model, but your `bitsandbytes` version doesn't support it. "
"If you want to save 4-bit models, make sure to have `bitsandbytes>=0.41.3` installed."
)
... | 344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py |
model = dequantize_and_replace(
model, self.modules_to_not_convert, quantization_config=self.quantization_config
)
return model | 344 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py |
class TorchAoHfQuantizer(HfQuantizer):
"""
Quantizer for torchao: https://github.com/pytorch/ao/
"""
requires_parameters_quantization = True
requires_calibration = False
required_packages = ["torchao"]
def __init__(self, quantization_config, **kwargs):
super().__init__(quantization... | 345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_torchao.py |
self.offload = False
device_map = kwargs.get("device_map", None)
if isinstance(device_map, dict):
if "cpu" in device_map.values() or "disk" in device_map.values():
if self.pre_quantized:
raise ValueError(
"You are attempting to perf... | 345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_torchao.py |
f"In order to use torchao pre-quantized model, you need to have torch>=2.5.0. However, the current version is {torch_version}."
f" You can also set with `weights_only=False` in `from_pretrained` if you don't want to update torch"
) | 345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_torchao.py |
def update_torch_dtype(self, torch_dtype):
if self.quantization_config.quant_type == "int4_weight_only":
if torch_dtype is not None and torch_dtype != torch.bfloat16:
logger.warning_once(
f"Setting torch_dtype to {torch_dtype} for int4_weight_only quantization, bu... | 345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_torchao.py |
"Setting torch_dtype to torch.float32 for int8_dynamic_activation_int8_weight quantization as no torch_dtype was specified in from_pretrained"
)
# we need to set the torch_dtype, otherwise we have dtype mismatch when performing the quantized linear op
torch_dtype = torch.... | 345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_torchao.py |
def adjust_target_dtype(self, target_dtype: "torch.dtype") -> "torch.dtype":
if version.parse(importlib.metadata.version("accelerate")) > version.parse("0.19.0"):
from accelerate.utils import CustomDtype
map_to_target_dtype = {
"int4_weight_only": CustomDtype.INT4,
... | 345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_torchao.py |
def adjust_max_memory(self, max_memory: Dict[str, Union[int, str]]) -> Dict[str, Union[int, str]]:
# need more space for the quantization parameters (e.g. scale). Tested with int4 wo and group size = 128
max_memory = {key: val * 0.9 for key, val in max_memory.items()}
return max_memory
def ... | 345 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_torchao.py |
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