code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def load_diffusion_weights_state_dict(
model: nn.Module | DiffusionModelStruct,
config: DiffusionQuantConfig,
state_dict: dict[str, torch.Tensor],
branch_state_dict: dict[str, dict[str, torch.Tensor]] | None = None,
) -> None:
"""Load the state dict of the weights of a diffusion model.
Args:
... | Load the state dict of the weights of a diffusion model.
Args:
model (`nn.Module` or `DiffusionModelStruct`):
The diffusion model to load the weights.
config (`DiffusionQuantConfig`):
The quantization configuration.
state_dict (`dict[str, torch.Tensor]`):
... | load_diffusion_weights_state_dict | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/weight.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/weight.py | Apache-2.0 |
def generate_calib_dirname(self) -> str:
"""Generate the name for quantization calibration.
Returns:
str: The name.
"""
name = ""
if self.static:
name += ".static"
if self.enabled_gptq:
name += ".gptq"
if self.enabled_low_rank:... | Generate the name for quantization calibration.
Returns:
str: The name.
| generate_calib_dirname | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/quantizer/config.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/quantizer/config.py | Apache-2.0 |
def generate_dirnames(
self,
*,
prefix: str = "",
shape: torch.Size | tuple[int, ...] = (1024, 1024, 16, 16),
default_dtype: torch.dtype = torch.float16,
**kwargs,
) -> list[str]:
"""Get the directory names of the quantization configuration.
Args:
... | Get the directory names of the quantization configuration.
Args:
prefix (`str`, *optional*, defaults to `""`):
The prefix for the directory names.
shape (`torch.Size` or `tuple[int, ...]`, *optional*, defaults to `(1024, 1024, 16, 16)`):
The shape of the ... | generate_dirnames | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/quantizer/config.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/quantizer/config.py | Apache-2.0 |
def for_unsigned(self) -> "DiffusionActivationQuantizerConfig":
"""get the quantizer configuration for unsigned activations.
Returns:
`DiffusionActivationQuantizerConfig`:
The quantizer configuration for unsigned activations.
"""
if isinstance(self.dtype, Qua... | get the quantizer configuration for unsigned activations.
Returns:
`DiffusionActivationQuantizerConfig`:
The quantizer configuration for unsigned activations.
| for_unsigned | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/quantizer/config.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/quantizer/config.py | Apache-2.0 |
def generate_calib_dirname(self) -> str:
"""Generate the name for quantization calibration.
Returns:
`str`:
The name.
"""
name = ""
if self.enabled_wgts:
calib_name = self.wgts.generate_calib_dirname()
if calib_name:
... | Generate the name for quantization calibration.
Returns:
`str`:
The name.
| generate_calib_dirname | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/quantizer/config.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/quantizer/config.py | Apache-2.0 |
def calibrate_dynamic_range(
self,
modules: tp.Sequence[nn.Module],
activations: TensorsCache,
weights: tp.Sequence[nn.Parameter] = None,
eval_inputs: TensorsCache | None = None,
eval_module: nn.Module | None = None,
eval_kwargs: dict[str, tp.Any] | None = None,
... | Calibrate the dynamic range.
Args:
modules (`Sequence[nn.Module]`):
The modules to calibrate.
activations (`TensorsCache`):
The inputs cache if the tensor type is not outputs, or the outputs cache if the tensor type is outputs.
weights (`Seque... | calibrate_dynamic_range | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/quantizer/quantizer.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/quantizer/quantizer.py | Apache-2.0 |
def calibrate_dynamic_range(
self,
module: nn.Module,
inputs: TensorsCache,
weight: nn.Parameter | None = None,
eval_inputs: TensorsCache | None = None,
eval_module: nn.Module | None = None,
eval_kwargs: dict[str, tp.Any] | None = None,
orig_inputs: Tensor... | Calibrate the dynamic range.
Args:
module (`nn.Module`):
The module to calibrate.
inputs (`TensorsCache`):
The inputs cache.
weight (`nn.Parameter` or `None`, *optional*, defaults to `None`):
The weight parameter to calibrate.
... | calibrate_dynamic_range | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/quantizer/quantizer.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/quantizer/quantizer.py | Apache-2.0 |
def calibrate_low_rank(
self,
input_quantizer: "DiffusionActivationQuantizer",
modules: tp.Sequence[nn.Module],
inputs: TensorsCache,
weights: tp.Sequence[nn.Parameter] = None,
eval_inputs: TensorsCache | None = None,
eval_module: nn.Module | None = None,
... | Calibrate the quantization low-rank branch. | calibrate_low_rank | python | mit-han-lab/deepcompressor | deepcompressor/app/diffusion/quant/quantizer/quantizer.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/diffusion/quant/quantizer/quantizer.py | Apache-2.0 |
def get_parser(cls) -> ConfigParser:
"""Get a parser for evaluating a large language model.
Returns:
`ConfigParser`: A parser for evaluating a large language model.
"""
parser = ConfigParser("Evaluate a large language model")
parser.add_config(cls)
return par... | Get a parser for evaluating a large language model.
Returns:
`ConfigParser`: A parser for evaluating a large language model.
| get_parser | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/config.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/config.py | Apache-2.0 |
def ptq( # noqa: C901
model: PreTrainedModel | LlmModelStruct,
/,
tokenizer: PreTrainedTokenizer,
config: LlmQuantConfig,
cache: LlmCacheConfig | None = None,
load_dirpath: str = "",
save_dirpath: str = "",
copy_on_save: bool = False,
save_model: bool = False,
) -> PreTrainedModel:
... | Post-training quantization of a large language model.
Args:
model (`PreTrainedModel` or `LlmStruct`):
The large language model.
tokenizer (`PreTrainedTokenizer`):
The large language model tokenizer.
config (`LlmQuantConfig`):
The large language model post... | ptq | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/ptq.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/ptq.py | Apache-2.0 |
def main(config: LlmPtqRunConfig, logging_level: int = tools.logging.DEBUG) -> None: # noqa: C901
"""Post-training quantization and evaluation of a large language model.
Args:
config (`LlmPtqConfig`):
The large language model post-training quantization configuration.
logging_level ... | Post-training quantization and evaluation of a large language model.
Args:
config (`LlmPtqConfig`):
The large language model post-training quantization configuration.
logging_level (`int`, *optional*, defaults to `logging.DEBUG`):
The logging level.
| main | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/ptq.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/ptq.py | Apache-2.0 |
def evaluate(
self,
model: PreTrainedModel,
/,
tokenizer: PreTrainedTokenizer,
model_name: str,
eos_token_ids: tp.Sequence[int] = (),
output_dirpath: str = "",
) -> dict[str, dict[int, dict[str, dict[tp.Any, dict[str, tp.Any]]]]]:
"""Evaluate the model... | Evaluate the model.
Args:
model (`PreTrainedModel`):
The model.
tokenizer (`PreTrainedTokenizer`):
The tokenizer.
model_name (`str`):
The name of the model.
eos_token_ids (`Sequence[int]`, *optional*, defaults to `(... | evaluate | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/eval/config.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/eval/config.py | Apache-2.0 |
def evaluate(
self, tasks: list[str], max_length: int | None = None, **kwargs
) -> dict[str, dict[str, dict[str, float]]]:
"""Evaluate the model on the given tasks.
Args:
tasks (`list[str]`): List of tasks to evaluate on.
max_length (`int`, optional, defaults to `Non... | Evaluate the model on the given tasks.
Args:
tasks (`list[str]`): List of tasks to evaluate on.
max_length (`int`, optional, defaults to `None`): Maximum length for the model.
Returns:
dict[str, dict[str, dict[str, float]]]: Evaluation results `{"results": {"task": ... | evaluate | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/eval/custom.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/eval/custom.py | Apache-2.0 |
def _eval_ppl_with_gptq_evaluator(
model: PreTrainedModel,
/,
tokenizer: PreTrainedTokenizer,
task: str,
seq_length: int = 2048,
max_num_samples: int = -1,
) -> float:
"""Evaluate the perplexity of a model on a task using GPTQ style evaluation.
Args:
model (`PreTrainedModel`):
... | Evaluate the perplexity of a model on a task using GPTQ style evaluation.
Args:
model (`PreTrainedModel`):
The model.
tokenizer (`PreTrainedTokenizer`):
The tokenizer.
task (`str`):
The task name.
seq_length (`int`, *optional*, defaults to `2048`)... | _eval_ppl_with_gptq_evaluator | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/eval/custom.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/eval/custom.py | Apache-2.0 |
def filter_tasks(self, tasks: list[str]) -> list[str]:
"""Filter the tasks to only include supported tasks."""
if "longbench-e" in tasks:
return ["longbench-e"]
if "longbench" in tasks:
return sorted(self.task2maxlen.keys(), key=lambda x: self.task2maxlen[x])
retu... | Filter the tasks to only include supported tasks. | filter_tasks | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/eval/longbench/eval.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/eval/longbench/eval.py | Apache-2.0 |
def evaluate(self, tasks: list[str], max_length: int, **kwargs) -> dict[str, dict[str, dict[str, float]]]:
"""Evaluate the model on the given tasks."""
...
tools.logging.Formatter.indent_inc()
longbench_e = False
if "longbench-e" in tasks:
assert len(tasks) == 1, "Lon... | Evaluate the model on the given tasks. | evaluate | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/eval/longbench/eval.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/eval/longbench/eval.py | Apache-2.0 |
def build_chat(self, prompt):
"""Build chat prompt for chat models."""
if "llama-2" in self.model_name:
prompt = f"[INST]{prompt}[/INST]"
return prompt | Build chat prompt for chat models. | build_chat | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/eval/longbench/eval.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/eval/longbench/eval.py | Apache-2.0 |
def build(self) -> tuple[PreTrainedModel, PreTrainedTokenizer]:
"""Build model and tokenizer.
Args:
dtype (`torch.dtype`, *optional*, defaults to `None`):
Data type of the model.
Returns:
`tuple[PreTrainedModel, PreTrainedTokenizer]`:
Mod... | Build model and tokenizer.
Args:
dtype (`torch.dtype`, *optional*, defaults to `None`):
Data type of the model.
Returns:
`tuple[PreTrainedModel, PreTrainedTokenizer]`:
Model and tokenizer.
| build | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/model/config.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/model/config.py | Apache-2.0 |
def register_model_factory(
cls,
names: str | tuple[str, ...],
/,
factory: tp.Callable[[str, torch.dtype], tuple[PreTrainedModel, PreTrainedTokenizer]],
*,
overwrite: bool = False,
) -> None:
"""Register a model factory.
Args:
names (`str`... | Register a model factory.
Args:
names (`str` or `tuple[str, ...]`):
Names of the model.
factory (`Callable[[str, torch.dtype], tuple[PreTrainedModel, PreTrainedTokenizer]]`):
Factory function.
overwrite (`bool`, *optional*, defaults to `False`... | register_model_factory | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/model/config.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/model/config.py | Apache-2.0 |
def update_rotary_cos_sin(
cos: torch.Tensor, sin: torch.Tensor, position_ids: torch.LongTensor | None, unsqueeze_dim: int = 1
) -> tuple[torch.Tensor, torch.Tensor]:
"""Update the cos and sin tensors with new position_ids.
Args:
cos (``torch.Tensor``):
Cosine tensor.
sin (``tor... | Update the cos and sin tensors with new position_ids.
Args:
cos (``torch.Tensor``):
Cosine tensor.
sin (``torch.Tensor``):
Sine tensor.
position_ids (``torch.LongTensor | None``):
Position ids.
unsqueeze_dim (``int``, *optional*, defaults to ``1``... | update_rotary_cos_sin | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/nn/patch.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/nn/patch.py | Apache-2.0 |
def forward(
self, states: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, unsqueeze_dim: int = 1
) -> torch.Tensor:
"""Apply rotary embedding to states.
Args:
states (torch.Tensor): States.
cos (torch.Tensor): Cosine tensor.
sin (torch.Tensor): Sine ... | Apply rotary embedding to states.
Args:
states (torch.Tensor): States.
cos (torch.Tensor): Cosine tensor.
sin (torch.Tensor): Sine tensor.
unsqueeze_dim (int, optional): The dimension along which to unsqueeze cos and sin.
Defaults to ``1``.
... | forward | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/nn/patch.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/nn/patch.py | Apache-2.0 |
def apply_rotary_pos_emb(
self,
q: torch.Tensor,
k: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
position_ids: torch.LongTensor = None,
unsqueeze_dim: int = 1,
) -> tuple[torch.Tensor, torch.Tensor]:
"""Apply Rotary Position Embedding to the query and key tensors.
Args:
... | Apply Rotary Position Embedding to the query and key tensors.
Args:
q (`torch.Tensor`):
The query tensor.
k (`torch.Tensor`):
The key tensor.
cos (`torch.Tensor`):
The cosine part of the rotary embedding.
sin (`torch.Tensor`):
The sine... | apply_rotary_pos_emb | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/nn/patch.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/nn/patch.py | Apache-2.0 |
def get_iter_layer_activations_args(
self, **kwargs
) -> tuple[list[nn.Module], list[LlmTransformerBlockStruct], list[bool], list[bool]]:
"""
Get the arguments for iterating over the layers and their activations.
Args:
skip_pre_modules (`bool`):
Whether t... |
Get the arguments for iterating over the layers and their activations.
Args:
skip_pre_modules (`bool`):
Whether to skip the pre-modules
skip_post_modules (`bool`):
Whether to skip the post-modules
Returns:
`tuple[list[nn.Modu... | get_iter_layer_activations_args | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/nn/struct.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/nn/struct.py | Apache-2.0 |
def _default_construct(
model: nn.Module,
/,
parent: tp.Optional[BaseModuleStruct] = None,
fname: str = "",
rname: str = "",
rkey: str = "",
idx: int = 0,
**kwargs,
) -> "LlmModelStruct":
"""Build the Large Language Model Structure."""
... | Build the Large Language Model Structure. | _default_construct | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/nn/struct.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/nn/struct.py | Apache-2.0 |
def quantize_llm_layer_activations( # noqa: C901
layer: LlmTransformerBlockStruct,
config: LlmQuantConfig,
quantizer_state_dict: dict[str, tp.Any],
layer_cache: dict[str, IOTensorsCache] | None = None,
layer_kwargs: dict[str, tp.Any] | None = None,
orig_state_dict: dict[str, torch.Tensor] | Non... | Calibrate the activation quantization ranges of modules in a layer.
Args:
layer (`LlmTransformerBlockStruct`):
Layer.
config (`LlmQuantConfig`):
Quantization configuration.
quantizer_state_dict (`dict[str, Any]`):
Activation quantizer state dict.
... | quantize_llm_layer_activations | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/quant/activation.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/quant/activation.py | Apache-2.0 |
def quantize_llm_activations(
model: nn.Module | LlmModelStruct,
config: LlmQuantConfig,
tokenizer: PreTrainedTokenizer | None = None,
quantizer_state_dict: dict[str, tp.Any] | None = None,
orig_state_dict: dict[str, torch.Tensor] | None = None,
) -> dict[str, tp.Any]:
"""Quantize the large foun... | Quantize the large foundation model activations.
Args:
model (`nn.Module` or `LlmStruct`):
Model to be quantized.
config (`LlmQuantConfig`):
Quantization configuration.
tokenizer (`PreTrainedTokenizer`, *optional*, defaults to `None`):
Tokenizer.
... | quantize_llm_activations | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/quant/activation.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/quant/activation.py | Apache-2.0 |
def generate_default_dirname(self) -> str: # noqa: C901
"""Generate directory name for a large language model quantization configuration."""
w_names = x_names = {"qkv_proj": "qkv", "out_proj": "out", "up_proj": "fc1", "down_proj": "fc2"}
y_names = {"attn_q": "q", "attn_k": "k", "attn_v": "v"}
... | Generate directory name for a large language model quantization configuration. | generate_default_dirname | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/quant/config.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/quant/config.py | Apache-2.0 |
def generate_dirnames(self, *, prefix: str = "", **kwargs) -> list[str]:
"""Get the names of the configuration fields."""
name = f"{self.data}.{self.num_samples}x{self.seq_length}.[{self.min_seq_length}-{self.max_seq_length}]"
return [f"{prefix}.{name}" if prefix else name] | Get the names of the configuration fields. | generate_dirnames | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/quant/dataset.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/quant/dataset.py | Apache-2.0 |
def build_dataset(self, tokenizer: PreTrainedTokenizer) -> "LlmCalibDataset":
"""Build calibration dataset.
Args:
tokenizer (`PreTrainedTokenizer`):
Tokenizer for encoding text.
Returns:
`LlmCalibDataset`:
Calibration dataset.
"""... | Build calibration dataset.
Args:
tokenizer (`PreTrainedTokenizer`):
Tokenizer for encoding text.
Returns:
`LlmCalibDataset`:
Calibration dataset.
| build_dataset | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/quant/dataset.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/quant/dataset.py | Apache-2.0 |
def __init__(self, config: LlmCalibDataLoaderConfig, tokenizer: PreTrainedTokenizer) -> None:
"""Initialize large language model calibration cache loader.
Args:
config (`LlmCalibDataLoaderConfig`):
Configuration for loading calibration dataset.
tokenizer (`PreTra... | Initialize large language model calibration cache loader.
Args:
config (`LlmCalibDataLoaderConfig`):
Configuration for loading calibration dataset.
tokenizer (`PreTrainedTokenizer`):
Tokenizer for encoding text.
| __init__ | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/quant/dataset.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/quant/dataset.py | Apache-2.0 |
def _init_cache(self, name: str, module: nn.Module) -> IOTensorsCache:
"""Initialize cache.
Args:
name (`str`):
Module name.
module (`nn.Module`):
Module.
Returns:
`IOTensorsCache`:
Input and output tensors cac... | Initialize cache.
Args:
name (`str`):
Module name.
module (`nn.Module`):
Module.
Returns:
`IOTensorsCache`:
Input and output tensors cache.
| _init_cache | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/quant/dataset.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/quant/dataset.py | Apache-2.0 |
def iter_samples(self) -> tp.Generator[ModuleForwardInput, None, None]:
"""Iterate over model input samples.
Args:
tokenizer (`nn.Module`):
Tokenizer for encoding text.
Yields:
`ModuleForwardInput`:
Module forward input.
"""
... | Iterate over model input samples.
Args:
tokenizer (`nn.Module`):
Tokenizer for encoding text.
Yields:
`ModuleForwardInput`:
Module forward input.
| iter_samples | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/quant/dataset.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/quant/dataset.py | Apache-2.0 |
def iter_layer_activations( # noqa: C901
self,
model: nn.Module | LlmModelStruct,
*args,
action: CacheAction | None = None,
needs_inputs_fn: tp.Callable[[str, nn.Module], bool] | bool | None = True,
needs_outputs_fn: tp.Callable[[str, nn.Module], bool] | bool | None = No... | Iterate over model activations for each layer.
Args:
model (`nn.Module`):
Model.
action (`CacheAction`, *optional*, defaults to `None`):
Action for caching activations. If ``None``, ``ConcatCacheAction("cpu")`` is used.
needs_inputs_fn (`Calla... | iter_layer_activations | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/quant/dataset.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/quant/dataset.py | Apache-2.0 |
def _extend_params_(
params: list[tuple[nn.Parameter, int]],
modules: list[nn.Linear | nn.Embedding, nn.LayerNorm],
out_channels_dim: int | None = None,
in_channels_dim: int | None = None,
) -> list[tuple[nn.Parameter, int]]:
"""Extend the parameters to be reordered."""
if out_channels_dim is no... | Extend the parameters to be reordered. | _extend_params_ | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/quant/reorder.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/quant/reorder.py | Apache-2.0 |
def reorder_llm_layer( # noqa: C901
layer: LlmTransformerBlockStruct,
config: LlmQuantConfig,
reorder_cache: dict[str, torch.Tensor],
residual_calibrator: ChannelOrderCalibrator | None = None,
layer_cache: dict[str, IOTensorsCache] | None = None,
layer_kwargs: dict[str, tp.Any] | None = None,
)... | Calibrate the channel order in a layer.
Args:
layer (`LlmTransformerBlockStruct`):
Large language model layer to be reordered.
config (`LlmQuantConfig`):
Quantization config.
reorder_cache (`dict[str, torch.Tensor]`):
Reorder indexes cache.
residu... | reorder_llm_layer | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/quant/reorder.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/quant/reorder.py | Apache-2.0 |
def reorder_llm( # noqa: C901
model: nn.Module | LlmModelStruct,
config: LlmQuantConfig,
tokenizer: PreTrainedTokenizer | None = None,
reorder_cache: dict[str, torch.Tensor] | None = None,
) -> dict[str, torch.Tensor]:
"""Quantize the large foundation model weights.
Args:
model (`nn.Mo... | Quantize the large foundation model weights.
Args:
model (`nn.Module` or `LlmStruct`):
Model to be reordered.
config (`LlmQuantConfig`):
Quantization config.
tokenizer (`PreTrainedTokenizer` or `None`, *optional*, defaults to `None`):
Tokenizer.
r... | reorder_llm | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/quant/reorder.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/quant/reorder.py | Apache-2.0 |
def rotate_llm( # noqa: C901
model: PreTrainedModel | LlmModelStruct,
/,
config: QuantRotationConfig,
rotation: torch.Tensor | None = None,
) -> torch.Tensor:
"""Rotate the weights of the large language model.
Args:
model (`PreTrainedModel` or `LlmStruct`):
Model to be rota... | Rotate the weights of the large language model.
Args:
model (`PreTrainedModel` or `LlmStruct`):
Model to be rotated.
config (`QuantRotationConfig`):
Rotation configuration.
rotation (`torch.Tensor` or `None`, *optional*, defaults to `None`):
Rotation matr... | rotate_llm | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/quant/rotate.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/quant/rotate.py | Apache-2.0 |
def smooth_llm_layer( # noqa: C901
layer: LlmTransformerBlockStruct,
config: LlmQuantConfig,
smooth_cache: dict[str, torch.Tensor],
layer_cache: dict[str, IOTensorsCache] | None = None,
layer_kwargs: dict[str, tp.Any] | None = None,
) -> None:
"""Smooth a large language model layer.
Args:
... | Smooth a large language model layer.
Args:
layer (`LlmTransformerBlockStruct`):
Large language model layer to smooth.
config (`LlmQuantConfig`):
Quantization configuration.
smooth_cache (`dict[str, torch.Tensor]`):
Smoothing scale caches.
layer_ca... | smooth_llm_layer | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/quant/smooth.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/quant/smooth.py | Apache-2.0 |
def smooth_llm(
model: nn.Module | LlmModelStruct,
/,
config: LlmQuantConfig,
tokenizer: PreTrainedTokenizer | None = None,
smooth_cache: dict[str, torch.Tensor] | None = None,
) -> dict[str, torch.Tensor]:
"""Smooth the large language model.
Args:
model (`nn.Module` or `LlmStruct`)... | Smooth the large language model.
Args:
model (`nn.Module` or `LlmStruct`):
Model to be smoothed.
config (`LlmQuantConfig`):
Quantization configuration.
tokenizer (`PreTrainedTokenizer`, *optional*, defaults to `None`):
Tokenizer.
smooth_cache (`di... | smooth_llm | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/quant/smooth.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/quant/smooth.py | Apache-2.0 |
def get_needs_inputs_fn(model: LlmModelStruct, config: LlmModuleQuantizerConfig) -> tp.Callable[[str, nn.Module], bool]:
"""Get function that checks if the module needs to cache the inputs.
Args:
model (`LlmStruct`):
Model struct.
config (`LlmModuleQuantizerConfig`):
Mod... | Get function that checks if the module needs to cache the inputs.
Args:
model (`LlmStruct`):
Model struct.
config (`LlmModuleQuantizerConfig`):
Module quantization config.
Returns:
`Callable[[str, nn.Module], bool]`:
Function to check if the module n... | get_needs_inputs_fn | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/quant/utils.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/quant/utils.py | Apache-2.0 |
def get_needs_outputs_fn(
model: LlmModelStruct, config: LlmModuleQuantizerConfig
) -> tp.Callable[[str, nn.Module], bool]:
"""Get function that checks if the module needs to cache the outputs.
Args:
model (`LlmStruct`):
Model struct.
config (`LlmModuleQuantizerConfig`):
... | Get function that checks if the module needs to cache the outputs.
Args:
model (`LlmStruct`):
Model struct.
config (`LlmModuleQuantizerConfig`):
Module quantization config.
Returns:
`Callable[[str, nn.Module], bool]`:
Function to check if the module ... | get_needs_outputs_fn | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/quant/utils.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/quant/utils.py | Apache-2.0 |
def quantize_llm_layer_weights( # noqa: C901
layer: LlmTransformerBlockStruct,
config: LlmQuantConfig,
quantizer_state_dict: dict[str, tp.Any],
layer_cache: dict[str, IOTensorsCache] | None = None,
layer_kwargs: dict[str, tp.Any] | None = None,
return_with_scale_state_dict: bool = False,
) -> d... | Calibrate the weight quantization ranges of modules in a layer.
Args:
layer (`LlmTransformerBlockStruct`):
Layer.
config (`LlmQuantConfig`):
Quantization config.
quantizer_state_dict (`dict[str, Any]`):
Weight quantizer.
layer_cache (`dict[str, IO... | quantize_llm_layer_weights | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/quant/weight.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/quant/weight.py | Apache-2.0 |
def quantize_llm_weights(
model: nn.Module | LlmModelStruct,
config: LlmQuantConfig,
tokenizer: PreTrainedTokenizer | None = None,
quantizer_state_dict: dict[str, tp.Any] | None = None,
return_with_scale_state_dict: bool = False,
) -> tuple[dict[str, tp.Any], dict[str, torch.Tensor | float | None]]:... | Quantize the large language model weights.
Args:
model (`nn.Module` or `LlmStruct`):
Model to be quantized.
config (`LlmQuantConfig`):
Quantization configuration.
tokenizer (`PreTrainedTokenizer`, *optional*, defaults to `None`):
Tokenizer.
quanti... | quantize_llm_weights | python | mit-han-lab/deepcompressor | deepcompressor/app/llm/quant/weight.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/app/llm/quant/weight.py | Apache-2.0 |
def load_state_dict_in_safetensors(
path: str, device: str | torch.device = "cpu", filter_prefix: str = ""
) -> dict[str, torch.Tensor]:
"""Load state dict in SafeTensors.
Args:
path (`str`):
file path.
device (`str` | `torch.device`, optional, defaults to `"cpu"`):
... | Load state dict in SafeTensors.
Args:
path (`str`):
file path.
device (`str` | `torch.device`, optional, defaults to `"cpu"`):
device.
filter_prefix (`str`, optional, defaults to `""`):
filter prefix.
Returns:
`dict`:
loaded SafeT... | load_state_dict_in_safetensors | python | mit-han-lab/deepcompressor | deepcompressor/backend/utils.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/backend/utils.py | Apache-2.0 |
def pack_lowrank_weight(self, weight: torch.Tensor, down: bool) -> torch.Tensor:
"""Pack Low-Rank Weight.
Args:
weight (`torch.Tensor`):
low-rank weight tensor.
down (`bool`):
whether the weight is for down projection in low-rank branch.
"... | Pack Low-Rank Weight.
Args:
weight (`torch.Tensor`):
low-rank weight tensor.
down (`bool`):
whether the weight is for down projection in low-rank branch.
| pack_lowrank_weight | python | mit-han-lab/deepcompressor | deepcompressor/backend/nunchaku/utils.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/backend/nunchaku/utils.py | Apache-2.0 |
def unpack_lowrank_weight(self, weight: torch.Tensor, down: bool) -> torch.Tensor:
"""Unpack Low-Rank Weight.
Args:
weight (`torch.Tensor`):
low-rank weight tensor.
down (`bool`):
whether the weight is for down projection in low-rank branch.
... | Unpack Low-Rank Weight.
Args:
weight (`torch.Tensor`):
low-rank weight tensor.
down (`bool`):
whether the weight is for down projection in low-rank branch.
| unpack_lowrank_weight | python | mit-han-lab/deepcompressor | deepcompressor/backend/nunchaku/utils.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/backend/nunchaku/utils.py | Apache-2.0 |
def convert_to_qserve_w4x8y16_linear_state_dict(
param_name: str,
weight: torch.Tensor,
scale: torch.Tensor,
zero: torch.Tensor,
subscale: torch.Tensor | None = None,
zero_pre_scaled: bool = False,
) -> dict[str, torch.Tensor]:
"""Convert a weight tensor to QServe W4-X8-Y16 linear state dict... | Convert a weight tensor to QServe W4-X8-Y16 linear state dictionary.
Args:
param_name (`str`):
parameter name.
weight (`torch.Tensor`):
weight tensor to be converted.
scale (`torch.Tensor`):
scale tensor for the weight tensor.
zero (`torch.Tensor`... | convert_to_qserve_w4x8y16_linear_state_dict | python | mit-han-lab/deepcompressor | deepcompressor/backend/qserve/convert.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/backend/qserve/convert.py | Apache-2.0 |
def convert_to_qserve_w8x8y16_linear_state_dict(
param_name: str, weight: torch.Tensor, scale: torch.Tensor
) -> dict[str, torch.Tensor]:
"""Convert a weight tensor to QServe W8-X8-Y16 linear state dictionary.
Args:
param_name (`str`):
parameter name.
weight (`torch.Tensor`):
... | Convert a weight tensor to QServe W8-X8-Y16 linear state dictionary.
Args:
param_name (`str`):
parameter name.
weight (`torch.Tensor`):
weight tensor to be converted.
scale (`torch.Tensor`):
scale tensor for the weight tensor.
Returns:
`dict[... | convert_to_qserve_w8x8y16_linear_state_dict | python | mit-han-lab/deepcompressor | deepcompressor/backend/qserve/convert.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/backend/qserve/convert.py | Apache-2.0 |
def convert_to_qserve_w4x8y16_linear_weight(
weight: torch.Tensor,
scale: torch.Tensor,
zero: torch.Tensor,
subscale: torch.Tensor | None = None,
zero_pre_scaled: bool = False,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor | None]:
"""Convert a weight tensor to QServe W4-X8-Y... | Convert a weight tensor to QServe W4-X8-Y16 linear weight format.
Args:
weight (`torch.Tensor`):
weight tensor to be converted.
scale (`torch.Tensor`):
scale tensor for the weight tensor.
zero (`torch.Tensor`):
zero point tensor for the weight tensor.
... | convert_to_qserve_w4x8y16_linear_weight | python | mit-han-lab/deepcompressor | deepcompressor/backend/qserve/utils.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/backend/qserve/utils.py | Apache-2.0 |
def convert_to_qserve_w8x8y16_linear_weight(
weight: torch.Tensor, scale: torch.Tensor
) -> tuple[torch.Tensor, torch.Tensor]:
"""Convert a weight tensor to QServe W8-X8-Y16 linear weight format.
Args:
weight (`torch.Tensor`):
weight tensor to be converted.
scale (`torch.Tensor`... | Convert a weight tensor to QServe W8-X8-Y16 linear weight format.
Args:
weight (`torch.Tensor`):
weight tensor to be converted.
scale (`torch.Tensor`):
scale tensor for the weight tensor.
Returns:
`tuple[torch.Tensor, torch.Tensor]`:
packed quantized... | convert_to_qserve_w8x8y16_linear_weight | python | mit-han-lab/deepcompressor | deepcompressor/backend/qserve/utils.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/backend/qserve/utils.py | Apache-2.0 |
def convert_to_tinychat_w4x16y16_linear_state_dict(
param_name: str,
weight: torch.Tensor,
scale: torch.Tensor,
zero: torch.Tensor,
zero_pre_scaled: bool = False,
) -> dict[str, torch.Tensor]:
"""Convert a weight tensor to TinyChat W4-X16-Y16 linear state dictionary.
Args:
param_nam... | Convert a weight tensor to TinyChat W4-X16-Y16 linear state dictionary.
Args:
param_name (`str`):
parameter name.
weight (`torch.Tensor`):
weight tensor to be converted.
scale (`torch.Tensor`):
scale tensor for the weight tensor.
zero (`torch.Tens... | convert_to_tinychat_w4x16y16_linear_state_dict | python | mit-han-lab/deepcompressor | deepcompressor/backend/tinychat/convert.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/backend/tinychat/convert.py | Apache-2.0 |
def from_linear(
linear: nn.Linear,
group_size: int,
init_only: bool = False,
weight: torch.Tensor | None = None,
scale: torch.Tensor | None = None,
zero: torch.Tensor | None = None,
zero_pre_scaled: bool = False,
) -> "W4Linear":
"""Convert a linear l... | Convert a linear layer to a TinyChat 4-bit weight-only quantized linear layer.
Args:
linear (`nn.Linear`):
linear layer to be converted.
group_size (`int`):
quantization group size.
init_only (`bool`, *optional*, defaults to `False`):
... | from_linear | python | mit-han-lab/deepcompressor | deepcompressor/backend/tinychat/linear.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/backend/tinychat/linear.py | Apache-2.0 |
def ceil_num_groups(in_features: int, group_size: int, weight_bits: int = 4) -> int:
"""Calculate the ceiling number of quantization groups.
Args:
in_features (`int`):
input channel size.
group_size (`int`):
quantization group size.
weight_bits (`int`, *optional*... | Calculate the ceiling number of quantization groups.
Args:
in_features (`int`):
input channel size.
group_size (`int`):
quantization group size.
weight_bits (`int`, *optional*, defaults to `4`):
quantized weight bits.
Returns:
`int`:
... | ceil_num_groups | python | mit-han-lab/deepcompressor | deepcompressor/backend/tinychat/utils.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/backend/tinychat/utils.py | Apache-2.0 |
def convert_to_tinychat_w4x16y16_linear_weight(
weight: torch.Tensor,
scale: torch.Tensor,
zero: torch.Tensor,
zero_pre_scaled: bool = False,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Convert a weight tensor to TinyChat W4-X16-Y16 linear weight format.
Args:
weight (`torc... | Convert a weight tensor to TinyChat W4-X16-Y16 linear weight format.
Args:
weight (`torch.Tensor`):
weight tensor to be converted.
scale (`torch.Tensor`):
scale tensor for the weight tensor.
zero (`torch.Tensor`):
zero point tensor for the weight tensor.
... | convert_to_tinychat_w4x16y16_linear_weight | python | mit-han-lab/deepcompressor | deepcompressor/backend/tinychat/utils.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/backend/tinychat/utils.py | Apache-2.0 |
def __init__(
self,
config: QuantLowRankCalibConfig,
w_quantizer: Quantizer,
x_quantizer: Quantizer | None,
develop_dtype: torch.dtype = torch.float32,
) -> None:
"""Initialize the calibrator.
Args:
config (`QuantLowRankCalibConfig`):
... | Initialize the calibrator.
Args:
config (`QuantLowRankCalibConfig`):
The configuration of the quantization low-rank branch calibrator.
w_quantizer (`Quantizer`):
The quantizer for weights.
x_quantizer (`Quantizer` or `None`):
T... | __init__ | python | mit-han-lab/deepcompressor | deepcompressor/calib/lowrank.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/lowrank.py | Apache-2.0 |
def _reset(self, x_wgts: list[torch.Tensor | nn.Parameter], **kwargs) -> None: # noqa: C901
"""Reset the calibrator.
Args:
x_wgts (`list[torch.Tensor | nn.Parameter]`):
The weights in x-w computation.
"""
self.best_branch: LowRankBranch = None
self.b... | Reset the calibrator.
Args:
x_wgts (`list[torch.Tensor | nn.Parameter]`):
The weights in x-w computation.
| _reset | python | mit-han-lab/deepcompressor | deepcompressor/calib/lowrank.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/lowrank.py | Apache-2.0 |
def _ask(self) -> LowRankBranch:
"""Ask for the next candidate.
Returns:
`LowRankBranch`:
The next candidate.
"""
branch = LowRankBranch(
self.w.shape[1],
self.w.shape[0],
rank=self.config.rank,
weight=self.w - ... | Ask for the next candidate.
Returns:
`LowRankBranch`:
The next candidate.
| _ask | python | mit-han-lab/deepcompressor | deepcompressor/calib/lowrank.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/lowrank.py | Apache-2.0 |
def _tell(self, error: list[torch.Tensor]) -> None: # noqa: C901
"""Tell the error of the last candidate and update the best candidate.
Args:
errors (list[torch.Tensor]): The error of the last candidate.
"""
if len(error) > 1:
error = [sum(error)]
error ... | Tell the error of the last candidate and update the best candidate.
Args:
errors (list[torch.Tensor]): The error of the last candidate.
| _tell | python | mit-han-lab/deepcompressor | deepcompressor/calib/lowrank.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/lowrank.py | Apache-2.0 |
def abs_max(
tensors: tp.Iterable[torch.Tensor] | torch.Tensor,
num_channels: int,
group_shape: tp.Sequence[int],
device: torch.device | str = None,
dtype: torch.dtype = torch.float32,
) -> torch.Tensor:
"""Get the absolute maximum of the tensors, where `R[i] = AbsMax... | Get the absolute maximum of the tensors, where `R[i] = AbsMax(T[i, :])`. | abs_max | python | mit-han-lab/deepcompressor | deepcompressor/calib/metric.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/metric.py | Apache-2.0 |
def abs_mean(
tensors: tp.Iterable[torch.Tensor] | torch.Tensor,
num_channels: int,
group_shape: tp.Sequence[int],
device: torch.device | str = None,
dtype: torch.dtype = torch.float32,
) -> torch.Tensor:
"""Get the absolute mean of the tensors, where `R[i] = AbsMean(... | Get the absolute mean of the tensors, where `R[i] = AbsMean(T[i, :])`. | abs_mean | python | mit-han-lab/deepcompressor | deepcompressor/calib/metric.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/metric.py | Apache-2.0 |
def abs_normalize_mean(
tensors: tp.Iterable[torch.Tensor] | torch.Tensor,
num_channels: int,
group_shape: tp.Sequence[int],
device: torch.device | str = None,
dtype: torch.dtype = torch.float32,
) -> torch.Tensor:
"""Get the absolute group normalized mean of the tens... | Get the absolute group normalized mean of the tensors, where `R[i] = Mean(U[i, :])`
and `U[i,j] = Abs(T[i, j]) / AbsMax(T[:, j]))`. | abs_normalize_mean | python | mit-han-lab/deepcompressor | deepcompressor/calib/metric.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/metric.py | Apache-2.0 |
def root_mean_square(
tensors: tp.Iterable[torch.Tensor] | torch.Tensor,
num_channels: int,
group_shape: tp.Sequence[int],
device: torch.device | str = None,
dtype: torch.dtype = torch.float32,
) -> torch.Tensor:
"""Get the root mean square of the tensors, where `R[i]... | Get the root mean square of the tensors, where `R[i] = Root(Mean(T[i, :]^2))`. | root_mean_square | python | mit-han-lab/deepcompressor | deepcompressor/calib/metric.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/metric.py | Apache-2.0 |
def __init__(
self,
tensor_type: TensorType,
config: DynamicRangeCalibConfig,
static: bool,
quantizer: Quantizer,
pre_scale: torch.Tensor | None = None,
) -> None:
"""Initialize the calibrator.
Args:
tensor_type (`TensorType`):
... | Initialize the calibrator.
Args:
tensor_type (`TensorType`):
The tensor type.
config (`DynamicRangeCalibConfig`):
The dynamic range calibration configuration.
static (`bool`):
Whether the dynamic range is static, i.e., whether ... | __init__ | python | mit-han-lab/deepcompressor | deepcompressor/calib/range.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/range.py | Apache-2.0 |
def _reset( # noqa: C901
self,
x_wgts: list[torch.Tensor | nn.Parameter],
x_acts: TensorsCache | None,
y_acts: TensorsCache | None,
**kwargs,
) -> None:
"""Reset the calibrator.
Args:
x_wgts (`list[torch.Tensor | nn.Parameter]`):
... | Reset the calibrator.
Args:
x_wgts (`list[torch.Tensor | nn.Parameter]`):
The weights in x-w computation.
x_acts (`TensorsCache` or `None`):
The x activations in x-w computation.
y_acts (`TensorsCache` or `None`):
The y activat... | _reset | python | mit-han-lab/deepcompressor | deepcompressor/calib/range.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/range.py | Apache-2.0 |
def get_best(self) -> DynamicRange:
"""Get the best candidate.
Returns:
`DynamicRange`:
The best candidate.
"""
if self.static:
return DynamicRange(min=self.best_range.min, max=self.best_range.max)
elif self.is_clamp_based():
r... | Get the best candidate.
Returns:
`DynamicRange`:
The best candidate.
| get_best | python | mit-han-lab/deepcompressor | deepcompressor/calib/range.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/range.py | Apache-2.0 |
def _ask(self) -> DynamicRange:
"""Ask for the next candidate.
Returns:
`DynamicRange`:
The next candidate.
"""
ratio = self.ratios[self.iter][self.candidate_id]
if self.is_clamp_based():
return self.base_range.scale(
ratio... | Ask for the next candidate.
Returns:
`DynamicRange`:
The next candidate.
| _ask | python | mit-han-lab/deepcompressor | deepcompressor/calib/range.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/range.py | Apache-2.0 |
def _tell(self, error: list[torch.Tensor]) -> None: # noqa: C901
"""Tell the error of the last candidate and update the best candidate.
Args:
errors (`list[torch.Tensor]`):
The error of the last candidate.
"""
assert len(error) == 1, "The error should only h... | Tell the error of the last candidate and update the best candidate.
Args:
errors (`list[torch.Tensor]`):
The error of the last candidate.
| _tell | python | mit-han-lab/deepcompressor | deepcompressor/calib/range.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/range.py | Apache-2.0 |
def calibrate_dynamic_range(
tensor_type: TensorType,
config: DynamicRangeCalibConfig | None,
static: bool,
quantizer: Quantizer,
modules: tp.Sequence[nn.Module],
activations: TensorsCache,
weights: tp.Sequence[nn.Parameter] | None = None,
eval_inputs: TensorsCache | None = None,
eva... | Calibrate the dynamic range.
Args:
tensor_type (`TensorType`):
The tensor type.
config (`DynamicRangeCalibConfig`):
The quantization dynamic range calibration configuration.
static (`bool`):
Whether the dynamic range is static.
quantizer (`Quantiz... | calibrate_dynamic_range | python | mit-han-lab/deepcompressor | deepcompressor/calib/range.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/range.py | Apache-2.0 |
def process(self, tensor: torch.Tensor) -> torch.Tensor:
"""Process the tensor.
Args:
tensor (torch.Tensor): The tensor to process.
Returns:
torch.Tensor: The processed tensor.
"""
self.index = self.index.to(device=tensor.device)
return tensor.in... | Process the tensor.
Args:
tensor (torch.Tensor): The tensor to process.
Returns:
torch.Tensor: The processed tensor.
| process | python | mit-han-lab/deepcompressor | deepcompressor/calib/reorder.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/reorder.py | Apache-2.0 |
def get_channel_index_from_rank(
rank: torch.Tensor,
num_channels: int,
num_groups: int,
index_mode: ChannelOrderCalibConfig.ChannelIndex,
) -> torch.Tensor:
"""Get the index from the rank.
Args:
rank (`torch.Tensor`):
The rank of the channels.
num_channels (`int`):
... | Get the index from the rank.
Args:
rank (`torch.Tensor`):
The rank of the channels.
num_channels (`int`):
The number of channels.
num_groups (`int`):
The number of groups.
index_mode (`ChannelOrderCalibConfig.ChannelIndex`):
The index ... | get_channel_index_from_rank | python | mit-han-lab/deepcompressor | deepcompressor/calib/reorder.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/reorder.py | Apache-2.0 |
def get_channel_metric(
inputs: TensorsCache,
weights: tp.Sequence[torch.Tensor],
metric_mode: ChannelOrderCalibConfig.ChannelMetric,
num_channels: int,
num_heads: int = 1,
device: torch.device | str | None = None,
dtype: torch.dtype = torch.float32,
) -> torch.Tensor:
"""Get the metric ... | Get the metric value of the channels.
Args:
inputs (`TensorsCache`):
The input activations.
weights (`Sequence[torch.Tensor]`):
The weight tensors.
metric_mode (`ChannelOrderCalibConfig.ChannelMetric`):
The channel metric mode.
num_channels (`int`... | get_channel_metric | python | mit-han-lab/deepcompressor | deepcompressor/calib/reorder.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/reorder.py | Apache-2.0 |
def update_channel_metric(
metric: torch.Tensor | None,
inputs: TensorsCache,
weights: tp.Sequence[torch.Tensor],
metric_mode: ChannelOrderCalibConfig.ChannelMetric,
num_channels: int,
num_heads: int = 1,
device: torch.device | str = None,
dtype: torch.dtype = torch.float32,
) -> torch.T... | Update the metric value of the channels.
Args:
metric (`torch.Tensor` or `None`):
The metric value of the channels.
inputs (`TensorsCache`):
The input activations.
weights (`Sequence[torch.Tensor]`):
The weight tensors.
metric_mode (`ChannelOrderC... | update_channel_metric | python | mit-han-lab/deepcompressor | deepcompressor/calib/reorder.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/reorder.py | Apache-2.0 |
def init_channel_index_from_metric(
metric: torch.Tensor,
/,
metric_mode: ChannelOrderCalibConfig.ChannelMetric,
index_mode: ChannelOrderCalibConfig.ChannelIndex,
group_size: int,
num_heads: int = 1,
num_head_repeats: int = 1,
) -> torch.Tensor:
"""Get the index of the channels.
Arg... | Get the index of the channels.
Args:
metric (`torch.Tensor`):
The metric value of the channels.
metric_mode (`ChannelOrderCalibConfig.ChannelMetric`):
The channel metric mode.
index_mode (`ChannelOrderCalibConfig.ChannelIndex`):
The index mode.
gr... | init_channel_index_from_metric | python | mit-han-lab/deepcompressor | deepcompressor/calib/reorder.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/reorder.py | Apache-2.0 |
def __init__(
self,
config: ChannelOrderCalibConfig,
weight_quantizer: Quantizer | None,
input_quantizer: Quantizer | None,
num_heads: int = 1,
num_head_repeats: int = 1,
develop_dtype: torch.dtype = torch.float32,
) -> None:
"""Initialize the calibrat... | Initialize the calibrator.
Args:
config (`ChannelOrderCalibConfig`):
The channel order calibration configuration.
weight_quantizer (`Quantizer` or `None`):
The quantizer for the weights.
input_quantizer (`Quantizer` or `None`):
... | __init__ | python | mit-han-lab/deepcompressor | deepcompressor/calib/reorder.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/reorder.py | Apache-2.0 |
def update_channel_metrics(self, weights: list[torch.Tensor | nn.Parameter], inputs: TensorsCache) -> None:
"""Update the metrics of the channels.
Args:
weights (list[torch.Tensor | nn.Parameter]): The weight tensors.
inputs (TensorsCache): The input activations.
"""
... | Update the metrics of the channels.
Args:
weights (list[torch.Tensor | nn.Parameter]): The weight tensors.
inputs (TensorsCache): The input activations.
| update_channel_metrics | python | mit-han-lab/deepcompressor | deepcompressor/calib/reorder.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/reorder.py | Apache-2.0 |
def _reset(self, x_wgts: list[torch.Tensor | nn.Parameter], x_acts: TensorsCache, **kwargs) -> None:
"""Reset the calibrator.
Args:
x_wgts (list[list[torch.Tensor | nn.Parameter]]): Weight tensors.
x_acts (TensorsCache): Input activations.
"""
if self.channel_ind... | Reset the calibrator.
Args:
x_wgts (list[list[torch.Tensor | nn.Parameter]]): Weight tensors.
x_acts (TensorsCache): Input activations.
| _reset | python | mit-han-lab/deepcompressor | deepcompressor/calib/reorder.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/reorder.py | Apache-2.0 |
def _ask(self) -> torch.Tensor:
"""Ask for the next candidate.
Returns:
torch.Tensor: The next candidate.
"""
channel_index = self.channel_indexes[self.candidate_id]
channel_index_inverse = None
if channel_index is not None:
channel_index_inverse ... | Ask for the next candidate.
Returns:
torch.Tensor: The next candidate.
| _ask | python | mit-han-lab/deepcompressor | deepcompressor/calib/reorder.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/reorder.py | Apache-2.0 |
def _tell(self, errors: list[tuple[torch.Tensor, ...]]) -> None: # noqa: C901
"""Tell the error of the last candidate and update the best candidate.
Args:
errors (list[tuple[torch.Tensor, ...]]): The error of the last candidate.
"""
errors = [tuple(math.root_(e.to(torch.flo... | Tell the error of the last candidate and update the best candidate.
Args:
errors (list[tuple[torch.Tensor, ...]]): The error of the last candidate.
| _tell | python | mit-han-lab/deepcompressor | deepcompressor/calib/reorder.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/reorder.py | Apache-2.0 |
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
"""Apply RMSNorm normalization to hidden states."""
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_stat... | Apply RMSNorm normalization to hidden states. | forward | python | mit-han-lab/deepcompressor | deepcompressor/calib/rotate.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/rotate.py | Apache-2.0 |
def rotate_in_channels(weight: nn.Parameter, /, *, rotation: torch.Tensor) -> None:
"""Rotate the input channels of a weight matrix."""
shape, dtype = weight.shape, weight.dtype
weight.data = (
torch.matmul(weight.data.view(-1, rotation.shape[0]).to(dtype=torch.float64), rotation.to(weight.device))
... | Rotate the input channels of a weight matrix. | rotate_in_channels | python | mit-han-lab/deepcompressor | deepcompressor/calib/rotate.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/rotate.py | Apache-2.0 |
def rotate_out_channels(weight: nn.Parameter, /, *, rotation: torch.Tensor, bias: nn.Parameter | None = None) -> None:
"""Rotate the output channels of a weight matrix."""
shape, dtype = weight.shape, weight.dtype
out_channels, head_channels = shape[0], rotation.shape[0]
num_heads = out_channels // head... | Rotate the output channels of a weight matrix. | rotate_out_channels | python | mit-han-lab/deepcompressor | deepcompressor/calib/rotate.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/rotate.py | Apache-2.0 |
def hadamard_in_channels(
modules: tp.Iterable[nn.Module],
packager: BaseInputPackager = None,
dtype: torch.dtype | None = None,
device: torch.device | str | None = None,
):
"""Apply Hadamard quantization to the input channels of the modules."""
for module in modules:
if isinstance(modul... | Apply Hadamard quantization to the input channels of the modules. | hadamard_in_channels | python | mit-han-lab/deepcompressor | deepcompressor/calib/rotate.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/rotate.py | Apache-2.0 |
def get_rotation_matrix(num_channels: int, random: bool = True, compatible: bool = True) -> torch.Tensor:
"""Get a random rotation matrix for the given number of channels."""
if random:
return random_hadamard_matrix(num_channels)
else:
rhs, lhs, k = HadamardMatrix.get(num_channels, scale=Fal... | Get a random rotation matrix for the given number of channels. | get_rotation_matrix | python | mit-han-lab/deepcompressor | deepcompressor/calib/rotate.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/rotate.py | Apache-2.0 |
def transform_rms_norm_and_linear(norm: nn.LayerNorm | RMSNorm, next_modules: tp.Iterable[nn.Linear]) -> None:
"""Fuse the weight multiplication of rms norm into the next adjacent linear modules.
Args:
norm (`nn.LayerNorm` or `RMSNorm`):
normalization module.
next_modules (`Iterable... | Fuse the weight multiplication of rms norm into the next adjacent linear modules.
Args:
norm (`nn.LayerNorm` or `RMSNorm`):
normalization module.
next_modules (`Iterable[nn.Linear]`):
modules after the normalization module.
| transform_rms_norm_and_linear | python | mit-han-lab/deepcompressor | deepcompressor/calib/rotate.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/rotate.py | Apache-2.0 |
def transform_layer_norm_to_rms_norm(
parent: nn.Module,
norm_name: str,
prev_modules: tp.Iterable[nn.Linear],
prev_out_channels_dims: int | tp.Iterable[int] = 0,
) -> None:
"""Transform LayerNorm to RMSNorm.
Args:
parent (`nn.Module`):
Parent module that contains the normal... | Transform LayerNorm to RMSNorm.
Args:
parent (`nn.Module`):
Parent module that contains the normalization module.
norm_name (`str`):
Name of the normalization module in `parent`.
prev_modules (`Iterable[nn.Linear]`):
Previous adjacent linear modules.
... | transform_layer_norm_to_rms_norm | python | mit-han-lab/deepcompressor | deepcompressor/calib/rotate.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/rotate.py | Apache-2.0 |
def transform_norm_and_linear(
parent: nn.Module,
norm_name: str,
next_modules: tp.Iterable[nn.Linear],
prev_modules: tp.Iterable[nn.Linear] | None = None,
prev_out_channels_dims: int | tp.Iterable[int] = 0,
):
"""Transform the normalization module and the next adjacent linear modules.
Args... | Transform the normalization module and the next adjacent linear modules.
Args:
parent (nn.Module): Parent module.
norm_name (str): Name of the normalization module.
next_modules (tp.Iterable[nn.Linear]): Next adjacent linear modules.
prev_modules (tp.Iterable[nn.Linear]): Previous a... | transform_norm_and_linear | python | mit-han-lab/deepcompressor | deepcompressor/calib/rotate.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/rotate.py | Apache-2.0 |
def __init__(
self,
tensor_type: TensorType,
config: _CONFIG,
w_quantizer: Quantizer | None,
x_quantizer: Quantizer | None,
y_quantizer: Quantizer | None,
develop_dtype: torch.dtype,
) -> None:
"""Initialize the search-based calibrator.
Args:
... | Initialize the search-based calibrator.
Args:
tensor_type (`TensorType`):
The tensor type.
config (`_CONFIG`):
The calibration configuration.
w_quantizer (`Quantizer` or `None`):
The w quantizer for x-w computation.
... | __init__ | python | mit-han-lab/deepcompressor | deepcompressor/calib/search.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/search.py | Apache-2.0 |
def tell(self, error: list[torch.Tensor]) -> None:
"""Tell the error of the last candidate and update the best candidate.
Args:
error (`list[torch.Tensor]`):
The error of the last candidate.
"""
self._tell(error)
self.candidate_id += 1
if self... | Tell the error of the last candidate and update the best candidate.
Args:
error (`list[torch.Tensor]`):
The error of the last candidate.
| tell | python | mit-han-lab/deepcompressor | deepcompressor/calib/search.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/search.py | Apache-2.0 |
def calibrate(
self,
x_wgts: list[nn.Parameter] | None = None,
y_wgts: list[nn.Parameter] | None = None,
x_acts: TensorsCache | None = None,
y_acts: TensorsCache | None = None,
x_mods: list[nn.Module] | None = None,
y_mods: list[nn.Module] | None = None,
e... | Calibrate the quantization parameters.
Args:
x_wgts (`list[nn.Parameter]` or `None`, *optional*, defaults to `None`):
The weights in x-w computation, or weights that generates x for y-x computation.
y_wgts (`list[nn.Parameter]` or `None`, *optional*, defaults to `None`):... | calibrate | python | mit-han-lab/deepcompressor | deepcompressor/calib/search.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/search.py | Apache-2.0 |
def process(self, tensor: torch.Tensor) -> torch.Tensor:
"""Process the tensor.
Args:
tensor (`torch.Tensor`):
The tensor to smooth.
Returns:
`torch.Tensor`:
The smoothed tensor.
"""
device, dtype = tensor.device, tensor.d... | Process the tensor.
Args:
tensor (`torch.Tensor`):
The tensor to smooth.
Returns:
`torch.Tensor`:
The smoothed tensor.
| process | python | mit-han-lab/deepcompressor | deepcompressor/calib/smooth.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/smooth.py | Apache-2.0 |
def get_smooth_span(
tensors: tp.Sequence[torch.Tensor],
/,
*,
group_shape: tp.Sequence[int],
span_mode: SmoothSpanMode,
device: torch.device | str | None = None,
dtype: torch.dtype = torch.float32,
) -> torch.Tensor:
"""Calculate the value span of tensors for calculating smoothing scale... | Calculate the value span of tensors for calculating smoothing scale.
Args:
tensors (`Sequence[torch.Tensor]`):
Tensors to calculate the span.
group_shape (`Sequence[int]`):
Quantization group shape.
span_mode (`SmoothSpanMode`):
The quantization smoothing... | get_smooth_span | python | mit-han-lab/deepcompressor | deepcompressor/calib/smooth.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/smooth.py | Apache-2.0 |
def get_smooth_scale(*, alpha_base: torch.Tensor, beta_base: torch.Tensor, alpha: float, beta: float) -> torch.Tensor:
"""Calculate the smoothing scale for quantization. Scale = alpha_base^alpha / beta_base^beta.
Args:
alpha_base (`torch.Tensor`):
Base span for alpha.
beta_base (`to... | Calculate the smoothing scale for quantization. Scale = alpha_base^alpha / beta_base^beta.
Args:
alpha_base (`torch.Tensor`):
Base span for alpha.
beta_base (`torch.Tensor`):
Base span for beta.
alpha (`float`):
Alpha.
beta (`float`):
... | get_smooth_scale | python | mit-han-lab/deepcompressor | deepcompressor/calib/smooth.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/smooth.py | Apache-2.0 |
def __init__(
self,
tensor_type: TensorType,
config: SmoothCalibConfig,
w_quantizer: Quantizer | None,
x_quantizer: Quantizer | None,
y_quantizer: Quantizer | None,
num_heads: int = 1,
num_head_repeats: int = 1,
with_rope: bool = False,
dev... | Initialize the calibrator.
Args:
tensor_type (`TensorType`):
The type of tensor to quantize. Choices are ``Weights`` and ``Outputs``.
config (`SmoothCalibConfig`):
The quantization smoothing calibration configuration.
w_quantizer (`Quantizer` ... | __init__ | python | mit-han-lab/deepcompressor | deepcompressor/calib/smooth.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/smooth.py | Apache-2.0 |
def _reset( # noqa: C901
self,
*,
x_wgts: list[torch.Tensor | nn.Parameter],
x_acts: TensorsCache,
y_wgts: list[torch.Tensor | nn.Parameter] = None,
y_acts: TensorsCache | None = None,
**kwargs,
) -> None:
"""Reset the calibrator.
Args:
... | Reset the calibrator.
Args:
x_wgts (`list[torch.Tensor | nn.Parameter]`):
The weights in x-w computation, or weights that generates x for y-x computation.
x_acts (`TensorsCache`):
The x activations. It should be x for x-w or y-x computation.
y... | _reset | python | mit-han-lab/deepcompressor | deepcompressor/calib/smooth.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/smooth.py | Apache-2.0 |
def _split_candidate_id(self, candidate_id: int) -> tuple[int, int]:
"""Split the candidate id into alpha_beta id and span_pair id.
Args:
candidate_id (`int`):
The candidate id.
Returns:
`tuple[int, int]`:
The alpha_beta id and span_mode ... | Split the candidate id into alpha_beta id and span_pair id.
Args:
candidate_id (`int`):
The candidate id.
Returns:
`tuple[int, int]`:
The alpha_beta id and span_mode id.
| _split_candidate_id | python | mit-han-lab/deepcompressor | deepcompressor/calib/smooth.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/smooth.py | Apache-2.0 |
def _ask(self) -> torch.Tensor:
"""Ask for the next candidate.
Returns:
`torch.Tensor`:
The next candidate.
"""
alpha_beta_id, span_pair_id = self._split_candidate_id(self.candidate_id)
alpha, beta = self.alpha_beta_pairs[alpha_beta_id]
a_span... | Ask for the next candidate.
Returns:
`torch.Tensor`:
The next candidate.
| _ask | python | mit-han-lab/deepcompressor | deepcompressor/calib/smooth.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/smooth.py | Apache-2.0 |
def __init__(
self,
config: SmoothCalibConfig,
weight_quantizer: Quantizer | None,
input_quantizer: Quantizer | None,
num_heads: int = 1,
num_head_repeats: int = 1,
develop_dtype: torch.dtype = torch.float32,
) -> None:
"""Initialize the calibrator.
... | Initialize the calibrator.
Args:
config (`SmoothCalibConfig`):
The quantization smoothing calibration configuration.
weight_quantizer (`Quantizer` or `None`):
The weight quantizer.
input_quantizer (`Quantizer` or `None`):
The i... | __init__ | python | mit-han-lab/deepcompressor | deepcompressor/calib/smooth.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/smooth.py | Apache-2.0 |
def __init__(
self,
config: SmoothCalibConfig,
query_quantizer: Quantizer | None,
key_quantizer: Quantizer | None,
num_heads: int = 1,
num_head_repeats: int = 1,
with_rope: bool = True,
develop_dtype: torch.dtype = torch.float32,
) -> None:
"""... | Initialize the calibrator.
Args:
config (`SmoothCalibConfig`):
The quantization smoothing calibration configuration.
query_quantizer (`Quantizer` or `None`):
The query quantizer.
key_quantizer (`Quantizer` or `None`):
The key q... | __init__ | python | mit-han-lab/deepcompressor | deepcompressor/calib/smooth.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/smooth.py | Apache-2.0 |
def calibrate(
self,
q_proj_weight: nn.Parameter,
k_proj_weight: nn.Parameter,
queries: TensorsCache,
keys: TensorsCache,
query_module: nn.Module,
key_module: nn.Module,
eval_module: nn.Module | None = None,
eval_inputs: TensorsCache | None = None,... | Calibrate the quantization for attention.
Args:
q_proj_weight (`nn.Parameter`):
The query projection weight.
k_proj_weight (`nn.Parameter`):
The key projection weight.
queries (`TensorsCache`):
The query activations.
... | calibrate | python | mit-han-lab/deepcompressor | deepcompressor/calib/smooth.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/smooth.py | Apache-2.0 |
def smooth_upscale_param(param: nn.Parameter, scale: torch.Tensor, channels_dim: int = 1) -> None:
"""In-place smooth the parameter by upscaling.
Args:
param (`nn.Parameter`):
The parameter to smooth.
scale (`torch.Tensor`):
The scale to upscale.
channels_dim (`i... | In-place smooth the parameter by upscaling.
Args:
param (`nn.Parameter`):
The parameter to smooth.
scale (`torch.Tensor`):
The scale to upscale.
channels_dim (`int`, *optional*, defaults to `1`):
The dimension of channels.
| smooth_upscale_param | python | mit-han-lab/deepcompressor | deepcompressor/calib/smooth.py | https://github.com/mit-han-lab/deepcompressor/blob/master/deepcompressor/calib/smooth.py | Apache-2.0 |
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