Kye
10 months ago
parent
b1598aa71a
commit
10829b03e2
@ -1,310 +0,0 @@
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from typing import Callable, Union
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import torch
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from torch import Tensor, nn
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from torch.distributed._tensor import (
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DeviceMesh,
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DTensor,
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Replicate,
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Shard,
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distribute_tensor,
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)
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from zeta.nn import QuantizedLN
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try:
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from peft.tuners.lora import Linear as LoRALinear
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except ImportError:
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class LoRALinear:
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pass
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def try_to_local(tensor: Union[Tensor, DTensor]):
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"""Try to convert DTensor to Tensor.
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Args:
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tensor (Tensor|DTensor): Tensor to convert.
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"""
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if isinstance(tensor, DTensor):
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tensor = tensor.to_local()
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return tensor
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def module_to_local(module: nn.Module):
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"""convert all DTensor parameters to Tensor parameters in module.
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Args:
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module (Module): Module to convert.
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"""
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for name, mod in module.named_children():
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module_to_local(mod)
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for name, param in module.named_parameters(recurse=False):
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module.register_parameter(
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name, nn.Parameter(try_to_local(param))
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)
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for name, buf in module.named_buffers(recurse=False):
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module.register_buffer(name, try_to_local(buf))
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def rowwise_parallelize_linear(
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module: nn.Module, device_mesh: DeviceMesh, to_local: bool = False
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) -> None:
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"""
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This function parallelizes the input :class:`nn.Linear` module in
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:class:`RowwiseParallel` style.
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Args:
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module (:class:`nn.Module`):
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The :class:`nn.Linear` module to be parallelized.
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device_mesh (:class:`DeviceMesh`):
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Object which describes the mesh topology of devices.
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Returns:
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None
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"""
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for name, param in module.named_parameters():
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dist_spec = (
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[Shard(1)] if name == "weight" else [Replicate()] # type: ignore[list-item]
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)
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dist_tensor = distribute_tensor(param, device_mesh, dist_spec)
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if to_local:
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dist_tensor = try_to_local(dist_tensor)
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if name == "bias":
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# rowwise linear would add bias more than ones.
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dist_tensor /= device_mesh.size()
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dist_param = torch.nn.Parameter(dist_tensor)
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module.register_parameter(name, dist_param)
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# Weight, bias and scale are registered as buffer in QLinear
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for name, buffer in module.named_buffers():
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dist_spec = (
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[Shard(1)] if name == "weight" else [Replicate()] # type: ignore[list-item]
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)
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dist_tensor = distribute_tensor(
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buffer, device_mesh, dist_spec
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)
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if to_local:
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dist_tensor = try_to_local(dist_tensor)
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if name == "bias":
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# rowwise linear would add bias more than ones.
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dist_tensor /= device_mesh.size()
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module.register_buffer(name, dist_tensor)
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dist_tensor = distribute_tensor(
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buffer, device_mesh, dist_spec
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)
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if to_local:
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dist_tensor = try_to_local(dist_tensor)
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module.register_buffer(name, dist_tensor)
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def rowwise_parallelize_loralinear(
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module: LoRALinear,
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device_mesh: DeviceMesh,
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to_local: bool = False,
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) -> None:
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"""rowwize parallelize lora linear.
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Read S-LoRA for more detail.
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"""
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rowwise_parallelize_linear(
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module.base_layer, device_mesh=device_mesh, to_local=to_local
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)
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for mod in module.lora_A.values():
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rowwise_parallelize_linear(
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mod, device_mesh=device_mesh, to_local=to_local
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)
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for mod in module.lora_B.values():
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colwise_parallelize_linear(
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mod, device_mesh=device_mesh, to_local=to_local
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)
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module._tp_mode = "rowwise"
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def rowwise_parallelize_linear_fn(
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module: nn.Module, device_mesh: DeviceMesh, to_local: bool = False
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) -> None:
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"""
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This function parallelizes the input :Linear module in
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:class:`RowwiseParallel` style.
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Args:
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module (:class:`nn.Module`):
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The :class:`nn.Linear` module to be parallelized.
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device_mesh (:class:`DeviceMesh`):
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Object which describes the mesh topology of devices.
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Returns:
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None
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"""
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if isinstance(module, (torch.nn.Linear, QuantizedLN)):
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return rowwise_parallelize_linear(
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module, device_mesh=device_mesh, to_local=to_local
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)
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elif isinstance(module, LoRALinear):
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return rowwise_parallelize_loralinear(
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module, device_mesh=device_mesh, to_local=to_local
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)
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else:
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raise TypeError(f"Unsupported module: {type(module)}")
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def colwise_parallelize_linear(
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module: nn.Module, device_mesh: DeviceMesh, to_local: bool = False
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) -> None:
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"""
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This function parallelizes the input :class:`nn.Linear` module in
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:class:`ColwiseParallel` style.
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Args:
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module (:class:`nn.Module`):
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The :class:`nn.Linear` module to be parallelized.
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device_mesh (:class:`DeviceMesh`):
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Object which describes the mesh topology of devices.
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Returns:
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None
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"""
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for name, param in module.named_parameters():
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dist_tensor = distribute_tensor(
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param, device_mesh, [Shard(0)]
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)
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if to_local:
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dist_tensor = try_to_local(dist_tensor)
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dist_param = torch.nn.Parameter(dist_tensor)
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module.register_parameter(name, dist_param)
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# Weight, bias and scale are registered as buffer in QLinear
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for name, buffer in module.named_buffers():
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dist_tensor = distribute_tensor(
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buffer, device_mesh, [Shard(0)]
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)
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if to_local:
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dist_tensor = try_to_local(dist_tensor)
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module.register_buffer(name, dist_tensor)
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def colwise_parallelize_loralinear(
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module: nn.Module, device_mesh: DeviceMesh, to_local: bool = False
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) -> None:
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"""colwise parallelize lora linear."""
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colwise_parallelize_linear(
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module.base_layer, device_mesh=device_mesh, to_local=to_local
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)
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for mod in module.lora_A.values():
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colwise_parallelize_linear(
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mod, device_mesh=device_mesh, to_local=to_local
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)
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for mod in module.lora_B.values():
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colwise_parallelize_linear(
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mod, device_mesh=device_mesh, to_local=to_local
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)
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module._tp_mode = "colwise"
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def colwise_parallelize_linear_fn(
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module: nn.Module, device_mesh: DeviceMesh, to_local: bool = False
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) -> None:
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"""
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This function parallelizes the input :Linear module in
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:class:`ColwiseParallel` style.
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Args:
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module (:class:`nn.Module`):
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The :class:`nn.Linear` module to be parallelized.
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device_mesh (:class:`DeviceMesh`):
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Object which describes the mesh topology of devices.
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Returns:
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None
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"""
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if isinstance(module, (torch.nn.Linear, QuantizedLN)):
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return colwise_parallelize_linear(
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module, device_mesh=device_mesh, to_local=to_local
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)
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elif isinstance(module, LoRALinear):
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return colwise_parallelize_loralinear(
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module, device_mesh=device_mesh, to_local=to_local
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)
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else:
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raise TypeError(f"Unsupported module: {type(module)}")
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def _partition_module(
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mod_name: str,
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prefix: str,
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module: nn.Module,
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device_mesh: DeviceMesh,
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func: Callable,
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):
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"""partition module.
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Parameters in module won't be force Replicated.
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Args:
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mod_name (str): module name.
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prefix (str): Parameter prefix.
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module (Module): Module to be partitioned.
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device_mesh (DeviceMesh): The device mesh.
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func (Callable): partition callback
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"""
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for name, mod in module.named_children():
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child_name = f"{prefix}{name}"
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_partition_module(
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child_name,
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child_name + ".",
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module=mod,
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device_mesh=device_mesh,
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func=func,
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)
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func(mod_name, module, device_mesh)
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def partition_module(
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module: nn.Module,
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device_mesh: DeviceMesh,
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func: Callable,
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to_local: bool = False,
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):
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"""partition module.
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Parameters in module won't be force Replicated.
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Args:
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module (Module): Module to be partitioned.
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device_mesh (DeviceMesh): The device mesh.
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func (Callable): partition callback.
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to_local (bool): Convert all DTensor parameters to Tensor parameters.
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"""
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_partition_module(
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"", "", module=module, device_mesh=device_mesh, func=func
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)
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if to_local:
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module_to_local(module)
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def replicate_module(model: nn.Module, device_mesh: DeviceMesh):
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"""Replicate all parameters in module.
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Args:
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model (Module): Module to perform replicate.
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device_mesh (DeviceMesh): The distribution device mesh.
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"""
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for name, param in model.named_parameters(recurse=False):
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param = distribute_tensor(
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param, device_mesh=device_mesh, placements=[Replicate()]
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).to_local()
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param = nn.Parameter(param)
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model.register_parameter(name, param)
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for name, buf in model.named_buffers(recurse=False):
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buf = distribute_tensor(
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buf, device_mesh=device_mesh, placements=[Replicate()]
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).to_local()
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model.register_buffer(name, buf)
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@ -1,10 +1,21 @@
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from loguru import logger
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from loguru import logger
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logger.add(
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logger.add(
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"MessagePool.log",
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"swarms.log",
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level="INFO",
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level="INFO",
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colorize=True,
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colorize=True,
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format="<green>{time}</green> <level>{message}</level>",
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format="<green>{time}</green> <level>{message}</level>",
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backtrace=True,
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backtrace=True,
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diagnose=True,
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diagnose=True,
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)
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)
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def loguru_logger(file_path: str = "swarms.log"):
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return logger.add(
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file_path,
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level="INFO",
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colorize=True,
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format="<green>{time}</green> <level>{message}</level>",
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backtrace=True,
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diagnose=True,
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)
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@ -1,259 +0,0 @@
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from enum import Enum
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import cv2
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import numpy as np
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import supervision as sv
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class FeatureType(Enum):
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"""
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An enumeration to represent the types of features for mask adjustment in image
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segmentation.
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"""
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ISLAND = "ISLAND"
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HOLE = "HOLE"
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@classmethod
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def list(cls):
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return list(map(lambda c: c.value, cls))
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def compute_mask_iou_vectorized(masks: np.ndarray) -> np.ndarray:
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"""
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Vectorized computation of the Intersection over Union (IoU) for all pairs of masks.
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Parameters:
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masks (np.ndarray): A 3D numpy array with shape `(N, H, W)`, where `N` is the
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number of masks, `H` is the height, and `W` is the width.
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Returns:
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np.ndarray: A 2D numpy array of shape `(N, N)` where each element `[i, j]` is
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the IoU between masks `i` and `j`.
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Raises:
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ValueError: If any of the masks is found to be empty.
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"""
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if np.any(masks.sum(axis=(1, 2)) == 0):
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raise ValueError(
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"One or more masks are empty. Please filter out empty"
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" masks before using `compute_iou_vectorized` function."
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)
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masks_bool = masks.astype(bool)
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masks_flat = masks_bool.reshape(masks.shape[0], -1)
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intersection = np.logical_and(
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masks_flat[:, None], masks_flat[None, :]
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).sum(axis=2)
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union = np.logical_or(
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masks_flat[:, None], masks_flat[None, :]
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).sum(axis=2)
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iou_matrix = intersection / union
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return iou_matrix
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def mask_non_max_suppression(
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masks: np.ndarray, iou_threshold: float = 0.6
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) -> np.ndarray:
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"""
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Performs Non-Max Suppression on a set of masks by prioritizing larger masks and
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removing smaller masks that overlap significantly.
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When the IoU between two masks exceeds the specified threshold, the smaller mask
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(in terms of area) is discarded. This process is repeated for each pair of masks,
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effectively filtering out masks that are significantly overlapped by larger ones.
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Parameters:
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masks (np.ndarray): A 3D numpy array with shape `(N, H, W)`, where `N` is the
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number of masks, `H` is the height, and `W` is the width.
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iou_threshold (float): The IoU threshold for determining significant overlap.
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Returns:
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np.ndarray: A 3D numpy array of filtered masks.
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"""
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num_masks = masks.shape[0]
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|
||||||
areas = masks.sum(axis=(1, 2))
|
|
||||||
sorted_idx = np.argsort(-areas)
|
|
||||||
keep_mask = np.ones(num_masks, dtype=bool)
|
|
||||||
iou_matrix = compute_mask_iou_vectorized(masks)
|
|
||||||
for i in range(num_masks):
|
|
||||||
if not keep_mask[sorted_idx[i]]:
|
|
||||||
continue
|
|
||||||
|
|
||||||
overlapping_masks = iou_matrix[sorted_idx[i]] > iou_threshold
|
|
||||||
overlapping_masks[sorted_idx[i]] = False
|
|
||||||
overlapping_indices = np.where(overlapping_masks)[0]
|
|
||||||
keep_mask[sorted_idx[overlapping_indices]] = False
|
|
||||||
|
|
||||||
return masks[keep_mask]
|
|
||||||
|
|
||||||
|
|
||||||
def filter_masks_by_relative_area(
|
|
||||||
masks: np.ndarray,
|
|
||||||
minimum_area: float = 0.01,
|
|
||||||
maximum_area: float = 1.0,
|
|
||||||
) -> np.ndarray:
|
|
||||||
"""
|
|
||||||
Filters masks based on their relative area within the total area of each mask.
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
masks (np.ndarray): A 3D numpy array with shape `(N, H, W)`, where `N` is the
|
|
||||||
number of masks, `H` is the height, and `W` is the width.
|
|
||||||
minimum_area (float): The minimum relative area threshold. Must be between `0`
|
|
||||||
and `1`.
|
|
||||||
maximum_area (float): The maximum relative area threshold. Must be between `0`
|
|
||||||
and `1`.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
np.ndarray: A 3D numpy array containing masks that fall within the specified
|
|
||||||
relative area range.
|
|
||||||
|
|
||||||
Raises:
|
|
||||||
ValueError: If `minimum_area` or `maximum_area` are outside the `0` to `1`
|
|
||||||
range, or if `minimum_area` is greater than `maximum_area`.
|
|
||||||
"""
|
|
||||||
|
|
||||||
if not (isinstance(masks, np.ndarray) and masks.ndim == 3):
|
|
||||||
raise ValueError("Input must be a 3D numpy array.")
|
|
||||||
|
|
||||||
if not (0 <= minimum_area <= 1) or not (0 <= maximum_area <= 1):
|
|
||||||
raise ValueError(
|
|
||||||
"`minimum_area` and `maximum_area` must be between 0"
|
|
||||||
" and 1."
|
|
||||||
)
|
|
||||||
|
|
||||||
if minimum_area > maximum_area:
|
|
||||||
raise ValueError(
|
|
||||||
"`minimum_area` must be less than or equal to"
|
|
||||||
" `maximum_area`."
|
|
||||||
)
|
|
||||||
|
|
||||||
total_area = masks.shape[1] * masks.shape[2]
|
|
||||||
relative_areas = masks.sum(axis=(1, 2)) / total_area
|
|
||||||
return masks[
|
|
||||||
(relative_areas >= minimum_area)
|
|
||||||
& (relative_areas <= maximum_area)
|
|
||||||
]
|
|
||||||
|
|
||||||
|
|
||||||
def adjust_mask_features_by_relative_area(
|
|
||||||
mask: np.ndarray,
|
|
||||||
area_threshold: float,
|
|
||||||
feature_type: FeatureType = FeatureType.ISLAND,
|
|
||||||
) -> np.ndarray:
|
|
||||||
"""
|
|
||||||
Adjusts a mask by removing small islands or filling small holes based on a relative
|
|
||||||
area threshold.
|
|
||||||
|
|
||||||
!!! warning
|
|
||||||
|
|
||||||
Running this function on a mask with small islands may result in empty masks.
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
mask (np.ndarray): A 2D numpy array with shape `(H, W)`, where `H` is the
|
|
||||||
height, and `W` is the width.
|
|
||||||
area_threshold (float): Threshold for relative area to remove or fill features.
|
|
||||||
feature_type (FeatureType): Type of feature to adjust (`ISLAND` for removing
|
|
||||||
islands, `HOLE` for filling holes).
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
np.ndarray: A 2D numpy array containing mask.
|
|
||||||
"""
|
|
||||||
height, width = mask.shape
|
|
||||||
total_area = width * height
|
|
||||||
|
|
||||||
mask = np.uint8(mask * 255)
|
|
||||||
operation = (
|
|
||||||
cv2.RETR_EXTERNAL
|
|
||||||
if feature_type == FeatureType.ISLAND
|
|
||||||
else cv2.RETR_CCOMP
|
|
||||||
)
|
|
||||||
contours, _ = cv2.findContours(
|
|
||||||
mask, operation, cv2.CHAIN_APPROX_SIMPLE
|
|
||||||
)
|
|
||||||
|
|
||||||
for contour in contours:
|
|
||||||
area = cv2.contourArea(contour)
|
|
||||||
relative_area = area / total_area
|
|
||||||
if relative_area < area_threshold:
|
|
||||||
cv2.drawContours(
|
|
||||||
image=mask,
|
|
||||||
contours=[contour],
|
|
||||||
contourIdx=-1,
|
|
||||||
color=(
|
|
||||||
0 if feature_type == FeatureType.ISLAND else 255
|
|
||||||
),
|
|
||||||
thickness=-1,
|
|
||||||
)
|
|
||||||
return np.where(mask > 0, 1, 0).astype(bool)
|
|
||||||
|
|
||||||
|
|
||||||
def masks_to_marks(masks: np.ndarray) -> sv.Detections:
|
|
||||||
"""
|
|
||||||
Converts a set of masks to a marks (sv.Detections) object.
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
masks (np.ndarray): A 3D numpy array with shape `(N, H, W)`, where `N` is the
|
|
||||||
number of masks, `H` is the height, and `W` is the width.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
sv.Detections: An object containing the masks and their bounding box
|
|
||||||
coordinates.
|
|
||||||
"""
|
|
||||||
if len(masks) == 0:
|
|
||||||
marks = sv.Detections.empty()
|
|
||||||
marks.mask = np.empty((0, 0, 0), dtype=bool)
|
|
||||||
return marks
|
|
||||||
return sv.Detections(
|
|
||||||
mask=masks, xyxy=sv.mask_to_xyxy(masks=masks)
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
def refine_marks(
|
|
||||||
marks: sv.Detections,
|
|
||||||
maximum_hole_area: float = 0.01,
|
|
||||||
maximum_island_area: float = 0.01,
|
|
||||||
minimum_mask_area: float = 0.02,
|
|
||||||
maximum_mask_area: float = 1.0,
|
|
||||||
) -> sv.Detections:
|
|
||||||
"""
|
|
||||||
Refines a set of masks by removing small islands and holes, and filtering by mask
|
|
||||||
area.
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
marks (sv.Detections): An object containing the masks and their bounding box
|
|
||||||
coordinates.
|
|
||||||
maximum_hole_area (float): The maximum relative area of holes to be filled in
|
|
||||||
each mask.
|
|
||||||
maximum_island_area (float): The maximum relative area of islands to be removed
|
|
||||||
from each mask.
|
|
||||||
minimum_mask_area (float): The minimum relative area for a mask to be retained.
|
|
||||||
maximum_mask_area (float): The maximum relative area for a mask to be retained.
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
sv.Detections: An object containing the masks and their bounding box
|
|
||||||
coordinates.
|
|
||||||
"""
|
|
||||||
result_masks = []
|
|
||||||
for mask in marks.mask:
|
|
||||||
mask = adjust_mask_features_by_relative_area(
|
|
||||||
mask=mask,
|
|
||||||
area_threshold=maximum_island_area,
|
|
||||||
feature_type=FeatureType.ISLAND,
|
|
||||||
)
|
|
||||||
mask = adjust_mask_features_by_relative_area(
|
|
||||||
mask=mask,
|
|
||||||
area_threshold=maximum_hole_area,
|
|
||||||
feature_type=FeatureType.HOLE,
|
|
||||||
)
|
|
||||||
if np.any(mask):
|
|
||||||
result_masks.append(mask)
|
|
||||||
result_masks = np.array(result_masks)
|
|
||||||
result_masks = filter_masks_by_relative_area(
|
|
||||||
masks=result_masks,
|
|
||||||
minimum_area=minimum_mask_area,
|
|
||||||
maximum_area=maximum_mask_area,
|
|
||||||
)
|
|
||||||
return sv.Detections(
|
|
||||||
mask=result_masks, xyxy=sv.mask_to_xyxy(masks=result_masks)
|
|
||||||
)
|
|
||||||
@ -1,27 +0,0 @@
|
|||||||
import tiktoken
|
|
||||||
|
|
||||||
|
|
||||||
def limit_tokens_from_string(
|
|
||||||
string: str, model: str = "gpt-4", limit: int = 500
|
|
||||||
) -> str:
|
|
||||||
"""Limits the number of tokens in a string
|
|
||||||
|
|
||||||
Args:
|
|
||||||
string (str): _description_
|
|
||||||
model (str): _description_
|
|
||||||
limit (int): _description_
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
str: _description_
|
|
||||||
"""
|
|
||||||
try:
|
|
||||||
encoding = tiktoken.encoding_for_model(model)
|
|
||||||
except Exception:
|
|
||||||
encoding = tiktoken.encoding_for_model(
|
|
||||||
"gpt2"
|
|
||||||
) # Fallback for others.
|
|
||||||
|
|
||||||
encoded = encoding.encode(string)
|
|
||||||
|
|
||||||
out = encoding.decode(encoded[:limit])
|
|
||||||
return out
|
|
||||||
Loading…
Reference in new issue