feat(func/min): Add function

lib/__init__.py

@@ -1,5 +1,8 @@
+from custom_nodes.KepPromptLang.lib.actions.abs_max import AbsMaxAction
 from custom_nodes.KepPromptLang.lib.actions.avg import AverageAction
 from custom_nodes.KepPromptLang.lib.actions.diff import DiffAction
+from custom_nodes.KepPromptLang.lib.actions.max import MaxAction
+from custom_nodes.KepPromptLang.lib.actions.min import MinAction
 from custom_nodes.KepPromptLang.lib.actions.mult import MultiplyAction
 from custom_nodes.KepPromptLang.lib.actions.neg import NegAction
 from custom_nodes.KepPromptLang.lib.actions.norm import NormAction
@@ -20,3 +23,6 @@ register_action(SlerpAction)
 register_action(AverageAction)
 register_action(ScaleDims)
 register_action(SetDims)
+register_action(MaxAction)
+register_action(AbsMaxAction)
+register_action(MinAction)

lib/actions/abs_max.py

@@ -0,0 +1,104 @@
+from typing import List, Union
+
+import torch
+from torch.nn import Embedding
+
+from custom_nodes.KepPromptLang.lib.action.base import Action, MultiArgAction
+from custom_nodes.KepPromptLang.lib.actions.action_utils import get_embedding
+from custom_nodes.KepPromptLang.lib.actions.utils import is_broadcastable
+from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
+
+
+class AbsMaxAction(MultiArgAction):
+    grammar = 'absMax(" arg ("|" arg)+ ")"'
+    name = "absMax"
+    chars = ["+", "+"]
+
+    def __init__(self, args: List[List[Union[PromptSegment, Action]]]) -> None:
+        super().__init__(args)
+
+        self.base_arg = args[0]
+        self.additional_args = args[1:]
+
+    def token_length(self) -> int:
+        # AbsMax modifies the base segment, so the length is the length of the base segment
+        return sum(seg_or_action.token_length() for seg_or_action in self.base_arg)
+
+    def get_result(self, embedding_module: Embedding) -> torch.Tensor:
+        # Calculate the embeddings for the base segment
+        all_base_embeddings = [
+            get_embedding(seg_or_action, embedding_module)
+            for seg_or_action in self.base_arg
+        ]
+
+        result = torch.cat(all_base_embeddings, dim=1)
+
+        for arg in self.additional_args:
+            all_arg_embeddings = [
+                get_embedding(seg_or_action, embedding_module) for seg_or_action in arg
+            ]
+
+            arg_embedding = torch.cat(all_arg_embeddings, dim=1)
+
+            if is_broadcastable(result, arg_embedding):
+                positive_mask1 = result > 0
+                positive_mask2 = arg_embedding > 0
+                negative_mask1 = result < 0
+                negative_mask2 = arg_embedding < 0
+                zero_mask1 = result == 0
+                zero_mask2 = arg_embedding == 0
+                
+                # For mixed signs, choose the one with the largest magnitude
+                mixed_mask_pos_neg = positive_mask1 & negative_mask2
+                mixed_mask_neg_pos = negative_mask1 & positive_mask2
+                mixed_mask = mixed_mask_pos_neg | mixed_mask_neg_pos
+                mixed_selection = torch.where(
+                    result.abs() > arg_embedding.abs(), result, arg_embedding
+                )
+                
+                # Apply max for positive dimensions, min for negative dimensions, and handle zeros and mixed signs
+                result = torch.where(
+                    positive_mask1 & positive_mask2,
+                    torch.max(result, arg_embedding),
+                    torch.where(
+                        negative_mask1 & negative_mask2,
+                        torch.min(result, arg_embedding),
+                        torch.where(
+                            positive_mask1 & zero_mask2,
+                            result,
+                            torch.where(
+                                zero_mask1 & positive_mask2,
+                                arg_embedding,
+                                torch.where(mixed_mask, mixed_selection, result),
+                            ),
+                        ),
+                    ),
+                )
+            else:
+                print(
+                    "WARNING: shape mismatch when trying to apply absMax, arg will be averaged"
+                )
+                result = torch.max(result, torch.mean(arg_embedding, dim=1, keepdim=True))
+        return result
+
+    # def __repr__(self):
+    #     return f"sum(\n\tbase_segment={self.base_segment},\n\targs={self.args}\n)"
+    def __repr__(self) -> str:
+        return f"sum({', '.join(map(str, self.additional_args))})"
+
+    def depth_repr(self, depth=1):
+        out = "NudgeAction(\n"
+        if isinstance(self.base_arg, Action):
+            base_segment_repr = self.base_arg.depth_repr(depth + 1)
+            out += "\t" * depth + f"base_segment={base_segment_repr}\n"
+        else:
+            out += "\t" * depth + f"base_segment={self.base_arg.depth_repr()},\n"
+
+        if isinstance(self.additional_args, Action):
+            target_repr = self.additional_args.depth_repr(depth + 1)
+            out += "\t" * depth + f"target={target_repr},\n"
+        else:
+            out += "\t" * depth + f"target={self.additional_args.depth_repr()},\n"
+        out += "\t" * depth + f"weight={self.weight},\n"
+        out += "\t" * (depth - 1) + ")"
+        return out

lib/actions/max.py

@@ -0,0 +1,72 @@
+from typing import List, Union
+
+import torch
+from torch.nn import Embedding
+
+from custom_nodes.KepPromptLang.lib.action.base import Action, MultiArgAction
+from custom_nodes.KepPromptLang.lib.actions.action_utils import get_embedding
+from custom_nodes.KepPromptLang.lib.actions.utils import is_broadcastable
+from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
+
+
+class MaxAction(MultiArgAction):
+    grammar = 'max(" arg ("|" arg)+ ")"'
+    name = "max"
+    chars = ["+", "+"]
+
+    def __init__(self, args: List[List[Union[PromptSegment, Action]]]) -> None:
+        super().__init__(args)
+
+        self.base_arg = args[0]
+        self.additional_args = args[1:]
+
+    def token_length(self) -> int:
+        # Max modifies the base segment, so the length is the length of the base segment
+        return sum(seg_or_action.token_length() for seg_or_action in self.base_arg)
+
+    def get_result(self, embedding_module: Embedding) -> torch.Tensor:
+        # Calculate the embeddings for the base segment
+        all_base_embeddings = [
+            get_embedding(seg_or_action, embedding_module)
+            for seg_or_action in self.base_arg
+        ]
+
+        result = torch.cat(all_base_embeddings, dim=1)
+
+        for arg in self.additional_args:
+            all_arg_embeddings = [
+                get_embedding(seg_or_action, embedding_module) for seg_or_action in arg
+            ]
+
+            arg_embedding = torch.cat(all_arg_embeddings, dim=1)
+
+            if is_broadcastable(result, arg_embedding):
+                result = torch.max(result, arg_embedding)
+            else:
+                print(
+                    "WARNING: shape mismatch when trying to apply max, arg will be averaged"
+                )
+                result = torch.max(result, torch.mean(arg_embedding, dim=1, keepdim=True))
+        return result
+
+    # def __repr__(self):
+    #     return f"sum(\n\tbase_segment={self.base_segment},\n\targs={self.args}\n)"
+    def __repr__(self) -> str:
+        return f"sum({', '.join(map(str, self.additional_args))})"
+
+    def depth_repr(self, depth=1):
+        out = "NudgeAction(\n"
+        if isinstance(self.base_arg, Action):
+            base_segment_repr = self.base_arg.depth_repr(depth + 1)
+            out += "\t" * depth + f"base_segment={base_segment_repr}\n"
+        else:
+            out += "\t" * depth + f"base_segment={self.base_arg.depth_repr()},\n"
+
+        if isinstance(self.additional_args, Action):
+            target_repr = self.additional_args.depth_repr(depth + 1)
+            out += "\t" * depth + f"target={target_repr},\n"
+        else:
+            out += "\t" * depth + f"target={self.additional_args.depth_repr()},\n"
+        out += "\t" * depth + f"weight={self.weight},\n"
+        out += "\t" * (depth - 1) + ")"
+        return out

lib/actions/min.py

@@ -0,0 +1,72 @@
+from typing import List, Union
+
+import torch
+from torch.nn import Embedding
+
+from custom_nodes.KepPromptLang.lib.action.base import Action, MultiArgAction
+from custom_nodes.KepPromptLang.lib.actions.action_utils import get_embedding
+from custom_nodes.KepPromptLang.lib.actions.utils import is_broadcastable
+from custom_nodes.KepPromptLang.lib.parser.prompt_segment import PromptSegment
+
+
+class MinAction(MultiArgAction):
+    grammar = 'min(" arg ("|" arg)+ ")"'
+    name = "min"
+    chars = ["+", "+"]
+
+    def __init__(self, args: List[List[Union[PromptSegment, Action]]]) -> None:
+        super().__init__(args)
+
+        self.base_arg = args[0]
+        self.additional_args = args[1:]
+
+    def token_length(self) -> int:
+        # Min modifies the base segment, so the length is the length of the base segment
+        return sum(seg_or_action.token_length() for seg_or_action in self.base_arg)
+
+    def get_result(self, embedding_module: Embedding) -> torch.Tensor:
+        # Calculate the embeddings for the base segment
+        all_base_embeddings = [
+            get_embedding(seg_or_action, embedding_module)
+            for seg_or_action in self.base_arg
+        ]
+
+        result = torch.cat(all_base_embeddings, dim=1)
+
+        for arg in self.additional_args:
+            all_arg_embeddings = [
+                get_embedding(seg_or_action, embedding_module) for seg_or_action in arg
+            ]
+
+            arg_embedding = torch.cat(all_arg_embeddings, dim=1)
+
+            if is_broadcastable(result, arg_embedding):
+                result = torch.min(result, arg_embedding)
+            else:
+                print(
+                    "WARNING: shape mismatch when trying to apply max, arg will be averaged"
+                )
+                result = torch.min(result, torch.mean(arg_embedding, dim=1, keepdim=True))
+        return result
+
+    # def __repr__(self):
+    #     return f"sum(\n\tbase_segment={self.base_segment},\n\targs={self.args}\n)"
+    def __repr__(self) -> str:
+        return f"sum({', '.join(map(str, self.additional_args))})"
+
+    def depth_repr(self, depth=1):
+        out = "NudgeAction(\n"
+        if isinstance(self.base_arg, Action):
+            base_segment_repr = self.base_arg.depth_repr(depth + 1)
+            out += "\t" * depth + f"base_segment={base_segment_repr}\n"
+        else:
+            out += "\t" * depth + f"base_segment={self.base_arg.depth_repr()},\n"
+
+        if isinstance(self.additional_args, Action):
+            target_repr = self.additional_args.depth_repr(depth + 1)
+            out += "\t" * depth + f"target={target_repr},\n"
+        else:
+            out += "\t" * depth + f"target={self.additional_args.depth_repr()},\n"
+        out += "\t" * depth + f"weight={self.weight},\n"
+        out += "\t" * (depth - 1) + ")"
+        return out

lib/actions/utils.py

@@ -36,3 +36,20 @@ def slerp(val: float, low: torch.Tensor, high: torch.Tensor, epsilon=1e-5):
     scale_1 = torch.where(close_condition, val, scale_1)
 
     return scale_0 * low + scale_1 * high
+
+def is_broadcastable(tensor1, tensor2) -> bool:
+    """
+    Check if two tensors are broadcastable.
+
+    Parameters:
+    - tensor1 (torch.Tensor): The target tensor against which broadcastability of tensor2 is checked.
+    - tensor2 (torch.Tensor): The tensor whose broadcastability is to be verified against tensor1.
+
+    Returns:
+    - bool: True if tensor2 is broadcastable to tensor1, False otherwise.
+    """
+    try:
+        broadcasted_shape = torch.broadcast_shapes(tensor1.shape, tensor2.shape)
+        return True
+    except RuntimeError:
+        return False