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configuration_kanllama.py

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+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""LLaMA model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class KANLlamaConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the LLaMA-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`LlamaModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens,
+            Llama 2 up to 4096, CodeLlama up to 16384.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        mlp_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.
+
+    ```python
+    >>> from transformers import LlamaModel, LlamaConfig
+
+    >>> # Initializing a LLaMA llama-7b style configuration
+    >>> configuration = LlamaConfig()
+
+    >>> # Initializing a model from the llama-7b style configuration
+    >>> model = LlamaModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "llama"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        mlp_bias=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.mlp_bias = mlp_bias
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with two fields, `type` and `factor`, " f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")

modeling_kanllama.py

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+import torch
+import torch.nn.functional as F
+import math
+
+from .configuration_kanllama import KANLlamaConfig
+
+######
+# KAN and KANLinear are take from efficient KAN
+# https://github.com/Blealtan/efficient-kan
+######
+
+class KANLinear(torch.nn.Module):
+    def __init__(
+        self,
+        in_features,
+        out_features,
+        grid_size=5,
+        spline_order=3,
+        scale_noise=0.1,
+        scale_base=1.0,
+        scale_spline=1.0,
+        enable_standalone_scale_spline=True,
+        base_activation=torch.nn.SiLU,
+        grid_eps=0.02,
+        grid_range=[-1, 1],
+    ):
+        super(KANLinear, self).__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.grid_size = grid_size
+        self.spline_order = spline_order
+
+        h = (grid_range[1] - grid_range[0]) / grid_size
+        grid = (
+            (
+                torch.arange(-spline_order, grid_size + spline_order + 1) * h
+                + grid_range[0]
+            )
+            .expand(in_features, -1)
+            .contiguous()
+        )
+        self.register_buffer("grid", grid)
+
+        self.base_weight = torch.nn.Parameter(torch.Tensor(out_features, in_features))
+        self.spline_weight = torch.nn.Parameter(
+            torch.Tensor(out_features, in_features, grid_size + spline_order)
+        )
+        if enable_standalone_scale_spline:
+            self.spline_scaler = torch.nn.Parameter(
+                torch.Tensor(out_features, in_features)
+            )
+
+        self.scale_noise = scale_noise
+        self.scale_base = scale_base
+        self.scale_spline = scale_spline
+        self.enable_standalone_scale_spline = enable_standalone_scale_spline
+        self.base_activation = base_activation()
+        self.grid_eps = grid_eps
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        torch.nn.init.kaiming_uniform_(self.base_weight, a=math.sqrt(5) * self.scale_base)
+        with torch.no_grad():
+            noise = (
+                (
+                    torch.rand(self.grid_size + 1, self.in_features, self.out_features)
+                    - 1 / 2
+                )
+                * self.scale_noise
+                / self.grid_size
+            )
+            self.spline_weight.data.copy_(
+                (self.scale_spline if not self.enable_standalone_scale_spline else 1.0)
+                * self.curve2coeff(
+                    self.grid.T[self.spline_order : -self.spline_order],
+                    noise,
+                )
+            )
+            if self.enable_standalone_scale_spline:
+                # torch.nn.init.constant_(self.spline_scaler, self.scale_spline)
+                torch.nn.init.kaiming_uniform_(self.spline_scaler, a=math.sqrt(5) * self.scale_spline)
+
+    def b_splines(self, x: torch.Tensor):
+        """
+        Compute the B-spline bases for the given input tensor.
+
+        Args:
+            x (torch.Tensor): Input tensor of shape (batch_size, in_features).
+
+        Returns:
+            torch.Tensor: B-spline bases tensor of shape (batch_size, in_features, grid_size + spline_order).
+        """
+        assert x.dim() == 2 and x.size(1) == self.in_features
+
+        grid: torch.Tensor = (
+            self.grid
+        )  # (in_features, grid_size + 2 * spline_order + 1)
+        x = x.unsqueeze(-1)
+        bases = ((x >= grid[:, :-1]) & (x < grid[:, 1:])).to(x.dtype)
+        for k in range(1, self.spline_order + 1):
+            bases = (
+                (x - grid[:, : -(k + 1)])
+                / (grid[:, k:-1] - grid[:, : -(k + 1)])
+                * bases[:, :, :-1]
+            ) + (
+                (grid[:, k + 1 :] - x)
+                / (grid[:, k + 1 :] - grid[:, 1:(-k)])
+                * bases[:, :, 1:]
+            )
+
+        assert bases.size() == (
+            x.size(0),
+            self.in_features,
+            self.grid_size + self.spline_order,
+        )
+        return bases.contiguous()
+
+    def curve2coeff(self, x: torch.Tensor, y: torch.Tensor):
+        """
+        Compute the coefficients of the curve that interpolates the given points.
+
+        Args:
+            x (torch.Tensor): Input tensor of shape (batch_size, in_features).
+            y (torch.Tensor): Output tensor of shape (batch_size, in_features, out_features).
+
+        Returns:
+            torch.Tensor: Coefficients tensor of shape (out_features, in_features, grid_size + spline_order).
+        """
+        assert x.dim() == 2 and x.size(1) == self.in_features
+        assert y.size() == (x.size(0), self.in_features, self.out_features)
+
+        A = self.b_splines(x).transpose(
+            0, 1
+        )  # (in_features, batch_size, grid_size + spline_order)
+        B = y.transpose(0, 1)  # (in_features, batch_size, out_features)
+        solution = torch.linalg.lstsq(
+            A, B
+        ).solution  # (in_features, grid_size + spline_order, out_features)
+        result = solution.permute(
+            2, 0, 1
+        )  # (out_features, in_features, grid_size + spline_order)
+
+        assert result.size() == (
+            self.out_features,
+            self.in_features,
+            self.grid_size + self.spline_order,
+        )
+        return result.contiguous()
+
+    @property
+    def scaled_spline_weight(self):
+        return self.spline_weight * (
+            self.spline_scaler.unsqueeze(-1)
+            if self.enable_standalone_scale_spline
+            else 1.0
+        )
+
+    def forward(self, x: torch.Tensor):
+        assert x.size(-1) == self.in_features
+        original_shape = x.shape
+        x = x.view(-1, self.in_features)
+
+        base_output = F.linear(self.base_activation(x), self.base_weight)
+        spline_output = F.linear(
+            self.b_splines(x).view(x.size(0), -1),
+            self.scaled_spline_weight.view(self.out_features, -1),
+        )
+        output = base_output + spline_output
+
+        output = output.view(*original_shape[:-1], self.out_features)
+        return output
+
+    @torch.no_grad()
+    def update_grid(self, x: torch.Tensor, margin=0.01):
+        assert x.dim() == 2 and x.size(1) == self.in_features
+        batch = x.size(0)
+
+        splines = self.b_splines(x)  # (batch, in, coeff)
+        splines = splines.permute(1, 0, 2)  # (in, batch, coeff)
+        orig_coeff = self.scaled_spline_weight  # (out, in, coeff)
+        orig_coeff = orig_coeff.permute(1, 2, 0)  # (in, coeff, out)
+        unreduced_spline_output = torch.bmm(splines, orig_coeff)  # (in, batch, out)
+        unreduced_spline_output = unreduced_spline_output.permute(
+            1, 0, 2
+        )  # (batch, in, out)
+
+        # sort each channel individually to collect data distribution
+        x_sorted = torch.sort(x, dim=0)[0]
+        grid_adaptive = x_sorted[
+            torch.linspace(
+                0, batch - 1, self.grid_size + 1, dtype=torch.int64, device=x.device
+            )
+        ]
+
+        uniform_step = (x_sorted[-1] - x_sorted[0] + 2 * margin) / self.grid_size
+        grid_uniform = (
+            torch.arange(
+                self.grid_size + 1, dtype=torch.float32, device=x.device
+            ).unsqueeze(1)
+            * uniform_step
+            + x_sorted[0]
+            - margin
+        )
+
+        grid = self.grid_eps * grid_uniform + (1 - self.grid_eps) * grid_adaptive
+        grid = torch.concatenate(
+            [
+                grid[:1]
+                - uniform_step
+                * torch.arange(self.spline_order, 0, -1, device=x.device).unsqueeze(1),
+                grid,
+                grid[-1:]
+                + uniform_step
+                * torch.arange(1, self.spline_order + 1, device=x.device).unsqueeze(1),
+            ],
+            dim=0,
+        )
+
+        self.grid.copy_(grid.T)
+        self.spline_weight.data.copy_(self.curve2coeff(x, unreduced_spline_output))
+
+    def regularization_loss(self, regularize_activation=1.0, regularize_entropy=1.0):
+        """
+        Compute the regularization loss.
+
+        This is a dumb simulation of the original L1 regularization as stated in the
+        paper, since the original one requires computing absolutes and entropy from the
+        expanded (batch, in_features, out_features) intermediate tensor, which is hidden
+        behind the F.linear function if we want an memory efficient implementation.
+
+        The L1 regularization is now computed as mean absolute value of the spline
+        weights. The authors implementation also includes this term in addition to the
+        sample-based regularization.
+        """
+        l1_fake = self.spline_weight.abs().mean(-1)
+        regularization_loss_activation = l1_fake.sum()
+        p = l1_fake / regularization_loss_activation
+        regularization_loss_entropy = -torch.sum(p * p.log())
+        return (
+            regularize_activation * regularization_loss_activation
+            + regularize_entropy * regularization_loss_entropy
+        )
+
+
+class KAN(torch.nn.Module):
+    def __init__(
+        self,
+        layers_hidden,
+        grid_size=5,
+        spline_order=3,
+        scale_noise=0.1,
+        scale_base=1.0,
+        scale_spline=1.0,
+        base_activation=torch.nn.SiLU,
+        grid_eps=0.02,
+        grid_range=[-1, 1],
+    ):
+        super(KAN, self).__init__()
+        self.grid_size = grid_size
+        self.spline_order = spline_order
+
+        self.layers = torch.nn.ModuleList()
+        for in_features, out_features in zip(layers_hidden, layers_hidden[1:]):
+            self.layers.append(
+                KANLinear(
+                    in_features,
+                    out_features,
+                    grid_size=grid_size,
+                    spline_order=spline_order,
+                    scale_noise=scale_noise,
+                    scale_base=scale_base,
+                    scale_spline=scale_spline,
+                    base_activation=base_activation,
+                    grid_eps=grid_eps,
+                    grid_range=grid_range,
+                )
+            )
+
+    def forward(self, x: torch.Tensor, update_grid=False):
+        for layer in self.layers:
+            if update_grid:
+                layer.update_grid(x)
+            x = layer(x)
+        return x
+
+    def regularization_loss(self, regularize_activation=1.0, regularize_entropy=1.0):
+        return sum(
+            layer.regularization_loss(regularize_activation, regularize_entropy)
+            for layer in self.layers
+        )
+
+"""## Build Kanformer"""
+
+from transformers import AutoConfig, AutoTokenizer, AutoModel
+
+from transformers.models.llama.modeling_llama import *
+
+class KANLlamaAttention(LlamaAttention):
+    def __init__(self, config, **args):
+        super().__init__(config, **args)
+        head_dim = config.hidden_size // config.num_attention_heads
+        self.q_proj = KANLinear(config.hidden_size, config.num_attention_heads * head_dim)
+        self.k_proj = KANLinear(config.hidden_size, config.num_key_value_heads * head_dim)
+        self.v_proj = KANLinear(config.hidden_size, config.num_key_value_heads * head_dim)
+        self.o_proj = KANLinear(config.hidden_size, config.hidden_size)
+        self._init_rope()
+
+class KANLlamaDecoderLayer(LlamaDecoderLayer):
+    def __init__(self,config, layer_idx):
+        super().__init__(config, layer_idx)
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = KANLlamaAttention(config=config, layer_idx=layer_idx)
+        self.mlp = KAN([config.hidden_size,config.intermediate_size,config.intermediate_size,config.hidden_size])
+        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+class KANLlamaModel(LlamaModel):
+    config_class = KANLlamaConfig
+    def __init__(self, config):
+        super().__init__(config)
+        self.layers = nn.ModuleList(
+            [KANLlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+
+class KANLlamaForSequenceClassification(LlamaForSequenceClassification):
+    config_class = KANLlamaConfig
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = KANLlamaModel(config)
+        self.score = KANLinear(config.hidden_size, self.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+class KANLlamaForCausalLM(LlamaForCausalLM):
+    config_class = KANLlamaConfig
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = KANLlamaModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = KANLinear(config.hidden_size, config.vocab_size)
+
+        # Initialize weights and apply final processing
+        self.post_init()