v1.0

code/REC/model/HLLM/modeling_mistral.py

+# coding=utf-8
+# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
+# Copyright (c) 2024 Bytedance Ltd. and/or its affiliate
+#
+# 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.
+
+""" PyTorch Mistral model."""
+import inspect
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from .activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+from transformers.configuration_utils import PretrainedConfig
+from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+
+try:
+    from .flash_self_attn import compute_flash_attention
+except ImportError as e:
+    compute_flash_attention = None
+
+
+class MistralConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MistralModel`]. It is used to instantiate an
+    Mistral 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 Mistral-7B-v0.1 or Mistral-7B-Instruct-v0.1.
+
+    [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
+    [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
+
+    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 Mistral model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`MistralModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 14336):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            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 `8`.
+        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 `4096*32`):
+            The maximum sequence length that this model might ever be used with. Mistral's sliding window attention
+            allows sequence of up to 4096*32 tokens.
+        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*):
+            The id of the padding token.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            The id of the "end-of-sequence" token.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        sliding_window (`int`, *optional*, defaults to 4096):
+            Sliding window attention window size. If not specified, will default to `4096`.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import MistralModel, MistralConfig
+
+    >>> # Initializing a Mistral 7B style configuration
+    >>> configuration = MistralConfig()
+
+    >>> # Initializing a model from the Mistral 7B style configuration
+    >>> model = MistralModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "mistral"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=14336,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        hidden_act="silu",
+        max_position_embeddings=4096 * 32,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        sliding_window=4096,
+        attention_dropout=0.0,
+        use_ft_flash_attn=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
+        self.sliding_window = sliding_window
+
+        # 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.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_dropout = attention_dropout
+        self.use_ft_flash_attn = use_ft_flash_attn
+
+        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,
+        )
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "MistralConfig"
+
+
+# Copied from transformers.models.llama.modeling_llama._get_unpad_data
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Mistral
+class MistralRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        MistralRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, 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_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+# copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Mistral
+# TODO @Arthur no longer copied from LLama after static cache
+class MistralRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
+        )
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2:]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+# TODO @Arthur no longer copied from LLama after static cache
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+    """Applies 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 part of the rotary embedding.
+        position_ids (`torch.Tensor`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class MistralMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class MistralAttention(nn.Module):
+    """
+    Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
+    and "Generating Long Sequences with Sparse Transformers".
+    """
+
+    def __init__(self, config: MistralConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True
+        self.attention_dropout = config.attention_dropout
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+        self.rotary_emb = MistralRotaryEmbedding(
+            self.head_dim,
+            max_position_embeddings=self.max_position_embeddings,
+            base=self.rope_theta,
+        )
+
+        self.use_ft_flash_attn = (
+            config.use_ft_flash_attn and compute_flash_attention is not None
+        )
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        cu_input_lens: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        if not self.use_ft_flash_attn:
+            attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+            if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                    f" {attn_weights.size()}"
+                )
+
+            if attention_mask is not None:
+                if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                    raise ValueError(
+                        f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                    )
+
+                attn_weights = attn_weights + attention_mask
+
+            # upcast attention to fp32
+            attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+            attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+            attn_output = torch.matmul(attn_weights, value_states)
+
+            if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+                raise ValueError(
+                    f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                    f" {attn_output.size()}"
+                )
+
+            attn_output = attn_output.transpose(1, 2).contiguous()
+            attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+        else:
+            assert output_attentions in [None, False]
+            # (batch, head, seq_length, head_features) -> (batch, seq_length, head, head_features)
+            query_states = query_states.transpose(1, 2)
+            key_states = key_states.transpose(1, 2)
+            value_states = value_states.transpose(1, 2)
+
+            if attention_mask is not None:
+                # [bsz, 1, tgt_seq_len, src_seq_len] -> [bsz, seq_len]
+                attention_mask = attention_mask[:, 0, -1].contiguous()
+
+            out_dtype = value_states.dtype
+            attn_output = compute_flash_attention(
+                query_states,
+                key_states,
+                value_states,
+                attention_mask,
+                cu_input_lens,
+                True,
+                self.training,
+            )
+            attn_output = attn_output.to(out_dtype)
+            attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class MistralDecoderLayer(nn.Module):
+    def __init__(self, config: MistralConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = MistralAttention(config, layer_idx)
+
+        self.mlp = MistralMLP(config)
+        self.input_layernorm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        cu_input_lens: Optional[torch.Tensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, sequence_length)` where padding elements are indicated by 0.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            cu_input_lens=cu_input_lens,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+MISTRAL_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`MistralConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare Mistral Model outputting raw hidden-states without any specific head on top.",
+    MISTRAL_START_DOCSTRING,
+)
+class MistralPreTrainedModel(PreTrainedModel):
+    config_class = MistralConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["MistralDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+MISTRAL_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare Mistral Model outputting raw hidden-states without any specific head on top.",
+    MISTRAL_START_DOCSTRING,
+)
+class MistralModel(MistralPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MistralDecoderLayer`]
+
+    Args:
+        config: MistralConfig
+    """
+
+    def __init__(self, config: MistralConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [MistralDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        cu_input_lens: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        past_key_values_length = 0
+
+        if use_cache:
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            if use_legacy_cache:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if cu_input_lens is None:
+            attention_mask = _prepare_4d_causal_attention_mask(
+                attention_mask,
+                (batch_size, seq_length),
+                inputs_embeds,
+                past_key_values_length,
+                sliding_window=self.config.sliding_window,
+            )
+
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    cu_input_lens,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    use_reentrant=False
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    cu_input_lens=cu_input_lens,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class MistralForCausalLM(MistralPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = MistralModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        cu_input_lens: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, MistralForCausalLM
+
+        >>> model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
+        >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cu_input_lens=cu_input_lens,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Ensure tensors are on the same device
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        # Omit tokens covered by past_key_values
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+                max_cache_length = past_key_values.get_max_length()
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
+            # input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length):]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1]:]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+
+
+@add_start_docstrings(
+    """
+    The Mistral Model transformer with a sequence classification head on top (linear layer).
+
+    [`MistralForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    MISTRAL_START_DOCSTRING,
+)
+# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Mistral, LLAMA->MISTRAL
+class MistralForSequenceClassification(MistralPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = MistralModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(MISTRAL_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
+                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                sequence_lengths = sequence_lengths % input_ids.shape[-1]
+                sequence_lengths = sequence_lengths.to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

code/REC/model/HLLM/modeling_rope_utils.py

+# Copyright 2024 The HuggingFace Team. All rights reserved.
+# Copyright (c) 2024 Bytedance Ltd. and/or its affiliate
+#
+# 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.
+
+import math
+from typing import Optional, Tuple
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import is_torch_available, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+if is_torch_available():
+    import torch
+
+
+def _compute_default_rope_parameters(
+    config: Optional[PretrainedConfig] = None,
+    device: Optional["torch.device"] = None,
+    seq_len: Optional[int] = None,
+    **rope_kwargs,
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies according to the original RoPE implementation
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+    """
+    if config is not None and len(rope_kwargs) > 0:
+        raise ValueError(
+            "Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in "
+            f"`_compute_default_rope_parameters`, got `rope_kwargs`={rope_kwargs} and `config`={config}"
+        )
+    if len(rope_kwargs) > 0:
+        base = rope_kwargs["base"]
+        dim = rope_kwargs["dim"]
+    elif config is not None:
+        base = config.rope_theta
+        partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
+        dim = int((config.hidden_size // config.num_attention_heads) * partial_rotary_factor)
+    attention_factor = 1.0  # Unused in this type of RoPE
+
+    # Compute the inverse frequencies
+    inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).float().to(device) / dim))
+    return inv_freq, attention_factor
+
+
+def _compute_linear_scaling_rope_parameters(
+    config: Optional[PretrainedConfig] = None,
+    device: Optional["torch.device"] = None,
+    seq_len: Optional[int] = None,
+    **rope_kwargs,
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies with linear scaling. Credits to the Reddit user /u/kaiokendev
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+    """
+    if config is not None and len(rope_kwargs) > 0:
+        raise ValueError(
+            "Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in "
+            f"`_compute_linear_scaling_rope_parameters`, got `rope_kwargs`={rope_kwargs} and `config`={config}"
+        )
+    if len(rope_kwargs) > 0:
+        factor = rope_kwargs["factor"]
+    elif config is not None:
+        factor = config.rope_scaling["factor"]
+
+    # Gets the default RoPE parameters
+    inv_freq, attention_factor = _compute_default_rope_parameters(config, device, seq_len, **rope_kwargs)
+
+    # Then applies linear scaling to the frequencies.
+    # NOTE: originally, scaling was applied to the position_ids. However, we get `embs = inv_freq @ position_ids`, so
+    # applying scaling to the inverse frequencies is equivalent.
+    inv_freq /= factor
+    return inv_freq, attention_factor
+
+
+def _compute_dynamic_ntk_parameters(
+    config: Optional[PretrainedConfig] = None,
+    device: Optional["torch.device"] = None,
+    seq_len: Optional[int] = None,
+    **rope_kwargs,
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies with NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length, used to update the dynamic RoPE at inference time.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+    """
+    # TODO (joao): use the new `original_max_position_embeddings` from rope_scaling
+    if config is not None and len(rope_kwargs) > 0:
+        raise ValueError(
+            "Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in "
+            f"`_compute_dynamic_ntk_parameters`, got `rope_kwargs`={rope_kwargs} and `config`={config}"
+        )
+    if len(rope_kwargs) > 0:
+        base = rope_kwargs["base"]
+        dim = rope_kwargs["dim"]
+        max_position_embeddings = rope_kwargs["max_position_embeddings"]
+        factor = rope_kwargs["factor"]
+    elif config is not None:
+        base = config.rope_theta
+        partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
+        dim = int((config.hidden_size // config.num_attention_heads) * partial_rotary_factor)
+        max_position_embeddings = config.max_position_embeddings
+        factor = config.rope_scaling["factor"]
+
+    attention_factor = 1.0  # Unused in this type of RoPE
+
+    # seq_len: default to max_position_embeddings, e.g. at init time
+    seq_len = seq_len if seq_len is not None else max_position_embeddings
+
+    # Compute the inverse frequencies
+    base = base * ((factor * seq_len / max_position_embeddings) - (factor - 1)) ** (dim / (dim - 2))
+    inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).float().to(device) / dim))
+    return inv_freq, attention_factor
+
+
+def _compute_yarn_parameters(
+    config: PretrainedConfig, device: "torch.device", seq_len: Optional[int] = None, **rope_kwargs
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies with NTK scaling. Please refer to the
+    [original paper](https://arxiv.org/abs/2309.00071)
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin.
+    """
+    # No need to keep BC with yarn, unreleased when this new pattern was created.
+    if len(rope_kwargs) > 0:
+        raise ValueError(
+            f"Unexpected arguments: `**rope_kwargs` should be unset in `_compute_yarn_parameters`, got {rope_kwargs}"
+        )
+
+    base = config.rope_theta
+    partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
+    dim = int((config.hidden_size // config.num_attention_heads) * partial_rotary_factor)
+    max_position_embeddings = config.max_position_embeddings
+    factor = config.rope_scaling["factor"]
+
+    # Sets the attention factor as suggested in the paper
+    attention_factor = config.rope_scaling.get("attention_factor")
+    if attention_factor is None:
+        attention_factor = 0.1 * math.log(factor) + 1.0
+
+    # Optional config options
+    # beta_fast/beta_slow: as suggested in the paper, default to 32/1 (correspondingly)
+    beta_fast = config.rope_scaling.get("beta_fast") or 32
+    beta_slow = config.rope_scaling.get("beta_slow") or 1
+
+    # Compute the inverse frequencies
+    def find_correction_dim(num_rotations, dim, base, max_position_embeddings):
+        """Inverse dimension formula to find the dimension based on the number of rotations"""
+        return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (2 * math.log(base))
+
+    def find_correction_range(low_rot, high_rot, dim, base, max_position_embeddings):
+        """Find dimension range bounds based on rotations"""
+        low = math.floor(find_correction_dim(low_rot, dim, base, max_position_embeddings))
+        high = math.ceil(find_correction_dim(high_rot, dim, base, max_position_embeddings))
+        return max(low, 0), min(high, dim - 1)
+
+    def linear_ramp_mask(min, max, dim):
+        if min == max:
+            max += 0.001  # Prevent singularity
+
+        linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
+        ramp_func = torch.clamp(linear_func, 0, 1)
+        return ramp_func
+
+    pos_freqs = base ** (torch.arange(0, dim, 2).float().to(device) / dim)
+    inv_freq_extrapolation = 1.0 / pos_freqs
+    inv_freq_interpolation = 1.0 / (factor * pos_freqs)
+
+    low, high = find_correction_range(beta_fast, beta_slow, dim, base, max_position_embeddings)
+
+    # Get n-dimensional rotational scaling corrected for extrapolation
+    inv_freq_mask = 1 - linear_ramp_mask(low, high, dim // 2).float().to(device)
+    inv_freq = inv_freq_interpolation * (1 - inv_freq_mask) + inv_freq_extrapolation * inv_freq_mask
+
+    return inv_freq, attention_factor
+
+
+def _compute_longrope_parameters(
+    config: PretrainedConfig, device: "torch.device", seq_len: Optional[int] = None, **rope_kwargs
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies with LongRoPE scaling. Please refer to the
+    [original implementation](https://github.com/microsoft/LongRoPE)
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin.
+    """
+    # TODO (joao): use the new `original_max_position_embeddings` from rope_scaling
+    # No need to keep BC with longrope, unreleased when this new pattern was created.
+    if len(rope_kwargs) > 0:
+        raise ValueError(
+            "Unexpected arguments: `**rope_kwargs` should be unset in `_compute_longrope_parameters`, got "
+            f"{rope_kwargs}"
+        )
+
+    base = config.rope_theta
+    partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
+    dim = int((config.hidden_size // config.num_attention_heads) * partial_rotary_factor)
+    long_factor = config.rope_scaling["long_factor"]
+    short_factor = config.rope_scaling["short_factor"]
+    factor = config.rope_scaling.get("factor")
+    attention_factor = config.rope_scaling.get("attention_factor")
+
+    # NOTE: Phi3 (and potentially other models) modify `max_position_embeddings` and have a
+    # `original_max_position_embeddings` field containing the pretrained value. They use the ratio between these two
+    # values to compute the default attention scaling factor, instead of using `factor`.
+    if hasattr(config, "original_max_position_embeddings"):
+        max_position_embeddings = config.original_max_position_embeddings
+        expanded_max_position_embeddings = config.max_position_embeddings
+        factor = expanded_max_position_embeddings / max_position_embeddings
+    else:
+        max_position_embeddings = config.max_position_embeddings
+        expanded_max_position_embeddings = max_position_embeddings * factor
+
+    # Sets the attention factor as suggested in the paper
+    if attention_factor is None:
+        if factor <= 1.0:
+            attention_factor = 1.0
+        else:
+            attention_factor = math.sqrt(1 + math.log(factor) / math.log(max_position_embeddings))
+
+    # Compute the inverse frequencies -- scaled based on the target sequence length
+    if expanded_max_position_embeddings > max_position_embeddings:
+        ext_factors = torch.tensor(long_factor, dtype=torch.float32, device=device)
+    else:
+        ext_factors = torch.tensor(short_factor, dtype=torch.float32, device=device)
+    inv_freq_shape = torch.arange(0, dim, 2, dtype=torch.int64, device=device).float() / dim
+    inv_freq = 1.0 / (ext_factors * base**inv_freq_shape)
+
+    return inv_freq, attention_factor
+
+
+def _compute_llama3_parameters(
+    config: PretrainedConfig, device: "torch.device", seq_len: Optional[int] = None, **rope_kwargs
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies for llama 3.1.
+
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin.
+    """
+    # Gets the default RoPE parameters
+    inv_freq, attention_factor = _compute_default_rope_parameters(config, device, seq_len, **rope_kwargs)
+
+    factor = config.rope_scaling["factor"]  # `8` in the original implementation
+    low_freq_factor = config.rope_scaling["low_freq_factor"]  # `1` in the original implementation
+    high_freq_factor = config.rope_scaling["high_freq_factor"]  # `4` in the original implementation
+    old_context_len = config.rope_scaling["original_max_position_embeddings"]  # `8192` in the original implementation
+
+    low_freq_wavelen = old_context_len / low_freq_factor
+    high_freq_wavelen = old_context_len / high_freq_factor
+    new_freqs = []
+    for freq in inv_freq:
+        wavelen = 2 * math.pi / freq
+        if wavelen < high_freq_wavelen:
+            new_freqs.append(freq)
+        elif wavelen > low_freq_wavelen:
+            new_freqs.append(freq / factor)
+        else:
+            assert low_freq_wavelen != high_freq_wavelen
+            smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+            new_freqs.append((1 - smooth) * freq / factor + smooth * freq)
+    inv_freq = torch.tensor(new_freqs, dtype=inv_freq.dtype, device=inv_freq.device)
+    return inv_freq, attention_factor
+
+
+# This maps the "rope_type" string field in rope config to the corresponding function to compute the RoPE parameters
+# from the model config. You can append new {'rope_type': callable} pairs to this dictionary to enable custom RoPE
+# parameterizations, as long as the callable has the same signature.
+ROPE_INIT_FUNCTIONS = {
+    "default": _compute_default_rope_parameters,
+    "linear": _compute_linear_scaling_rope_parameters,
+    "dynamic": _compute_dynamic_ntk_parameters,
+    "yarn": _compute_yarn_parameters,
+    "longrope": _compute_longrope_parameters,
+    "llama3": _compute_llama3_parameters,
+}
+
+
+def _check_received_keys(rope_type: str, received_keys: set, required_keys: set, optional_keys: Optional[set] = None):
+    """Compare the received keys in `config.rope_scaling` against the expected and optional keys"""
+    # BC: "rope_type" was originally "type" -- let's gracefully handle it
+    if "rope_type" not in received_keys and "type" in received_keys:
+        received_keys -= {"type"}
+        received_keys.add("rope_type")
+
+    missing_keys = required_keys - received_keys
+    if missing_keys:
+        raise KeyError(f"Missing required keys in `rope_scaling` for 'rope_type'='{rope_type}': {missing_keys}")
+
+    if optional_keys is not None:
+        unused_keys = received_keys - required_keys - optional_keys
+    else:
+        unused_keys = received_keys - required_keys
+    if unused_keys:
+        logger.warning(f"Unrecognized keys in `rope_scaling` for 'rope_type'='{rope_type}': {unused_keys}")
+
+
+def _validate_default_rope_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys)
+
+
+def _validate_linear_scaling_rope_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "factor"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys)
+
+    factor = rope_scaling["factor"]
+    if factor is None or not isinstance(factor, float) or factor < 1.0:
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+
+def _validate_dynamic_scaling_rope_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "factor"}
+    # TODO (joao): update logic for the inclusion of `original_max_position_embeddings`
+    optional_keys = {"original_max_position_embeddings"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys, optional_keys)
+
+    factor = rope_scaling["factor"]
+    if factor is None or not isinstance(factor, float) or factor < 1.0:
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+
+def _validate_yarn_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "factor"}
+    optional_keys = {"attention_factor", "beta_fast", "beta_slow"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys, optional_keys)
+
+    factor = rope_scaling["factor"]
+    if factor is None or not isinstance(factor, float) or factor < 1.0:
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+    attention_factor = rope_scaling.get("attention_factor")
+    if attention_factor is not None and (not isinstance(attention_factor, float) or attention_factor < 0):
+        logger.warning(
+            f"`rope_scaling`'s attention_factor field must be a float greater than 0, got {attention_factor}"
+        )
+    beta_fast = rope_scaling.get("beta_fast")
+    if beta_fast is not None and not isinstance(beta_fast, float):
+        logger.warning(f"`rope_scaling`'s beta_fast field must be a float, got {beta_fast}")
+    beta_slow = rope_scaling.get("beta_slow")
+    if beta_slow is not None and not isinstance(beta_slow, float):
+        logger.warning(f"`rope_scaling`'s beta_slow field must be a float, got {beta_slow}")
+
+    if (beta_fast or 32) < (beta_slow or 1):
+        logger.warning(
+            f"`rope_scaling`'s beta_fast field must be greater than beta_slow, got beta_fast={beta_fast} "
+            f"(defaults to 32 if None) and beta_slow={beta_slow} (defaults to 1 if None)"
+        )
+
+
+def _validate_longrope_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "short_factor", "long_factor"}
+    # TODO (joao): update logic for the inclusion of `original_max_position_embeddings`
+    optional_keys = {"attention_factor", "factor", "original_max_position_embeddings"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys, optional_keys)
+
+    partial_rotary_factor = config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
+    dim = int((config.hidden_size // config.num_attention_heads) * partial_rotary_factor)
+
+    short_factor = rope_scaling.get("short_factor")
+    if not isinstance(short_factor, list) and all(isinstance(x, (int, float)) for x in short_factor):
+        logger.warning(f"`rope_scaling`'s short_factor field must be a list of numbers, got {short_factor}")
+    if not len(short_factor) == dim // 2:
+        logger.warning(f"`rope_scaling`'s short_factor field must have length {dim // 2}, got {len(short_factor)}")
+
+    long_factor = rope_scaling.get("long_factor")
+    if not isinstance(long_factor, list) and all(isinstance(x, (int, float)) for x in long_factor):
+        logger.warning(f"`rope_scaling`'s long_factor field must be a list of numbers, got {long_factor}")
+    if not len(long_factor) == dim // 2:
+        logger.warning(f"`rope_scaling`'s long_factor field must have length {dim // 2}, got {len(long_factor)}")
+
+    # Handle Phi3 divergence: prefer the use of `attention_factor` and/or `factor` over
+    # `original_max_position_embeddings` to compute internal variables. The latter lives outside `rope_scaling` and is
+    # unique to longrope (= undesirable)
+    if hasattr(config, "original_max_position_embeddings"):
+        logger.warning_once(
+            "This model has set a `original_max_position_embeddings` field, to be used together with "
+            "`max_position_embeddings` to determine a scaling factor. Please set the `factor` field of `rope_scaling`"
+            "with this ratio instead -- we recommend the use of this field over `original_max_position_embeddings`, "
+            "as it is compatible with most model architectures."
+        )
+    else:
+        factor = rope_scaling.get("factor")
+        if factor is None:
+            logger.warning("Missing required keys in `rope_scaling`: 'factor'")
+        elif not isinstance(factor, float) or factor < 1.0:
+            logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+        attention_factor = rope_scaling.get("attention_factor")
+        if attention_factor is not None and not isinstance(attention_factor, float) or attention_factor < 0:
+            logger.warning(
+                f"`rope_scaling`'s attention_factor field must be a float greater than 0, got {attention_factor}"
+            )
+
+
+def _validate_llama3_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "factor", "original_max_position_embeddings", "low_freq_factor", "high_freq_factor"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys)
+
+    factor = rope_scaling["factor"]
+    if factor is None or not isinstance(factor, float) or factor < 1.0:
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+    low_freq_factor = rope_scaling["low_freq_factor"]
+    high_freq_factor = rope_scaling["high_freq_factor"]
+    if low_freq_factor is None or not isinstance(low_freq_factor, float):
+        logger.warning(f"`rope_scaling`'s low_freq_factor field must be a float, got {low_freq_factor}")
+    if high_freq_factor is None or not isinstance(high_freq_factor, float):
+        logger.warning(f"`rope_scaling`'s high_freq_factor field must be a float, got {high_freq_factor}")
+    if high_freq_factor < low_freq_factor:
+        logger.warning(
+            "`rope_scaling`'s high_freq_factor field must be greater than low_freq_factor, got high_freq_factor="
+            f"{high_freq_factor} and low_freq_factor={low_freq_factor}"
+        )
+
+    original_max_position_embeddings = rope_scaling["original_max_position_embeddings"]
+    if original_max_position_embeddings is None or not isinstance(original_max_position_embeddings, int):
+        logger.warning(
+            "`rope_scaling`'s original_max_position_embeddings field must be an integer, got "
+            f"{original_max_position_embeddings}"
+        )
+    if original_max_position_embeddings >= config.max_position_embeddings:
+        logger.warning(
+            "`rope_scaling`'s original_max_position_embeddings field must be less than max_position_embeddings, got "
+            f"{original_max_position_embeddings} and max_position_embeddings={config.max_position_embeddings}"
+        )
+
+
+# Like `ROPE_INIT_FUNCTIONS`, this validation function mapping can be dynamically updated for custom RoPE types.
+ROPE_VALIDATION_FUNCTIONS = {
+    "default": _validate_default_rope_parameters,
+    "linear": _validate_linear_scaling_rope_parameters,
+    "dynamic": _validate_dynamic_scaling_rope_parameters,
+    "yarn": _validate_yarn_parameters,
+    "longrope": _validate_longrope_parameters,
+    "llama3": _validate_llama3_parameters,
+}
+
+
+def rope_config_validation(config: PretrainedConfig):
+    """
+    Validate the RoPE config arguments, given a `PretrainedConfig` object
+    """
+    rope_scaling = getattr(config, "rope_scaling", None)  # not a default parameter in `PretrainedConfig`
+    if rope_scaling is None:
+        return
+
+    # BC: "rope_type" was originally "type"
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", "default"))
+    validation_fn = ROPE_VALIDATION_FUNCTIONS.get(rope_type)
+    if validation_fn is not None:
+        validation_fn(config)
+    else:
+        logger.warning(
+            f"Missing validation function mapping in `ROPE_VALIDATION_FUNCTIONS` for 'rope_type'='{rope_type}'"
+        )

code/REC/model/IDNet/hstu.py

+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# Copyright (c) 2024 Bytedance Ltd. and/or its affiliate
+#
+# 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.
+
+"""
+Implements HSTU (Hierarchical Sequential Transduction Unit) in
+Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations
+(https://arxiv.org/abs/2402.17152).
+"""
+
+import abc
+import math
+from typing import Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+from logging import getLogger
+import fbgemm_gpu
+
+from REC.utils.enum_type import InputType
+from REC.model.basemodel import BaseModel, l2_norm, all_gather
+
+
+def truncated_normal(x: torch.Tensor, mean: float, std: float) -> torch.Tensor:
+    with torch.no_grad():
+        size = x.shape
+        tmp = x.new_empty(size + (4,)).normal_()
+        valid = (tmp < 2) & (tmp > -2)
+        ind = valid.max(-1, keepdim=True)[1]
+        x.data.copy_(tmp.gather(-1, ind).squeeze(-1))
+        x.data.mul_(std).add_(mean)
+        return x
+
+
+TIMESTAMPS_KEY = "timestamps"
+
+
+class RelativeAttentionBiasModule(torch.nn.Module):
+
+    @abc.abstractmethod
+    def forward(
+        self,
+        all_timestamps: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Args:
+            all_timestamps: [B, N] x int64
+        Returns:
+            torch.float tensor broadcastable to [B, N, N]
+        """
+        pass
+
+
+class RelativePositionalBias(RelativeAttentionBiasModule):
+    def __init__(self, max_seq_len: int) -> None:
+        super().__init__()
+
+        self._max_seq_len: int = max_seq_len
+        self._w = torch.nn.Parameter(
+            torch.empty(2 * max_seq_len - 1).normal_(mean=0, std=0.02),
+        )
+
+    def forward(
+        self,
+        all_timestamps: torch.Tensor,
+    ) -> torch.Tensor:
+        del all_timestamps
+        n: int = self._max_seq_len
+        t = F.pad(self._w[: 2 * n - 1], [0, n]).repeat(n)
+        t = t[..., :-n].reshape(1, n, 3 * n - 2)
+        r = (2 * n - 1) // 2
+        return t[..., r:-r]
+
+
+class RelativeBucketedTimeAndPositionBasedBias(RelativeAttentionBiasModule):
+    """
+    Bucketizes timespans based on ts(next-item) - ts(current-item).
+    """
+
+    def __init__(
+        self,
+        max_seq_len: int,
+        num_buckets: int,
+        bucketization_fn: Callable[[torch.Tensor], torch.Tensor],
+    ) -> None:
+        super().__init__()
+
+        self._max_seq_len: int = max_seq_len
+        self._ts_w = torch.nn.Parameter(
+            torch.empty(num_buckets + 1).normal_(mean=0, std=0.02),
+        )
+        self._pos_w = torch.nn.Parameter(
+            torch.empty(2 * max_seq_len - 1).normal_(mean=0, std=0.02),
+        )
+        self._num_buckets: int = num_buckets
+        self._bucketization_fn: Callable[[torch.Tensor], torch.Tensor] = (
+            bucketization_fn
+        )
+
+    def forward(
+        self,
+        all_timestamps: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Args:
+            all_timestamps: (B, N).
+        Returns:
+            (B, N, N).
+        """
+        B = all_timestamps.size(0)
+        N = self._max_seq_len
+        t = F.pad(self._pos_w[: 2 * N - 1], [0, N]).repeat(N)
+        t = t[..., :-N].reshape(1, N, 3 * N - 2)
+        r = (2 * N - 1) // 2
+
+        # [B, N + 1] to simplify tensor manipulations.
+        ext_timestamps = torch.cat(
+            [all_timestamps, all_timestamps[:, N - 1: N]], dim=1
+        )
+        # causal masking. Otherwise [:, :-1] - [:, 1:] works
+        bucketed_timestamps = torch.clamp(
+            self._bucketization_fn(
+                ext_timestamps[:, 1:].unsqueeze(2) - ext_timestamps[:, :-1].unsqueeze(1)
+            ),
+            min=0,
+            max=self._num_buckets,
+        ).detach()
+        rel_pos_bias = t[:, :, r:-r]
+        rel_ts_bias = torch.index_select(
+            self._ts_w, dim=0, index=bucketed_timestamps.view(-1)
+        ).view(B, N, N)
+        return rel_pos_bias + rel_ts_bias
+
+
+HSTUCacheState = Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]
+
+
+def _hstu_attention_maybe_from_cache(
+    num_heads: int,
+    attention_dim: int,
+    linear_dim: int,
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    attention_mask: torch.Tensor  # [bs, 1, n, n]
+):
+    B, _, n, _ = attention_mask.size()
+
+    qk_attn = torch.einsum(
+        "bnhd,bmhd->bhnm",
+        q.view(B, n, num_heads, attention_dim),
+        k.view(B, n, num_heads, attention_dim),
+    )
+    qk_attn = F.silu(qk_attn) / n
+    qk_attn = qk_attn * attention_mask
+    # print(f"{qk_attn.size() = } {v.size() = }")
+    attn_output = torch.einsum(
+        "bhnm,bmhd->bnhd",
+        qk_attn,
+        v.reshape(B, n, num_heads, linear_dim),
+    ).reshape(B, n, num_heads * linear_dim)
+    return attn_output
+
+
+class SequentialTransductionUnitJagged(torch.nn.Module):
+    def __init__(
+        self,
+        embedding_dim: int,
+        linear_hidden_dim: int,
+        attention_dim: int,
+        dropout_ratio: float,
+        attn_dropout_ratio: float,
+        num_heads: int,
+        linear_activation: str,
+        relative_attention_bias_module: Optional[RelativeAttentionBiasModule] = None,
+        normalization: str = "rel_bias",
+        linear_config: str = "uvqk",
+        concat_ua: bool = False,
+        epsilon: float = 1e-6,
+        max_length: Optional[int] = None,
+    ) -> None:
+        super().__init__()
+        self._embedding_dim: int = embedding_dim
+        self._linear_dim: int = linear_hidden_dim
+        self._attention_dim: int = attention_dim
+        self._dropout_ratio: float = dropout_ratio
+        self._attn_dropout_ratio: float = attn_dropout_ratio
+        self._num_heads: int = num_heads
+        self._rel_attn_bias: Optional[RelativeAttentionBiasModule] = (
+            relative_attention_bias_module
+        )
+        self._normalization: str = normalization
+        self._linear_config: str = linear_config
+        if self._linear_config == "uvqk":
+            self._uvqk = torch.nn.Parameter(
+                torch.empty(
+                    (
+                        embedding_dim,
+                        linear_hidden_dim * 2 * num_heads
+                        + attention_dim * num_heads * 2,
+                    )
+                ).normal_(mean=0, std=0.02),
+            )
+        else:
+            raise ValueError(f"Unknown linear_config {self._linear_config}")
+        self._linear_activation: str = linear_activation
+        self._concat_ua: bool = concat_ua
+        self._o = torch.nn.Linear(
+            in_features=linear_hidden_dim * num_heads * (3 if concat_ua else 1),
+            out_features=embedding_dim,
+        )
+        torch.nn.init.xavier_uniform_(self._o.weight)
+        self._eps: float = epsilon
+
+    def _norm_input(self, x: torch.Tensor) -> torch.Tensor:
+        return F.layer_norm(x, normalized_shape=[self._embedding_dim], eps=self._eps)
+
+    def _norm_attn_output(self, x: torch.Tensor) -> torch.Tensor:
+        return F.layer_norm(
+            x, normalized_shape=[self._linear_dim * self._num_heads], eps=self._eps
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        Args:
+            x: (\sum_i N_i, D) x float.
+            x_offsets: (B + 1) x int32.
+            all_timestamps: optional (B, N) x int64.
+            invalid_attn_mask: (B, N, N) x float, each element in {0, 1}.
+            delta_x_offsets: optional 2-tuple ((B,) x int32, (B,) x int32).
+                For the 1st element in the tuple, each element is in [0, x_offsets[-1]). For the
+                2nd element in the tuple, each element is in [0, N).
+            cache: Optional 4-tuple of (v, padded_q, padded_k, output) from prior runs,
+                where all except padded_q, padded_k are jagged.
+        Returns:
+            x' = f(x), (\sum_i N_i, D) x float.
+        """
+
+        normed_x = self._norm_input(x)
+        if self._linear_config == "uvqk":
+            batched_mm_output = torch.matmul(normed_x, self._uvqk)
+            if self._linear_activation == "silu":
+                batched_mm_output = F.silu(batched_mm_output)
+            elif self._linear_activation == "none":
+                batched_mm_output = batched_mm_output
+            u, v, q, k = torch.split(
+                batched_mm_output,
+                [
+                    self._linear_dim * self._num_heads,
+                    self._linear_dim * self._num_heads,
+                    self._attention_dim * self._num_heads,
+                    self._attention_dim * self._num_heads,
+                ],
+                dim=-1,
+            )
+        else:
+            raise ValueError(f"Unknown self._linear_config {self._linear_config}")
+
+        B: int = attention_mask.size(0)
+        if self._normalization == "rel_bias" or self._normalization == "hstu_rel_bias":
+            attn_output = _hstu_attention_maybe_from_cache(
+                num_heads=self._num_heads,
+                attention_dim=self._attention_dim,
+                linear_dim=self._linear_dim,
+                q=q,
+                k=k,
+                v=v,
+                attention_mask=attention_mask
+            )
+
+        if self._concat_ua:
+            a = self._norm_attn_output(attn_output)
+            o_input = torch.cat([u, a, u * a], dim=-1)
+        else:
+            o_input = u * self._norm_attn_output(attn_output)
+
+        new_outputs = (
+            self._o(
+                F.dropout(
+                    o_input,
+                    p=self._dropout_ratio,
+                    training=self.training,
+                )
+            )
+            + x
+        )
+
+        return new_outputs
+
+
+class HSTUJagged(torch.nn.Module):
+
+    def __init__(
+        self,
+        modules: List[SequentialTransductionUnitJagged],
+        autocast_dtype: torch.dtype,
+    ) -> None:
+        super().__init__()
+
+        self._attention_layers: torch.nn.ModuleList = torch.nn.ModuleList(
+            modules=modules
+        )
+        self._autocast_dtype: torch.dtype = autocast_dtype
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: torch.Tensor,
+    ) -> Tuple[torch.Tensor, List[HSTUCacheState]]:
+        """
+        Args:
+            x: (B, N, D) x float.
+            x_offsets: (B + 1) x int32.
+            all_timestamps: (B, 1 + N) x int64
+            invalid_attn_mask: (B, N, N) x float, each element in {0, 1}.
+        Returns:
+            x' = f(x), (B, N, D) x float
+        """
+
+        for i, layer in enumerate(self._attention_layers):
+            x = layer(
+                x=x,
+                attention_mask=attention_mask
+            )
+
+        return x
+
+
+class HSTU(BaseModel):
+    """
+    Implements HSTU (Hierarchical Sequential Transduction Unit) in
+    Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations,
+    https://arxiv.org/abs/2402.17152.
+
+    Note that this implementation is intended for reproducing experiments in
+    the traditional sequential recommender setting (Section 4.1.1), and does
+    not yet use optimized kernels discussed in the paper.
+    """
+    input_type = InputType.SEQ
+
+    def __init__(self, config, dataload):
+        super().__init__()
+        self.logger = getLogger()
+        self.item_num = dataload.item_num
+        self._item_embedding_dim: int = config['item_embedding_size']
+        self._hstu_embedding_dim: int = config['hstu_embedding_size']
+        self._max_sequence_length: int = config['MAX_ITEM_LIST_LENGTH']
+        self._num_blocks: int = config['n_layers']
+        self._num_heads: int = config['n_heads']
+        self._dqk: int = config['hstu_embedding_size'] // config['n_heads']
+        self._dv: int = config['hstu_embedding_size'] // config['n_heads']
+        self._linear_activation: str = config['hidden_act'] if config['hidden_act'] else "silu"
+        self._linear_dropout_rate: float = config['hidden_dropout_prob']
+        self._attn_dropout_rate: float = config['attn_dropout_prob']
+        self._enable_relative_attention_bias: bool = config['enable_relative_attention_bias'] if config['enable_relative_attention_bias'] else False
+        self._linear_config = 'uvqk'
+        self._normalization = 'rel_bias'
+        self.position_embedding = nn.Embedding(self._max_sequence_length+1, self._hstu_embedding_dim)
+        self._hstu = HSTUJagged(
+            modules=[
+                SequentialTransductionUnitJagged(
+                    embedding_dim=self._hstu_embedding_dim,
+                    linear_hidden_dim=self._dv,
+                    attention_dim=self._dqk,
+                    normalization=self._normalization,
+                    linear_config=self._linear_config,
+                    linear_activation=self._linear_activation,
+                    num_heads=self._num_heads,
+                    # TODO: change to lambda x.
+                    relative_attention_bias_module=(
+                        RelativeBucketedTimeAndPositionBasedBias(
+                            max_seq_len=self._max_sequence_length
+                            + self._max_sequence_length,  # accounts for next item.
+                            num_buckets=128,
+                            bucketization_fn=lambda x: (
+                                torch.log(torch.abs(x).clamp(min=1)) / 0.301
+                            ).long(),
+                        )
+                        if self._enable_relative_attention_bias
+                        else None
+                    ),
+                    dropout_ratio=self._linear_dropout_rate,
+                    attn_dropout_ratio=self._attn_dropout_rate,
+                    concat_ua=False,
+                )
+                for _ in range(self._num_blocks)
+            ],
+            autocast_dtype=None,
+        )
+
+        self.item_embedding = nn.Embedding(self.item_num, self._item_embedding_dim, padding_idx=0)
+        self.item_id_proj_tower = nn.Identity() if config['item_embedding_size'] == config['hstu_embedding_size'] else nn.Linear(config['item_embedding_size'], config['hstu_embedding_size'], bias=False)
+        self.loss = config['loss']
+        if self.loss == 'nce':
+            if config['fix_temp']:
+                self.logger.info(f"Fixed logit_scale 20")
+                self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.05), requires_grad=False)
+            else:
+                self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
+            self.nce_thres = config['nce_thres'] if config['nce_thres'] else 0.99
+            self.num_negatives = config['num_negatives']
+            self.logger.info(f"nce thres setting to {self.nce_thres}")
+        else:
+            raise NotImplementedError(f"Only nce is supported")
+
+        # causal forward, w/ +1 for padding.
+        self.register_buffer(
+            "_attn_mask",
+            torch.triu(
+                torch.ones(
+                    (
+                        self._max_sequence_length,
+                        self._max_sequence_length,
+                    ),
+                    dtype=torch.bool,
+                ),
+                diagonal=1,
+            ),
+        )
+        self._verbose: bool = True
+        self.reset_params()
+
+    def reset_params(self):
+        for name, params in self.named_parameters():
+            if ("_hstu" in name) or ("_embedding_module" in name) or ('logit_scale' in name):
+                if self._verbose:
+                    print(f"Skipping init for {name}")
+                continue
+            try:
+                truncated_normal(params.data, mean=0.0, std=0.02)
+                if self._verbose:
+                    print(
+                        f"Initialize {name} as trunc normal: {params.data.size()} params"
+                    )
+            except:
+                if self._verbose:
+                    print(f"Failed to initialize {name}: {params.data.size()} params")
+
+    def debug_str(self) -> str:
+        debug_str = (
+            f"HSTU-b{self._num_blocks}-h{self._num_heads}-dqk{self._dqk}-dv{self._dv}"
+            + f"-l{self._linear_activation}d{self._linear_dropout_rate}"
+            + f"-ad{self._attn_dropout_rate}"
+        )
+        if not self._enable_relative_attention_bias:
+            debug_str += "-norab"
+        return debug_str
+
+    def forward(self, interaction):
+        items, neg_items, masked_index = interaction  # [batch, 2, seq_len]    #[batch, max_seq_len-1]
+        if self.num_negatives:
+            neg_items = torch.randint(
+                low=1,
+                high=self.item_num,
+                size=(items.size(0), items.size(1) - 1, self.num_negatives),
+                dtype=items.dtype,
+                device=items.device,
+            )
+
+        pos_items_embs = self.item_id_proj_tower(self.item_embedding(items))  # [batch, 2, max_seq_len+1, dim]
+        neg_items_embs = self.item_id_proj_tower(self.item_embedding(neg_items))  # [128, 200, 1024, 50]
+        input_emb = pos_items_embs[:, :-1, :]  # [batch, max_seq_len, dim]
+
+        position_ids = torch.arange(masked_index.size(1), dtype=torch.long, device=masked_index.device)
+        position_ids = position_ids.unsqueeze(0).expand_as(masked_index)
+        position_embedding = self.position_embedding(position_ids)
+        input_emb = input_emb + position_embedding
+
+        attention_mask = self.get_attention_mask(masked_index)
+        output_embs = self._hstu(
+            x=input_emb,
+            attention_mask=attention_mask
+        )
+
+        target_pos_embs = pos_items_embs[:, 1:, :]  # [batch, max_seq_len, dim]
+        neg_embedding_all = neg_items_embs  # [batch, max_seq_len, dim]
+
+        with torch.no_grad():
+            self.logit_scale.clamp_(0, np.log(100))
+        logit_scale = self.logit_scale.exp()
+        output_embs = output_embs / output_embs.norm(dim=-1, keepdim=True)
+        target_pos_embs = target_pos_embs / target_pos_embs.norm(dim=-1, keepdim=True)
+        neg_embedding_all = neg_embedding_all / neg_embedding_all.norm(dim=-1, keepdim=True)
+        pos_logits = F.cosine_similarity(output_embs, target_pos_embs, dim=-1).unsqueeze(-1)
+        if self.num_negatives:
+            neg_logits = F.cosine_similarity(output_embs.unsqueeze(2), neg_embedding_all, dim=-1)
+            fix_logits = F.cosine_similarity(target_pos_embs.unsqueeze(2), neg_embedding_all, dim=-1)
+        else:
+            D = neg_embedding_all.size(-1)
+            neg_embedding_all = all_gather(neg_embedding_all, sync_grads=True).reshape(-1, D)  # [num, dim]
+            neg_embedding_all = neg_embedding_all.transpose(-1, -2)
+            neg_logits = torch.matmul(output_embs, neg_embedding_all)
+            fix_logits = torch.matmul(target_pos_embs, neg_embedding_all)
+
+        neg_logits[fix_logits > self.nce_thres] = torch.finfo(neg_logits.dtype).min
+        logits = torch.cat([pos_logits, neg_logits], dim=-1)
+        logits = logits[masked_index.bool()] * logit_scale
+        labels = torch.zeros(logits.size(0), device=logits.device, dtype=torch.int64)
+        model_out = {}
+        model_out['loss'] = F.cross_entropy(logits, labels)
+        model_out['nce_samples'] = (logits > torch.finfo(logits.dtype).min/100).sum(dim=1).float().mean()
+        for k in [1, 5, 10, 50, 100]:
+            if k > logits.size(1):
+                break
+            indices = logits.topk(k, dim=1).indices
+            model_out[f"nce_top{k}_acc"] = labels.view(-1, 1).eq(indices).any(dim=1).float().mean()
+        return model_out
+
+    @torch.no_grad()
+    def predict(self, item_seq, time_seq, item_feature):
+        position_ids = torch.arange(item_seq.size(1), dtype=torch.long, device=item_seq.device)
+        position_ids = position_ids.unsqueeze(0).expand_as(item_seq)
+        position_embedding = self.position_embedding(position_ids)
+
+        item_emb = self.item_id_proj_tower(self.item_embedding(item_seq))
+        item_emb = item_emb + position_embedding
+        attention_mask = self.get_attention_mask(item_seq)
+        output_embs = self._hstu(
+            x=item_emb,
+            attention_mask=attention_mask
+        )
+        seq_output = output_embs[:, -1]
+        seq_output = seq_output / seq_output.norm(dim=-1, keepdim=True)
+
+        scores = torch.matmul(seq_output, item_feature.t())
+        return scores
+
+    @torch.no_grad()
+    def compute_item_all(self):
+        weight = self.item_id_proj_tower(self.item_embedding.weight)
+        return weight / weight.norm(dim=-1, keepdim=True)
+
+    def get_attention_mask(self, item_seq, bidirectional=False):
+        """Generate left-to-right uni-directional or bidirectional attention mask for multi-head attention."""
+        attention_mask = (item_seq != 0)
+        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)  # torch.bool
+        if not bidirectional:
+            extended_attention_mask = torch.tril(extended_attention_mask.expand((-1, -1, item_seq.size(-1), -1)))
+        # extended_attention_mask = torch.where(extended_attention_mask, 0., -1e9)
+        return extended_attention_mask

code/REC/model/IDNet/llmidrec.py

+# Copyright (c) 2024 Bytedance Ltd. and/or its affiliate
+#
+# 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.
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+import numpy as np
+import transformers
+from transformers import AutoConfig, AutoModelForCausalLM
+from logging import getLogger
+
+from REC.utils.enum_type import InputType
+from REC.model.basemodel import BaseModel, all_gather
+from REC.model.HLLM.modeling_llama import LlamaForCausalLM
+from REC.model.HLLM.modeling_bert import BertModel
+
+
+class LLMIDRec(BaseModel):
+    input_type = InputType.SEQ
+
+    def __init__(self, config, dataload):
+        super(LLMIDRec, self).__init__()
+        self.logger = getLogger()
+
+        self.user_pretrain_dir = config['user_pretrain_dir']
+        self.gradient_checkpointing = config['gradient_checkpointing']
+        self.use_ft_flash_attn = config['use_ft_flash_attn']
+        self.logger.info(f"create user llm")
+        self.user_llm = self.create_llm(self.user_pretrain_dir, config['user_llm_init'])
+
+        self.item_num = dataload.item_num
+        self.item_embedding = nn.Embedding(self.item_num, config['item_embed_dim'], padding_idx=0)
+        self.item_id_proj_tower = nn.Identity() if config['item_embed_dim'] == self.user_llm.config.hidden_size else nn.Linear(config['item_embed_dim'], self.user_llm.config.hidden_size, bias=False)
+        self.item_embedding.weight.data.normal_(mean=0.0, std=0.02)
+
+        self.loss = config['loss']
+        if self.loss == 'nce':
+            if config['fix_temp']:
+                self.logger.info(f"Fixed logit_scale 20")
+                self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.05), requires_grad=False)
+            else:
+                self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
+            self.nce_thres = config['nce_thres'] if config['nce_thres'] else 0.99
+            self.num_negatives = config['num_negatives']
+            self.logger.info(f"nce thres setting to {self.nce_thres}")
+        else:
+            raise NotImplementedError(f"Only nce is supported")
+
+    def create_llm(self, pretrain_dir, init=True):
+        self.logger.info(f"******* create LLM {pretrain_dir} *******")
+        hf_config = AutoConfig.from_pretrained(pretrain_dir, trust_remote_code=True)
+        self.logger.info(f"hf_config: {hf_config}")
+        hf_config.gradient_checkpointing = self.gradient_checkpointing
+        hf_config.use_cache = False
+        hf_config.output_hidden_states = True
+        hf_config.return_dict = True
+
+        self.logger.info("xxxxx starting loading checkpoint")
+        if isinstance(hf_config, transformers.LlamaConfig):
+            hf_config.use_ft_flash_attn = self.use_ft_flash_attn
+            self.logger.info(f'Using flash attention {hf_config.use_ft_flash_attn} for llama')
+            self.logger.info(f'Init {init} for llama')
+            if init:
+                return LlamaForCausalLM.from_pretrained(pretrain_dir, config=hf_config)
+            else:
+                return LlamaForCausalLM(config=hf_config).bfloat16()
+        elif isinstance(hf_config, transformers.BertConfig):
+            hf_config.use_ft_flash_attn = self.use_ft_flash_attn
+            self.logger.info(f'Using flash attention {hf_config.use_ft_flash_attn} for bert')
+            self.logger.info(f'Init {init} for bert')
+            if init:
+                return BertModel.from_pretrained(pretrain_dir, config=hf_config)
+            else:
+                return BertModel(config=hf_config).bfloat16()
+        else:
+            return AutoModelForCausalLM.from_pretrained(
+                self.local_dir, config=hf_config
+            )
+
+    def forward(self, interaction):
+        items, neg_items, masked_index = interaction  # [batch, 2, seq_len]    #[batch, max_seq_len-1]
+        if self.num_negatives:
+            neg_items = torch.randint(
+                low=1,
+                high=self.item_num,
+                size=(items.size(0), items.size(1) - 1, self.num_negatives),
+                dtype=items.dtype,
+                device=items.device,
+            )
+
+        pos_items_embs = self.item_id_proj_tower(self.item_embedding(items))  # [batch, 2, max_seq_len+1, dim]
+        neg_items_embs = self.item_id_proj_tower(self.item_embedding(neg_items))  # [batch, 2, max_seq_len+1, dim]
+
+        input_emb = pos_items_embs[:, :-1, :]  # [batch, max_seq_len, dim]
+        target_pos_embs = pos_items_embs[:, 1:, :]  # [batch, max_seq_len, dim]
+        neg_embedding_all = neg_items_embs  # [batch, max_seq_len, dim]
+        output_embs = self.user_llm(inputs_embeds=input_emb, attention_mask=masked_index).hidden_states[-1]
+
+        with torch.no_grad():
+            self.logit_scale.clamp_(0, np.log(100))
+        logit_scale = self.logit_scale.exp()
+        output_embs = output_embs / output_embs.norm(dim=-1, keepdim=True)
+        target_pos_embs = target_pos_embs / target_pos_embs.norm(dim=-1, keepdim=True)
+        neg_embedding_all = neg_embedding_all / neg_embedding_all.norm(dim=-1, keepdim=True)
+        pos_logits = F.cosine_similarity(output_embs, target_pos_embs, dim=-1).unsqueeze(-1)
+        if self.num_negatives:
+            neg_logits = F.cosine_similarity(output_embs.unsqueeze(2), neg_embedding_all, dim=-1)
+            fix_logits = F.cosine_similarity(target_pos_embs.unsqueeze(2), neg_embedding_all, dim=-1)
+        else:
+            D = neg_embedding_all.size(-1)
+            neg_embedding_all = all_gather(neg_embedding_all, sync_grads=True).reshape(-1, D)  # [num, dim]
+            neg_embedding_all = neg_embedding_all.transpose(-1, -2)
+            neg_logits = torch.matmul(output_embs, neg_embedding_all)
+            fix_logits = torch.matmul(target_pos_embs, neg_embedding_all)
+        neg_logits[fix_logits > self.nce_thres] = torch.finfo(neg_logits.dtype).min
+        logits = torch.cat([pos_logits, neg_logits], dim=-1)
+        logits = logits[masked_index.bool()] * logit_scale
+        labels = torch.zeros(logits.size(0), device=logits.device, dtype=torch.int64)
+        model_out = {}
+        model_out['loss'] = F.cross_entropy(logits, labels)
+        model_out['nce_samples'] = (logits > torch.finfo(logits.dtype).min/100).sum(dim=1).float().mean()
+        for k in [1, 5, 10, 50, 100]:
+            if k > logits.size(1):
+                break
+            indices = logits.topk(k, dim=1).indices
+            model_out[f"nce_top{k}_acc"] = labels.view(-1, 1).eq(indices).any(dim=1).float().mean()
+        return model_out
+
+    @torch.no_grad()
+    def predict(self, item_seq, time_seq, item_feature):
+
+        item_emb = self.item_id_proj_tower(self.item_embedding(item_seq))
+        attention_mask = (item_seq > 0).int()
+        output_embs = self.user_llm(inputs_embeds=item_emb, attention_mask=attention_mask).hidden_states[-1]
+        seq_output = output_embs[:, -1]
+        seq_output = seq_output / seq_output.norm(dim=-1, keepdim=True)
+
+        scores = torch.matmul(seq_output, item_feature.t())
+        return scores
+
+    @torch.no_grad()
+    def compute_item_all(self):
+        weight = self.item_id_proj_tower(self.item_embedding(torch.arange(self.item_num, device=self.item_embedding.weight.device)))
+        return weight / weight.norm(dim=-1, keepdim=True)
+
+    def get_attention_mask(self, item_seq, bidirectional=False):
+        """Generate left-to-right uni-directional or bidirectional attention mask for multi-head attention."""
+        attention_mask = (item_seq != 0)
+        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)  # torch.bool
+        if not bidirectional:
+            extended_attention_mask = torch.tril(extended_attention_mask.expand((-1, -1, item_seq.size(-1), -1)))
+        extended_attention_mask = torch.where(extended_attention_mask, 0., -1e9)
+        return extended_attention_mask

code/REC/model/IDNet/sasrec.py

+# Copyright (c) 2024 westlake-repl
+# Copyright (c) 2024 Bytedance Ltd. and/or its affiliate
+# SPDX-License-Identifier: MIT
+# This file has been modified by Junyi Chen.
+#
+# Original file was released under MIT, with the full license text
+# available at https://choosealicense.com/licenses/mit/.
+#
+# This modified file is released under the same license.
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+import numpy as np
+from logging import getLogger
+
+from REC.model.layers import TransformerEncoder
+from REC.utils.enum_type import InputType
+from REC.model.basemodel import BaseModel, all_gather
+
+
+class SASRec(BaseModel):
+    input_type = InputType.SEQ
+
+    def __init__(self, config, dataload):
+        super(SASRec, self).__init__()
+        self.logger = getLogger()
+        # load parameters info
+        self.n_layers = config['n_layers']
+        self.n_heads = config['n_heads']
+        self.hidden_size = config['embedding_size']  # same as embedding_size
+        self.inner_size = config['inner_size']  # the dimensionality in feed-forward layer
+        self.inner_size *= self.hidden_size
+        self.hidden_dropout_prob = config['hidden_dropout_prob']
+        self.attn_dropout_prob = config['attn_dropout_prob']
+        self.hidden_act = config['hidden_act']
+        self.layer_norm_eps = config['layer_norm_eps']
+
+        self.initializer_range = config['initializer_range']
+        self.max_seq_length = config['MAX_ITEM_LIST_LENGTH']
+        self.item_num = dataload.item_num
+        # define layers and loss
+        self.item_embedding = nn.Embedding(self.item_num, self.hidden_size, padding_idx=0)
+        self.position_embedding = nn.Embedding(self.max_seq_length, self.hidden_size)
+        self.trm_encoder = TransformerEncoder(
+            n_layers=self.n_layers,
+            n_heads=self.n_heads,
+            hidden_size=self.hidden_size,
+            inner_size=self.inner_size,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attn_dropout_prob=self.attn_dropout_prob,
+            hidden_act=self.hidden_act,
+            layer_norm_eps=self.layer_norm_eps
+        )
+
+        self.LayerNorm = nn.LayerNorm(self.hidden_size, eps=self.layer_norm_eps)
+        self.dropout = nn.Dropout(self.hidden_dropout_prob)
+
+        self.loss = config['loss']
+        if self.loss == 'nce':
+            if config['fix_temp']:
+                self.logger.info(f"Fixed logit_scale 20")
+                self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.05), requires_grad=False)
+            else:
+                self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
+            self.nce_thres = config['nce_thres'] if config['nce_thres'] else 0.99
+            self.num_negatives = config['num_negatives']
+            self.logger.info(f"nce thres setting to {self.nce_thres}")
+        else:
+            raise NotImplementedError(f"Only nce is supported")
+
+        # parameters initialization
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        """ Initialize the weights """
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.initializer_range)
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        if isinstance(module, nn.Linear) and module.bias is not None:
+            module.bias.data.zero_()
+
+    def forward(self, interaction):
+        items, neg_items, masked_index = interaction  # [batch, 2, seq_len]    #[batch, max_seq_len-1]
+        if self.num_negatives:
+            neg_items = torch.randint(
+                low=1,
+                high=self.item_num,
+                size=(items.size(0), items.size(1) - 1, self.num_negatives),
+                dtype=items.dtype,
+                device=items.device,
+            )
+
+        pos_items_embs = self.item_embedding(items)  # [batch, 2, max_seq_len+1, dim]
+        neg_items_embs = self.item_embedding(neg_items)  # [batch, 2, max_seq_len+1, dim]
+
+        input_emb = pos_items_embs[:, :-1, :]  # [batch, max_seq_len, dim]
+        target_pos_embs = pos_items_embs[:, 1:, :]  # [batch, max_seq_len, dim]
+        neg_embedding_all = neg_items_embs  # [batch, max_seq_len, dim]
+
+        position_ids = torch.arange(masked_index.size(1), dtype=torch.long, device=masked_index.device)
+        position_ids = position_ids.unsqueeze(0).expand_as(masked_index)
+        position_embedding = self.position_embedding(position_ids)
+        input_emb = input_emb + position_embedding
+        input_emb = self.LayerNorm(input_emb)
+        input_emb = self.dropout(input_emb)
+
+        extended_attention_mask = self.get_attention_mask(masked_index, bidirectional=False)
+
+        output_embs = self.trm_encoder(input_emb, extended_attention_mask, output_all_encoded_layers=False)  # [batch, max_seq_len-1, dim]
+        output_embs = output_embs[-1]
+
+        with torch.no_grad():
+            self.logit_scale.clamp_(0, np.log(100))
+        logit_scale = self.logit_scale.exp()
+        output_embs = output_embs / output_embs.norm(dim=-1, keepdim=True)
+        target_pos_embs = target_pos_embs / target_pos_embs.norm(dim=-1, keepdim=True)
+        neg_embedding_all = neg_embedding_all / neg_embedding_all.norm(dim=-1, keepdim=True)
+        pos_logits = F.cosine_similarity(output_embs, target_pos_embs, dim=-1).unsqueeze(-1)
+        if self.num_negatives:
+            neg_logits = F.cosine_similarity(output_embs.unsqueeze(2), neg_embedding_all, dim=-1)
+            fix_logits = F.cosine_similarity(target_pos_embs.unsqueeze(2), neg_embedding_all, dim=-1)
+        else:
+            D = neg_embedding_all.size(-1)
+            neg_embedding_all = all_gather(neg_embedding_all, sync_grads=True).reshape(-1, D)  # [num, dim]
+            neg_embedding_all = neg_embedding_all.transpose(-1, -2)
+            neg_logits = torch.matmul(output_embs, neg_embedding_all)
+            fix_logits = torch.matmul(target_pos_embs, neg_embedding_all)
+
+        neg_logits[fix_logits > self.nce_thres] = torch.finfo(neg_logits.dtype).min
+        logits = torch.cat([pos_logits, neg_logits], dim=-1)
+        logits = logits[masked_index.bool()] * logit_scale
+        labels = torch.zeros(logits.size(0), device=logits.device, dtype=torch.int64)
+        model_out = {}
+        model_out['loss'] = F.cross_entropy(logits, labels)
+        model_out['nce_samples'] = (logits > torch.finfo(logits.dtype).min/100).sum(dim=1).float().mean()
+        for k in [1, 5, 10, 50, 100]:
+            if k > logits.size(1):
+                break
+            indices = logits.topk(k, dim=1).indices
+            model_out[f"nce_top{k}_acc"] = labels.view(-1, 1).eq(indices).any(dim=1).float().mean()
+        return model_out
+
+    @torch.no_grad()
+    def predict(self, item_seq, time_seq, item_feature):
+
+        position_ids = torch.arange(item_seq.size(1), dtype=torch.long, device=item_seq.device)
+        position_ids = position_ids.unsqueeze(0).expand_as(item_seq)
+        position_embedding = self.position_embedding(position_ids)
+
+        item_emb = self.item_embedding(item_seq)
+        input_emb = item_emb + position_embedding
+        input_emb = self.LayerNorm(input_emb)
+        input_emb = self.dropout(input_emb)
+
+        extended_attention_mask = self.get_attention_mask(item_seq, bidirectional=False)
+
+        output = self.trm_encoder(input_emb, extended_attention_mask, output_all_encoded_layers=False)
+        output_embs = output[-1]
+        seq_output = output_embs[:, -1]
+        seq_output = seq_output / seq_output.norm(dim=-1, keepdim=True)
+
+        scores = torch.matmul(seq_output, item_feature.t())
+        return scores
+
+    @torch.no_grad()
+    def compute_item_all(self):
+        weight = self.item_embedding.weight
+        return weight / weight.norm(dim=-1, keepdim=True)
+
+    def get_attention_mask(self, item_seq, bidirectional=False):
+        """Generate left-to-right uni-directional or bidirectional attention mask for multi-head attention."""
+        attention_mask = (item_seq != 0)
+        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)  # torch.bool
+        if not bidirectional:
+            extended_attention_mask = torch.tril(extended_attention_mask.expand((-1, -1, item_seq.size(-1), -1)))
+        extended_attention_mask = torch.where(extended_attention_mask, 0., -1e9)
+        return extended_attention_mask

code/REC/model/basemodel.py

+# Copyright (c) 2024 westlake-repl
+# SPDX-License-Identifier: MIT
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from REC.utils import set_color
+
+
+def all_gather(data,
+               group=None,
+               sync_grads=False):
+    group = group if group is not None else torch.distributed.group.WORLD
+    if torch.distributed.get_world_size() > 1:
+        from torch.distributed import nn
+        if sync_grads:
+            return torch.stack(nn.functional.all_gather(data, group=group), dim=0)
+        with torch.no_grad():
+            return torch.stack(nn.functional.all_gather(data, group=group), dim=0)
+    else:
+        return data.unsqueeze(0)
+
+
+def l2_norm(x, eps=1e-6):
+    x = x / torch.clamp(
+        torch.linalg.norm(x, ord=2, dim=-1, keepdim=True),
+        min=eps,
+    )
+    return x
+
+
+class BaseModel(nn.Module):
+
+    def __init__(self):
+        super(BaseModel, self).__init__()
+
+    def load_weights(self, path):
+        checkpoint = torch.load(path, map_location='cpu')
+        pretrained_dicts = checkpoint['state_dict']
+        self.load_state_dict({k.replace('item_embedding.rec_fc', 'visual_encoder.item_encoder.fc'): v for k, v in pretrained_dicts.items()}, strict=False)
+
+    def __str__(self):
+        """
+        Model prints with number of trainable parameters
+        """
+        model_parameters = filter(lambda p: p.requires_grad, self.parameters())
+        params = sum([np.prod(p.size()) for p in model_parameters])
+        return super().__str__() + set_color('\nTrainable parameters', 'blue') + f': {params}'

code/REC/model/layers.py

+# Copyright (c) 2024 westlake-repl
+# SPDX-License-Identifier: MIT
+
+import math
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as fn
+from torch.nn.init import normal_
+import copy
+
+# [64, 128, 512] -> (64,128), (128,512)
+# [64]
+
+
+class MLPLayers(nn.Module):
+    r""" MLPLayers
+
+    Args:
+        - layers(list): a list contains the size of each layer in mlp layers
+        - dropout(float): probability of an element to be zeroed. Default: 0
+        - activation(str): activation function after each layer in mlp layers. Default: 'relu'.
+                           candidates: 'sigmoid', 'tanh', 'relu', 'leekyrelu', 'none'
+
+    Shape:
+
+        - Input: (:math:`N`, \*, :math:`H_{in}`) where \* means any number of additional dimensions
+          :math:`H_{in}` must equal to the first value in `layers`
+        - Output: (:math:`N`, \*, :math:`H_{out}`) where :math:`H_{out}` equals to the last value in `layers`
+
+    Examples::
+
+        >>> m = MLPLayers([64, 32, 16], 0.2, 'relu')
+        >>> input = torch.randn(128, 64)
+        >>> output = m(input)
+        >>> print(output.size())
+        >>> torch.Size([128, 16])
+    """
+
+    def __init__(self, layers, dropout=0., activation='relu', bn=False, init_method=None):
+        super(MLPLayers, self).__init__()
+        self.layers = layers
+        self.dropout = dropout
+        self.activation = activation
+        self.use_bn = bn
+        self.init_method = init_method
+
+        mlp_modules = []
+        for idx, (input_size, output_size) in enumerate(zip(self.layers[:-1], self.layers[1:])):
+            mlp_modules.append(nn.Dropout(p=self.dropout))
+            mlp_modules.append(nn.Linear(input_size, output_size))
+            if self.use_bn:
+                mlp_modules.append(nn.BatchNorm1d(num_features=output_size))
+            activation_func = activation_layer(self.activation, output_size)
+            if activation_func is not None:
+                mlp_modules.append(activation_func)
+
+        self.mlp_layers = nn.Sequential(*mlp_modules)
+        if self.init_method is not None:
+            self.apply(self.init_weights)
+
+    def init_weights(self, module):
+        # We just initialize the module with normal distribution as the paper said
+        if isinstance(module, nn.Linear):
+            if self.init_method == 'norm':
+                normal_(module.weight.data, 0, 0.01)
+            if module.bias is not None:
+                module.bias.data.fill_(0.0)
+
+    def forward(self, input_feature):
+        return self.mlp_layers(input_feature)
+
+
+def activation_layer(activation_name='relu', emb_dim=None):
+    """Construct activation layers
+
+    Args:
+        activation_name: str, name of activation function
+        emb_dim: int, used for Dice activation
+
+    Return:
+        activation: activation layer
+    """
+    if activation_name is None:
+        activation = None
+    elif isinstance(activation_name, str):
+        if activation_name.lower() == 'sigmoid':
+            activation = nn.Sigmoid()
+        elif activation_name.lower() == 'tanh':
+            activation = nn.Tanh()
+        elif activation_name.lower() == 'relu':
+            activation = nn.ReLU()
+        elif activation_name.lower() == 'leakyrelu':
+            activation = nn.LeakyReLU()
+        elif activation_name.lower() == 'dice':
+            activation = Dice(emb_dim)
+        elif activation_name.lower() == 'none':
+            activation = None
+    elif issubclass(activation_name, nn.Module):
+        activation = activation_name()
+    else:
+        raise NotImplementedError("activation function {} is not implemented".format(activation_name))
+
+    return activation
+
+
+class FMEmbedding(nn.Module):
+    r""" Embedding for token fields.
+
+    Args:
+        field_dims: list, the number of tokens in each token fields
+        offsets: list, the dimension offset of each token field
+        embed_dim: int, the dimension of output embedding vectors
+
+    Input:
+        input_x: tensor, A 3D tensor with shape:``(batch_size,field_size)``.
+
+    Return:
+        output: tensor,  A 3D tensor with shape: ``(batch_size,field_size,embed_dim)``.
+    """
+
+    def __init__(self, field_dims, offsets, embed_dim):
+        super(FMEmbedding, self).__init__()
+        self.embedding = nn.Embedding(sum(field_dims), embed_dim)
+        self.offsets = offsets
+
+    def forward(self, input_x):
+        input_x = input_x + input_x.new_tensor(self.offsets).unsqueeze(0)
+        output = self.embedding(input_x)
+        return output
+
+
+class BaseFactorizationMachine(nn.Module):
+    r"""Calculate FM result over the embeddings
+
+    Args:
+        reduce_sum: bool, whether to sum the result, default is True.
+
+    Input:
+        input_x: tensor, A 3D tensor with shape:``(batch_size,field_size,embed_dim)``.
+
+    Output
+        output: tensor, A 3D tensor with shape: ``(batch_size,1)`` or ``(batch_size, embed_dim)``.
+    """
+
+    def __init__(self, reduce_sum=True):
+        super(BaseFactorizationMachine, self).__init__()
+        self.reduce_sum = reduce_sum
+
+    def forward(self, input_x):
+        square_of_sum = torch.sum(input_x, dim=1) ** 2
+        sum_of_square = torch.sum(input_x ** 2, dim=1)
+        output = square_of_sum - sum_of_square
+        if self.reduce_sum:
+            output = torch.sum(output, dim=1, keepdim=True)
+        output = 0.5 * output
+        return output
+
+
+class BiGNNLayer(nn.Module):
+    r"""Propagate a layer of Bi-interaction GNN
+
+    .. math::
+        output = (L+I)EW_1 + LE \otimes EW_2
+    """
+
+    def __init__(self, in_dim, out_dim):
+        super(BiGNNLayer, self).__init__()
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+        self.linear = torch.nn.Linear(in_features=in_dim, out_features=out_dim)
+        self.interActTransform = torch.nn.Linear(in_features=in_dim, out_features=out_dim)
+
+    def forward(self, lap_matrix, eye_matrix, features):
+        # for GCF ajdMat is a (N+M) by (N+M) mat
+        # lap_matrix L = D^-1(A)D^-1 # 拉普拉斯矩阵
+        x = torch.sparse.mm(lap_matrix, features)
+
+        inter_part1 = self.linear(features + x)
+        inter_feature = torch.mul(x, features)
+        inter_part2 = self.interActTransform(inter_feature)
+
+        return inter_part1 + inter_part2
+
+
+class AttLayer(nn.Module):
+    """Calculate the attention signal(weight) according the input tensor.
+
+    Args:
+        infeatures (torch.FloatTensor): A 3D input tensor with shape of[batch_size, M, embed_dim].
+
+    Returns:
+        torch.FloatTensor: Attention weight of input. shape of [batch_size, M].
+    """
+
+    def __init__(self, in_dim, att_dim):
+        super(AttLayer, self).__init__()
+        self.in_dim = in_dim
+        self.att_dim = att_dim
+        self.w = torch.nn.Linear(in_features=in_dim, out_features=att_dim, bias=False)
+        self.h = nn.Parameter(torch.randn(att_dim), requires_grad=True)
+
+    def forward(self, infeatures):
+        att_signal = self.w(infeatures)  # [batch_size, M, att_dim]
+        att_signal = fn.relu(att_signal)  # [batch_size, M, att_dim]
+
+        att_signal = torch.mul(att_signal, self.h)  # [batch_size, M, att_dim]
+        att_signal = torch.sum(att_signal, dim=2)  # [batch_size, M]
+        att_signal = fn.softmax(att_signal, dim=1)  # [batch_size, M]
+
+        return att_signal
+
+
+class Dice(nn.Module):
+    r"""Dice activation function
+
+    .. math::
+        f(s)=p(s) \cdot s+(1-p(s)) \cdot \alpha s
+
+    .. math::
+        p(s)=\frac{1} {1 + e^{-\frac{s-E[s]} {\sqrt {Var[s] + \epsilon}}}}
+    """
+
+    def __init__(self, emb_size):
+        super(Dice, self).__init__()
+
+        self.sigmoid = nn.Sigmoid()
+        self.alpha = torch.zeros((emb_size,))
+
+    def forward(self, score):
+        self.alpha = self.alpha.to(score.device)
+        score_p = self.sigmoid(score)
+
+        return self.alpha * (1 - score_p) * score + score_p * score
+
+
+class SequenceAttLayer(nn.Module):
+    """Attention Layer. Get the representation of each user in the batch.
+
+    Args:
+        queries (torch.Tensor): candidate ads, [B, H], H means embedding_size * feat_num
+        keys (torch.Tensor): user_hist, [B, T, H]
+        keys_length (torch.Tensor): mask, [B]
+
+    Returns:
+        torch.Tensor: result
+    """
+
+    def __init__(
+        self, att_hidden_size=(80, 40), activation='sigmoid', softmax_stag=False, return_seq_weight=True
+    ):
+        super(SequenceAttLayer, self).__init__()
+        self.att_hidden_size = att_hidden_size
+        self.activation = activation
+        self.softmax_stag = softmax_stag
+        self.return_seq_weight = return_seq_weight
+        self.att_mlp_layers = MLPLayers(self.att_hidden_size, activation='Sigmoid', bn=False)
+        self.dense = nn.Linear(self.att_hidden_size[-1], 1)
+
+    def forward(self, queries, keys, mask):
+        embedding_size = queries.shape[-1]  # H
+        hist_len = keys.shape[1]  # T
+        queries = queries.repeat(1, hist_len)
+
+        queries = queries.view(-1, hist_len, embedding_size)
+
+        # MLP Layer
+        input_tensor = torch.cat([queries, keys, queries - keys, queries * keys], dim=-1)
+        output = self.att_mlp_layers(input_tensor)
+        output = torch.transpose(self.dense(output), -1, -2)
+
+        # get mask
+        output = output.squeeze(1)
+        # mask
+        if self.softmax_stag:
+            mask_value = -np.inf
+        else:
+            mask_value = 0.0
+
+        output = output.masked_fill(mask=mask, value=torch.tensor(mask_value))
+        output = output.unsqueeze(1)
+        output = output / (embedding_size ** 0.5)
+
+        # get the weight of each user's history list about the target item
+        if self.softmax_stag:
+            output = fn.softmax(output, dim=2)  # [B, 1, T]
+
+        if not self.return_seq_weight:
+            output = torch.matmul(output, keys)  # [B, 1, H]
+
+        return output
+
+
+class VanillaAttention(nn.Module):
+    """
+    Vanilla attention layer is implemented by linear layer.
+
+    Args:
+        input_tensor (torch.Tensor): the input of the attention layer
+
+    Returns:
+        hidden_states (torch.Tensor): the outputs of the attention layer
+        weights (torch.Tensor): the attention weights
+
+    """
+
+    def __init__(self, hidden_dim, attn_dim):
+        super().__init__()
+        self.projection = nn.Sequential(nn.Linear(hidden_dim, attn_dim), nn.ReLU(True), nn.Linear(attn_dim, 1))
+
+    def forward(self, input_tensor):
+        # (B, Len, num, H) -> (B, Len, num, 1)
+        energy = self.projection(input_tensor)
+        weights = torch.softmax(energy.squeeze(-1), dim=-1)
+        # (B, Len, num, H) * (B, Len, num, 1) -> (B, len, H)
+        hidden_states = (input_tensor * weights.unsqueeze(-1)).sum(dim=-2)
+        return hidden_states, weights
+
+
+class MultiHeadAttention(nn.Module):
+    """
+    Multi-head Self-attention layers, a attention score dropout layer is introduced.
+
+    Args:
+        input_tensor (torch.Tensor): the input of the multi-head self-attention layer
+        attention_mask (torch.Tensor): the attention mask for input tensor
+
+    Returns:
+        hidden_states (torch.Tensor): the output of the multi-head self-attention layer
+
+    """
+
+    def __init__(self, n_heads, hidden_size, hidden_dropout_prob, attn_dropout_prob, layer_norm_eps):
+        super(MultiHeadAttention, self).__init__()
+        if hidden_size % n_heads != 0:
+            raise ValueError(
+                "The hidden size (%d) is not a multiple of the number of attention "
+                "heads (%d)" % (hidden_size, n_heads)
+            )
+
+        self.num_attention_heads = n_heads
+        self.attention_head_size = int(hidden_size / n_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+        self.sqrt_attention_head_size = math.sqrt(self.attention_head_size)
+
+        self.query = nn.Linear(hidden_size, self.all_head_size)
+        self.key = nn.Linear(hidden_size, self.all_head_size)
+        self.value = nn.Linear(hidden_size, self.all_head_size)
+
+        self.softmax = nn.Softmax(dim=-1)
+        self.attn_dropout = nn.Dropout(attn_dropout_prob)
+
+        self.dense = nn.Linear(hidden_size, hidden_size)
+        self.LayerNorm = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
+        self.out_dropout = nn.Dropout(hidden_dropout_prob)
+
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x
+
+    def forward(self, input_tensor, attention_mask):
+        mixed_query_layer = self.query(input_tensor)
+        mixed_key_layer = self.key(input_tensor)
+        mixed_value_layer = self.value(input_tensor)
+
+        query_layer = self.transpose_for_scores(mixed_query_layer).permute(0, 2, 1, 3)
+        key_layer = self.transpose_for_scores(mixed_key_layer).permute(0, 2, 3, 1)
+        value_layer = self.transpose_for_scores(mixed_value_layer).permute(0, 2, 1, 3)
+
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_layer, key_layer)
+
+        attention_scores = attention_scores / self.sqrt_attention_head_size
+        # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+        # [batch_size heads seq_len seq_len] scores
+        # [batch_size 1 1 seq_len]
+        attention_scores = attention_scores + attention_mask
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = self.softmax(attention_scores).to(attention_scores.dtype)
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+
+        attention_probs = self.attn_dropout(attention_probs)
+        context_layer = torch.matmul(attention_probs, value_layer)
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(*new_context_layer_shape)
+        hidden_states = self.dense(context_layer)
+        hidden_states = self.out_dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+
+        return hidden_states
+
+
+class FeedForward(nn.Module):
+    """
+    Point-wise feed-forward layer is implemented by two dense layers.
+
+    Args:
+        input_tensor (torch.Tensor): the input of the point-wise feed-forward layer
+
+    Returns:
+        hidden_states (torch.Tensor): the output of the point-wise feed-forward layer
+
+    """
+
+    def __init__(self, hidden_size, inner_size, hidden_dropout_prob, hidden_act, layer_norm_eps):
+        super(FeedForward, self).__init__()
+        self.dense_1 = nn.Linear(hidden_size, inner_size)
+        self.intermediate_act_fn = self.get_hidden_act(hidden_act)
+
+        self.dense_2 = nn.Linear(inner_size, hidden_size)
+        self.LayerNorm = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
+        self.dropout = nn.Dropout(hidden_dropout_prob)
+
+    def get_hidden_act(self, act):
+        ACT2FN = {
+            "gelu": self.gelu,
+            "relu": fn.relu,
+            "swish": self.swish,
+            "tanh": torch.tanh,
+            "sigmoid": torch.sigmoid,
+        }
+        return ACT2FN[act]
+
+    def gelu(self, x):
+        """Implementation of the gelu activation function.
+
+        For information: OpenAI GPT's gelu is slightly different (and gives slightly different results)::
+
+            0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
+
+        Also see https://arxiv.org/abs/1606.08415
+        """
+        return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
+
+    def swish(self, x):
+        return x * torch.sigmoid(x)
+
+    def forward(self, input_tensor):
+        hidden_states = self.dense_1(input_tensor)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+
+        hidden_states = self.dense_2(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+
+        return hidden_states
+
+
+class TransformerLayer(nn.Module):
+    """
+    One transformer layer consists of a multi-head self-attention layer and a point-wise feed-forward layer.
+
+    Args:
+        hidden_states (torch.Tensor): the input of the multi-head self-attention sublayer
+        attention_mask (torch.Tensor): the attention mask for the multi-head self-attention sublayer
+
+    Returns:
+        feedforward_output (torch.Tensor): The output of the point-wise feed-forward sublayer,
+                                           is the output of the transformer layer.
+
+    """
+
+    def __init__(
+        self, n_heads, hidden_size, intermediate_size, hidden_dropout_prob, attn_dropout_prob, hidden_act,
+        layer_norm_eps
+    ):
+        super(TransformerLayer, self).__init__()
+        self.multi_head_attention = MultiHeadAttention(
+            n_heads, hidden_size, hidden_dropout_prob, attn_dropout_prob, layer_norm_eps
+        )
+        self.feed_forward = FeedForward(hidden_size, intermediate_size, hidden_dropout_prob, hidden_act, layer_norm_eps)
+
+    def forward(self, hidden_states, attention_mask):
+        attention_output = self.multi_head_attention(hidden_states, attention_mask)
+        feedforward_output = self.feed_forward(attention_output)
+        return feedforward_output
+
+
+class TransformerEncoder(nn.Module):
+    r""" One TransformerEncoder consists of several TransformerLayers.
+
+    Args:
+        n_layers(num): num of transformer layers in transformer encoder. Default: 2
+        n_heads(num): num of attention heads for multi-head attention layer. Default: 2
+        hidden_size(num): the input and output hidden size. Default: 64
+        inner_size(num): the dimensionality in feed-forward layer. Default: 256
+        hidden_dropout_prob(float): probability of an element to be zeroed. Default: 0.5
+        attn_dropout_prob(float): probability of an attention score to be zeroed. Default: 0.5
+        hidden_act(str): activation function in feed-forward layer. Default: 'gelu'
+                      candidates: 'gelu', 'relu', 'swish', 'tanh', 'sigmoid'
+        layer_norm_eps(float): a value added to the denominator for numerical stability. Default: 1e-12
+
+    """
+
+    def __init__(
+        self,
+        n_layers=2,
+        n_heads=2,
+        hidden_size=64,
+        inner_size=256,
+        hidden_dropout_prob=0.5,
+        attn_dropout_prob=0.5,
+        hidden_act='gelu',
+        layer_norm_eps=1e-12
+    ):
+
+        super(TransformerEncoder, self).__init__()
+        layer = TransformerLayer(
+            n_heads, hidden_size, inner_size, hidden_dropout_prob, attn_dropout_prob, hidden_act, layer_norm_eps
+        )
+        self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(n_layers)])
+
+    def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):
+        """
+        Args:
+            hidden_states (torch.Tensor): the input of the TransformerEncoder
+            attention_mask (torch.Tensor): the attention mask for the input hidden_states
+            output_all_encoded_layers (Bool): whether output all transformer layers' output
+
+        Returns:
+            all_encoder_layers (list): if output_all_encoded_layers is True, return a list consists of all transformer
+            layers' output, otherwise return a list only consists of the output of last transformer layer.
+
+        """
+        all_encoder_layers = []
+        for layer_module in self.layer:
+            hidden_states = layer_module(hidden_states, attention_mask)
+            if output_all_encoded_layers:
+                all_encoder_layers.append(hidden_states)
+        if not output_all_encoded_layers:
+            all_encoder_layers.append(hidden_states)
+        return all_encoder_layers
+
+
+class ItemToInterestAggregation(nn.Module):
+    def __init__(self, seq_len, hidden_size, k_interests=5):
+        super().__init__()
+        self.k_interests = k_interests  # k latent interests
+        self.theta = nn.Parameter(torch.randn([hidden_size, k_interests]))
+
+    def forward(self, input_tensor):  # [B, L, d] -> [B, k, d]
+        D_matrix = torch.matmul(input_tensor, self.theta)  # [B, L, k]
+        D_matrix = nn.Softmax(dim=-2)(D_matrix)
+        result = torch.einsum('nij, nik -> nkj', input_tensor, D_matrix)  # [B, k, d]
+
+        return result
+
+
+class LightMultiHeadAttention(nn.Module):
+    def __init__(self, n_heads, k_interests, hidden_size, seq_len, hidden_dropout_prob, attn_dropout_prob, layer_norm_eps):
+        super(LightMultiHeadAttention, self).__init__()
+        if hidden_size % n_heads != 0:
+            raise ValueError(
+                "The hidden size (%d) is not a multiple of the number of attention "
+                "heads (%d)" % (hidden_size, n_heads))
+
+        self.num_attention_heads = n_heads
+        self.attention_head_size = int(hidden_size / n_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        # initialization for low-rank decomposed self-attention
+        self.query = nn.Linear(hidden_size, self.all_head_size)
+        self.key = nn.Linear(hidden_size, self.all_head_size)
+        self.value = nn.Linear(hidden_size, self.all_head_size)
+
+        self.attpooling_key = ItemToInterestAggregation(seq_len, hidden_size, k_interests)
+        self.attpooling_value = ItemToInterestAggregation(seq_len, hidden_size, k_interests)
+
+        # initialization for decoupled position encoding
+        self.attn_scale_factor = 2
+        self.pos_q_linear = nn.Linear(hidden_size, self.all_head_size)
+        self.pos_k_linear = nn.Linear(hidden_size, self.all_head_size)
+        self.pos_scaling = float(self.attention_head_size * self.attn_scale_factor) ** -0.5
+        self.pos_ln = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
+
+        self.attn_dropout = nn.Dropout(attn_dropout_prob)
+
+        self.dense = nn.Linear(hidden_size, hidden_size)
+        self.LayerNorm = nn.LayerNorm(hidden_size, eps=layer_norm_eps)
+        self.out_dropout = nn.Dropout(hidden_dropout_prob)
+
+    def transpose_for_scores(self, x):  # transfor to multihead
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def forward(self, input_tensor, pos_emb):
+        # linear map
+        mixed_query_layer = self.query(input_tensor)
+        mixed_key_layer = self.key(input_tensor)
+        mixed_value_layer = self.value(input_tensor)
+
+        # low-rank decomposed self-attention: relation of items
+        query_layer = self.transpose_for_scores(mixed_query_layer)
+        key_layer = self.transpose_for_scores(self.attpooling_key(mixed_key_layer))
+        value_layer = self.transpose_for_scores(self.attpooling_value(mixed_value_layer))
+
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+
+        # normalize the attention scores to probabilities.
+        attention_probs = nn.Softmax(dim=-2)(attention_scores)
+        attention_probs = self.attn_dropout(attention_probs)
+        context_layer_item = torch.matmul(attention_probs, value_layer)
+
+        # decoupled position encoding: relation of positions
+        value_layer_pos = self.transpose_for_scores(mixed_value_layer)
+        pos_emb = self.pos_ln(pos_emb).unsqueeze(0)
+        pos_query_layer = self.transpose_for_scores(self.pos_q_linear(pos_emb)) * self.pos_scaling
+        pos_key_layer = self.transpose_for_scores(self.pos_k_linear(pos_emb))
+
+        abs_pos_bias = torch.matmul(pos_query_layer, pos_key_layer.transpose(-1, -2))
+        abs_pos_bias = abs_pos_bias / math.sqrt(self.attention_head_size)
+        abs_pos_bias = nn.Softmax(dim=-2)(abs_pos_bias)
+
+        context_layer_pos = torch.matmul(abs_pos_bias, value_layer_pos)
+
+        context_layer = context_layer_item + context_layer_pos
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(*new_context_layer_shape)
+        hidden_states = self.dense(context_layer)
+        hidden_states = self.out_dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+
+        return hidden_states
+
+
+class LightTransformerLayer(nn.Module):
+    """
+    One transformer layer consists of a multi-head self-attention layer and a point-wise feed-forward layer.
+
+    Args:
+        hidden_states (torch.Tensor): the input of the multi-head self-attention sublayer
+        attention_mask (torch.Tensor): the attention mask for the multi-head self-attention sublayer
+
+    Returns:
+        feedforward_output (torch.Tensor): the output of the point-wise feed-forward sublayer, is the output of the transformer layer
+    """
+
+    def __init__(self, n_heads, k_interests, hidden_size, seq_len, intermediate_size,
+                 hidden_dropout_prob, attn_dropout_prob, hidden_act, layer_norm_eps):
+        super(LightTransformerLayer, self).__init__()
+        self.multi_head_attention = LightMultiHeadAttention(n_heads, k_interests, hidden_size,
+                                                            seq_len, hidden_dropout_prob, attn_dropout_prob, layer_norm_eps)
+        self.feed_forward = FeedForward(hidden_size, intermediate_size,
+                                        hidden_dropout_prob, hidden_act, layer_norm_eps)
+
+    def forward(self, hidden_states, pos_emb):
+        attention_output = self.multi_head_attention(hidden_states, pos_emb)
+        feedforward_output = self.feed_forward(attention_output)
+        return feedforward_output
+
+
+class LightTransformerEncoder(nn.Module):
+    r""" One LightTransformerEncoder consists of several LightTransformerLayers.
+
+    Args:
+        n_layers(num): num of transformer layers in transformer encoder. Default: 2
+        n_heads(num): num of attention heads for multi-head attention layer. Default: 2
+        hidden_size(num): the input and output hidden size. Default: 64
+        inner_size(num): the dimensionality in feed-forward layer. Default: 256
+        hidden_dropout_prob(float): probability of an element to be zeroed. Default: 0.5
+        attn_dropout_prob(float): probability of an attention score to be zeroed. Default: 0.5
+        hidden_act(str): activation function in feed-forward layer. Default: 'gelu'.
+            candidates: 'gelu', 'relu', 'swish', 'tanh', 'sigmoid'
+        layer_norm_eps(float): a value added to the denominator for numerical stability. Default: 1e-12
+    """
+
+    def __init__(self,
+                 n_layers=2,
+                 n_heads=2,
+                 k_interests=5,
+                 hidden_size=64,
+                 seq_len=50,
+                 inner_size=256,
+                 hidden_dropout_prob=0.5,
+                 attn_dropout_prob=0.5,
+                 hidden_act='gelu',
+                 layer_norm_eps=1e-12):
+
+        super(LightTransformerEncoder, self).__init__()
+        layer = LightTransformerLayer(n_heads, k_interests, hidden_size, seq_len, inner_size,
+                                      hidden_dropout_prob, attn_dropout_prob, hidden_act, layer_norm_eps)
+        self.layer = nn.ModuleList([copy.deepcopy(layer)
+                                    for _ in range(n_layers)])
+
+    def forward(self, hidden_states, pos_emb, output_all_encoded_layers=True):
+        """
+        Args:
+            hidden_states (torch.Tensor): the input of the TrandformerEncoder
+            attention_mask (torch.Tensor): the attention mask for the input hidden_states
+            output_all_encoded_layers (Bool): whether output all transformer layers' output
+
+        Returns:
+            all_encoder_layers (list): if output_all_encoded_layers is True, return a list consists of all transformer layers' output,
+            otherwise return a list only consists of the output of last transformer layer.
+        """
+        all_encoder_layers = []
+        for layer_module in self.layer:
+            hidden_states = layer_module(hidden_states, pos_emb)
+            if output_all_encoded_layers:
+                all_encoder_layers.append(hidden_states)
+        if not output_all_encoded_layers:
+            all_encoder_layers.append(hidden_states)
+        return all_encoder_layers
+
+
+class CNNLayers(nn.Module):
+    r""" CNNLayers
+
+    Args:
+        - channels(list): a list contains the channels of each layer in cnn layers
+        - kernel(list): a list contains the kernels of each layer in cnn layers
+        - strides(list): a list contains the channels of each layer in cnn layers
+        - activation(str): activation function after each layer in mlp layers. Default: 'relu'
+                      candidates: 'sigmoid', 'tanh', 'relu', 'leekyrelu', 'none'
+
+    Shape:
+        - Input: :math:`(N, C_{in}, H_{in}, W_{in})`
+        - Output: :math:`(N, C_{out}, H_{out}, W_{out})` where
+
+        .. math::
+            H_{out} = \left\lfloor\frac{H_{in}  + 2 \times \text{padding}[0] - \text{dilation}[0]
+                      \times (\text{kernel\_size}[0] - 1) - 1}{\text{stride}[0]} + 1\right\rfloor
+
+        .. math::
+            W_{out} = \left\lfloor\frac{W_{in}  + 2 \times \text{padding}[1] - \text{dilation}[1]
+                      \times (\text{kernel\_size}[1] - 1) - 1}{\text{stride}[1]} + 1\right\rfloor
+
+    Examples::
+
+        >>> m = CNNLayers([1, 32, 32], [2,2], [2,2], 'relu')
+        >>> input = torch.randn(128, 1, 64, 64)
+        >>> output = m(input)
+        >>> print(output.size())
+        >>> torch.Size([128, 32, 16, 16])
+    """
+
+    def __init__(self, channels, kernels, strides, activation='relu', init_method=None):
+        super(CNNLayers, self).__init__()
+        self.channels = channels
+        self.kernels = kernels
+        self.strides = strides
+        self.activation = activation
+        self.init_method = init_method
+        self.num_of_nets = len(self.channels) - 1
+
+        if len(kernels) != len(strides) or self.num_of_nets != (len(kernels)):
+            raise RuntimeError('channels, kernels and strides don\'t match\n')
+
+        cnn_modules = []
+
+        for i in range(self.num_of_nets):
+            cnn_modules.append(
+                nn.Conv2d(self.channels[i], self.channels[i + 1], self.kernels[i], stride=self.strides[i])
+            )
+            if self.activation.lower() == 'sigmoid':
+                cnn_modules.append(nn.Sigmoid())
+            elif self.activation.lower() == 'tanh':
+                cnn_modules.append(nn.Tanh())
+            elif self.activation.lower() == 'relu':
+                cnn_modules.append(nn.ReLU())
+            elif self.activation.lower() == 'leakyrelu':
+                cnn_modules.append(nn.LeakyReLU())
+            elif self.activation.lower() == 'none':
+                pass
+
+        self.cnn_layers = nn.Sequential(*cnn_modules)
+
+        if self.init_method is not None:
+            self.apply(self.init_weights)
+
+    def init_weights(self, module):
+        # We just initialize the module with normal distribution as the paper said
+        if isinstance(module, nn.Conv2d):
+            if self.init_method == 'norm':
+                normal_(module.weight.data, 0, 0.01)
+            if module.bias is not None:
+                module.bias.data.fill_(0.0)
+
+    def forward(self, input_feature):
+        return self.cnn_layers(input_feature)
+
+
+class SparseDropout(nn.Module):
+    """
+    This is a Module that execute Dropout on Pytorch sparse tensor.
+    """
+
+    def __init__(self, p=0.5):
+        super(SparseDropout, self).__init__()
+        # p is ratio of dropout
+        # convert to keep probability
+        self.kprob = 1 - p
+
+    def forward(self, x):
+        if not self.training:
+            return x
+
+        mask = ((torch.rand(x._values().size()) + self.kprob).floor()).type(torch.bool)
+        rc = x._indices()[:, mask]
+        val = x._values()[mask] * (1.0 / self.kprob)
+        return torch.sparse.FloatTensor(rc, val, x.shape)

code/REC/trainer/__init__.py

+from .trainer import *
+
+
+__all__ = ['Trainer']

code/REC/trainer/trainer.py

+# Copyright (c) 2024 westlake-repl
+# Copyright (c) 2024 Bytedance Ltd. and/or its affiliate
+# SPDX-License-Identifier: MIT
+# This file has been modified by Junyi Chen.
+#
+# Original file was released under MIT, with the full license text
+# available at https://choosealicense.com/licenses/mit/.
+#
+# This modified file is released under the same license.
+
+import os
+import sys
+from logging import getLogger
+from time import time
+import time as t
+import numpy as np
+import torch
+import torch.optim as optim
+import torch.distributed as dist
+from tqdm import tqdm
+import deepspeed
+
+from REC.data.dataset import BatchTextDataset
+from REC.data.dataset.collate_fn import customize_rmpad_collate
+from torch.utils.data import DataLoader
+from REC.evaluator import Evaluator, Collector
+from REC.utils import ensure_dir, get_local_time, early_stopping, calculate_valid_score, dict2str, \
+    get_tensorboard, set_color, get_gpu_usage, WandbLogger
+from REC.utils.lr_scheduler import *
+
+import lightning as L
+from lightning.fabric.strategies import DeepSpeedStrategy, DDPStrategy
+
+
+class Trainer(object):
+    def __init__(self, config, model):
+        super(Trainer, self).__init__()
+        self.config = config
+        self.model = model
+        self.logger = getLogger()
+
+        self.wandblogger = WandbLogger(config)
+
+        self.optim_args = config['optim_args']
+        self.epochs = config['epochs']
+        self.eval_step = min(config['eval_step'], self.epochs)
+        self.stopping_step = config['stopping_step']
+        self.clip_grad_norm = config.get('clip_grad_norm', 1.0)
+        self.valid_metric = config['valid_metric'].lower()
+        self.valid_metric_bigger = config['valid_metric_bigger']
+        self.test_batch_size = config['eval_batch_size']
+        self.gpu_available = torch.cuda.is_available() and config['use_gpu']
+        self.device = config['device']
+
+        self.rank = torch.distributed.get_rank()
+
+        if self.rank == 0:
+            self.tensorboard = get_tensorboard(self.logger)
+
+        self.checkpoint_dir = config['checkpoint_dir']
+        if self.rank == 0:
+            ensure_dir(self.checkpoint_dir)
+
+        self.saved_model_name = '{}-{}.pth'.format(self.config['model'], 0)
+        self.saved_model_file = os.path.join(self.checkpoint_dir, self.saved_model_name)
+
+        self.use_text = config['use_text']
+
+        self.start_epoch = 0
+        self.cur_step = 0
+        self.best_valid_score = -np.inf if self.valid_metric_bigger else np.inf
+        self.best_valid_result = None
+        self.train_loss_dict = dict()
+        self.optimizer = self._build_optimizer()
+        self.update_interval = config['update_interval'] if config['update_interval'] else 20
+        self.scheduler_config = config['scheduler_args']
+        if config['freeze_prefix'] or config['freeze_ad']:
+            freeze_prefix = config['freeze_prefix'] if config['freeze_prefix'] else []
+            if config['freeze_ad']:
+                freeze_prefix.extend(['item_llm', 'item_emb_tokens'])
+            if not config['ft_item']:
+                freeze_prefix.extend(['item_embedding'])
+
+            self._freeze_params(freeze_prefix)
+
+        for n, p in self.model.named_parameters():
+            self.logger.info(f"{n} {p.size()} {p.requires_grad}")
+
+        self.eval_collector = Collector(config)
+        self.evaluator = Evaluator(config)
+        self.item_feature = None
+        self.tot_item_num = None
+
+    def _freeze_params(self, freeze_prefix):
+        for name, param in self.model.named_parameters():
+            for prefix in freeze_prefix:
+                if name.startswith(prefix):
+                    self.logger.info(f"freeze_params: {name}")
+                    param.requires_grad = False
+
+    def _build_scheduler(self, warmup_steps=None, tot_steps=None):
+        if self.scheduler_config['type'] == 'cosine':
+            self.logger.info(f"Use consine scheduler with {warmup_steps} warmup {tot_steps} total steps")
+            return get_cosine_schedule_with_warmup(self.optimizer, warmup_steps, tot_steps)
+        elif self.scheduler_config['type'] == 'liner':
+            self.logger.info(f"Use linear scheduler with {warmup_steps} warmup {tot_steps} total steps")
+            return get_linear_schedule_with_warmup(self.optimizer, warmup_steps, tot_steps)
+        else:
+            self.logger.info(f"Use constant scheduler")
+            return get_constant_schedule(self.optimizer)
+
+    def _build_optimizer(self):
+        if len(self.optim_args) == 4:
+            params = self.model.named_parameters()
+            modal_params = []
+            recsys_params = []
+            modal_decay_params = []
+            recsys_decay_params = []
+            decay_check_name = self.config['decay_check_name']
+            for index, (name, param) in enumerate(params):
+                if param.requires_grad:
+                    if 'visual_encoder' in name:
+                        modal_params.append(param)
+                    else:
+                        recsys_params.append(param)
+                    if decay_check_name:
+                        if decay_check_name in name:
+                            modal_decay_params.append(param)
+                        else:
+                            recsys_decay_params.append(param)
+            if decay_check_name:
+                optimizer = optim.AdamW([
+                    {'params': modal_decay_params, 'lr': self.optim_args['modal_lr'], 'weight_decay': self.optim_args['modal_decay']},
+                    {'params': recsys_decay_params, 'lr': self.optim_args['rec_lr'], 'weight_decay': self.optim_args['rec_decay']}
+                ])
+                optim_output = set_color(f'recsys_decay_params_len: {len(recsys_decay_params)}  modal_params_decay_len: {len(modal_decay_params)}', 'blue')
+                self.logger.info(optim_output)
+            else:
+                optimizer = optim.AdamW([
+                    {'params': modal_params, 'lr': self.optim_args['modal_lr'], 'weight_decay': self.optim_args['modal_decay']},
+                    {'params': recsys_params, 'lr': self.optim_args['rec_lr'], 'weight_decay': self.optim_args['rec_decay']}
+                ])
+                optim_output = set_color(f'recsys_lr_params_len: {len(recsys_params)}  modal_lr_params_len: {len(modal_params)}', 'blue')
+                self.logger.info(optim_output)
+        elif self.config['lr_mult_prefix'] and self.config['lr_mult_rate']:
+            normal_params_dict = {
+                "params": [],
+                "lr": self.optim_args['learning_rate'],
+                "weight_decay": self.optim_args['weight_decay']
+            }
+            high_lr_params_dict = {
+                "params": [],
+                "lr": self.optim_args['learning_rate'] * self.config['lr_mult_rate'],
+                "weight_decay": self.optim_args['weight_decay']
+            }
+            self.logger.info(f'Use higher lr rate {self.config["lr_mult_rate"]} x {self.optim_args["learning_rate"]} for prefix {self.config["lr_mult_prefix"]}')
+
+            for n, p in self.model.named_parameters():
+                if any(n.startswith(x) for x in self.config['lr_mult_prefix']):
+                    self.logger.info(f"high lr param: {n} {self.optim_args['learning_rate'] * self.config['lr_mult_rate']}")
+                    high_lr_params_dict["params"].append(p)
+                else:
+                    normal_params_dict["params"].append(p)
+            optimizer = optim.AdamW([normal_params_dict, high_lr_params_dict])
+        elif self.config['optimizer_kwargs']:
+            params = self.model.parameters()
+            self.config['optimizer_kwargs']['optimizer']['params']['lr'] = self.optim_args['learning_rate']
+            self.config['optimizer_kwargs']['optimizer']['params']['weight_decay'] = self.optim_args['weight_decay']
+            optimizer = deepspeed.ops.adam.cpu_adam.DeepSpeedCPUAdam(params, **self.config['optimizer_kwargs']['optimizer']['params'])
+        else:
+            params = self.model.parameters()
+            optimizer = optim.AdamW(params, lr=self.optim_args['learning_rate'], weight_decay=self.optim_args['weight_decay'])
+        return optimizer
+
+    def _train_epoch(self, train_data, epoch_idx, show_progress=False):
+        self.model.train()
+        total_loss = 0
+        if self.rank == 0:
+            pbar = tqdm(
+                total=len(train_data),
+                miniters=self.update_interval,
+                desc=set_color(f"Train [{epoch_idx:>3}/{self.epochs:>3}]", 'pink'),
+                file=sys.stdout
+            )
+        bwd_time = t.time()
+        for batch_idx, data in enumerate(train_data):
+            start_time = bwd_time
+            self.optimizer.zero_grad()
+            data = self.to_device(data)
+            data_time = t.time()
+            losses = self.model(data)
+            fwd_time = t.time()
+            if self.config['loss'] and self.config['loss'] == 'nce':
+                model_out = losses
+                losses = model_out.pop('loss')
+            self._check_nan(losses)
+            total_loss = total_loss + losses.item()
+            self.lite.backward(losses)
+            grad_norm = self.optimizer.step()
+            bwd_time = t.time()
+            if self.scheduler_config:
+                self.lr_scheduler.step()
+            if show_progress and self.rank == 0 and batch_idx % self.update_interval == 0:
+                msg = f"loss: {losses:.4f} data: {data_time-start_time:.3f} fwd: {fwd_time-data_time:.3f} bwd: {bwd_time-fwd_time:.3f}"
+                if self.scheduler_config:
+                    msg = f"lr: {self.lr_scheduler.get_lr()[0]:.7f} " + msg
+                if self.config['loss'] and self.config['loss'] == 'nce':
+                    for k, v in model_out.items():
+                        msg += f" {k}: {v:.3f}"
+                if grad_norm:
+                    msg = msg + f" grad_norm: {grad_norm.sum():.4f}"
+                pbar.set_postfix_str(msg, refresh=False)
+                pbar.update(self.update_interval)
+                self.logger.info("\n" + "-"*50)
+            if self.config['debug'] and batch_idx >= 10:
+                break
+
+        return total_loss
+
+    def _valid_epoch(self, valid_data, show_progress=False):
+        torch.distributed.barrier()
+        valid_result = self.evaluate(valid_data, load_best_model=False, show_progress=show_progress)
+        valid_score = calculate_valid_score(valid_result, self.valid_metric)
+        torch.distributed.barrier()
+        return valid_score, valid_result
+
+    def _save_checkpoint(self, epoch, verbose=True):
+        r"""Store the model parameters information and training information.
+
+        Args:
+            epoch (int): the current epoch id
+
+        """
+        state = {
+            "model": self.model,
+            "optimizer": self.optimizer,
+            'config': self.config,
+            'epoch': epoch,
+            'cur_step': self.cur_step,
+            'best_valid_score': self.best_valid_score,
+            'rng_state': torch.get_rng_state(),
+            'cuda_rng_state': torch.cuda.get_rng_state()
+        }
+
+        self.lite.save(os.path.join(self.checkpoint_dir, self.saved_model_name), state=state)
+        if self.rank == 0 and verbose:
+            self.logger.info(set_color('Saving current', 'blue') + f': {self.saved_model_file}')
+
+    def _check_nan(self, loss):
+        if torch.isnan(loss):
+            raise ValueError('Training loss is nan')
+
+    def _generate_train_loss_output(self, epoch_idx, s_time, e_time, losses):
+        des = self.config['loss_decimal_place'] or 4
+        train_loss_output = (set_color('epoch %d training', 'green') + ' [' + set_color('time', 'blue') +
+                             ': %.2fs, ') % (epoch_idx, e_time - s_time)
+        if isinstance(losses, tuple):
+            des = (set_color('train_loss%d', 'blue') + ': %.' + str(des) + 'f')
+            train_loss_output += ', '.join(des % (idx + 1, loss) for idx, loss in enumerate(losses))
+        else:
+            des = '%.' + str(des) + 'f'
+            train_loss_output += set_color('train loss', 'blue') + ': ' + des % losses
+        return train_loss_output + ']'
+
+    def _add_train_loss_to_tensorboard(self, epoch_idx, losses, tag='Loss/Train'):
+        if isinstance(losses, tuple):
+            for idx, loss in enumerate(losses):
+                self.tensorboard.add_scalar(tag + str(idx), loss, epoch_idx)
+        else:
+            self.tensorboard.add_scalar(tag, losses, epoch_idx)
+
+    def _add_hparam_to_tensorboard(self, best_valid_result):
+        # base hparam
+        hparam_dict = {
+            'learning_rate': self.config['learning_rate'],
+            'weight_decay': self.config['weight_decay'],
+            'train_batch_size': self.config['train_batch_size']
+        }
+        # unrecorded parameter
+        unrecorded_parameter = {
+            parameter
+            for parameters in self.config.parameters.values() for parameter in parameters
+        }.union({'model', 'dataset', 'config_files', 'device'})
+        # other model-specific hparam
+        hparam_dict.update({
+            para: val
+            for para, val in self.config.final_config_dict.items() if para not in unrecorded_parameter
+        })
+        for k in hparam_dict:
+            k = k.replace('@', '_')
+            if hparam_dict[k] is not None and not isinstance(hparam_dict[k], (bool, str, float, int)):
+                hparam_dict[k] = str(hparam_dict[k])
+
+        self.tensorboard.add_hparams(hparam_dict, {'hparam/best_valid_result': best_valid_result})
+
+    def to_device(self, data):
+        device = self.device
+        if isinstance(data, tuple) or isinstance(data, list):
+            tdata = ()
+            for d in data:
+                d = d.to(device)
+                tdata += (d,)
+            return tdata
+        elif isinstance(data, dict):
+            for k, v in data.items():
+                data[k] = v.to(device)
+            return data
+        else:
+            return data.to(device)
+
+    def fit(self, train_data, valid_data=None, verbose=True, saved=True, show_progress=False, callback_fn=None):
+        if self.scheduler_config:
+            warmup_rate = self.scheduler_config.get('warmup', 0.001)
+            tot_steps = len(train_data) * self.epochs
+            warmup_steps = tot_steps * warmup_rate
+            self.lr_scheduler = self._build_scheduler(warmup_steps=warmup_steps, tot_steps=tot_steps)
+
+        world_size, local_world_size = int(os.environ['WORLD_SIZE']), int(os.environ['LOCAL_WORLD_SIZE'])
+        nnodes = world_size // local_world_size
+        precision = self.config['precision'] if self.config['precision'] else '32'
+        if self.config['strategy'] == 'deepspeed':
+            self.logger.info(f"Use deepspeed strategy")
+            strategy = DeepSpeedStrategy(stage=self.config["stage"], precision=precision)
+            self.lite = L.Fabric(accelerator='gpu', strategy=strategy, precision=precision, num_nodes=nnodes)
+        else:
+            self.logger.info(f"Use DDP strategy")
+            strategy = DDPStrategy(find_unused_parameters=True)
+            self.lite = L.Fabric(accelerator='gpu', strategy=strategy, precision=precision, num_nodes=nnodes)
+        self.lite.launch()
+        self.model, self.optimizer = self.lite.setup(self.model, self.optimizer)
+
+        if self.config['auto_resume']:
+            raise NotImplementedError
+
+        valid_step = 0
+
+        for epoch_idx in range(self.start_epoch, self.epochs):
+            # train
+            if self.config['need_training'] == None or self.config['need_training']:
+                train_data.sampler.set_epoch(epoch_idx)
+                training_start_time = time()
+                train_loss = self._train_epoch(train_data, epoch_idx, show_progress=show_progress)
+                self.train_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss
+                training_end_time = time()
+                train_loss_output = \
+                    self._generate_train_loss_output(epoch_idx, training_start_time, training_end_time, train_loss)
+                if verbose:
+                    self.logger.info(train_loss_output)
+                if self.rank == 0:
+                    self._add_train_loss_to_tensorboard(epoch_idx, train_loss)
+                self.wandblogger.log_metrics({'epoch': epoch_idx, 'train_loss': train_loss, 'train_step': epoch_idx}, head='train')
+
+            if self.eval_step <= 0 or not valid_data:
+                if saved:
+                    self._save_checkpoint(epoch_idx, verbose=verbose)
+                continue
+            if (epoch_idx + 1) % self.eval_step == 0:
+                valid_start_time = time()
+                valid_score, valid_result = self._valid_epoch(valid_data, show_progress=show_progress)
+                self.best_valid_score, self.cur_step, stop_flag, update_flag = early_stopping(
+                    valid_score,
+                    self.best_valid_score,
+                    self.cur_step,
+                    max_step=self.stopping_step,
+                    bigger=self.valid_metric_bigger
+                )
+                valid_end_time = time()
+                valid_score_output = (set_color("epoch %d evaluating", 'green') + " [" + set_color("time", 'blue')
+                                      + ": %.2fs, " + set_color("valid_score", 'blue') + ": %f]") % \
+                    (epoch_idx, valid_end_time - valid_start_time, valid_score)
+                valid_result_output = set_color('valid result', 'blue') + ': \n' + dict2str(valid_result)
+                if verbose:
+                    self.logger.info(valid_score_output)
+                    self.logger.info(valid_result_output)
+                if self.rank == 0:
+                    self.tensorboard.add_scalar('Vaild_score', valid_score, epoch_idx)
+                    for name, value in valid_result.items():
+                        self.tensorboard.add_scalar(name.replace('@', '_'), value, epoch_idx)
+                self.wandblogger.log_metrics({**valid_result, 'valid_step': valid_step}, head='valid')
+
+                if update_flag:
+                    if saved:
+                        self._save_checkpoint(epoch_idx, verbose=verbose)
+                    self.best_valid_result = valid_result
+
+                if callback_fn:
+                    callback_fn(epoch_idx, valid_score)
+
+                if stop_flag:
+                    stop_output = 'Finished training, best eval result in epoch %d' % \
+                        (epoch_idx - self.cur_step * self.eval_step)
+                    if verbose:
+                        self.logger.info(stop_output)
+                    break
+
+                valid_step += 1
+
+        return self.best_valid_score, self.best_valid_result
+
+    @torch.no_grad()
+    def _full_sort_batch_eval(self, batched_data):
+        user, time_seq, history_index, positive_u, positive_i = batched_data
+        interaction = self.to_device(user)
+        time_seq = self.to_device(time_seq)
+        if self.config['model'] == 'HLLM':
+            if self.config['stage'] == 3:
+                scores = self.model.module.predict(interaction, time_seq, self.item_feature)
+            else:
+                scores = self.model((interaction, time_seq, self.item_feature), mode='predict')
+        else:
+            scores = self.model.module.predict(interaction, time_seq, self.item_feature)
+        scores = scores.view(-1, self.tot_item_num)
+        scores[:, 0] = -np.inf
+        if history_index is not None:
+            scores[history_index] = -np.inf
+        return scores, positive_u, positive_i
+
+    @torch.no_grad()
+    def compute_item_feature(self, config, data):
+        if self.use_text:
+            item_data = BatchTextDataset(config, data)
+            item_batch_size = config['MAX_ITEM_LIST_LENGTH'] * config['train_batch_size']
+            item_loader = DataLoader(item_data, batch_size=item_batch_size, num_workers=8, shuffle=False, pin_memory=True, collate_fn=customize_rmpad_collate)
+            self.logger.info(f"Inference item_data with {item_batch_size = } {len(item_loader) = }")
+            self.item_feature = []
+            with torch.no_grad():
+                for idx, items in tqdm(enumerate(item_loader), total=len(item_loader)):
+                    items = self.to_device(items)
+                    items = self.model(items, mode='compute_item')
+                    self.item_feature.append(items)
+                if isinstance(items, tuple):
+                    self.item_feature = torch.cat([x[0] for x in self.item_feature]), torch.cat([x[1] for x in self.item_feature])
+                else:
+                    self.item_feature = torch.cat(self.item_feature)
+                if self.config['stage'] == 3:
+                    self.item_feature = self.item_feature.bfloat16()
+        else:
+            with torch.no_grad():
+                self.item_feature = self.model.module.compute_item_all()
+
+    def distributed_concat(self, tensor, num_total_examples):
+        output_tensors = [tensor.clone() for _ in range(torch.distributed.get_world_size())]
+        torch.distributed.all_gather(output_tensors, tensor)
+        concat = torch.cat(output_tensors, dim=0)
+        return concat.sum() / num_total_examples
+
+    def evaluate(self, eval_data, load_best_model=True, model_file=None, show_progress=False, init_model=False):
+        if not eval_data:
+            return
+        if init_model:
+            world_size, local_world_size = int(os.environ['WORLD_SIZE']), int(os.environ['LOCAL_WORLD_SIZE'])
+            nnodes = world_size // local_world_size
+            if self.config['strategy'] == 'deepspeed':
+                self.logger.info(f"Use deepspeed strategy")
+                precision = self.config['precision'] if self.config['precision'] else '32'
+                strategy = DeepSpeedStrategy(stage=self.config['stage'], precision=precision)
+                self.lite = L.Fabric(accelerator='gpu', strategy=strategy, precision=precision, num_nodes=nnodes)
+                self.lite.launch()
+                self.model, self.optimizer = self.lite.setup(self.model, self.optimizer)
+            else:
+                self.logger.info(f"Use DDP strategy")
+                precision = self.config['precision'] if self.config['precision'] else '32'
+                strategy = DDPStrategy(find_unused_parameters=True)
+                self.lite = L.Fabric(accelerator='gpu', strategy=strategy, precision=precision, num_nodes=nnodes)
+                self.lite.launch()
+                self.model = self.lite.setup(self.model)
+
+        if load_best_model:
+            checkpoint_file = model_file or self.saved_model_file
+            state = {"model": self.model}
+            self.lite.load(checkpoint_file, state)
+            message_output = 'Loading model structure and parameters from {}'.format(checkpoint_file)
+            self.logger.info(message_output)
+
+        with torch.no_grad():
+            self.model.eval()
+            eval_func = self._full_sort_batch_eval
+
+            self.tot_item_num = eval_data.dataset.dataload.item_num
+            self.compute_item_feature(self.config, eval_data.dataset.dataload)
+            iter_data = (
+                tqdm(
+                    eval_data,
+                    total=len(eval_data),
+                    ncols=150,
+                    desc=set_color(f"Evaluate   ", 'pink'),
+                    file=sys.stdout
+                ) if show_progress and self.rank == 0 else eval_data
+            )
+            fwd_time = t.time()
+            for batch_idx, batched_data in enumerate(iter_data):
+                start_time = fwd_time
+                data_time = t.time()
+                scores, positive_u, positive_i = eval_func(batched_data)
+                fwd_time = t.time()
+
+                if show_progress and self.rank == 0:
+                    iter_data.set_postfix_str(f"data: {data_time-start_time:.3f} fwd: {fwd_time-data_time:.3f}", refresh=False)
+                self.eval_collector.eval_batch_collect(scores, positive_u, positive_i)
+            num_total_examples = len(eval_data.sampler.dataset)
+            struct = self.eval_collector.get_data_struct()
+            result = self.evaluator.evaluate(struct)
+
+            metric_decimal_place = 5 if self.config['metric_decimal_place'] == None else self.config['metric_decimal_place']
+            for k, v in result.items():
+                result_cpu = self.distributed_concat(torch.tensor([v]).to(self.device), num_total_examples).cpu()
+                result[k] = round(result_cpu.item(), metric_decimal_place)
+            self.wandblogger.log_eval_metrics(result, head='eval')
+
+            return result

code/REC/utils/__init__.py

+from .logger import init_logger, set_color
+from .utils import get_local_time, ensure_dir, get_model, \
+    early_stopping, calculate_valid_score, dict2str, init_seed, get_tensorboard, get_gpu_usage
+
+from .enum_type import *
+from .argument_list import *
+from .wandblogger import WandbLogger
+
+__all__ = [
+    'init_logger', 'get_local_time', 'ensure_dir', 'get_model', 'early_stopping',
+    'calculate_valid_score', 'dict2str', 'Enum',  'EvaluatorType', 'InputType',
+    'init_seed', 'general_arguments', 'training_arguments', 'evaluation_arguments',
+    'dataset_arguments', 'get_tensorboard', 'set_color', 'get_gpu_usage', 'WandbLogger'
+]