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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,163 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+- ko
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+- embedding
+- gte
+- text-embedding
+- retrieval
+- matryoshka
+- academic-search
+- scientific-search
+library_name: transformers
+base_model: Snowflake/snowflake-arctic-embed-m-v2.0
+datasets:
+- ms_marco
+---
+
+# LinerAI/snowflake-arctic-embed-m-v2.0-academic for Academic Search
+
+This is a fine-tuned version of [Snowflake/snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) optimized for **academic and scientific literature search**. The model has been trained using contrastive learning with hard negative mining, specifically curated for academic search scenarios.
+
+## Highlights
+
+- **Optimized for Academic Search**: Fine-tuned on datasets specifically designed for academic literature retrieval
+- **Hard Negative Mining**: Trained with carefully mined hard negatives to improve discrimination between similar academic papers
+- **Matryoshka Representation Learning (MRL)**: Supports flexible embedding dimensions (768, 512, 256, 128) for efficiency
+- **Efficient & Fast**: Medium-sized model offering excellent speed-performance trade-off
+- **Long Context**: Supports up to 4096 tokens
+
+## Model Description
+
+| Attribute | Value |
+|-----------|-------|
+| Base Model | Snowflake/snowflake-arctic-embed-m-v2.0 |
+| Architecture | GTE |
+| Embedding Dimension | 768 |
+| MRL Dimensions | 768, 512, 256, 128 |
+| Max Sequence Length | 4096 |
+| Pooling | CLS token |
+| Precision | float16 |
+
+## Evaluation Results
+
+| **Model** | Avg. | SciFact: Recall@10 | TRECCOVID: Recall@10 | NFCorpus: Recall@10 | SCIDOCS: Recall@10 | LitSearch: Recall@10 | QASA: Recall@10 |
+| --- | --- | --- | --- | --- | --- | --- | --- |
+| snowflake-arctic-embed-m-v2.0-academic | 0.3729 | 0.8609 | 0.0219 | 0.177 | 0.2129 | 0.6435 | 0.321 |
+| snowflake-arctic-embed-m-v2.0 | 0.3654 | 0.8353 | 0.0224 | 0.1669 | 0.2122 | 0.6508 | 0.3046 |
+
+## Training Details
+
+### Training Configuration
+
+| Parameter | Value |
+|-----------|-------|
+| Learning Rate | 2e-5 |
+| Batch Size | 8192 (effective) |
+| Per-Device Batch Size | 32 |
+| Warmup Steps | 100 |
+| Weight Decay | 0.1 |
+| Precision | fp16 |
+| Max Length | 4096 |
+| Loss Function | InfoNCE (Contrastive) |
+| Temperature (τ) | 0.02 |
+
+### Training Data
+
+The model was trained on [LEAD (Liner Embedding Academic Dataset)](https://huggingface.co/datasets/LinerAI/LEAD), a combination of ~55,560 samples tailored for academic search:
+- **MS MARCO** (49%): General passage retrieval dataset with hard negatives
+- **Academic Search Dataset** (51%): Custom dataset built specifically for academic literature search, with two-stage hard negative mining
+
+### Matryoshka Representation Learning (MRL)
+
+This model supports [Matryoshka Representation Learning](https://arxiv.org/abs/2205.13147). You can truncate embeddings to smaller dimensions (512, 256, 128) for faster computation and reduced storage.
+
+```python
+# Full dimension (768)
+full_embedding = embeddings[:, :768]
+
+# MRL dimensions
+embedding_512 = embeddings[:, :512]
+embedding_256 = embeddings[:, :256]
+embedding_128 = embeddings[:, :128]
+```
+
+## Usage
+
+### Using Transformers
+
+```python
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+model_path = "LinerAI/snowflake-arctic-embed-l-v2.0-academic"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModel.from_pretrained(model_path, torch_dtype=torch.float16, trust_remote_code=True)
+model.eval()
+
+# For queries
+def encode_query(text):
+    input_text = f"query: {text}"
+    inputs = tokenizer(input_text, return_tensors="pt", max_length=4096, truncation=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        embeddings = outputs.last_hidden_state[:, 0]  # CLS token
+        embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
+    return embeddings
+
+# For passages
+def encode_passage(text):
+    inputs = tokenizer(text, return_tensors="pt", max_length=4096, truncation=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        embeddings = outputs.last_hidden_state[:, 0]  # CLS token
+        embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
+    return embeddings
+
+# Example: Academic search
+query = "transformer models for protein structure prediction"
+abstract = "We introduce AlphaFold, a deep learning system that predicts protein structures..."
+
+query_emb = encode_query(query)
+passage_emb = encode_passage(abstract)
+
+similarity = torch.nn.functional.cosine_similarity(query_emb, passage_emb)
+print(f"Similarity: {similarity.item():.4f}")
+```
+
+## Input Format
+
+### Query Format
+```
+query: {your_query_text}
+```
+
+### Passage Format
+```
+{your_passage_text}
+```
+
+## Intended Use
+
+- **Academic Paper Search**: Finding relevant research papers given a research query
+- **Literature Review**: Discovering related work in academic literature
+- **Scientific Document Retrieval**: Retrieving scientific documents, abstracts, and articles
+- **Research Question Answering**: Finding papers that address specific research questions
+
+## Limitations
+
+- Maximum sequence length is 4096 tokens
+- Best performance achieved when using the specific input formats described above
+- The model uses asymmetric encoding (query prefix for queries, no prefix for passages)
+- Requires `trust_remote_code=True` for loading
+
+## License
+
+This model is released under the Apache 2.0 license.

config.json

@@ -0,0 +1,38 @@
+{
+  "architectures": [
+    "GteModel"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_hf_alibaba_nlp_gte.GteConfig",
+    "AutoModel": "modeling_hf_alibaba_nlp_gte.GteModel"
+  },
+  "classifier_dropout": 0.1,
+  "dtype": "float16",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "layer_norm_type": "layer_norm",
+  "logn_attention_clip1": false,
+  "logn_attention_scale": false,
+  "matryoshka_dimensions": [
+    256
+  ],
+  "max_position_embeddings": 8192,
+  "model_type": "gte",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pack_qkv": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "rope",
+  "rope_scaling": null,
+  "rope_theta": 160000,
+  "transformers_version": "4.57.1",
+  "type_vocab_size": 1,
+  "unpad_inputs": false,
+  "use_memory_efficient_attention": false,
+  "vocab_size": 250048
+}

configuration_hf_alibaba_nlp_gte.py

@@ -0,0 +1,145 @@
+# coding=utf-8
+# Copyright 2024 The GTE Team Authors and Alibaba Group.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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.
+""" GTE model configuration"""
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class GteConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`NewModel`] or a [`TFNewModel`]. It is used to
+    instantiate a NEW 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 NEW
+    [izhx/new-base-en](https://huggingface.co/izhx/new-base-en) architecture.
+
+    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 30522):
+            Vocabulary size of the NEW model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`NewModel`] or [`TFNewModel`].
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+        hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"silu"` and `"gelu_new"` are supported.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for the attention probabilities.
+        max_position_embeddings (`int`, *optional*, defaults to 512):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        type_vocab_size (`int`, *optional*, defaults to 2):
+            The vocabulary size of the `token_type_ids` passed when calling [`NewModel`] or [`TFNewModel`].
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the layer normalization layers.
+        position_embedding_type (`str`, *optional*, defaults to `"rope"`):
+            Type of position embedding. Choose one of `"absolute"`, `"rope"`.
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+        classifier_dropout (`float`, *optional*):
+            The dropout ratio for the classification head.
+
+    Examples:
+
+    ```python
+    >>> from transformers import NewConfig, NewModel
+
+    >>> # Initializing a NEW izhx/new-base-en style configuration
+    >>> configuration = NewConfig()
+
+    >>> # Initializing a model (with random weights) from the izhx/new-base-en style configuration
+    >>> model = NewModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "gte"
+
+    def __init__(
+        self,
+        vocab_size=30528,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.0,
+        max_position_embeddings=2048,
+        type_vocab_size=1,
+        initializer_range=0.02,
+        layer_norm_type='layer_norm',
+        layer_norm_eps=1e-12,
+        # pad_token_id=0,
+        position_embedding_type="rope",
+        rope_theta=10000.0,
+        rope_scaling=None,
+        classifier_dropout=None,
+        pack_qkv=True,
+        unpad_inputs=False,
+        use_memory_efficient_attention=False,
+        logn_attention_scale=False,
+        logn_attention_clip1=False,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_act = hidden_act
+        self.intermediate_size = intermediate_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.initializer_range = initializer_range
+        self.layer_norm_type = layer_norm_type
+        self.layer_norm_eps = layer_norm_eps
+        self.position_embedding_type = position_embedding_type
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.classifier_dropout = classifier_dropout
+
+        self.pack_qkv = pack_qkv
+        self.unpad_inputs = unpad_inputs
+        self.use_memory_efficient_attention = use_memory_efficient_attention
+        self.logn_attention_scale = logn_attention_scale
+        self.logn_attention_clip1 = logn_attention_clip1

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latest

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+global_step30

model.safetensors

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modeling_hf_alibaba_nlp_gte.py

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+# coding=utf-8
+# Copyright 2024 The GTE Team Authors and Alibaba Group.
+# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
+#
+# 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 dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (
+    BaseModelOutput,
+    BaseModelOutputWithPooling,
+    MaskedLMOutput,
+    MultipleChoiceModelOutput,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutput,
+    ModelOutput,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+
+try:
+    import xformers.ops as xops
+except ImportError as e:
+    xops = None
+
+from .configuration_hf_alibaba_nlp_gte import GteConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+# Adapted from https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/bert_padding.py
+# Which was adapted from https://github.com/mlcommons/training_results_v1.1/blob/main/NVIDIA/benchmarks/bert/implementations/pytorch/padding.py
+class IndexFirstAxis(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input, indices):
+        ctx.save_for_backward(indices)
+        assert input.ndim >= 2
+        ctx.first_axis_dim, other_shape = input.shape[0], input.shape[1:]
+        second_dim = other_shape.numel()
+        # TD [2022-03-04] For some reason torch.gather is a bit faster than indexing.
+        # return input[indices]
+        # return torch.gather(
+        #     rearrange(input, "b ... -> b (...)"), 0, repeat(indices, "z -> z d", d=second_dim)
+        # ).reshape(-1, *other_shape)
+        return torch.gather(
+            input.view(ctx.first_axis_dim, second_dim),
+            0,
+            indices.unsqueeze(-1).expand(indices.size(0), second_dim)
+        ).reshape(-1, *other_shape)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        (indices,) = ctx.saved_tensors
+        assert grad_output.ndim >= 2
+        other_shape = grad_output.shape[1:]
+        # grad_output = rearrange(grad_output, "b ... -> b (...)")
+        grad_output = grad_output.view(grad_output.size(0), other_shape.numel())
+        grad_input = torch.zeros(
+            [ctx.first_axis_dim, grad_output.shape[1]],
+            device=grad_output.device,
+            dtype=grad_output.dtype,
+        )
+        # TD [2022-03-04] For some reason torch.scatter is a bit faster than indexing.
+        # grad_input[indices] = grad_output
+        # grad_input.scatter_(0, repeat(indices, "z -> z d", d=grad_output.shape[1]), grad_output)
+        grad_input.scatter_(
+            0, indices.unsqueeze(-1).expand(indices.size(0), grad_output.size(1)), grad_output
+        )
+        return grad_input.reshape(ctx.first_axis_dim, *other_shape), None
+
+
+index_first_axis = IndexFirstAxis.apply
+
+
+def unpad_input(hidden_states, attention_mask=None, indices=None):
+    """
+    Arguments:
+        hidden_states: (batch, seqlen, ...)
+        attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid.
+        indices: (total_nnz), the indices of non-masked tokens from the flattened input sequence.
+    Return:
+        hidden_states: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask.
+    """
+    if indices is None:
+        assert attention_mask is not None
+        indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+
+    # TD [2022-03-04] We don't want to index with a bool mask, because Pytorch will expand the
+    # bool mask, then call nonzero to get the indices, then index with those. The indices is @dim
+    # times larger than it needs to be, wasting memory. It's faster and more memory-efficient to
+    # index with integer indices. Moreover, torch's index is a bit slower than it needs to be,
+    # so we write custom forward and backward to make it a bit faster.
+    hidden_states = hidden_states.view(-1, *hidden_states.shape[2:])
+    return index_first_axis(hidden_states, indices)
+
+
+class IndexPutFirstAxis(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        values: torch.Tensor,
+        indices: torch.Tensor,
+        first_axis_dim
+    ) -> torch.Tensor:
+        ctx.save_for_backward(indices)
+        assert indices.ndim == 1
+        assert values.ndim >= 2
+        output = torch.zeros(
+            first_axis_dim, *values.shape[1:], device=values.device, dtype=values.dtype
+        )
+        output[indices] = values
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output: torch.Tensor) -> Tuple[torch.Tensor, None, None]:
+        indices, = ctx.saved_tensors
+        grad_values = grad_output[indices]
+        return grad_values, None, None
+
+
+index_put_first_axis = IndexPutFirstAxis.apply
+
+
+def pad_input(inputs: torch.Tensor, indices: torch.Tensor, batch: int, seqlen: int) -> torch.Tensor:
+    """Add padding to sequences.
+
+    Arguments:
+        inputs: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask.
+        indices: (total_nnz), `indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()`
+        batch: int batch_size
+        seqlen: int max sequence length
+
+    Returns:
+        inputs: (batch, seqlen, ...)
+    """
+    output = index_put_first_axis(inputs, indices, batch * seqlen)
+    return output.view(batch, seqlen, *inputs.shape[1:])
+
+
+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)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin):
+    """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.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos, sin = cos.to(q.dtype), sin.to(q.dtype)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class RotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim, max_position_embeddings=512, base=10000.0, 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).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.float32)
+
+        freqs = torch.einsum("i,j->ij", 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),
+        )
+
+
+class NTKScalingRotaryEmbedding(RotaryEmbedding):
+    """RotaryEmbedding extended with fixed and mixed NTK scaling. https://kexue.fm/archives/9706 """
+
+    def __init__(self, dim, max_position_embeddings=512, base=10000, device=None, scaling_factor=1.0, mixed_b=None):
+        self.scaling_factor = scaling_factor
+        self.mixed_b = mixed_b
+        super().__init__(dim, max_position_embeddings, base, device)
+        max_position_embeddings = max_position_embeddings * self.scaling_factor
+        self._set_cos_sin_cache(max_position_embeddings, self.inv_freq.device, torch.get_default_dtype())
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (self.scaling_factor if self.mixed_b is None else 1)
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+
+            if self.mixed_b is None:
+                inv_freq = inv_freq / self.scaling_factor ** (2 / self.dim)  # (6)
+            else:
+                a = torch.tensor(self.scaling_factor).log() / (self.dim / 2) ** self.mixed_b  # (13)
+                lambda_1_m = (a * torch.arange(1, self.dim // 2 + 1).float().to(device) ** self.mixed_b).exp()  # (12)
+                inv_freq = inv_freq / lambda_1_m  # (10)
+
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
+
+        freqs = torch.einsum("i,j->ij", 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)
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        RMSNorm 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)
+
+
+LAYER_NORM = {
+    'layer_norm': nn.LayerNorm,
+    'rms_norm': RMSNorm
+}
+
+
+class GteEmbeddings(nn.Module):
+    """
+    Embedding and Unpadding.
+    """
+
+    def __init__(self, config: GteConfig):
+        super().__init__()
+        self.padding_idx = config.pad_token_id
+        self.word_embeddings = nn.Embedding(
+            config.vocab_size, config.hidden_size, padding_idx=self.padding_idx
+        )
+
+        self.position_embedding_type = config.position_embedding_type
+        if self.position_embedding_type == 'absolute':
+            self.position_embeddings = nn.Embedding(
+                config.max_position_embeddings, config.hidden_size, padding_idx=self.padding_idx
+            )
+        elif self.position_embedding_type == 'rope':
+            self._init_rope(config)
+        else:
+            raise ValueError
+
+        self.type_vocab_size = config.type_vocab_size
+        if self.type_vocab_size > 0:
+            self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
+        # any TensorFlow checkpoint file
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        # position_ids is contiguous in memory and excluded when serialized
+        self.register_buffer(
+            "position_ids", torch.arange(config.max_position_embeddings), persistent=False
+        )
+
+    def _init_rope(self, config):
+        kwargs = dict(
+            dim=int(config.hidden_size / config.num_attention_heads),
+            max_position_embeddings=config.max_position_embeddings,
+            base=config.rope_theta
+        )
+        if config.rope_scaling is None:
+            self.rotary_emb = RotaryEmbedding(**kwargs)
+        else:
+            kwargs.update(scaling_factor=config.rope_scaling["factor"])
+            scaling_type = config.rope_scaling["type"]
+            if scaling_type == 'ntk':
+                kwargs.update(mixed_b=config.rope_scaling.get('mixed_b', None))
+                self.rotary_emb = NTKScalingRotaryEmbedding(**kwargs)
+            # elif scaling_type == "linear":
+            #     self.rotary_emb = LinearScalingRotaryEmbedding(**kwargs)
+            # elif scaling_type == "dynamic":
+            #     self.rotary_emb = DynamicNTKScalingRotaryEmbedding(**kwargs)
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+    def forward(
+        self,
+        unpad_inputs: bool,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        length: Optional[List[int]] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, Optional[Tuple], Optional[List[int]]]:
+        """
+        """
+        if inputs_embeds is None:
+            device, input_shape = input_ids.device, input_ids.shape
+        else:
+            device, input_shape = inputs_embeds.device, inputs_embeds.shape[:2]
+        batch_size, seq_length = input_shape
+
+        # Set attention_mask if it's None
+        if attention_mask is None:
+            attention_mask = torch.ones(input_shape, device=device)
+            if length is not None:
+                for i, l in enumerate(length):
+                    attention_mask[i, l:] = 0
+
+        # Set attention_mask_bool for unpadding
+        if unpad_inputs:
+            attention_mask_bool = attention_mask.bool()
+            if length is None:
+                length = attention_mask.sum(-1).tolist()
+
+        # Get word embeddings
+        if inputs_embeds is None:
+            if unpad_inputs:
+                input_ids = input_ids[attention_mask_bool].unsqueeze(0)
+            inputs_embeds = self.word_embeddings(input_ids)
+        else:
+            if unpad_inputs:
+                inputs_embeds = inputs_embeds[attention_mask_bool].unsqueeze(0)
+        embeddings = inputs_embeds
+
+        # Set and unpad position_ids
+        if position_ids is None:
+            if seq_length > self.position_ids.size(0):
+                self.register_buffer(
+                    "position_ids", torch.arange(seq_length, device=embeddings.device), persistent=False
+                )
+            if unpad_inputs:
+                # [1, cumsum_seq_len]
+                position_ids = torch.cat([self.position_ids[:l] for l in length]).unsqueeze(0)
+            else:
+                # [bs, seq_len]
+                position_ids = self.position_ids[:seq_length].expand(batch_size, -1)
+        elif unpad_inputs:
+            position_ids = position_ids[attention_mask_bool].unsqueeze(0)  # [1, cumsum_seq_len]
+
+        # Compute rotary embedding
+        if self.position_embedding_type == 'rope':
+            rope_cos, rope_sin = self.rotary_emb(inputs_embeds, seq_len=seq_length)
+            rope_cos = rope_cos[position_ids].unsqueeze(2)  # [bs, seq_len, 1, dim]
+            rope_sin = rope_sin[position_ids].unsqueeze(2)  # [bs, seq_len, 1, dim]
+            rope_embeds = rope_cos, rope_sin
+        else:
+            rope_embeds = None
+
+        if self.type_vocab_size > 0:
+            if token_type_ids is None:
+                token_type_ids = position_ids.mul(0)
+            else:
+                if self.type_vocab_size < 2:
+                    token_type_ids.mul_(0)
+                if unpad_inputs:
+                    token_type_ids = token_type_ids[attention_mask_bool].unsqueeze(0)
+
+            token_type_embeddings = self.token_type_embeddings(token_type_ids)
+            embeddings = embeddings + token_type_embeddings
+
+        # BERT position
+        if self.position_embedding_type == "absolute":
+            position_embeddings = self.position_embeddings(position_ids)
+            embeddings = embeddings + position_embeddings
+
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+
+        return embeddings, attention_mask, rope_embeds, length
+
+
+class GteAttention(nn.Module):
+    def __init__(self, config: GteConfig, pack_qkv=None, use_memory_efficient_attention=None):
+        super().__init__()
+        self.config = config
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+            raise ValueError(
+                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+                f"heads ({config.num_attention_heads})"
+            )
+
+        self.hidden_size = config.hidden_size
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        if pack_qkv is None:
+            pack_qkv = config.pack_qkv
+        self.pack_qkv = pack_qkv
+
+        if self.pack_qkv:
+            self.qkv_proj = nn.Linear(config.hidden_size, self.all_head_size * 3, bias=True)
+        else:
+            self.q_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)
+            self.k_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)
+            self.v_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.o_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=True)
+
+        if use_memory_efficient_attention is None:
+            use_memory_efficient_attention = self.config.use_memory_efficient_attention
+        self.use_memory_efficient_attention = use_memory_efficient_attention
+        self.memory_efficient_attention = None if xops is None else xops.memory_efficient_attention
+        if self.use_memory_efficient_attention:
+            assert self.memory_efficient_attention is not None, 'please install xformers'
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_bias: torch.FloatTensor,
+        rope_embeds: Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] = None,
+        padding_inputs: Optional[Tuple] = None,  # indices, batch, seqlen
+        attention_scale: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = False,
+        qkv_inputs: Optional[Tuple] = None,  # For RetroMAE
+    ) -> Tuple[torch.Tensor, ...]:
+        shape_hd = (self.num_attention_heads, self.attention_head_size)
+        # qkv
+        if self.pack_qkv and qkv_inputs is None:
+            qkv_pack = self.qkv_proj(hidden_states).split(self.all_head_size, dim=-1)
+        else:
+            if qkv_inputs is None:
+                qkv_inputs = (hidden_states, hidden_states, hidden_states)
+            qkv_pack = [
+                getattr(self, n + '_proj')(s) for s, n in zip(qkv_inputs, 'qkv')
+            ]
+        query_states, key_states, value_states = [t.view(t.shape[:-1] + shape_hd) for t in qkv_pack]
+
+        if self.config.position_embedding_type == 'rope':
+            query_states, key_states = apply_rotary_pos_emb(query_states, key_states, *rope_embeds)
+
+        dtype = query_states.dtype
+
+        if self.config.logn_attention_scale and attention_scale is not None:
+            # https://kexue.fm/archives/8823
+            query_states = query_states * attention_scale.to(dtype)
+
+        if padding_inputs is not None:
+            query_states = pad_input(query_states.squeeze(), *padding_inputs)
+            key_states = pad_input(key_states.squeeze(), *padding_inputs)
+            value_states = pad_input(value_states.squeeze(), *padding_inputs)
+
+        if self.use_memory_efficient_attention:
+            assert self.memory_efficient_attention is not None, "xformers is not loaded"
+            assert output_attentions is False, "memory_efficient_attention do not output attentions"
+            assert head_mask is None, "Not support yet"
+            attention_probs = None
+            if torch.is_tensor(attention_bias):
+                attention_bias = attention_bias.to(dtype)
+            context_layer = self.memory_efficient_attention(
+                query_states,
+                key_states,
+                value_states,
+                attn_bias=attention_bias,
+                p=self.dropout.p
+            )
+        else:
+            if output_attentions and isinstance(self, GteSdpaAttention):
+                raise RuntimeError("SDPA do not output attentions")
+            context_layer, attention_probs = self._attention(
+                query_states, key_states, value_states, attention_bias, head_mask
+            )
+
+        if padding_inputs is not None:
+            context_layer = unpad_input(context_layer, indices=padding_inputs[0])
+
+        gte_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(gte_context_layer_shape)
+
+        # output proj
+        attn_output = self.o_proj(context_layer)
+
+        # add attentions if we output them
+        outputs = (attn_output, attention_probs) if output_attentions else (attn_output,)
+        return outputs
+
+    def _attention(self, query_states, key_states, value_states, attention_bias, head_mask):
+        """
+        Args:
+            q/k/v: (B, L, n_head, head_dim),
+        Returns:
+            attn_output: (B L, n_head, head_dim)
+        """
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+        # Take the dot product between "query" and "key" to get the raw attention scores.
+        attention_scores = torch.matmul(query_states, key_states.transpose(-1, -2))
+
+        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+        if attention_bias is not None:
+            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+            attention_scores = attention_scores + attention_bias
+
+        # Normalize the attention scores to probabilities.
+        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        if self.dropout.p > 0:
+            attention_probs = self.dropout(attention_probs)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attention_probs = attention_probs * head_mask
+
+        context_layer = torch.matmul(attention_probs, value_states)
+
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        return context_layer, attention_probs
+
+
+class GteSdpaAttention(GteAttention):
+    """
+    Gte attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `GteAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+    def __init__(self, config: GteConfig, **kwargs):
+        super().__init__(config, **kwargs)
+        # torch.backends.cuda.enable_mem_efficient_sdp(False)
+        # logger.warning(
+        #     "Disable memory efficient attention kernel for `GteSdpaAttention`, you can set "
+        #     "`use_memory_efficient_attention=True` if it expected to use."
+        # )
+
+    def _attention(self, query_states, key_states, value_states, attention_bias, head_mask):
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states.transpose(1, 2),
+            key_states.transpose(1, 2),
+            value_states.transpose(1, 2),
+            attn_mask=attention_bias,
+            dropout_p=self.dropout.p if self.training else 0.0,
+        )
+        attn_output = attn_output.permute(0, 2, 1, 3).contiguous()
+        return attn_output, None
+
+
+GTE_ATTENTION_CLASSES = {
+    "eager": GteAttention,
+    # "flash_attention_2": ,  # TODO
+    "sdpa": GteSdpaAttention,
+}
+
+
+class GteGatedMLP(nn.Module):
+    """
+    GLU Variants Improve Transformer.
+    """
+
+    def __init__(self, config: GteConfig):
+        super().__init__()
+        self.intermediate_size = config.intermediate_size
+        self.up_gate_proj = nn.Linear(config.hidden_size, self.intermediate_size * 2, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, config.hidden_size, bias=True)
+        self.act_fn = ACT2FN[config.hidden_act]
+        if config.hidden_dropout_prob > 0:
+            self.hidden_dropout = nn.Dropout(config.hidden_dropout_prob)
+        else:
+            self.hidden_dropout = None
+
+    def forward(self, hidden_states):
+        up_gate = self.up_gate_proj(hidden_states)
+        up_states, gate = torch.split(up_gate, self.intermediate_size, dim=-1)
+        gate = self.act_fn(gate)
+        gated_states = gate * up_states
+        if self.hidden_dropout is not None:
+            gated_states = self.hidden_dropout(gated_states)
+        down_states = self.down_proj(gated_states)
+        return down_states
+
+
+class GteLayer(nn.Module):
+    def __init__(
+        self,
+        config: GteConfig,
+        pack_qkv=None,
+        use_memory_efficient_attention=None,
+        attn_implementation=None
+    ):
+        super().__init__()
+        if attn_implementation is None:
+            attn_implementation = config._attn_implementation
+        if use_memory_efficient_attention is None:
+            use_memory_efficient_attention = config.use_memory_efficient_attention
+        if use_memory_efficient_attention:
+            if attn_implementation != 'eager':
+                logger.warning_once(f"Override {attn_implementation=} to 'eager' as {use_memory_efficient_attention=}")
+                attn_implementation = 'eager'  # Since it will be SDPA by default for torch>=2.1.1
+        self.attention = GTE_ATTENTION_CLASSES[attn_implementation](
+            config, pack_qkv=pack_qkv, use_memory_efficient_attention=use_memory_efficient_attention
+        )
+        self.mlp = GteGatedMLP(config)
+
+        ln_class = LAYER_NORM[config.layer_norm_type]
+        self.attn_ln = ln_class(config.hidden_size, eps=config.layer_norm_eps)
+        self.mlp_ln = ln_class(config.hidden_size, eps=config.layer_norm_eps)
+
+        if config.hidden_dropout_prob > 0:
+            self.hidden_dropout = nn.Dropout(config.hidden_dropout_prob)
+        else:
+            self.hidden_dropout = None
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_bias: torch.FloatTensor,
+        rope_embeds: Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] = None,
+        padding_inputs: Optional[Tuple] = None,  # indices, batch, seqlen
+        attention_scale: Optional[torch.FloatTensor] = None,
+        subset_indices: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = False,
+        qkv_inputs: Optional[Tuple] = None,  # For RetroMAE
+    ) -> Tuple[torch.Tensor, ...]:
+        # Multi head self attention
+        residual = hidden_states if qkv_inputs is None else qkv_inputs[0]
+        attention_outputs = self.attention(
+            hidden_states,
+            attention_bias,
+            rope_embeds,
+            padding_inputs,
+            attention_scale,
+            head_mask,
+            output_attentions=output_attentions,
+            qkv_inputs=qkv_inputs,
+        )
+        hidden_states = attention_outputs[0]
+        if self.hidden_dropout is not None:
+            hidden_states = self.hidden_dropout(hidden_states)
+        hidden_states = residual + hidden_states
+
+        # In pretraining, after the attention of last layer, we only need the masked tokens.
+        if subset_indices is not None:
+            hidden_states = hidden_states[subset_indices]
+
+        hidden_states = self.attn_ln(hidden_states)
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.mlp(hidden_states)
+        if self.hidden_dropout is not None:
+            hidden_states = self.hidden_dropout(hidden_states)
+        hidden_states = residual + hidden_states
+        hidden_states = self.mlp_ln(hidden_states)
+
+        # add self attentions if we output attention weights
+        outputs = (hidden_states,) + attention_outputs[1:]
+        return outputs
+
+
+class GteEncoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList([GteLayer(config) for _ in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_bias: Optional[torch.FloatTensor] = None,
+        rope_embeds: Optional[Tuple[torch.FloatTensor, torch.FloatTensor]] = None,
+        padding_inputs: Optional[Tuple] = None,  # indices, batch, seqlen
+        attention_scale: Optional[torch.FloatTensor] = None,
+        subset_indices: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = False,
+        output_hidden_states: Optional[bool] = False,
+        return_dict: Optional[bool] = True,
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutput]:
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            if i >= len(self.layer) - 1:
+                layer_subset_indices = subset_indices
+            else:
+                layer_subset_indices = None
+
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    layer_module.__call__,
+                    hidden_states,
+                    attention_bias,
+                    rope_embeds,
+                    padding_inputs,
+                    attention_scale,
+                    layer_subset_indices,
+                    layer_head_mask,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_bias,
+                    rope_embeds,
+                    padding_inputs,
+                    attention_scale,
+                    layer_subset_indices,
+                    layer_head_mask,
+                    output_attentions,
+                )
+
+            hidden_states = layer_outputs[0]
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [
+                    hidden_states,
+                    all_hidden_states,
+                    all_self_attentions,
+                ]
+                if v is not None
+            )
+        return BaseModelOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+        )
+
+
+# Copied from transformers.models.bert.modeling_bert.BertPooler with Bert->Gte
+class GtePooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # We "pool" the model by simply taking the hidden state corresponding
+        # to the first token.
+        first_token_tensor = hidden_states[:, 0]
+        pooled_output = self.dense(first_token_tensor)
+        pooled_output = self.activation(pooled_output)
+        return pooled_output
+
+
+class GtePreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = GteConfig
+    base_model_prefix = "gte"
+    supports_gradient_checkpointing = True
+    _supports_sdpa = True
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            # 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.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+
+class GteModel(GtePreTrainedModel):
+    """
+    The bare Gte Model transformer outputting raw hidden-states without any specific head on top.
+    """
+
+    def __init__(self, config: GteConfig, add_pooling_layer=False):
+        super().__init__(config)
+        self.config = config
+
+        self.embeddings = GteEmbeddings(config)
+        self.encoder = GteEncoder(config)
+
+        self.pooler = GtePooler(config) if add_pooling_layer else None
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.word_embeddings = value
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        length: Optional[List[int]] = None,
+        subset_indices: Optional[torch.LongTensor] = None,
+        token_type_ids: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.Tensor] = None,
+        head_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        unpad_inputs: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPooling]:
+        r"""
+        length  (`list` of length `batch_size`, *optional*):
+            If is `None`, return padded `last_hidden_state`.
+        subset_indices  ():
+            pass
+        unpad_inputs  (`bool`, *optional*):
+            pass
+        """
+        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
+        unpad_inputs = unpad_inputs if unpad_inputs is not None else self.config.unpad_inputs
+        output_padded = length is None
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+            input_shape = input_ids.size()
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        # TODO: not used
+        # # Prepare head mask if needed
+        # # 1.0 in head_mask indicate we keep the head
+        # # attention_probs has shape bsz x n_heads x N x N
+        # # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        # head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        # Get embeddings, may unpad them
+        (embedding_output, attention_mask, rope_embeds, length) = self.embeddings(
+            unpad_inputs,
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            length=length,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds
+        )
+
+        batch_size, seq_length = input_shape
+        if unpad_inputs and self.config.use_memory_efficient_attention:
+            attention_bias = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens(length)
+        else:
+            # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+            # ourselves in which case we just need to make it broadcastable to all heads.
+            attention_bias = self.get_extended_attention_mask(attention_mask, input_shape)
+            if self.config.use_memory_efficient_attention:
+                # Invalid shape for attention bias: torch.Size([48, 1, 1, 512]) (expected (48, 12, 512, 512))
+                attention_bias = attention_bias.expand(-1, self.config.num_attention_heads, seq_length, -1)
+
+        padding_inputs = None
+        if unpad_inputs and (output_padded or not self.config.use_memory_efficient_attention):
+            indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+            if not self.config.use_memory_efficient_attention:
+                padding_inputs = (indices, *input_shape)
+
+        attention_scale = None
+        if self.config.logn_attention_scale:
+            logger.warning_once("TODO: logn_attention_scale")
+        #     # attention scale log_512(input_len)
+        #     attention_scale = attention_mask.sum(1).log() / torch.tensor(self.config.max_position_embeddings).log()
+        #     # inference-time logn scale need clip 1
+        #     if self.config.logn_attention_clip1:
+        #         attention_scale.clip_(1)
+        #     attention_scale = attention_scale[:, None, None, None]
+        # else:
+        #     attention_scale = None
+
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_bias=attention_bias,
+            rope_embeds=rope_embeds,
+            padding_inputs=padding_inputs,
+            attention_scale=attention_scale,
+            subset_indices=subset_indices,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = encoder_outputs[0]
+        if unpad_inputs and output_padded:
+            sequence_output = pad_input(
+                sequence_output.squeeze(), indices, batch_size, seq_length
+            )
+
+        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )

rng_state_0.pth

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rng_state_1.pth

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special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+size 17082734

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "250001": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 512,
+  "model_max_length": 32768,
+  "pad_to_multiple_of": null,
+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "XLMRobertaTokenizerFast",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}

trainer_state.json

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+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 4.2949308755760365,
+  "eval_steps": 500,
+  "global_step": 30,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
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+      "learning_rate": 9.999999999999999e-06,
+      "loss": 0.257,
+      "step": 10
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+      "loss": 0.2261,
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+      "learning_rate": 1.4771212547196623e-05,
+      "loss": 0.1678,
+      "step": 30
+    }
+  ],
+  "logging_steps": 5,
+  "max_steps": 35,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 5,
+  "save_steps": 5,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": false
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 481142620815360.0,
+  "train_batch_size": 32,
+  "trial_name": null,
+  "trial_params": null
+}

training_args.bin

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zero_to_fp32.py

@@ -0,0 +1,760 @@
+#!/usr/bin/env python
+
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
+# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
+# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
+# application.
+#
+# example:
+#   python zero_to_fp32.py . output_dir/
+#   or
+#   python zero_to_fp32.py . output_dir/ --safe_serialization
+
+import argparse
+import torch
+import glob
+import math
+import os
+import re
+import gc
+import json
+import numpy as np
+from tqdm import tqdm
+from collections import OrderedDict
+from dataclasses import dataclass
+
+# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
+# DeepSpeed data structures it has to be available in the current python environment.
+from deepspeed.utils import logger
+from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
+                                            FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
+                                            FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
+
+
+@dataclass
+class zero_model_state:
+    buffers: dict()
+    param_shapes: dict()
+    shared_params: list
+    ds_version: int
+    frozen_param_shapes: dict()
+    frozen_param_fragments: dict()
+
+
+debug = 0
+
+# load to cpu
+device = torch.device('cpu')
+
+
+def atoi(text):
+    return int(text) if text.isdigit() else text
+
+
+def natural_keys(text):
+    '''
+    alist.sort(key=natural_keys) sorts in human order
+    http://nedbatchelder.com/blog/200712/human_sorting.html
+    (See Toothy's implementation in the comments)
+    '''
+    return [atoi(c) for c in re.split(r'(\d+)', text)]
+
+
+def get_model_state_file(checkpoint_dir, zero_stage):
+    if not os.path.isdir(checkpoint_dir):
+        raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
+
+    # there should be only one file
+    if zero_stage <= 2:
+        file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
+    elif zero_stage == 3:
+        file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
+
+    if not os.path.exists(file):
+        raise FileNotFoundError(f"can't find model states file at '{file}'")
+
+    return file
+
+
+def get_checkpoint_files(checkpoint_dir, glob_pattern):
+    # XXX: need to test that this simple glob rule works for multi-node setup too
+    ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
+
+    if len(ckpt_files) == 0:
+        raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
+
+    return ckpt_files
+
+
+def get_optim_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
+
+
+def get_model_state_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
+
+
+def parse_model_states(files):
+    zero_model_states = []
+    for file in files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+
+        if BUFFER_NAMES not in state_dict:
+            raise ValueError(f"{file} is not a model state checkpoint")
+        buffer_names = state_dict[BUFFER_NAMES]
+        if debug:
+            print("Found buffers:", buffer_names)
+
+        # recover just the buffers while restoring them to fp32 if they were saved in fp16
+        buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
+        param_shapes = state_dict[PARAM_SHAPES]
+
+        # collect parameters that are included in param_shapes
+        param_names = []
+        for s in param_shapes:
+            for name in s.keys():
+                param_names.append(name)
+
+        # update with frozen parameters
+        frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
+        if frozen_param_shapes is not None:
+            if debug:
+                print(f"Found frozen_param_shapes: {frozen_param_shapes}")
+            param_names += list(frozen_param_shapes.keys())
+
+        # handle shared params
+        shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
+
+        ds_version = state_dict.get(DS_VERSION, None)
+
+        frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
+
+        z_model_state = zero_model_state(buffers=buffers,
+                                         param_shapes=param_shapes,
+                                         shared_params=shared_params,
+                                         ds_version=ds_version,
+                                         frozen_param_shapes=frozen_param_shapes,
+                                         frozen_param_fragments=frozen_param_fragments)
+        zero_model_states.append(z_model_state)
+
+    return zero_model_states
+
+
+def parse_optim_states(files, ds_checkpoint_dir):
+    total_files = len(files)
+    state_dicts = []
+    for f in tqdm(files, desc='Loading checkpoint shards'):
+        state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
+        # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
+        # and also handle the case where it was already removed by another helper script
+        state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
+        state_dicts.append(state_dict)
+
+    if ZERO_STAGE not in state_dicts[0][OPTIMIZER_STATE_DICT]:
+        raise ValueError(f"{files[0]} is not a zero checkpoint")
+    zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
+    world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
+
+    # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
+    # parameters can be different from data parallelism for non-expert parameters. So we can just
+    # use the max of the partition_count to get the dp world_size.
+
+    if type(world_size) is list:
+        world_size = max(world_size)
+
+    if world_size != total_files:
+        raise ValueError(
+            f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
+            "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
+        )
+
+    # the groups are named differently in each stage
+    if zero_stage <= 2:
+        fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
+    elif zero_stage == 3:
+        fp32_groups_key = FP32_FLAT_GROUPS
+    else:
+        raise ValueError(f"unknown zero stage {zero_stage}")
+
+    fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
+    return zero_stage, world_size, fp32_flat_groups
+
+
+def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
+    """
+    Returns fp32 state_dict reconstructed from ds checkpoint
+
+    Args:
+        - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
+
+    """
+    print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
+
+    optim_files = get_optim_files(ds_checkpoint_dir)
+    zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
+    print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
+
+    model_files = get_model_state_files(ds_checkpoint_dir)
+
+    zero_model_states = parse_model_states(model_files)
+    print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
+
+    if zero_stage <= 2:
+        return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                                          exclude_frozen_parameters)
+    elif zero_stage == 3:
+        return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                                          exclude_frozen_parameters)
+
+
+def _zero2_merge_frozen_params(state_dict, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+    frozen_param_fragments = zero_model_states[0].frozen_param_fragments
+
+    if debug:
+        num_elem = sum(s.numel() for s in frozen_param_shapes.values())
+        print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        state_dict[name] = frozen_param_fragments[name]
+
+        if debug:
+            print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _has_callable(obj, fn):
+    attr = getattr(obj, fn, None)
+    return callable(attr)
+
+
+def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
+
+    # Reconstruction protocol:
+    #
+    # XXX: document this
+
+    if debug:
+        for i in range(world_size):
+            for j in range(len(fp32_flat_groups[0])):
+                print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
+
+    # XXX: memory usage doubles here (zero2)
+    num_param_groups = len(fp32_flat_groups[0])
+    merged_single_partition_of_fp32_groups = []
+    for i in range(num_param_groups):
+        merged_partitions = [sd[i] for sd in fp32_flat_groups]
+        full_single_fp32_vector = torch.cat(merged_partitions, 0)
+        merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
+    avail_numel = sum(
+        [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
+
+    if debug:
+        wanted_params = sum([len(shapes) for shapes in param_shapes])
+        wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
+        # not asserting if there is a mismatch due to possible padding
+        print(f"Have {avail_numel} numels to process.")
+        print(f"Need {wanted_numel} numels in {wanted_params} params.")
+
+    # params
+    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
+    # out-of-core computing solution
+    total_numel = 0
+    total_params = 0
+    for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
+        offset = 0
+        avail_numel = full_single_fp32_vector.numel()
+        for name, shape in shapes.items():
+
+            unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)
+            total_numel += unpartitioned_numel
+            total_params += 1
+
+            if debug:
+                print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+            state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
+            offset += unpartitioned_numel
+
+        # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
+        # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
+        # paddings performed in the code it's almost impossible to predict the exact numbers w/o the
+        # live optimizer object, so we are checking that the numbers are within the right range
+        align_to = 2 * world_size
+
+        def zero2_align(x):
+            return align_to * math.ceil(x / align_to)
+
+        if debug:
+            print(f"original offset={offset}, avail_numel={avail_numel}")
+
+        offset = zero2_align(offset)
+        avail_numel = zero2_align(avail_numel)
+
+        if debug:
+            print(f"aligned  offset={offset}, avail_numel={avail_numel}")
+
+        # Sanity check
+        if offset != avail_numel:
+            raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
+
+    print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                               exclude_frozen_parameters):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    if not exclude_frozen_parameters:
+        _zero2_merge_frozen_params(state_dict, zero_model_states)
+
+    _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
+
+    return state_dict
+
+
+def zero3_partitioned_param_info(unpartitioned_numel, world_size):
+    remainder = unpartitioned_numel % world_size
+    padding_numel = (world_size - remainder) if remainder else 0
+    partitioned_numel = math.ceil(unpartitioned_numel / world_size)
+    return partitioned_numel, padding_numel
+
+
+def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    if debug:
+        for i in range(world_size):
+            num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
+            print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in zero_model_states[0].frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
+        state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
+
+        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
+
+        if debug:
+            print(
+                f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+            )
+
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
+
+
+class GatheredTensor:
+    """
+    A pseudo tensor that collects partitioned weights.
+    It is more memory efficient when there are multiple groups.
+    """
+
+    def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
+        self.flat_groups = flat_groups
+        self.flat_groups_offset = flat_groups_offset
+        self.offset = offset
+        self.partitioned_numel = partitioned_numel
+        self.shape = shape
+        self.dtype = self.flat_groups[0][0].dtype
+
+    def contiguous(self):
+        """
+        Merge partitioned weights from flat_groups into a single tensor.
+        """
+        end_idx = self.offset + self.partitioned_numel
+        world_size = len(self.flat_groups)
+        pad_flat_param_chunks = []
+
+        for rank_i in range(world_size):
+            # for each rank, we need to collect weights from related group/groups
+            flat_groups_at_rank_i = self.flat_groups[rank_i]
+            start_group_id = None
+            end_group_id = None
+            for group_id in range(len(self.flat_groups_offset)):
+                if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
+                    start_group_id = group_id
+                if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
+                    end_group_id = group_id
+                    break
+            # collect weights from related group/groups
+            for group_id in range(start_group_id, end_group_id + 1):
+                flat_tensor = flat_groups_at_rank_i[group_id]
+                start_offset = self.offset - self.flat_groups_offset[group_id]
+                end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
+                pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
+
+        # collect weights from all ranks
+        pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
+        param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
+        return param
+
+
+def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
+    avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size
+
+    # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
+    # param, re-consolidating each param, while dealing with padding if any
+
+    # merge list of dicts, preserving order
+    param_shapes = {k: v for d in param_shapes for k, v in d.items()}
+
+    if debug:
+        for i in range(world_size):
+            print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
+
+        wanted_params = len(param_shapes)
+        wanted_numel = sum(shape.numel() for shape in param_shapes.values())
+        # not asserting if there is a mismatch due to possible padding
+        avail_numel = fp32_flat_groups[0].numel() * world_size
+        print(f"Trainable params: Have {avail_numel} numels to process.")
+        print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
+
+    # params
+    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
+    # out-of-core computing solution
+    offset = 0
+    total_numel = 0
+    total_params = 0
+    flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]]))
+    for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'):
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+        total_params += 1
+        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
+
+        if debug:
+            print(
+                f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+            )
+
+        # memory efficient tensor
+        tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
+        state_dict[name] = tensor
+        offset += partitioned_numel
+
+    offset *= world_size
+
+    # Sanity check
+    if offset != avail_numel:
+        raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
+
+    print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                               exclude_frozen_parameters):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    if not exclude_frozen_parameters:
+        _zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
+
+    _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
+
+    return state_dict
+
+
+def to_torch_tensor(state_dict, return_empty_tensor=False):
+    """
+    Convert state_dict of GatheredTensor to torch tensor
+    """
+    torch_state_dict = {}
+    converted_tensors = {}
+    for name, tensor in state_dict.items():
+        tensor_id = id(tensor)
+        if tensor_id in converted_tensors:  # shared tensors
+            shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
+            torch_state_dict[name] = shared_tensor
+        else:
+            converted_tensors[tensor_id] = name
+            if return_empty_tensor:
+                torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
+            else:
+                torch_state_dict[name] = tensor.contiguous()
+    return torch_state_dict
+
+
+def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                             tag=None,
+                                             exclude_frozen_parameters=False,
+                                             lazy_mode=False):
+    """
+    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
+    ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
+    via a model hub.
+
+    Args:
+        - ``checkpoint_dir``: path to the desired checkpoint folder
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
+        - ``exclude_frozen_parameters``: exclude frozen parameters
+        - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
+          Convert the pesduo tensor to torch tensor by ``.contiguous()``
+
+    Returns:
+        - pytorch ``state_dict``
+
+    A typical usage might be ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        # do the training and checkpoint saving
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
+        model = model.cpu() # move to cpu
+        model.load_state_dict(state_dict)
+        # submit to model hub or save the model to share with others
+
+    In this example the ``model`` will no longer be usable in the deepspeed context of the same
+    application. i.e. you will need to re-initialize the deepspeed engine, since
+    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
+
+    If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
+
+    Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
+    You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
+    the checkpoint. Or you can load state_dict in lazy mode ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
+        for name, lazy_tensor in state_dict.item():
+            tensor = lazy_tensor.contiguous()  # to cpu
+            print(name, tensor)
+            # del tensor to release memory if it no longer in use
+    """
+    if tag is None:
+        latest_path = os.path.join(checkpoint_dir, 'latest')
+        if os.path.isfile(latest_path):
+            with open(latest_path, 'r') as fd:
+                tag = fd.read().strip()
+        else:
+            raise ValueError(f"Unable to find 'latest' file at {latest_path}")
+
+    ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
+
+    if not os.path.isdir(ds_checkpoint_dir):
+        raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
+
+    state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
+    if lazy_mode:
+        return state_dict
+    else:
+        return to_torch_tensor(state_dict)
+
+
+def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir,
+                                               output_dir,
+                                               max_shard_size="5GB",
+                                               safe_serialization=False,
+                                               tag=None,
+                                               exclude_frozen_parameters=False):
+    """
+    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
+    loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
+
+    Args:
+        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
+        - ``output_dir``: directory to the pytorch fp32 state_dict output files
+        - ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB
+        - ``safe_serialization``:  whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
+        - ``exclude_frozen_parameters``: exclude frozen parameters
+    """
+
+    # Dependency pre-check
+    if safe_serialization:
+        try:
+            from safetensors.torch import save_file
+        except ImportError:
+            print('If you want to use `safe_serialization`, please `pip install safetensors`')
+            raise
+    if max_shard_size is not None:
+        try:
+            from huggingface_hub import split_torch_state_dict_into_shards
+        except ImportError:
+            print('If you want to use `max_shard_size`, please `pip install huggingface_hub`')
+            raise
+
+    # Convert zero checkpoint to state_dict
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                                          tag,
+                                                          exclude_frozen_parameters,
+                                                          lazy_mode=True)
+
+    # Shard the model if it is too big.
+    weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin"
+    if max_shard_size is not None:
+        filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
+        # an memory-efficient approach for sharding
+        empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
+        state_dict_split = split_torch_state_dict_into_shards(empty_state_dict,
+                                                              filename_pattern=filename_pattern,
+                                                              max_shard_size=max_shard_size)
+    else:
+        from collections import namedtuple
+        StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"])
+        state_dict_split = StateDictSplit(is_sharded=False,
+                                          filename_to_tensors={weights_name: list(state_dict.keys())})
+
+    # Save the model by shard
+    os.makedirs(output_dir, exist_ok=True)
+    filename_to_tensors = state_dict_split.filename_to_tensors.items()
+    for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
+        shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
+        shard_state_dict = to_torch_tensor(shard_state_dict)
+        output_path = os.path.join(output_dir, shard_file)
+        if safe_serialization:
+            save_file(shard_state_dict, output_path, metadata={"format": "pt"})
+        else:
+            torch.save(shard_state_dict, output_path)
+        # release the memory of current shard
+        for tensor_name in list(shard_state_dict.keys()):
+            del state_dict[tensor_name]
+            del shard_state_dict[tensor_name]
+        del shard_state_dict
+        gc.collect()
+
+    # Save index if sharded
+    if state_dict_split.is_sharded:
+        index = {
+            "metadata": state_dict_split.metadata,
+            "weight_map": state_dict_split.tensor_to_filename,
+        }
+        save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json"
+        save_index_file = os.path.join(output_dir, save_index_file)
+        with open(save_index_file, "w", encoding="utf-8") as f:
+            content = json.dumps(index, indent=2, sort_keys=True) + "\n"
+            f.write(content)
+
+
+def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
+    """
+    1. Put the provided model to cpu
+    2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
+    3. Load it into the provided model
+
+    Args:
+        - ``model``: the model object to update
+        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
+
+    Returns:
+        - ``model`: modified model
+
+    Make sure you have plenty of CPU memory available before you call this function. If you don't
+    have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
+    conveniently placed for you in the checkpoint folder.
+
+    A typical usage might be ::
+
+        from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
+        model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
+        # submit to model hub or save the model to share with others
+
+    Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
+    of the same application. i.e. you will need to re-initialize the deepspeed engine, since
+    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
+
+    """
+    logger.info("Extracting fp32 weights")
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+
+    logger.info("Overwriting model with fp32 weights")
+    model = model.cpu()
+    model.load_state_dict(state_dict, strict=False)
+
+    return model
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("checkpoint_dir",
+                        type=str,
+                        help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
+    parser.add_argument("output_dir",
+                        type=str,
+                        help="directory to the pytorch fp32 state_dict output files"
+                        "(e.g. path/checkpoint-12-output/)")
+    parser.add_argument(
+        "--max_shard_size",
+        type=str,
+        default="5GB",
+        help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size"
+        "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`"
+        "We default it to 5GB in order for models to be able to run easily on free-tier google colab instances"
+        "without CPU OOM issues.")
+    parser.add_argument(
+        "--safe_serialization",
+        default=False,
+        action='store_true',
+        help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).")
+    parser.add_argument("-t",
+                        "--tag",
+                        type=str,
+                        default=None,
+                        help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
+    parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters")
+    parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
+    args = parser.parse_args()
+
+    debug = args.debug
+
+    convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
+                                               args.output_dir,
+                                               max_shard_size=args.max_shard_size,
+                                               safe_serialization=args.safe_serialization,
+                                               tag=args.tag,
+                                               exclude_frozen_parameters=args.exclude_frozen_parameters)