update model

config.json

@@ -1,31 +0,0 @@
-{
-  "_name_or_path": "scutcyr/SoulChat",
-  "architectures": [
-    "ChatGLMForConditionalGeneration"
-  ],
-  "auto_map": {
-    "AutoConfig": "configuration_chatglm.ChatGLMConfig",
-    "AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration",
-    "AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration"
-  },
-  "bos_token_id": 130004,
-  "eos_token_id": 130005,
-  "gmask_token_id": 130001,
-  "hidden_size": 4096,
-  "inner_hidden_size": 16384,
-  "layernorm_epsilon": 1e-05,
-  "mask_token_id": 130000,
-  "max_sequence_length": 2048,
-  "model_type": "chatglm",
-  "num_attention_heads": 32,
-  "num_layers": 28,
-  "pad_token_id": 3,
-  "position_encoding_2d": true,
-  "pre_seq_len": null,
-  "prefix_projection": false,
-  "quantization_bit": 0,
-  "torch_dtype": "float16",
-  "transformers_version": "4.28.0",
-  "use_cache": true,
-  "vocab_size": 130528
-}

configuration_chatglm.py

@@ -1,103 +0,0 @@
-""" ChatGLM model configuration """
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-logger = logging.get_logger(__name__)
-
-
-class ChatGLMConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`~ChatGLMModel`].
-    It is used to instantiate an ChatGLM 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 ChatGLM-6B [THUDM/ChatGLM-6B](https://huggingface.co/THUDM/chatglm-6b) 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 150528):
-            Vocabulary size of the ChatGLM-6B model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`~ChatGLMModel`] or
-            [`~TFChatGLMModel`].
-        hidden_size (`int`, *optional*, defaults to 4096):
-            Dimension of the encoder layers and the pooler layer.
-        num_hidden_layers (`int`, *optional*, defaults to 28):
-            Number of hidden layers in the Transformer encoder.
-        num_attention_heads (`int`, *optional*, defaults to 32):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        inner_hidden_size (`int`, *optional*, defaults to 16384):
-            Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
-        max_sequence_length (`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).
-        layernorm_epsilon (`float`, *optional*, defaults to 1e-5):
-            The epsilon used by the layer normalization layers.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether the model should return the last key/values attentions (not used by all models).
-        Example:
-
-    ```python
-    >>> from configuration_chatglm import ChatGLMConfig
-    >>> from modeling_chatglm import ChatGLMModel
-
-    >>> # Initializing a ChatGLM-6B THUDM/ChatGLM-6B style configuration
-    >>> configuration = ChatGLMConfig()
-
-    >>> # Initializing a model from the THUDM/ChatGLM-6B style configuration
-    >>> model = ChatGLMModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```
-"""
-    model_type = "chatglm"
-
-    def __init__(
-            self,
-            vocab_size=150528,
-            hidden_size=4096,
-            num_layers=28,
-            num_attention_heads=32,
-            layernorm_epsilon=1e-5,
-            use_cache=False,
-            bos_token_id=150004,
-            eos_token_id=150005,
-            mask_token_id=150000,
-            gmask_token_id=150001,
-            pad_token_id=0,
-            max_sequence_length=2048,
-            inner_hidden_size=16384,
-            position_encoding_2d=True,
-            quantization_bit=0,
-            pre_seq_len=None,
-            prefix_projection=False,
-            **kwargs
-    ):
-        self.num_layers = num_layers
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.num_attention_heads = num_attention_heads
-        self.max_sequence_length = max_sequence_length
-        self.layernorm_epsilon = layernorm_epsilon
-        self.inner_hidden_size = inner_hidden_size
-        self.use_cache = use_cache
-        self.bos_token_id = bos_token_id
-        self.eos_token_id = eos_token_id
-        self.pad_token_id = pad_token_id
-        self.mask_token_id = mask_token_id
-        self.gmask_token_id = gmask_token_id
-        self.position_encoding_2d = position_encoding_2d
-        self.quantization_bit = quantization_bit
-        self.pre_seq_len = pre_seq_len
-        self.prefix_projection = prefix_projection
-
-        super().__init__(
-            pad_token_id=pad_token_id,
-            bos_token_id=bos_token_id,
-            eos_token_id=eos_token_id,
-            **kwargs
-        )

generation_config.json

@@ -1,7 +0,0 @@
-{
-  "_from_model_config": true,
-  "bos_token_id": 130004,
-  "eos_token_id": 130005,
-  "pad_token_id": 3,
-  "transformers_version": "4.28.0"
-}

modeling_chatglm.py

@@ -1,1435 +0,0 @@
-""" PyTorch ChatGLM model. """
-
-import math
-import copy
-import os
-import warnings
-import re
-import sys
-
-import torch
-import torch.utils.checkpoint
-import torch.nn.functional as F
-from torch import nn
-from torch.nn import CrossEntropyLoss, LayerNorm
-from torch.nn.utils import skip_init
-from typing import Optional, Tuple, Union, List, Callable, Dict, Any
-
-from transformers.utils import (
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-)
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-    BaseModelOutputWithPastAndCrossAttentions,
-)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import logging
-from transformers.generation.logits_process import LogitsProcessor
-from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput
-
-from .configuration_chatglm import ChatGLMConfig
-
-# flags required to enable jit fusion kernels
-
-if sys.platform != 'darwin':
-    torch._C._jit_set_profiling_mode(False)
-    torch._C._jit_set_profiling_executor(False)
-    torch._C._jit_override_can_fuse_on_cpu(True)
-    torch._C._jit_override_can_fuse_on_gpu(True)
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM-6B"
-_CONFIG_FOR_DOC = "ChatGLM6BConfig"
-
-CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "THUDM/chatglm-6b",
-    # See all ChatGLM-6B models at https://huggingface.co/models?filter=chatglm
-]
-
-
-class InvalidScoreLogitsProcessor(LogitsProcessor):
-    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
-        if torch.isnan(scores).any() or torch.isinf(scores).any():
-            scores.zero_()
-            scores[..., 5] = 5e4
-        return scores
-
-
-def load_tf_weights_in_chatglm_6b(model, config, tf_checkpoint_path):
-    """Load tf checkpoints in a pytorch model."""
-    try:
-        import re
-
-        import numpy as np
-        import tensorflow as tf
-    except ImportError:
-        logger.error(
-            "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
-            "https://www.tensorflow.org/install/ for installation instructions."
-        )
-        raise
-    tf_path = os.path.abspath(tf_checkpoint_path)
-    logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
-    # Load weights from TF model
-    init_vars = tf.train.list_variables(tf_path)
-    names = []
-    arrays = []
-    for name, shape in init_vars:
-        logger.info(f"Loading TF weight {name} with shape {shape}")
-        array = tf.train.load_variable(tf_path, name)
-        names.append(name)
-        arrays.append(array)
-
-    for name, array in zip(names, arrays):
-        name = name.split("/")
-        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
-        # which are not required for using pretrained model
-        if any(
-                n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
-                for n in name
-        ):
-            logger.info(f"Skipping {'/'.join(name)}")
-            continue
-        pointer = model
-        for m_name in name:
-            if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
-                scope_names = re.split(r"_(\d+)", m_name)
-            else:
-                scope_names = [m_name]
-            if scope_names[0] == "kernel" or scope_names[0] == "gamma":
-                pointer = getattr(pointer, "weight")
-            elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
-                pointer = getattr(pointer, "bias")
-            elif scope_names[0] == "output_weights":
-                pointer = getattr(pointer, "weight")
-            elif scope_names[0] == "squad":
-                pointer = getattr(pointer, "classifier")
-            else:
-                try:
-                    pointer = getattr(pointer, scope_names[0])
-                except AttributeError:
-                    logger.info(f"Skipping {'/'.join(name)}")
-                    continue
-            if len(scope_names) >= 2:
-                num = int(scope_names[1])
-                pointer = pointer[num]
-        if m_name[-11:] == "_embeddings":
-            pointer = getattr(pointer, "weight")
-        elif m_name == "kernel":
-            array = np.transpose(array)
-        try:
-            assert (
-                    pointer.shape == array.shape
-            ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
-        except AssertionError as e:
-            e.args += (pointer.shape, array.shape)
-            raise
-        logger.info(f"Initialize PyTorch weight {name}")
-        pointer.data = torch.from_numpy(array)
-    return model
-
-
-class PrefixEncoder(torch.nn.Module):
-    """
-    The torch.nn model to encode the prefix
-    Input shape: (batch-size, prefix-length)
-    Output shape: (batch-size, prefix-length, 2*layers*hidden)
-    """
-
-    def __init__(self, config):
-        super().__init__()
-        self.prefix_projection = config.prefix_projection
-        if self.prefix_projection:
-            # Use a two-layer MLP to encode the prefix
-            self.embedding = torch.nn.Embedding(config.pre_seq_len, config.hidden_size)
-            self.trans = torch.nn.Sequential(
-                torch.nn.Linear(config.hidden_size, config.hidden_size),
-                torch.nn.Tanh(),
-                torch.nn.Linear(config.hidden_size, config.num_layers * config.hidden_size * 2)
-            )
-        else:
-            self.embedding = torch.nn.Embedding(config.pre_seq_len, config.num_layers * config.hidden_size * 2)
-
-    def forward(self, prefix: torch.Tensor):
-        if self.prefix_projection:
-            prefix_tokens = self.embedding(prefix)
-            past_key_values = self.trans(prefix_tokens)
-        else:
-            past_key_values = self.embedding(prefix)
-        return past_key_values
-
-
-@torch.jit.script
-def gelu_impl(x):
-    """OpenAI's gelu implementation."""
-    return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x *
-                                       (1.0 + 0.044715 * x * x)))
-
-
-def gelu(x):
-    return gelu_impl(x)
-
-
-class RotaryEmbedding(torch.nn.Module):
-    def __init__(self, dim, base=10000, precision=torch.half, learnable=False):
-        super().__init__()
-        inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
-        inv_freq = inv_freq.half()
-        self.learnable = learnable
-        if learnable:
-            self.inv_freq = torch.nn.Parameter(inv_freq)
-            self.max_seq_len_cached = None
-        else:
-            self.register_buffer('inv_freq', inv_freq)
-            self.max_seq_len_cached = None
-            self.cos_cached = None
-            self.sin_cached = None
-        self.precision = precision
-
-    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
-                              error_msgs):
-        pass
-
-    def forward(self, x, seq_dim=1, seq_len=None):
-        if seq_len is None:
-            seq_len = x.shape[seq_dim]
-        if self.max_seq_len_cached is None or (seq_len > self.max_seq_len_cached):
-            self.max_seq_len_cached = None if self.learnable else seq_len
-            t = torch.arange(seq_len, device=x.device, dtype=self.inv_freq.dtype)
-            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).to(x.device)
-            if self.precision == torch.bfloat16:
-                emb = emb.float()
-
-            # [sx, 1 (b * np), hn]
-            cos_cached = emb.cos()[:, None, :]
-            sin_cached = emb.sin()[:, None, :]
-            if self.precision == torch.bfloat16:
-                cos_cached = cos_cached.bfloat16()
-                sin_cached = sin_cached.bfloat16()
-            if self.learnable:
-                return cos_cached, sin_cached
-            self.cos_cached, self.sin_cached = cos_cached, sin_cached
-        return self.cos_cached[:seq_len, ...], self.sin_cached[:seq_len, ...]
-
-    def _apply(self, fn):
-        if self.cos_cached is not None:
-            self.cos_cached = fn(self.cos_cached)
-        if self.sin_cached is not None:
-            self.sin_cached = fn(self.sin_cached)
-        return super()._apply(fn)
-
-
-def rotate_half(x):
-    x1, x2 = x[..., :x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
-    return torch.cat((-x2, x1), dim=x1.ndim - 1)  # dim=-1 triggers a bug in earlier torch versions
-
-
-@torch.jit.script
-def apply_rotary_pos_emb_index(q, k, cos, sin, position_id):
-    # position_id: [sq, b], q, k: [sq, b, np, hn], cos: [sq, 1, hn] -> [sq, b, 1, hn]
-    cos, sin = F.embedding(position_id, cos.squeeze(1)).unsqueeze(2), \
-        F.embedding(position_id, sin.squeeze(1)).unsqueeze(2)
-    q, k = (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
-    return q, k
-
-
-def attention_fn(
-        self,
-        query_layer,
-        key_layer,
-        value_layer,
-        attention_mask,
-        hidden_size_per_partition,
-        layer_id,
-        layer_past=None,
-        scaling_attention_score=True,
-        use_cache=False,
-):
-    if layer_past is not None:
-        past_key, past_value = layer_past[0], layer_past[1]
-        key_layer = torch.cat((past_key, key_layer), dim=0)
-        value_layer = torch.cat((past_value, value_layer), dim=0)
-
-    # seqlen, batch, num_attention_heads, hidden_size_per_attention_head
-    seq_len, b, nh, hidden_size = key_layer.shape
-
-    if use_cache:
-        present = (key_layer, value_layer)
-    else:
-        present = None
-
-    query_key_layer_scaling_coeff = float(layer_id + 1)
-    if scaling_attention_score:
-        query_layer = query_layer / (math.sqrt(hidden_size) * query_key_layer_scaling_coeff)
-
-    # ===================================
-    # Raw attention scores. [b, np, s, s]
-    # ===================================
-
-    # [b, np, sq, sk]
-    output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0))
-
-    # [sq, b, np, hn] -> [sq, b * np, hn]
-    query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1)
-    # [sk, b, np, hn] -> [sk, b * np, hn]
-    key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1)
-
-    matmul_result = torch.zeros(
-        1, 1, 1,
-        dtype=query_layer.dtype,
-        device=query_layer.device,
-    )
-
-    matmul_result = torch.baddbmm(
-        matmul_result,
-        query_layer.transpose(0, 1),  # [b * np, sq, hn]
-        key_layer.transpose(0, 1).transpose(1, 2),  # [b * np, hn, sk]
-        beta=0.0,
-        alpha=1.0,
-    )
-
-    # change view to [b, np, sq, sk]
-    attention_scores = matmul_result.view(*output_size)
-
-    if self.scale_mask_softmax:
-        self.scale_mask_softmax.scale = query_key_layer_scaling_coeff
-        attention_probs = self.scale_mask_softmax(attention_scores, attention_mask.contiguous())
-    else:
-        if not (attention_mask == 0).all():
-            # if auto-regressive, skip
-            attention_scores.masked_fill_(attention_mask, -10000.0)
-        dtype = attention_scores.dtype
-        attention_scores = attention_scores.float()
-        attention_scores = attention_scores * query_key_layer_scaling_coeff
-
-        attention_probs = F.softmax(attention_scores, dim=-1)
-
-        attention_probs = attention_probs.type(dtype)
-
-    # =========================
-    # Context layer. [sq, b, hp]
-    # =========================
-
-    # value_layer -> context layer.
-    # [sk, b, np, hn] --> [b, np, sq, hn]
-
-    # context layer shape: [b, np, sq, hn]
-    output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3))
-
-    # change view [sk, b * np, hn]
-    value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1)
-
-    # change view [b * np, sq, sk]
-    attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
-
-    # matmul: [b * np, sq, hn]
-    context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1))
-
-    # change view [b, np, sq, hn]
-    context_layer = context_layer.view(*output_size)
-
-    # [b, np, sq, hn] --> [sq, b, np, hn]
-    context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
-
-    # [sq, b, np, hn] --> [sq, b, hp]
-    new_context_layer_shape = context_layer.size()[:-2] + (hidden_size_per_partition,)
-    context_layer = context_layer.view(*new_context_layer_shape)
-
-    outputs = (context_layer, present, attention_probs)
-
-    return outputs
-
-
-def default_init(cls, *args, **kwargs):
-    return cls(*args, **kwargs)
-
-
-class SelfAttention(torch.nn.Module):
-    def __init__(self, hidden_size, num_attention_heads,
-                 layer_id, hidden_size_per_attention_head=None, bias=True,
-                 params_dtype=torch.float, position_encoding_2d=True, empty_init=True):
-        if empty_init:
-            init_method = skip_init
-        else:
-            init_method = default_init
-        super(SelfAttention, self).__init__()
-
-        self.layer_id = layer_id
-        self.hidden_size = hidden_size
-        self.hidden_size_per_partition = hidden_size
-        self.num_attention_heads = num_attention_heads
-        self.num_attention_heads_per_partition = num_attention_heads
-        self.position_encoding_2d = position_encoding_2d
-        self.rotary_emb = RotaryEmbedding(
-            self.hidden_size // (self.num_attention_heads * 2)
-            if position_encoding_2d
-            else self.hidden_size // self.num_attention_heads,
-            base=10000,
-            precision=torch.half,
-            learnable=False,
-        )
-
-        self.scale_mask_softmax = None
-
-        if hidden_size_per_attention_head is None:
-            self.hidden_size_per_attention_head = hidden_size // num_attention_heads
-        else:
-            self.hidden_size_per_attention_head = hidden_size_per_attention_head
-
-        self.inner_hidden_size = num_attention_heads * self.hidden_size_per_attention_head
-
-        # Strided linear layer.
-        self.query_key_value = init_method(
-            torch.nn.Linear,
-            hidden_size,
-            3 * self.inner_hidden_size,
-            bias=bias,
-            dtype=params_dtype,
-        )
-
-        self.dense = init_method(
-            torch.nn.Linear,
-            self.inner_hidden_size,
-            hidden_size,
-            bias=bias,
-            dtype=params_dtype,
-        )
-
-    @staticmethod
-    def attention_mask_func(attention_scores, attention_mask):
-        attention_scores.masked_fill_(attention_mask, -10000.0)
-        return attention_scores
-
-    def split_tensor_along_last_dim(self, tensor, num_partitions,
-                                    contiguous_split_chunks=False):
-        """Split a tensor along its last dimension.
-        Arguments:
-            tensor: input tensor.
-            num_partitions: number of partitions to split the tensor
-            contiguous_split_chunks: If True, make each chunk contiguous
-                                    in memory.
-        """
-        # Get the size and dimension.
-        last_dim = tensor.dim() - 1
-        last_dim_size = tensor.size()[last_dim] // num_partitions
-        # Split.
-        tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
-        # Note: torch.split does not create contiguous tensors by default.
-        if contiguous_split_chunks:
-            return tuple(chunk.contiguous() for chunk in tensor_list)
-
-        return tensor_list
-
-    def forward(
-            self,
-            hidden_states: torch.Tensor,
-            position_ids,
-            attention_mask: torch.Tensor,
-            layer_id,
-            layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-            use_cache: bool = False,
-            output_attentions: bool = False,
-    ):
-        """
-        hidden_states: [seq_len, batch, hidden_size]
-        attention_mask: [(1, 1), seq_len, seq_len]
-        """
-
-        # [seq_len, batch, 3 * hidden_size]
-        mixed_raw_layer = self.query_key_value(hidden_states)
-
-        # [seq_len, batch, 3 * hidden_size] --> [seq_len, batch, num_attention_heads, 3 * hidden_size_per_attention_head]
-        new_tensor_shape = mixed_raw_layer.size()[:-1] + (
-            self.num_attention_heads_per_partition,
-            3 * self.hidden_size_per_attention_head,
-        )
-        mixed_raw_layer = mixed_raw_layer.view(*new_tensor_shape)
-
-        # [seq_len, batch, num_attention_heads, hidden_size_per_attention_head]
-        (query_layer, key_layer, value_layer) = self.split_tensor_along_last_dim(mixed_raw_layer, 3)
-
-        if self.position_encoding_2d:
-            q1, q2 = query_layer.chunk(2, dim=(query_layer.ndim - 1))
-            k1, k2 = key_layer.chunk(2, dim=(key_layer.ndim - 1))
-            cos, sin = self.rotary_emb(q1, seq_len=position_ids.max() + 1)
-            position_ids, block_position_ids = position_ids[:, 0, :].transpose(0, 1).contiguous(), \
-                position_ids[:, 1, :].transpose(0, 1).contiguous()
-            q1, k1 = apply_rotary_pos_emb_index(q1, k1, cos, sin, position_ids)
-            q2, k2 = apply_rotary_pos_emb_index(q2, k2, cos, sin, block_position_ids)
-            query_layer = torch.concat([q1, q2], dim=(q1.ndim - 1))
-            key_layer = torch.concat([k1, k2], dim=(k1.ndim - 1))
-        else:
-            position_ids = position_ids.transpose(0, 1)
-            cos, sin = self.rotary_emb(value_layer, seq_len=position_ids.max() + 1)
-            # [seq_len, batch, num_attention_heads, hidden_size_per_attention_head]
-            query_layer, key_layer = apply_rotary_pos_emb_index(query_layer, key_layer, cos, sin, position_ids)
-
-        # [seq_len, batch, hidden_size]
-        context_layer, present, attention_probs = attention_fn(
-            self=self,
-            query_layer=query_layer,
-            key_layer=key_layer,
-            value_layer=value_layer,
-            attention_mask=attention_mask,
-            hidden_size_per_partition=self.hidden_size_per_partition,
-            layer_id=layer_id,
-            layer_past=layer_past,
-            use_cache=use_cache
-        )
-
-        output = self.dense(context_layer)
-
-        outputs = (output, present)
-
-        if output_attentions:
-            outputs += (attention_probs,)
-
-        return outputs  # output, present, attention_probs
-
-
-class GEGLU(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.activation_fn = F.gelu
-
-    def forward(self, x):
-        # dim=-1 breaks in jit for pt<1.10
-        x1, x2 = x.chunk(2, dim=(x.ndim - 1))
-        return x1 * self.activation_fn(x2)
-
-
-class GLU(torch.nn.Module):
-    def __init__(self, hidden_size, inner_hidden_size=None,
-                 layer_id=None, bias=True, activation_func=gelu, params_dtype=torch.float, empty_init=True):
-        super(GLU, self).__init__()
-        if empty_init:
-            init_method = skip_init
-        else:
-            init_method = default_init
-        self.layer_id = layer_id
-        self.activation_func = activation_func
-
-        # Project to 4h.
-        self.hidden_size = hidden_size
-        if inner_hidden_size is None:
-            inner_hidden_size = 4 * hidden_size
-        self.inner_hidden_size = inner_hidden_size
-        self.dense_h_to_4h = init_method(
-            torch.nn.Linear,
-            self.hidden_size,
-            self.inner_hidden_size,
-            bias=bias,
-            dtype=params_dtype,
-        )
-        # Project back to h.
-        self.dense_4h_to_h = init_method(
-            torch.nn.Linear,
-            self.inner_hidden_size,
-            self.hidden_size,
-            bias=bias,
-            dtype=params_dtype,
-        )
-
-    def forward(self, hidden_states):
-        """
-        hidden_states: [seq_len, batch, hidden_size]
-        """
-
-        # [seq_len, batch, inner_hidden_size]
-        intermediate_parallel = self.dense_h_to_4h(hidden_states)
-
-        intermediate_parallel = self.activation_func(intermediate_parallel)
-
-        output = self.dense_4h_to_h(intermediate_parallel)
-
-        return output
-
-
-class GLMBlock(torch.nn.Module):
-    def __init__(
-            self,
-            hidden_size,
-            num_attention_heads,
-            layernorm_epsilon,
-            layer_id,
-            inner_hidden_size=None,
-            hidden_size_per_attention_head=None,
-            layernorm=LayerNorm,
-            use_bias=True,
-            params_dtype=torch.float,
-            num_layers=28,
-            position_encoding_2d=True,
-            empty_init=True
-    ):
-        super(GLMBlock, self).__init__()
-        # Set output layer initialization if not provided.
-
-        self.layer_id = layer_id
-
-        # Layernorm on the input data.
-        self.input_layernorm = layernorm(hidden_size, eps=layernorm_epsilon)
-
-        self.position_encoding_2d = position_encoding_2d
-
-        # Self attention.
-        self.attention = SelfAttention(
-            hidden_size,
-            num_attention_heads,
-            layer_id,
-            hidden_size_per_attention_head=hidden_size_per_attention_head,
-            bias=use_bias,
-            params_dtype=params_dtype,
-            position_encoding_2d=self.position_encoding_2d,
-            empty_init=empty_init
-        )
-
-        # Layernorm on the input data.
-        self.post_attention_layernorm = layernorm(hidden_size, eps=layernorm_epsilon)
-
-        self.num_layers = num_layers
-
-        # GLU
-        self.mlp = GLU(
-            hidden_size,
-            inner_hidden_size=inner_hidden_size,
-            bias=use_bias,
-            layer_id=layer_id,
-            params_dtype=params_dtype,
-            empty_init=empty_init
-        )
-
-    def forward(
-            self,
-            hidden_states: torch.Tensor,
-            position_ids,
-            attention_mask: torch.Tensor,
-            layer_id,
-            layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-            use_cache: bool = False,
-            output_attentions: bool = False,
-    ):
-        """
-        hidden_states: [seq_len, batch, hidden_size]
-        attention_mask: [(1, 1), seq_len, seq_len]
-        """
-
-        # Layer norm at the begining of the transformer layer.
-        # [seq_len, batch, hidden_size]
-        attention_input = self.input_layernorm(hidden_states)
-
-        # Self attention.
-        attention_outputs = self.attention(
-            attention_input,
-            position_ids,
-            attention_mask=attention_mask,
-            layer_id=layer_id,
-            layer_past=layer_past,
-            use_cache=use_cache,
-            output_attentions=output_attentions
-        )
-
-        attention_output = attention_outputs[0]
-
-        outputs = attention_outputs[1:]
-
-        # Residual connection.
-        alpha = (2 * self.num_layers) ** 0.5
-        hidden_states = attention_input * alpha + attention_output
-
-        mlp_input = self.post_attention_layernorm(hidden_states)
-
-        # MLP.
-        mlp_output = self.mlp(mlp_input)
-
-        # Second residual connection.
-        output = mlp_input * alpha + mlp_output
-
-        if use_cache:
-            outputs = (output,) + outputs
-        else:
-            outputs = (output,) + outputs[1:]
-
-        return outputs  # hidden_states, present, attentions
-
-
-class ChatGLMPreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and
-    a simple interface for downloading and loading pretrained models.
-    """
-
-    is_parallelizable = False
-    supports_gradient_checkpointing = True
-    config_class = ChatGLMConfig
-    base_model_prefix = "transformer"
-    _no_split_modules = ["GLMBlock"]
-
-    def __init__(self, *inputs, **kwargs):
-        super().__init__(*inputs, **kwargs)
-
-    def _init_weights(self, module: nn.Module):
-        """Initialize the weights."""
-        return
-
-    def get_masks(self, input_ids, device):
-        batch_size, seq_length = input_ids.shape
-        context_lengths = [seq.tolist().index(self.config.bos_token_id) for seq in input_ids]
-        attention_mask = torch.ones((batch_size, seq_length, seq_length), device=device)
-        attention_mask.tril_()
-        for i, context_length in enumerate(context_lengths):
-            attention_mask[i, :, :context_length] = 1
-        attention_mask.unsqueeze_(1)
-        attention_mask = (attention_mask < 0.5).bool()
-
-        return attention_mask
-
-    def get_position_ids(self, input_ids, mask_positions, device, use_gmasks=None):
-        batch_size, seq_length = input_ids.shape
-        if use_gmasks is None:
-            use_gmasks = [False] * batch_size
-        context_lengths = [seq.tolist().index(self.config.bos_token_id) for seq in input_ids]
-        if self.position_encoding_2d:
-            position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1)
-            for i, context_length in enumerate(context_lengths):
-                position_ids[i, context_length:] = mask_positions[i]
-            block_position_ids = [torch.cat((
-                torch.zeros(context_length, dtype=torch.long, device=device),
-                torch.arange(seq_length - context_length, dtype=torch.long, device=device) + 1
-            )) for context_length in context_lengths]
-            block_position_ids = torch.stack(block_position_ids, dim=0)
-            position_ids = torch.stack((position_ids, block_position_ids), dim=1)
-        else:
-            position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1)
-            for i, context_length in enumerate(context_lengths):
-                if not use_gmasks[i]:
-                    position_ids[context_length:] = mask_positions[i]
-
-        return position_ids
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, ChatGLMModel):
-            module.gradient_checkpointing = value
-
-
-CHATGLM_6B_START_DOCSTRING = r"""
-    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
-    usage and behavior.
-
-    Parameters:
-        config ([`~ChatGLM6BConfig`]): Model configuration class with all the parameters of the model.
-            Initializing with a config file does not load the weights associated with the model, only the configuration.
-            Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-CHATGLM_6B_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `({0})`):
-            Indices of input sequence tokens in the vocabulary.
-
-            Indices can be obtained using [`ChatGLM6BTokenizer`].
-            See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
-            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
-
-            - 0 corresponds to a *sentence A* token,
-            - 1 corresponds to a *sentence B* token.
-
-            [What are token type IDs?](../glossary#token-type-ids)
-        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings.
-            Selected in the range `[0, config.max_position_embeddings - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
-            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-
-        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
-            This is useful if you want more control over how to convert *input_ids* indices into associated vectors
-            than the model's internal embedding lookup matrix.
-        output_attentions (`bool`, *optional*):
-            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
-            tensors for more detail.
-        output_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-@add_start_docstrings(
-    "The bare ChatGLM-6B Model transformer outputting raw hidden-states without any specific head on top.",
-    CHATGLM_6B_START_DOCSTRING,
-)
-class ChatGLMModel(ChatGLMPreTrainedModel):
-    """
-
-    The model can behave as an encoder (with only self-attention) as well
-    as a decoder, in which case a layer of cross-attention is added between
-    the self-attention layers, following the architecture described in [Attention is
-    all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani,
-    Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
-
-    To behave as an decoder the model needs to be initialized with the
-    `is_decoder` argument of the configuration set to `True`.
-    To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder`
-    argument and `add_cross_attention` set to `True`; an
-    `encoder_hidden_states` is then expected as an input to the forward pass.
-    """
-
-    def __init__(self, config: ChatGLMConfig, empty_init=True):
-        super().__init__(config)
-        if empty_init:
-            init_method = skip_init
-        else:
-            init_method = default_init
-        # recording parameters
-        self.max_sequence_length = config.max_sequence_length
-        self.hidden_size = config.hidden_size
-        self.params_dtype = torch.half
-        self.num_attention_heads = config.num_attention_heads
-        self.vocab_size = config.vocab_size
-        self.num_layers = config.num_layers
-        self.layernorm_epsilon = config.layernorm_epsilon
-        self.inner_hidden_size = config.inner_hidden_size
-        self.hidden_size_per_attention_head = self.hidden_size // self.num_attention_heads
-        self.position_encoding_2d = config.position_encoding_2d
-        self.pre_seq_len = config.pre_seq_len
-        self.prefix_projection = config.prefix_projection
-
-        self.word_embeddings = init_method(
-            torch.nn.Embedding,
-            num_embeddings=self.vocab_size, embedding_dim=self.hidden_size,
-            dtype=self.params_dtype
-        )
-        self.gradient_checkpointing = False
-
-        def get_layer(layer_id):
-            return GLMBlock(
-                self.hidden_size,
-                self.num_attention_heads,
-                self.layernorm_epsilon,
-                layer_id,
-                inner_hidden_size=self.inner_hidden_size,
-                hidden_size_per_attention_head=self.hidden_size_per_attention_head,
-                layernorm=LayerNorm,
-                use_bias=True,
-                params_dtype=self.params_dtype,
-                position_encoding_2d=self.position_encoding_2d,
-                empty_init=empty_init
-            )
-
-        self.layers = torch.nn.ModuleList(
-            [get_layer(layer_id) for layer_id in range(self.num_layers)]
-        )
-
-        # Final layer norm before output.
-        self.final_layernorm = LayerNorm(self.hidden_size, eps=self.layernorm_epsilon)
-
-        if self.pre_seq_len is not None:
-            for param in self.parameters():
-                param.requires_grad = False
-            self.prefix_tokens = torch.arange(self.pre_seq_len).long()
-            self.prefix_encoder = PrefixEncoder(config)
-            self.dropout = torch.nn.Dropout(0.1)
-
-            # total_params = sum(p.numel() for p in self.parameters())
-            # trainable_params = sum(p.numel() for p in self.parameters() if p.requires_grad)
-            # print("Using p-tuning v2: # trainable_params = {} / {}".format(trainable_params, total_params))
-
-    def get_input_embeddings(self):
-        return self.word_embeddings
-
-    def set_input_embeddings(self, new_embeddings: torch.Tensor):
-        self.word_embeddings = new_embeddings
-
-    def get_prompt(self, batch_size, device, dtype=torch.half):
-        prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device)
-        past_key_values = self.prefix_encoder(prefix_tokens).type(dtype)
-        past_key_values = past_key_values.view(
-            batch_size,
-            self.pre_seq_len,
-            self.num_layers * 2,
-            self.num_attention_heads,
-            self.hidden_size // self.num_attention_heads
-        )
-        # seq_len, b, nh, hidden_size
-        past_key_values = self.dropout(past_key_values)
-        past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2)
-        # past_key_values = [(v[0], v[1]) for v in past_key_values]
-        return past_key_values
-
-    @add_start_docstrings_to_model_forward(CHATGLM_6B_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        checkpoint=_CHECKPOINT_FOR_DOC,
-        output_type=BaseModelOutputWithPastAndCrossAttentions,
-        config_class=_CONFIG_FOR_DOC,
-    )
-    def forward(
-            self,
-            input_ids: Optional[torch.LongTensor] = None,
-            position_ids: Optional[torch.LongTensor] = None,
-            attention_mask: Optional[torch.Tensor] = None,
-            past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
-            inputs_embeds: Optional[torch.LongTensor] = None,
-            use_cache: Optional[bool] = None,
-            output_attentions: Optional[bool] = None,
-            output_hidden_states: Optional[bool] = None,
-            return_dict: Optional[bool] = None,
-    ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPast]:
-
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if self.gradient_checkpointing and self.training:
-            if use_cache:
-                logger.warning_once(
-                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
-                )
-                use_cache = False
-
-        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:
-            batch_size, seq_length = input_ids.shape[:2]
-        elif inputs_embeds is not None:
-            batch_size, seq_length = inputs_embeds.shape[:2]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        if inputs_embeds is None:
-            inputs_embeds = self.word_embeddings(input_ids)
-
-        if past_key_values is None:
-            if self.pre_seq_len is not None:
-                past_key_values = self.get_prompt(batch_size=input_ids.shape[0], device=input_ids.device,
-                                                  dtype=inputs_embeds.dtype)
-            else:
-                past_key_values = tuple([None] * len(self.layers))
-
-            if attention_mask is None:
-                attention_mask = self.get_masks(
-                    input_ids,
-                    device=input_ids.device
-                )
-
-
-            if position_ids is None:
-                MASK, gMASK = self.config.mask_token_id, self.config.gmask_token_id
-                seqs = input_ids.tolist()
-
-                mask_positions, use_gmasks = [], []
-                for seq in seqs:
-                    mask_token = gMASK if gMASK in seq else MASK
-                    use_gmask = mask_token == gMASK
-                    mask_positions.append(seq.index(mask_token))
-                    use_gmasks.append(use_gmask)
-
-                position_ids = self.get_position_ids(
-                    input_ids,
-                    mask_positions=mask_positions,
-                    device=input_ids.device,
-                    use_gmasks=use_gmasks
-                )
-
-        if self.pre_seq_len is not None and attention_mask is not None:
-            prefix_attention_mask = torch.ones(batch_size, 1, input_ids.size(-1), self.pre_seq_len).to(
-                attention_mask.device)
-            prefix_attention_mask = (prefix_attention_mask < 0.5).bool()
-            attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=3)
-
-        # [seq_len, batch, hidden_size]
-        hidden_states = inputs_embeds.transpose(0, 1)
-
-        presents = () if use_cache else None
-        all_self_attentions = () if output_attentions else None
-        all_hidden_states = () if output_hidden_states else None
-
-        if attention_mask is None:
-            attention_mask = torch.zeros(1, 1, device=input_ids.device).bool()
-        else:
-            attention_mask = attention_mask.to(hidden_states.device)
-
-        for i, layer in enumerate(self.layers):
-
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-            layer_past = past_key_values[i]
-
-            if self.gradient_checkpointing and self.training:
-                layer_ret = torch.utils.checkpoint.checkpoint(
-                    layer,
-                    hidden_states,
-                    position_ids,
-                    attention_mask,
-                    torch.tensor(i),
-                    layer_past,
-                    use_cache,
-                    output_attentions
-                )
-            else:
-                layer_ret = layer(
-                    hidden_states,
-                    position_ids=position_ids,
-                    attention_mask=attention_mask,
-                    layer_id=torch.tensor(i),
-                    layer_past=layer_past,
-                    use_cache=use_cache,
-                    output_attentions=output_attentions
-                )
-
-            hidden_states = layer_ret[0]
-
-            if use_cache:
-                presents = presents + (layer_ret[1],)
-
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (layer_ret[2 if use_cache else 1],)
-
-        # Final layer norm.
-        hidden_states = self.final_layernorm(hidden_states)
-
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
-
-        return BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=presents,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attentions,
-        )
-
-
-class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
-    def __init__(self, config: ChatGLMConfig, empty_init=True):
-        super().__init__(config)
-        if empty_init:
-            init_method = skip_init
-        else:
-            init_method = default_init
-
-        # self.hidden_size = config.hidden_size
-        # self.params_dtype = torch.half
-        # self.vocab_size = config.vocab_size
-        self.max_sequence_length = config.max_sequence_length
-
-        self.position_encoding_2d = config.position_encoding_2d
-
-        self.transformer = ChatGLMModel(config, empty_init=empty_init)
-
-        self.lm_head = init_method(
-            nn.Linear,
-            config.hidden_size,
-            config.vocab_size,
-            bias=False,
-            dtype=torch.half
-        )
-
-        self.config = config
-
-        self.quantized = False
-
-        if self.config.quantization_bit:
-            self.quantize(self.config.quantization_bit, empty_init=True)
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def _update_model_kwargs_for_generation(
-        self,
-        outputs: ModelOutput,
-        model_kwargs: Dict[str, Any],
-        is_encoder_decoder: bool = False,
-        standardize_cache_format: bool = False,
-    ) -> Dict[str, Any]:
-        # update past_key_values
-        model_kwargs["past_key_values"] = self._extract_past_from_model_output(
-            outputs, standardize_cache_format=standardize_cache_format
-        )
-
-        # update attention mask
-        if "attention_mask" in model_kwargs:
-            attention_mask = model_kwargs["attention_mask"]
-            if attention_mask is not None and attention_mask.dtype == torch.bool:
-                attention_mask = torch.cat(
-                    [attention_mask, attention_mask.new_ones((*attention_mask.shape[:3], 1))], dim=3)
-                new_attention_mask = attention_mask[:, :, -1:].clone()
-                new_attention_mask[..., -1] = False
-                model_kwargs["attention_mask"] = torch.cat(
-                    [attention_mask, new_attention_mask], dim=2
-                )
-
-        # update position ids
-        if "position_ids" in model_kwargs:
-            position_ids = model_kwargs["position_ids"]
-            new_position_id = position_ids[..., -1:].clone()
-            new_position_id[:, 1, :] += 1
-            model_kwargs["position_ids"] = torch.cat(
-                [position_ids, new_position_id], dim=-1
-            )
-
-        return model_kwargs
-
-    def prepare_inputs_for_generation(
-            self,
-            input_ids: torch.LongTensor,
-            past: Optional[torch.Tensor] = None,
-            past_key_values: Optional[torch.Tensor] = None,
-            attention_mask: Optional[torch.Tensor] = None,
-            position_ids: Optional[torch.Tensor] = None,
-            **kwargs
-    ) -> dict:
-        batch_size, seq_length = input_ids.shape
-        MASK, gMASK = self.config.mask_token_id, self.config.gmask_token_id
-        seqs = input_ids.tolist()
-        mask_positions, use_gmasks = [], []
-        for seq in seqs:
-            mask_token = gMASK if gMASK in seq else MASK
-            use_gmask = mask_token == gMASK
-            mask_positions.append(seq.index(mask_token))
-            use_gmasks.append(use_gmask)
-
-        # only last token for input_ids if past is not None
-        if past is not None or past_key_values is not None:
-            last_token = input_ids[:, -1].unsqueeze(-1)
-            if attention_mask is not None and attention_mask.dtype == torch.bool:
-                attention_mask = attention_mask[:, :, -1:]
-            else:
-                attention_mask = None
-            if position_ids is not None:
-                position_ids = position_ids[..., -1:]
-            else:
-                context_lengths = [seq.index(self.config.bos_token_id) for seq in seqs]
-                if self.position_encoding_2d:
-                    position_ids = torch.tensor(
-                        [[mask_position, seq_length - context_length] for mask_position, context_length in
-                         zip(mask_positions, context_lengths)], dtype=torch.long, device=input_ids.device).unsqueeze(-1)
-                else:
-                    position_ids = torch.tensor([mask_position for mask_position in mask_positions], dtype=torch.long,
-                                                device=input_ids.device).unsqueeze(-1)
-
-            if past is None:
-                past = past_key_values
-            return {
-                "input_ids": last_token,
-                "past_key_values": past,
-                "position_ids": position_ids,
-                "attention_mask": attention_mask
-            }
-        else:
-            if attention_mask is not None and attention_mask.dtype != torch.bool:
-                logger.warning_once(f"The dtype of attention mask ({attention_mask.dtype}) is not bool")
-                attention_mask = None
-            if attention_mask is None:
-                attention_mask = self.get_masks(
-                    input_ids,
-                    device=input_ids.device
-                )
-            if position_ids is None:
-                position_ids = self.get_position_ids(
-                    input_ids,
-                    device=input_ids.device,
-                    mask_positions=mask_positions,
-                    use_gmasks=use_gmasks
-                )
-
-            return {
-                "input_ids": input_ids,
-                "past_key_values": past,
-                "position_ids": position_ids,
-                "attention_mask": attention_mask
-            }
-
-    def forward(
-            self,
-            input_ids: Optional[torch.Tensor] = None,
-            position_ids: Optional[torch.Tensor] = None,
-            attention_mask: Optional[torch.Tensor] = None,
-            past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
-            inputs_embeds: Optional[torch.Tensor] = None,
-            labels: Optional[torch.Tensor] = None,
-            use_cache: Optional[bool] = None,
-            output_attentions: Optional[bool] = None,
-            output_hidden_states: Optional[bool] = None,
-            return_dict: Optional[bool] = None,
-    ):
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        transformer_outputs = self.transformer(
-            input_ids=input_ids,
-            position_ids=position_ids,
-            attention_mask=attention_mask,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        hidden_states = transformer_outputs[0]
-
-        lm_logits = self.lm_head(hidden_states).permute(1, 0, 2).contiguous()
-
-        loss = None
-        if labels is not None:
-            lm_logits = lm_logits.to(torch.float32)
-
-            # Shift so that tokens < n predict n
-            shift_logits = lm_logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss(ignore_index=-100)
-            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
-
-            lm_logits = lm_logits.to(hidden_states.dtype)
-            loss = loss.to(hidden_states.dtype)
-
-        if not return_dict:
-            output = (lm_logits,) + transformer_outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=lm_logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )
-
-    @staticmethod
-    def _reorder_cache(
-            past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
-    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
-        """
-        This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
-        [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
-        beam_idx at every generation step.
-
-        Output shares the same memory storage as `past`.
-        """
-        return tuple(
-            (
-                layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)),
-                layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)),
-            )
-            for layer_past in past
-        )
-
-    def process_response(self, response):
-        response = response.strip()
-        response = response.replace("[[训练时间]]", "2023年")
-        punkts = [
-            [",", "，"],
-            ["!", "！"],
-            [":", "："],
-            [";", "；"],
-            ["\?", "？"],
-        ]
-        for item in punkts:
-            response = re.sub(r"([\u4e00-\u9fff])%s" % item[0], r"\1%s" % item[1], response)
-            response = re.sub(r"%s([\u4e00-\u9fff])" % item[0], r"%s\1" % item[1], response)
-        return response
-
-    @torch.no_grad()
-    def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 2048, num_beams=1,
-             do_sample=True, top_p=0.7, temperature=0.95, logits_processor=None, **kwargs):
-        if history is None:
-            history = []
-        if logits_processor is None:
-            logits_processor = LogitsProcessorList()
-        logits_processor.append(InvalidScoreLogitsProcessor())
-        gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
-                      "temperature": temperature, "logits_processor": logits_processor, **kwargs}
-        if not history:
-            prompt = query
-        else:
-            prompt = ""
-            for i, (old_query, response) in enumerate(history):
-                prompt += "[Round {}]\n问：{}\n答：{}\n".format(i, old_query, response)
-            prompt += "[Round {}]\n问：{}\n答：".format(len(history), query)
-        inputs = tokenizer([prompt], return_tensors="pt")
-        inputs = inputs.to(self.device)
-        outputs = self.generate(**inputs, **gen_kwargs)
-        outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):]
-        response = tokenizer.decode(outputs)
-        response = self.process_response(response)
-        history = history + [(query, response)]
-        return response, history
-
-    @torch.no_grad()
-    def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 2048,
-                    do_sample=True, top_p=0.7, temperature=0.95, logits_processor=None, **kwargs):
-        if history is None:
-            history = []
-        if logits_processor is None:
-            logits_processor = LogitsProcessorList()
-        logits_processor.append(InvalidScoreLogitsProcessor())
-        gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p,
-                      "temperature": temperature, "logits_processor": logits_processor, **kwargs}
-        if not history:
-            prompt = query
-        else:
-            prompt = ""
-            for i, (old_query, response) in enumerate(history):
-                prompt += "[Round {}]\n问：{}\n答：{}\n".format(i, old_query, response)
-            prompt += "[Round {}]\n问：{}\n答：".format(len(history), query)
-        inputs = tokenizer([prompt], return_tensors="pt")
-        inputs = inputs.to(self.device)
-        for outputs in self.stream_generate(**inputs, **gen_kwargs):
-            outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):]
-            response = tokenizer.decode(outputs)
-            response = self.process_response(response)
-            new_history = history + [(query, response)]
-            yield response, new_history
-
-    @torch.no_grad()
-    def stream_generate(
-            self,
-            input_ids,
-            generation_config: Optional[GenerationConfig] = None,
-            logits_processor: Optional[LogitsProcessorList] = None,
-            stopping_criteria: Optional[StoppingCriteriaList] = None,
-            prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
-            **kwargs,
-    ):
-        batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
-
-        if generation_config is None:
-            generation_config = self.generation_config
-        generation_config = copy.deepcopy(generation_config)
-        model_kwargs = generation_config.update(**kwargs)
-        bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id
-
-        if isinstance(eos_token_id, int):
-            eos_token_id = [eos_token_id]
-
-        has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
-        if has_default_max_length and generation_config.max_new_tokens is None:
-            warnings.warn(
-                f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
-                "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
-                " recommend using `max_new_tokens` to control the maximum length of the generation.",
-                UserWarning,
-            )
-        elif generation_config.max_new_tokens is not None:
-            generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
-            if not has_default_max_length:
-                logger.warn(
-                    f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
-                    f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
-                    "Please refer to the documentation for more information. "
-                    "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
-                    UserWarning,
-                )
-
-        if input_ids_seq_length >= generation_config.max_length:
-            input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
-            logger.warning(
-                f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
-                f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
-                " increasing `max_new_tokens`."
-            )
-
-        # 2. Set generation parameters if not already defined
-        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
-        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
-
-        logits_processor = self._get_logits_processor(
-            generation_config=generation_config,
-            input_ids_seq_length=input_ids_seq_length,
-            encoder_input_ids=input_ids,
-            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
-            logits_processor=logits_processor,
-        )
-
-        stopping_criteria = self._get_stopping_criteria(
-            generation_config=generation_config, stopping_criteria=stopping_criteria
-        )
-        logits_warper = self._get_logits_warper(generation_config)
-
-        unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
-        scores = None
-        while True:
-            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
-            # forward pass to get next token
-            outputs = self(
-                **model_inputs,
-                return_dict=True,
-                output_attentions=False,
-                output_hidden_states=False,
-            )
-
-            next_token_logits = outputs.logits[:, -1, :]
-
-            # pre-process distribution
-            next_token_scores = logits_processor(input_ids, next_token_logits)
-            next_token_scores = logits_warper(input_ids, next_token_scores)
-
-            # sample
-            probs = nn.functional.softmax(next_token_scores, dim=-1)
-            if generation_config.do_sample:
-                next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
-            else:
-                next_tokens = torch.argmax(probs, dim=-1)
-
-            # update generated ids, model inputs, and length for next step
-            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
-            model_kwargs = self._update_model_kwargs_for_generation(
-                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
-            )
-            unfinished_sequences = unfinished_sequences.mul((sum(next_tokens != i for i in eos_token_id)).long())
-
-            # stop when each sentence is finished, or if we exceed the maximum length
-            if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
-                break
-            yield input_ids
-
-    def quantize(self, bits: int, empty_init=False, **kwargs):
-        if bits == 0:
-            return
-
-        from .quantization import quantize
-
-        if self.quantized:
-            logger.info("Already quantized.")
-            return self
-
-        self.quantized = True
-
-        self.config.quantization_bit = bits
-
-        self.transformer = quantize(self.transformer, bits, empty_init=empty_init, **kwargs)
-        return self

pytorch_model-00001-of-00002.bin

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pytorch_model.bin.index.json

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-    "transformer.layers.5.attention.dense.weight": "pytorch_model-00001-of-00002.bin",
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-    "transformer.layers.6.attention.dense.weight": "pytorch_model-00001-of-00002.bin",
-    "transformer.layers.6.attention.query_key_value.bias": "pytorch_model-00001-of-00002.bin",
-    "transformer.layers.6.attention.query_key_value.weight": "pytorch_model-00001-of-00002.bin",
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-    "transformer.layers.6.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
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-    "transformer.layers.9.attention.dense.weight": "pytorch_model-00001-of-00002.bin",
-    "transformer.layers.9.attention.query_key_value.bias": "pytorch_model-00001-of-00002.bin",
-    "transformer.layers.9.attention.query_key_value.weight": "pytorch_model-00001-of-00002.bin",
-    "transformer.layers.9.attention.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
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-    "transformer.layers.9.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
-    "transformer.layers.9.mlp.dense_4h_to_h.bias": "pytorch_model-00001-of-00002.bin",
-    "transformer.layers.9.mlp.dense_4h_to_h.weight": "pytorch_model-00001-of-00002.bin",
-    "transformer.layers.9.mlp.dense_h_to_4h.bias": "pytorch_model-00001-of-00002.bin",
-    "transformer.layers.9.mlp.dense_h_to_4h.weight": "pytorch_model-00001-of-00002.bin",
-    "transformer.layers.9.post_attention_layernorm.bias": "pytorch_model-00001-of-00002.bin",
-    "transformer.layers.9.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
-    "transformer.word_embeddings.weight": "pytorch_model-00001-of-00002.bin"
-  }
-}

quantization.py

@@ -1,201 +0,0 @@
-from torch.nn import Linear
-from torch.nn.parameter import Parameter
-
-import bz2
-import torch
-import base64
-import ctypes
-from transformers.utils import logging
-
-from typing import List
-from functools import partial
-
-logger = logging.get_logger(__name__)
-
-try:
-    from cpm_kernels.kernels.base import LazyKernelCModule, KernelFunction, round_up
-
-    class Kernel:
-        def __init__(self, code: bytes, function_names: List[str]):
-            self.code = code
-            self._function_names = function_names
-            self._cmodule = LazyKernelCModule(self.code)
-
-            for name in self._function_names:
-                setattr(self, name, KernelFunction(self._cmodule, name))
-
-    quantization_code = "$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"
-
-    kernels = Kernel(
-        bz2.decompress(base64.b64decode(quantization_code)),
-        [
-            "int4WeightCompression",
-            "int4WeightExtractionFloat",
-            "int4WeightExtractionHalf",
-            "int8WeightExtractionFloat",
-            "int8WeightExtractionHalf",
-        ],
-    )
-except Exception as exception:
-    kernels = None
-    logger.warning("Failed to load cpm_kernels:" + str(exception))
-
-
-class W8A16Linear(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, inp: torch.Tensor, quant_w: torch.Tensor, scale_w: torch.Tensor, weight_bit_width):
-        ctx.inp_shape = inp.size()
-        ctx.weight_bit_width = weight_bit_width
-        out_features = quant_w.size(0)
-        inp = inp.contiguous().view(-1, inp.size(-1))
-        weight = extract_weight_to_half(quant_w, scale_w, weight_bit_width)
-        ctx.weight_shape = weight.size()
-        output = inp.mm(weight.t())
-        ctx.save_for_backward(inp, quant_w, scale_w)
-        return output.view(*(ctx.inp_shape[:-1] + (out_features,)))
-
-    @staticmethod
-    def backward(ctx, grad_output: torch.Tensor):
-        inp, quant_w, scale_w = ctx.saved_tensors
-        weight = extract_weight_to_half(quant_w, scale_w, ctx.weight_bit_width)
-        grad_output = grad_output.contiguous().view(-1, weight.size(0))
-        grad_input = grad_output.mm(weight)
-        grad_weight = grad_output.t().mm(inp)
-        return grad_input.view(ctx.inp_shape), grad_weight.view(ctx.weight_shape), None, None
-
-
-def compress_int4_weight(weight: torch.Tensor):  # (n, m)
-    with torch.cuda.device(weight.device):
-        n, m = weight.size(0), weight.size(1)
-        assert m % 2 == 0
-        m = m // 2
-        out = torch.empty(n, m, dtype=torch.int8, device="cuda")
-        stream = torch.cuda.current_stream()
-
-        gridDim = (n, 1, 1)
-        blockDim = (min(round_up(m, 32), 1024), 1, 1)
-
-        kernels.int4WeightCompression(
-            gridDim,
-            blockDim,
-            0,
-            stream,
-            [ctypes.c_void_p(weight.data_ptr()), ctypes.c_void_p(out.data_ptr()), ctypes.c_int32(n), ctypes.c_int32(m)],
-        )
-        return out
-
-
-def extract_weight_to_half(weight: torch.Tensor, scale_list: torch.Tensor, source_bit_width: int):
-    if source_bit_width == 8:
-        func = kernels.int8WeightExtractionHalf
-    elif source_bit_width == 4:
-        func = kernels.int4WeightExtractionHalf
-    else:
-        assert False, "Unsupported bit-width"
-
-    with torch.cuda.device(weight.device):
-        n, m = weight.size(0), weight.size(1)
-        out = torch.empty(n, m * (8 // source_bit_width), dtype=torch.half, device="cuda")
-        stream = torch.cuda.current_stream()
-
-        gridDim = (n, 1, 1)
-        blockDim = (min(round_up(m, 32), 1024), 1, 1)
-
-        func(
-            gridDim,
-            blockDim,
-            0,
-            stream,
-            [
-                ctypes.c_void_p(weight.data_ptr()),
-                ctypes.c_void_p(scale_list.data_ptr()),
-                ctypes.c_void_p(out.data_ptr()),
-                ctypes.c_int32(n),
-                ctypes.c_int32(m),
-            ],
-        )
-        return out
-
-
-class QuantizedLinear(Linear):
-    def __init__(self, weight_bit_width: int, weight_tensor=None, bias_tensor=None, empty_init=False, *args, **kwargs):
-        super(QuantizedLinear, self).__init__(*args, **kwargs)
-        self.weight_bit_width = weight_bit_width
-
-        shape = self.weight.shape
-        del self.weight
-
-        if weight_tensor is None or empty_init:
-            self.weight = torch.empty(
-                shape[0], shape[1] * weight_bit_width // 8, dtype=torch.int8, device=kwargs["device"]
-            )
-            self.weight_scale = torch.empty(shape[0], dtype=kwargs["dtype"], device=kwargs["device"])
-        else:
-            self.weight_scale = (weight_tensor.abs().max(dim=-1).values / ((2 ** (weight_bit_width - 1)) - 1)).half()
-            self.weight = torch.round(weight_tensor / self.weight_scale[:, None]).to(torch.int8)
-            if weight_bit_width == 4:
-                self.weight = compress_int4_weight(self.weight)
-
-        self.weight = Parameter(self.weight.to(kwargs["device"]), requires_grad=False)
-        self.weight_scale = Parameter(self.weight_scale.to(kwargs["device"]), requires_grad=False)
-        if bias_tensor is not None:
-            self.bias = Parameter(bias_tensor.to(kwargs["device"]), requires_grad=False)
-        else:
-            self.bias = None
-
-    def forward(self, input):
-        output = W8A16Linear.apply(input, self.weight, self.weight_scale, self.weight_bit_width)
-        if self.bias is not None:
-            output = output + self.bias
-        return output
-
-
-def quantize(model, weight_bit_width, empty_init=False, **kwargs):
-    """Replace fp16 linear with quantized linear"""
-
-    for layer in model.layers:
-        layer.attention.query_key_value = QuantizedLinear(
-            weight_bit_width=weight_bit_width,
-            weight_tensor=layer.attention.query_key_value.weight.to(torch.cuda.current_device()),
-            bias_tensor=layer.attention.query_key_value.bias,
-            in_features=layer.attention.query_key_value.in_features,
-            out_features=layer.attention.query_key_value.out_features,
-            bias=True,
-            dtype=torch.half,
-            device=layer.attention.query_key_value.weight.device,
-            empty_init=empty_init
-        )
-        layer.attention.dense = QuantizedLinear(
-            weight_bit_width=weight_bit_width,
-            weight_tensor=layer.attention.dense.weight.to(torch.cuda.current_device()),
-            bias_tensor=layer.attention.dense.bias,
-            in_features=layer.attention.dense.in_features,
-            out_features=layer.attention.dense.out_features,
-            bias=True,
-            dtype=torch.half,
-            device=layer.attention.dense.weight.device,
-            empty_init=empty_init
-        )
-        layer.mlp.dense_h_to_4h = QuantizedLinear(
-            weight_bit_width=weight_bit_width,
-            weight_tensor=layer.mlp.dense_h_to_4h.weight.to(torch.cuda.current_device()),
-            bias_tensor=layer.mlp.dense_h_to_4h.bias,
-            in_features=layer.mlp.dense_h_to_4h.in_features,
-            out_features=layer.mlp.dense_h_to_4h.out_features,
-            bias=True,
-            dtype=torch.half,
-            device=layer.mlp.dense_h_to_4h.weight.device,
-            empty_init=empty_init
-        )
-        layer.mlp.dense_4h_to_h = QuantizedLinear(
-            weight_bit_width=weight_bit_width,
-            weight_tensor=layer.mlp.dense_4h_to_h.weight.to(torch.cuda.current_device()),
-            bias_tensor=layer.mlp.dense_4h_to_h.bias,
-            in_features=layer.mlp.dense_4h_to_h.in_features,
-            out_features=layer.mlp.dense_4h_to_h.out_features,
-            bias=True,
-            dtype=torch.half,
-            device=layer.mlp.dense_4h_to_h.weight.device,
-            empty_init=empty_init
-        )
-    return model

rng_state_0.pth

@@ -1,3 +0,0 @@
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-oid sha256:e75c96f06b249e57a701db73ce821398e69672027a86d3a44063830602a29ab4
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rng_state_1.pth

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rng_state_4.pth

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rng_state_6.pth

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rng_state_7.pth

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special_tokens_map.json

@@ -1,7 +0,0 @@
-{
-  "bos_token": "<sop>",
-  "eos_token": "<eop>",
-  "mask_token": "[MASK]",
-  "pad_token": "<pad>",
-  "unk_token": "<unk>"
-}

tokenization_chatglm.py

@@ -1,430 +0,0 @@
-"""Tokenization classes for ChatGLM."""
-from typing import List, Optional, Union
-import os
-
-from transformers.tokenization_utils import PreTrainedTokenizer
-from transformers.utils import logging, PaddingStrategy
-from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
-from typing import Dict
-import sentencepiece as spm
-import numpy as np
-
-logger = logging.get_logger(__name__)
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "THUDM/chatglm-6b": 2048,
-}
-
-
-class TextTokenizer:
-    def __init__(self, model_path):
-        self.sp = spm.SentencePieceProcessor()
-        self.sp.Load(model_path)
-        self.num_tokens = self.sp.vocab_size()
-
-    def encode(self, text):
-        return self.sp.EncodeAsIds(text)
-
-    def decode(self, ids: List[int]):
-        return self.sp.DecodeIds(ids)
-
-    def tokenize(self, text):
-        return self.sp.EncodeAsPieces(text)
-
-    def convert_tokens_to_ids(self, tokens):
-        return [self.sp.PieceToId(token) for token in tokens]
-
-    def convert_token_to_id(self, token):
-        return self.sp.PieceToId(token)
-
-    def convert_id_to_token(self, idx):
-        return self.sp.IdToPiece(idx)
-
-    def __len__(self):
-        return self.num_tokens
-
-
-class SPTokenizer:
-    def __init__(
-            self,
-            vocab_file,
-            num_image_tokens=20000,
-            max_blank_length=80,
-            byte_fallback=True,
-    ):
-        assert vocab_file is not None
-        self.vocab_file = vocab_file
-        self.num_image_tokens = num_image_tokens
-        self.special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "<unused_0>", "<sop>", "<eop>", "<ENC>", "<dBLOCK>"]
-        self.max_blank_length = max_blank_length
-        self.byte_fallback = byte_fallback
-        self.text_tokenizer = TextTokenizer(vocab_file)
-
-    def _get_text_tokenizer(self):
-        return self.text_tokenizer
-
-    @staticmethod
-    def get_blank_token(length: int):
-        assert length >= 2
-        return f"<|blank_{length}|>"
-
-    @staticmethod
-    def get_tab_token():
-        return f"<|tab|>"
-
-    @property
-    def num_text_tokens(self):
-        return self.text_tokenizer.num_tokens
-
-    @property
-    def num_tokens(self):
-        return self.num_image_tokens + self.num_text_tokens
-
-    @staticmethod
-    def _encode_whitespaces(text: str, max_len: int = 80):
-        text = text.replace("\t", SPTokenizer.get_tab_token())
-        for i in range(max_len, 1, -1):
-            text = text.replace(" " * i, SPTokenizer.get_blank_token(i))
-        return text
-
-    def _preprocess(self, text: str, linebreak=True, whitespaces=True):
-        if linebreak:
-            text = text.replace("\n", "<n>")
-        if whitespaces:
-            text = self._encode_whitespaces(text, max_len=self.max_blank_length)
-        return text
-
-    def encode(
-            self, text: str, linebreak=True, whitespaces=True, add_dummy_prefix=True
-    ) -> List[int]:
-        """
-        @param text: Text to encode.
-        @param linebreak: Whether to encode newline (\n) in text.
-        @param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding.
-        @param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text.
-        @param add_dummy_prefix: Whether to add dummy blank space in the beginning.
-        """
-        text = self._preprocess(text, linebreak, whitespaces)
-        if not add_dummy_prefix:
-            text = "<n>" + text
-        tmp = self._get_text_tokenizer().encode(text)
-        tokens = [x + self.num_image_tokens for x in tmp]
-        return tokens if add_dummy_prefix else tokens[2:]
-
-    def decode(self, text_ids: List[int]) -> str:
-        ids = [int(_id) - self.num_image_tokens for _id in text_ids]
-        ids = [_id for _id in ids if _id >= 0]
-        text = self._get_text_tokenizer().decode(ids)
-        text = text.replace("<n>", "\n")
-        text = text.replace(SPTokenizer.get_tab_token(), "\t")
-        for i in range(2, self.max_blank_length + 1):
-            text = text.replace(self.get_blank_token(i), " " * i)
-        return text
-
-    def tokenize(
-            self, text: str, linebreak=True, whitespaces=True, add_dummy_prefix=True
-    ) -> List[str]:
-        """
-        @param text: Text to encode.
-        @param linebreak: Whether to encode newline (\n) in text.
-        @param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding.
-        @param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text.
-        @param add_dummy_prefix: Whether to add dummy blank space in the beginning.
-        """
-        text = self._preprocess(text, linebreak, whitespaces)
-        if not add_dummy_prefix:
-            text = "<n>" + text
-        tokens = self._get_text_tokenizer().tokenize(text)
-        return tokens if add_dummy_prefix else tokens[2:]
-
-    def __getitem__(self, x: Union[int, str]):
-        if isinstance(x, int):
-            if x < self.num_image_tokens:
-                return "<image_{}>".format(x)
-            else:
-                return self.text_tokenizer.convert_id_to_token(x - self.num_image_tokens)
-        elif isinstance(x, str):
-            if x.startswith("<image_") and x.endswith(">") and x[7:-1].isdigit():
-                return int(x[7:-1])
-            else:
-                return self.text_tokenizer.convert_token_to_id(x) + self.num_image_tokens
-        else:
-            raise ValueError("The key should be str or int.")
-
-
-class ChatGLMTokenizer(PreTrainedTokenizer):
-    """
-    Construct a ChatGLM tokenizer. Based on byte-level Byte-Pair-Encoding.
-
-    Args:
-        vocab_file (`str`):
-            Path to the vocabulary file.
-    """
-
-    vocab_files_names = {"vocab_file": "ice_text.model"}
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["input_ids", "attention_mask", "position_ids"]
-
-    def __init__(
-            self,
-            vocab_file,
-            do_lower_case=False,
-            remove_space=False,
-            bos_token='<sop>',
-            eos_token='<eop>',
-            end_token='</s>',
-            mask_token='[MASK]',
-            gmask_token='[gMASK]',
-            padding_side="left",
-            pad_token="<pad>",
-            unk_token="<unk>",
-            num_image_tokens=20000,
-            **kwargs
-    ) -> None:
-        super().__init__(
-            do_lower_case=do_lower_case,
-            remove_space=remove_space,
-            padding_side=padding_side,
-            bos_token=bos_token,
-            eos_token=eos_token,
-            end_token=end_token,
-            mask_token=mask_token,
-            gmask_token=gmask_token,
-            pad_token=pad_token,
-            unk_token=unk_token,
-            num_image_tokens=num_image_tokens,
-            **kwargs
-        )
-
-        self.do_lower_case = do_lower_case
-        self.remove_space = remove_space
-        self.vocab_file = vocab_file
-
-        self.bos_token = bos_token
-        self.eos_token = eos_token
-        self.end_token = end_token
-        self.mask_token = mask_token
-        self.gmask_token = gmask_token
-
-        self.sp_tokenizer = SPTokenizer(vocab_file, num_image_tokens=num_image_tokens)
-
-        """ Initialisation """
-
-    @property
-    def gmask_token_id(self) -> Optional[int]:
-        if self.gmask_token is None:
-            return None
-        return self.convert_tokens_to_ids(self.gmask_token)
-
-    @property
-    def end_token_id(self) -> Optional[int]:
-        """
-        `Optional[int]`: Id of the end of context token in the vocabulary. Returns `None` if the token has not been
-        set.
-        """
-        if self.end_token is None:
-            return None
-        return self.convert_tokens_to_ids(self.end_token)
-
-    @property
-    def vocab_size(self):
-        """ Returns vocab size """
-        return self.sp_tokenizer.num_tokens
-
-    def get_vocab(self):
-        """ Returns vocab as a dict """
-        vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
-        vocab.update(self.added_tokens_encoder)
-        return vocab
-
-    def preprocess_text(self, inputs):
-        if self.remove_space:
-            outputs = " ".join(inputs.strip().split())
-        else:
-            outputs = inputs
-
-        if self.do_lower_case:
-            outputs = outputs.lower()
-
-        return outputs
-
-    def _tokenize(self, text, **kwargs):
-        """ Returns a tokenized string. """
-        text = self.preprocess_text(text)
-
-        seq = self.sp_tokenizer.tokenize(text)
-
-        return seq
-
-    def _decode(
-            self,
-            token_ids: Union[int, List[int]],
-            skip_special_tokens: bool = False,
-            clean_up_tokenization_spaces: bool = True,
-            **kwargs
-    ) -> str:
-        if isinstance(token_ids, int):
-            token_ids = [token_ids]
-        if len(token_ids) == 0:
-            return ""
-        if self.pad_token_id in token_ids:  # remove pad
-            token_ids = list(filter((self.pad_token_id).__ne__, token_ids))
-        return self.sp_tokenizer.decode(token_ids)
-
-    def _convert_token_to_id(self, token):
-        """ Converts a token (str) in an id using the vocab. """
-        return self.sp_tokenizer[token]
-
-    def _convert_id_to_token(self, index):
-        """Converts an index (integer) in a token (str) using the vocab."""
-        return self.sp_tokenizer[index]
-
-    def save_vocabulary(self, save_directory, filename_prefix=None):
-        """
-        Save the vocabulary and special tokens file to a directory.
-
-        Args:
-            save_directory (`str`):
-                The directory in which to save the vocabulary.
-            filename_prefix (`str`, *optional*):
-                An optional prefix to add to the named of the saved files.
-
-        Returns:
-            `Tuple(str)`: Paths to the files saved.
-        """
-        if os.path.isdir(save_directory):
-            vocab_file = os.path.join(
-                save_directory, self.vocab_files_names["vocab_file"]
-            )
-        else:
-            vocab_file = save_directory
-
-        with open(self.vocab_file, 'rb') as fin:
-            proto_str = fin.read()
-
-        with open(vocab_file, "wb") as writer:
-            writer.write(proto_str)
-
-        return (vocab_file,)
-
-    def build_inputs_with_special_tokens(
-            self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
-        adding special tokens. A BERT sequence has the following format:
-
-        - single sequence: `[CLS] X [SEP]`
-        - pair of sequences: `[CLS] A [SEP] B [SEP]`
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs to which the special tokens will be added.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-
-        Returns:
-            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
-        """
-        gmask_id = self.sp_tokenizer[self.gmask_token]
-        eos_id = self.sp_tokenizer[self.eos_token]
-        token_ids_0 = token_ids_0 + [gmask_id, self.sp_tokenizer[self.bos_token]]
-        if token_ids_1 is not None:
-            token_ids_0 = token_ids_0 + token_ids_1 + [eos_id]
-        return token_ids_0
-
-    def _pad(
-            self,
-            encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
-            max_length: Optional[int] = None,
-            padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
-            pad_to_multiple_of: Optional[int] = None,
-            return_attention_mask: Optional[bool] = None,
-    ) -> dict:
-        """
-        Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
-
-        Args:
-            encoded_inputs:
-                Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
-            max_length: maximum length of the returned list and optionally padding length (see below).
-                Will truncate by taking into account the special tokens.
-            padding_strategy: PaddingStrategy to use for padding.
-
-                - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
-                - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
-                - PaddingStrategy.DO_NOT_PAD: Do not pad
-                The tokenizer padding sides are defined in self.padding_side:
-
-                    - 'left': pads on the left of the sequences
-                    - 'right': pads on the right of the sequences
-            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
-                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
-                `>= 7.5` (Volta).
-            return_attention_mask:
-                (optional) Set to False to avoid returning attention mask (default: set to model specifics)
-        """
-        # Load from model defaults
-        bos_token_id = self.sp_tokenizer[self.bos_token]
-        mask_token_id = self.sp_tokenizer[self.mask_token]
-        gmask_token_id = self.sp_tokenizer[self.gmask_token]
-        assert self.padding_side == "left"
-
-        required_input = encoded_inputs[self.model_input_names[0]]
-        seq_length = len(required_input)
-
-        if padding_strategy == PaddingStrategy.LONGEST:
-            max_length = len(required_input)
-
-        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
-            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
-
-        needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
-
-        # Initialize attention mask if not present.
-        if max_length is not None:
-            if "attention_mask" not in encoded_inputs:
-                if bos_token_id in required_input:
-                    context_length = required_input.index(bos_token_id)
-                else:
-                    context_length = seq_length
-                attention_mask = np.ones((1, seq_length, seq_length))
-                attention_mask = np.tril(attention_mask)
-                attention_mask[:, :, :context_length] = 1
-                attention_mask = np.bool_(attention_mask < 0.5)
-                encoded_inputs["attention_mask"] = attention_mask
-
-            if "position_ids" not in encoded_inputs:
-                if bos_token_id in required_input:
-                    context_length = required_input.index(bos_token_id)
-                else:
-                    context_length = seq_length
-                position_ids = np.arange(seq_length, dtype=np.int64)
-                mask_token = mask_token_id if mask_token_id in required_input else gmask_token_id
-                if mask_token in required_input:
-                    mask_position = required_input.index(mask_token)
-                    position_ids[context_length:] = mask_position
-                block_position_ids = np.concatenate(
-                    [np.zeros(context_length, dtype=np.int64),
-                     np.arange(1, seq_length - context_length + 1, dtype=np.int64)])
-                encoded_inputs["position_ids"] = np.stack([position_ids, block_position_ids], axis=0)
-
-        if needs_to_be_padded:
-            difference = max_length - len(required_input)
-
-            if "attention_mask" in encoded_inputs:
-                encoded_inputs["attention_mask"] = np.pad(encoded_inputs["attention_mask"],
-                                                          pad_width=[(0, 0), (difference, 0), (difference, 0)],
-                                                          mode='constant', constant_values=True)
-            if "token_type_ids" in encoded_inputs:
-                encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
-                    "token_type_ids"
-                ]
-            if "special_tokens_mask" in encoded_inputs:
-                encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
-            if "position_ids" in encoded_inputs:
-                encoded_inputs["position_ids"] = np.pad(encoded_inputs["position_ids"],
-                                                        pad_width=[(0, 0), (difference, 0)])
-            encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
-
-        return encoded_inputs

tokenizer_config.json

@@ -1,22 +0,0 @@
-{
-  "auto_map": {
-    "AutoTokenizer": [
-      "tokenization_chatglm.ChatGLMTokenizer",
-      null
-    ]
-  },
-  "bos_token": "<sop>",
-  "clean_up_tokenization_spaces": true,
-  "do_lower_case": false,
-  "end_token": "</s>",
-  "eos_token": "<eop>",
-  "gmask_token": "[gMASK]",
-  "mask_token": "[MASK]",
-  "model_max_length": 1000000000000000019884624838656,
-  "num_image_tokens": 0,
-  "pad_token": "<pad>",
-  "padding_side": "left",
-  "remove_space": false,
-  "tokenizer_class": "ChatGLMTokenizer",
-  "unk_token": "<unk>"
-}

zero_to_fp32.py

@@ -1,578 +0,0 @@
-#!/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 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 . pytorch_model.bin
-
-import argparse
-import torch
-import glob
-import math
-import os
-import re
-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)
-
-        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 files:
-        state_dicts.append(torch.load(f, map_location=device))
-
-    if not ZERO_STAGE 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}")
-
-    if zero_stage == 2:
-        fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
-    elif zero_stage == 3:
-        # if there is more than one param group, there will be multiple flattened tensors - one
-        # flattened tensor per group - for simplicity merge them into a single tensor
-        #
-        # XXX: could make the script more memory efficient for when there are multiple groups - it
-        # will require matching the sub-lists of param_shapes for each param group flattened tensor
-
-        fp32_flat_groups = [
-            torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) 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):
-    """
-    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)
-    elif zero_stage == 3:
-        return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states)
-
-
-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 _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()
-            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):
-    state_dict = OrderedDict()
-
-    # buffers
-    buffers = zero_model_states[0].buffers
-    state_dict.update(buffers)
-    if debug:
-        print(f"added {len(buffers)} buffers")
-
-    _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")
-
-
-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 = fp32_flat_groups[0].numel() * 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
-    for name, shape in param_shapes.items():
-
-        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}"
-            )
-
-        # XXX: memory usage doubles here
-        state_dict[name] = torch.cat(
-            tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)),
-            0).narrow(0, 0, unpartitioned_numel).view(shape)
-        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):
-    state_dict = OrderedDict()
-
-    # buffers
-    buffers = zero_model_states[0].buffers
-    state_dict.update(buffers)
-    if debug:
-        print(f"added {len(buffers)} buffers")
-
-    _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 get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None):
-    """
-    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``
-
-    Returns:
-        - pytorch ``state_dict``
-
-    Note: this approach may not work if your application doesn't have sufficient free CPU memory and
-    you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
-    the checkpoint.
-
-    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.
-
-    """
-    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")
-
-    return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir)
-
-
-def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None):
-    """
-    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_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
-        - ``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``
-    """
-
-    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
-    print(f"Saving fp32 state dict to {output_file}")
-    torch.save(state_dict, output_file)
-
-
-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(f"Extracting fp32 weights")
-    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
-
-    logger.info(f"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_file",
-        type=str,
-        help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)")
-    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_file)