Init code.

.gitignore

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+__pycache__
+.vscode

chatglm/__init__.py

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+
+from chatglm.configuration_chatglm import ChatGLMConfig
+from chatglm.tokenization_chatglm import ChatGLMTokenizer
+from chatglm.modeling_chatglm import ChatGLMForConditionalGeneration
+
+
+

chatglm/configuration_chatglm.py

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+from transformers import PretrainedConfig
+
+
+class ChatGLMConfig(PretrainedConfig):
+    model_type = "chatglm"
+    def __init__(
+        self,
+        num_layers=28,
+        padded_vocab_size=65024,
+        hidden_size=4096,
+        ffn_hidden_size=13696,
+        kv_channels=128,
+        num_attention_heads=32,
+        seq_length=2048,
+        hidden_dropout=0.0,
+        classifier_dropout=None,
+        attention_dropout=0.0,
+        layernorm_epsilon=1e-5,
+        rmsnorm=True,
+        apply_residual_connection_post_layernorm=False,
+        post_layer_norm=True,
+        add_bias_linear=False,
+        add_qkv_bias=False,
+        bias_dropout_fusion=True,
+        multi_query_attention=False,
+        multi_query_group_num=1,
+        apply_query_key_layer_scaling=True,
+        attention_softmax_in_fp32=True,
+        fp32_residual_connection=False,
+        quantization_bit=0,
+        pre_seq_len=None,
+        prefix_projection=False,
+        **kwargs
+    ):
+        self.num_layers = num_layers
+        self.vocab_size = padded_vocab_size
+        self.padded_vocab_size = padded_vocab_size
+        self.hidden_size = hidden_size
+        self.ffn_hidden_size = ffn_hidden_size
+        self.kv_channels = kv_channels
+        self.num_attention_heads = num_attention_heads
+        self.seq_length = seq_length
+        self.hidden_dropout = hidden_dropout
+        self.classifier_dropout = classifier_dropout
+        self.attention_dropout = attention_dropout
+        self.layernorm_epsilon = layernorm_epsilon
+        self.rmsnorm = rmsnorm
+        self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
+        self.post_layer_norm = post_layer_norm
+        self.add_bias_linear = add_bias_linear
+        self.add_qkv_bias = add_qkv_bias
+        self.bias_dropout_fusion = bias_dropout_fusion
+        self.multi_query_attention = multi_query_attention
+        self.multi_query_group_num = multi_query_group_num
+        self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
+        self.attention_softmax_in_fp32 = attention_softmax_in_fp32
+        self.fp32_residual_connection = fp32_residual_connection
+        self.quantization_bit = quantization_bit
+        self.pre_seq_len = pre_seq_len
+        self.prefix_projection = prefix_projection
+        super().__init__(**kwargs)

chatglm/modeling_chatglm.py

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+import collections
+import math
+import copy
+import os
+import gc
+import json
+
+import torch
+import torch.utils.checkpoint
+import torch.nn.functional as F
+from torch import nn
+from torch.nn.utils import skip_init
+from typing import Optional, Tuple, Union, List, Dict, Any
+
+from tqdm import auto as tqdm_lib
+from safetensors.torch import storage_ptr, storage_size
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.generation import GenerationConfig
+
+from chatglm import ChatGLMConfig
+
+WEIGHTS_INDEX_NAME = "pytorch_model.bin.index.json"
+
+
+class RotaryEmbedding(nn.Module):
+    def __init__(self, dim: int, original_impl=False, device=None, dtype=None):
+        super().__init__()
+        inv_freq = 1.0 / (
+            10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim)
+        )
+        self.register_buffer("inv_freq", inv_freq)
+        self.dim = dim
+        self.original_impl = original_impl
+
+    def forward(self, max_seq_len: int, base: int = 10000):
+        dtype = self.inv_freq.dtype
+        device = self.inv_freq.device
+        # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$
+        theta = 1.0 / (
+            base
+            ** (
+                torch.arange(0, self.dim, 2, dtype=torch.float, device=device)
+                / self.dim
+            )
+        )
+        # Create position indexes `[0, 1, ..., max_seq_len - 1]`
+        seq_idx = torch.arange(max_seq_len, dtype=torch.float, device=device)
+        # Calculate the product of position index and $\theta_i$
+        idx_theta = torch.outer(seq_idx, theta).float()
+        cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1)
+        # this is to mimic the behaviour of complex32, else we will get different results
+        if dtype in (torch.float16, torch.bfloat16, torch.int8):
+            cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half()
+        return cache
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs):
+        super().__init__()
+        self.weight = torch.nn.Parameter(
+            torch.empty(normalized_shape, device=device, dtype=dtype)
+        )
+        self.eps = eps
+
+    def forward(self, hidden_states: torch.Tensor):
+        input_dtype = hidden_states.dtype
+        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
+        return (self.weight * hidden_states).to(input_dtype)
+
+
+class CoreAttention(torch.nn.Module):
+    def __init__(self, config: ChatGLMConfig, layer_number):
+        super(CoreAttention, self).__init__()
+
+        self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
+        self.layer_number = max(1, layer_number)
+        projection_size = config.kv_channels * config.num_attention_heads
+        # Per attention head and per partition values.
+        self.hidden_size_per_partition = projection_size
+        self.hidden_size_per_attention_head = (
+            projection_size // config.num_attention_heads
+        )
+        self.num_attention_heads_per_partition = config.num_attention_heads
+
+        coeff = None
+        self.norm_factor = math.sqrt(self.hidden_size_per_attention_head)
+        coeff = self.layer_number
+        self.norm_factor *= coeff
+        self.coeff = coeff
+
+        self.attention_dropout = torch.nn.Dropout(config.attention_dropout)
+
+    def forward(self, query_layer, key_layer, value_layer, attention_mask):
+        query_layer, key_layer, value_layer = [
+            k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]
+        ]
+        if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]:
+            context_layer = torch.nn.functional.scaled_dot_product_attention(
+                query_layer, key_layer, value_layer, is_causal=True
+            )
+        context_layer = context_layer.permute(2, 0, 1, 3)
+        new_context_layer_shape = context_layer.size()[:-2] + (
+            self.hidden_size_per_partition,
+        )
+        context_layer = context_layer.reshape(*new_context_layer_shape)
+        return context_layer
+
+
+class SelfAttention(torch.nn.Module):
+    """Parallel self-attention layer abstract class.
+
+    Self-attention layer takes input with size [s, b, h]
+    and returns output of the same size.
+    """
+
+    def __init__(self, config: ChatGLMConfig, layer_number, device=None):
+        super(SelfAttention, self).__init__()
+        self.layer_number = max(1, layer_number)
+        self.projection_size = config.kv_channels * config.num_attention_heads
+        self.hidden_size_per_attention_head = (
+            self.projection_size // config.num_attention_heads
+        )
+        self.num_attention_heads_per_partition = config.num_attention_heads
+        self.multi_query_attention = config.multi_query_attention
+        self.qkv_hidden_size = 3 * self.projection_size
+        self.num_multi_query_groups_per_partition = config.multi_query_group_num
+        self.qkv_hidden_size = (
+            self.projection_size
+            + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num
+        )
+        self.query_key_value = nn.Linear(
+            config.hidden_size,
+            self.qkv_hidden_size,
+            bias=config.add_bias_linear or config.add_qkv_bias,
+            device=device,
+            dtype=config.torch_dtype,
+        )
+        self.core_attention = CoreAttention(config, self.layer_number)
+        self.dense = nn.Linear(
+            self.projection_size,
+            config.hidden_size,
+            bias=config.add_bias_linear,
+            device=device,
+            dtype=config.torch_dtype,
+        )
+
+    def apply_rotary_pos_emb(
+        self, x: torch.Tensor, rope_cache: torch.Tensor
+    ) -> torch.Tensor:
+        # x: [sq, b, np, hn]
+        sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3)
+        rot_dim = rope_cache.shape[-2] * 2
+        x, x_pass = x[..., :rot_dim], x[..., rot_dim:]
+        # truncate to support variable sizes
+        rope_cache = rope_cache[:sq]
+        xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2)
+        rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2)
+        x_out2 = torch.stack(
+            [
+                xshaped[..., 0] * rope_cache[..., 0]
+                - xshaped[..., 1] * rope_cache[..., 1],
+                xshaped[..., 1] * rope_cache[..., 0]
+                + xshaped[..., 0] * rope_cache[..., 1],
+            ],
+            -1,
+        )
+        x_out2 = x_out2.flatten(3)
+        return torch.cat((x_out2, x_pass), dim=-1)
+
+    def forward(self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None):
+        # hidden_states: [sq, b, h]
+        # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)]
+        mixed_x_layer = self.query_key_value(hidden_states)
+
+        (query_layer, key_layer, value_layer) = mixed_x_layer.split(
+            [
+                self.num_attention_heads_per_partition
+                * self.hidden_size_per_attention_head,
+                self.num_multi_query_groups_per_partition
+                * self.hidden_size_per_attention_head,
+                self.num_multi_query_groups_per_partition
+                * self.hidden_size_per_attention_head,
+            ],
+            dim=-1,
+        )
+        query_layer = query_layer.view(
+            query_layer.size()[:-1]
+            + (
+                self.num_attention_heads_per_partition,
+                self.hidden_size_per_attention_head,
+            )
+        )
+        key_layer = key_layer.view(
+            key_layer.size()[:-1]
+            + (
+                self.num_multi_query_groups_per_partition,
+                self.hidden_size_per_attention_head,
+            )
+        )
+        value_layer = value_layer.view(
+            value_layer.size()[:-1]
+            + (
+                self.num_multi_query_groups_per_partition,
+                self.hidden_size_per_attention_head,
+            )
+        )
+
+        # apply relative positional encoding (rotary embedding)
+        if rotary_pos_emb is not None:
+            query_layer = self.apply_rotary_pos_emb(query_layer, rotary_pos_emb)
+            key_layer = self.apply_rotary_pos_emb(key_layer, rotary_pos_emb)
+
+        kv_cache = (key_layer, value_layer)
+
+        key_layer = key_layer.unsqueeze(-2)
+        key_layer = key_layer.expand(
+            -1,
+            -1,
+            -1,
+            self.num_attention_heads_per_partition
+            // self.num_multi_query_groups_per_partition,
+            -1,
+        )
+        key_layer = key_layer.contiguous().view(
+            key_layer.size()[:2]
+            + (
+                self.num_attention_heads_per_partition,
+                self.hidden_size_per_attention_head,
+            )
+        )
+        value_layer = value_layer.unsqueeze(-2)
+        value_layer = value_layer.expand(
+            -1,
+            -1,
+            -1,
+            self.num_attention_heads_per_partition
+            // self.num_multi_query_groups_per_partition,
+            -1,
+        )
+        value_layer = value_layer.contiguous().view(
+            value_layer.size()[:2]
+            + (
+                self.num_attention_heads_per_partition,
+                self.hidden_size_per_attention_head,
+            )
+        )
+        # ==================================
+        # core attention computation
+        # ==================================
+        context_layer = self.core_attention(
+            query_layer, key_layer, value_layer, attention_mask
+        )
+        # =================
+        # Output. [sq, b, h]
+        # =================
+        output = self.dense(context_layer)
+        return output, kv_cache
+
+
+class MLP(torch.nn.Module):
+    """MLP.
+
+    MLP will take the input with h hidden state, project it to 4*h
+    hidden dimension, perform nonlinear transformation, and project the
+    state back into h hidden dimension.
+    """
+
+    def __init__(self, config: ChatGLMConfig, device=None):
+        super(MLP, self).__init__()
+
+        self.add_bias = config.add_bias_linear
+
+        # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf
+        self.dense_h_to_4h = nn.Linear(
+            config.hidden_size,
+            config.ffn_hidden_size * 2,
+            bias=self.add_bias,
+            device=device,
+            dtype=config.torch_dtype,
+        )
+
+        def swiglu(x):
+            x = torch.chunk(x, 2, dim=-1)
+            return F.silu(x[0]) * x[1]
+
+        self.activation_func = swiglu
+
+        # Project back to h.
+        self.dense_4h_to_h = nn.Linear(
+            config.ffn_hidden_size,
+            config.hidden_size,
+            bias=self.add_bias,
+            device=device,
+            dtype=config.torch_dtype,
+        )
+
+    def forward(self, hidden_states):
+        # [s, b, 4hp]
+        intermediate_parallel = self.dense_h_to_4h(hidden_states)
+        intermediate_parallel = self.activation_func(intermediate_parallel)
+        # [s, b, h]
+        output = self.dense_4h_to_h(intermediate_parallel)
+        return output
+
+
+class GLMBlock(torch.nn.Module):
+    """A single transformer layer.
+
+    Transformer layer takes input with size [s, b, h] and returns an
+    output of the same size.
+    """
+
+    def __init__(self, config: ChatGLMConfig, layer_number, device=None):
+        super(GLMBlock, self).__init__()
+        self.layer_number = layer_number
+
+        self.apply_residual_connection_post_layernorm = (
+            config.apply_residual_connection_post_layernorm
+        )
+
+        self.fp32_residual_connection = config.fp32_residual_connection
+
+        LayerNormFunc = RMSNorm
+        # Layernorm on the input data.
+        self.input_layernorm = LayerNormFunc(
+            config.hidden_size,
+            eps=config.layernorm_epsilon,
+            device=device,
+            dtype=config.torch_dtype,
+        )
+        # Self attention.
+        self.self_attention = SelfAttention(config, layer_number, device=device)
+        self.hidden_dropout = config.hidden_dropout
+        # Layernorm on the attention output
+        self.post_attention_layernorm = LayerNormFunc(
+            config.hidden_size,
+            eps=config.layernorm_epsilon,
+            device=device,
+            dtype=config.torch_dtype,
+        )
+        # MLP
+        self.mlp = MLP(config, device=device)
+
+    def forward(self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None):
+        # hidden_states: [s, b, h]
+        # Layer norm at the beginning of the transformer layer.
+        layernorm_output = self.input_layernorm(hidden_states)
+        # Self attention.
+        attention_output, kv_cache = self.self_attention(
+            layernorm_output, attention_mask, rotary_pos_emb, kv_cache=kv_cache
+        )
+        residual = hidden_states
+
+        layernorm_input = torch.nn.functional.dropout(
+            attention_output, p=self.hidden_dropout, training=self.training
+        )
+        layernorm_input = residual + layernorm_input
+
+        # Layer norm post the self attention.
+        layernorm_output = self.post_attention_layernorm(layernorm_input)
+
+        # MLP.
+        mlp_output = self.mlp(layernorm_output)
+
+        residual = layernorm_input
+
+        output = torch.nn.functional.dropout(
+            mlp_output, p=self.hidden_dropout, training=self.training
+        )
+        output = residual + output
+        return output, kv_cache
+
+
+class GLMTransformer(torch.nn.Module):
+    def __init__(self, config: ChatGLMConfig, device=None):
+        super(GLMTransformer, self).__init__()
+        self.fp32_residual_connection = config.fp32_residual_connection
+        self.post_layer_norm = config.post_layer_norm
+        self.num_layers = config.num_layers
+        self.layers = []
+        for i in range(self.num_layers):
+            self.layers.append(GLMBlock(config, i + 1, device=device))
+        self.layers = torch.nn.ModuleList(self.layers)
+        self.final_layernorm = RMSNorm(
+            config.hidden_size,
+            eps=config.layernorm_epsilon,
+            device=device,
+            dtype=config.torch_dtype,
+        )
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask,
+        rotary_pos_emb,
+        kv_caches=None,
+        use_cache: Optional[bool] = True,
+    ):
+        kv_caches = [None for _ in range(self.num_layers)]
+        presents = () if use_cache else None
+
+        for index in range(self.num_layers):
+            layer = self.layers[index]
+            hidden_states, kv_cache = layer(
+                hidden_states, attention_mask, rotary_pos_emb, kv_cache=kv_caches[index]
+            )
+            if use_cache:
+                presents = presents + (kv_cache,)
+        hidden_states = self.final_layernorm(hidden_states)
+        return hidden_states
+
+
+class Embedding(torch.nn.Module):
+    def __init__(self, config: ChatGLMConfig, device=None):
+        super(Embedding, self).__init__()
+
+        self.hidden_size = config.hidden_size
+        self.word_embeddings = nn.Embedding(
+            config.padded_vocab_size,
+            self.hidden_size,
+            dtype=config.torch_dtype,
+            device=device,
+        )
+
+    def forward(self, input_ids):
+        # Embeddings.
+        words_embeddings = self.word_embeddings(input_ids)
+        embeddings = words_embeddings
+        # Data format change to avoid explicit tranposes : [b s h] --> [s b h].
+        embeddings = embeddings.transpose(0, 1).contiguous()
+        # If the input flag for fp32 residual connection is set, convert for float.
+        return embeddings
+
+
+class ChatGLMModel(nn.Module):
+    def __init__(self, config: ChatGLMConfig, device=None, empty_init=True):
+        super().__init__()
+        init_method = skip_init
+        init_kwargs = {}
+        if device is not None:
+            init_kwargs["device"] = device
+        self.embedding = init_method(Embedding, config, **init_kwargs)
+        self.num_layers = config.num_layers
+        self.multi_query_group_num = config.multi_query_group_num
+        self.kv_channels = config.kv_channels
+        self.config = config
+
+        # Rotary positional embeddings
+        self.seq_length = config.seq_length
+        rotary_dim = (
+            config.hidden_size // config.num_attention_heads
+            if config.kv_channels is None
+            else config.kv_channels
+        )
+
+        self.rotary_pos_emb = RotaryEmbedding(
+            rotary_dim // 2,
+            original_impl=config.original_rope,
+            device=device,
+            dtype=config.torch_dtype,
+        )
+        self.encoder = init_method(GLMTransformer, config, **init_kwargs)
+        self.output_layer = init_method(
+            nn.Linear,
+            config.hidden_size,
+            config.padded_vocab_size,
+            bias=False,
+            dtype=config.torch_dtype,
+            **init_kwargs,
+        )
+
+    def forward(
+        self,
+        input_ids,
+        position_ids: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.BoolTensor] = None,
+        full_attention_mask: Optional[torch.BoolTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        use_cache: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        return_last_logit: Optional[bool] = False,
+    ):
+        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
+        )
+
+        batch_size, seq_length = input_ids.shape
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embedding(input_ids)
+
+        # Rotary positional embeddings
+        rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
+
+
+        import plotly_express as px
+        img = px.imshow((rotary_pos_emb[:,:,0]*256).byte().cpu())
+        img.write_image("plot.png")
+
+
+
+
+        if position_ids is not None:
+            rotary_pos_emb = rotary_pos_emb[position_ids]
+        else:
+            rotary_pos_emb = rotary_pos_emb[None, :seq_length]
+        rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous()
+        hidden_states = self.encoder(
+            inputs_embeds,
+            full_attention_mask,
+            rotary_pos_emb=rotary_pos_emb,
+            kv_caches=past_key_values,
+            use_cache=use_cache,
+        )
+        if return_last_logit:
+            hidden_states = hidden_states[-1:]
+        lm_logits = self.output_layer(hidden_states)
+        lm_logits = lm_logits.transpose(0, 1).contiguous()
+
+        return lm_logits
+
+
+class ChatGLMForConditionalGeneration(nn.Module):
+    def __init__(self, config: ChatGLMConfig, empty_init=True, device=None):
+        super().__init__()
+
+        self.max_sequence_length = config.max_length
+        self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device)
+        self.config = config
+
+        self.main_input_name = "input_ids"
+        self.config = config
+        self.name_or_path = config.name_or_path
+        self.warnings_issued = {}
+        self.generation_config = GenerationConfig.from_model_config(config)
+
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
+        config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
+        **kwargs,
+    ):
+        state_dict = kwargs.pop("state_dict", None)
+        _ = kwargs.pop("mirror", None)
+        load_in_8bit = kwargs.pop("load_in_8bit", False)
+        load_in_4bit = kwargs.pop("load_in_4bit", False)
+        subfolder = kwargs.pop("subfolder", "")
+
+        # This variable will flag if we're loading a sharded checkpoint. In this case the archive file is just the
+        # index of the files.
+        sharded_metadata = None
+
+        # if pretrained_model_name_or_path is not None:
+        pretrained_model_name_or_path = str(pretrained_model_name_or_path)
+        archive_file = os.path.join(
+            pretrained_model_name_or_path,
+            subfolder,
+            WEIGHTS_INDEX_NAME,
+        )
+        print(f"loading weights file {archive_file}")
+        resolved_archive_file = archive_file
+
+        with open(resolved_archive_file, "r") as f:
+            index = json.loads(f.read())
+
+        shard_filenames = sorted(set(index["weight_map"].values()))
+        sharded_metadata = index["metadata"]
+        sharded_metadata["all_checkpoint_keys"] = list(index["weight_map"].keys())
+        sharded_metadata["weight_map"] = index["weight_map"].copy()
+
+        resolved_archive_file = [
+            os.path.join(pretrained_model_name_or_path, subfolder, f)
+            for f in shard_filenames
+        ]
+
+        loaded_state_dict_keys = sharded_metadata["all_checkpoint_keys"]
+        config.name_or_path = pretrained_model_name_or_path
+        config = copy.deepcopy(config)
+        # make sure we use the model's config since the __init__ call might have copied it
+        config = cls.config
+        # restore default dtype
+        model = cls._load_pretrained_model(
+            state_dict,
+            loaded_state_dict_keys,
+            resolved_archive_file,
+            pretrained_model_name_or_path,
+        )
+        model.is_loaded_in_4bit = load_in_4bit
+        model.is_loaded_in_8bit = load_in_8bit
+        # Set model in evaluation mode to deactivate DropOut modules by default
+        model.eval()
+        return model
+
+    def _load_state_dict_into_model(self, model_to_load, state_dict, start_prefix):
+        metadata = getattr(state_dict, "_metadata", None)
+        state_dict = state_dict.copy()
+        if metadata is not None:
+            state_dict._metadata = metadata
+        error_msgs = []
+
+        def load(module: nn.Module, state_dict, prefix=""):
+            local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
+            args = (state_dict, prefix, local_metadata, True, [], [], error_msgs)
+            if len([key for key in state_dict if key.startswith(prefix)]) > 0:
+                module._load_from_state_dict(*args)
+
+            for name, child in module._modules.items():
+                if child is not None:
+                    load(child, state_dict, prefix + name + ".")
+
+        load(model_to_load, state_dict, prefix=start_prefix)
+        del state_dict
+        return error_msgs
+
+    def _load_pretrained_model(
+        cls,
+        state_dict,
+        loaded_keys,
+        resolved_archive_file,
+        pretrained_model_name_or_path,
+    ):
+        # Retrieve missing & unexpected_keys
+        model_state_dict = cls.state_dict()
+        expected_keys = list(model_state_dict.keys())
+        loaded_keys = [key for key in loaded_keys]
+
+        unexpected_keys = set(loaded_keys) - set(expected_keys)
+        model_buffers = {n for n, _ in cls.named_buffers()}
+        unexpected_keys = list(unexpected_keys - model_buffers)
+
+        ptrs = collections.defaultdict(list)
+        for name, tensor in cls.state_dict().items():
+            ptrs[(tensor.device, storage_ptr(tensor), storage_size(tensor))].append(
+                name
+            )
+        # retrieve unintialized modules and initialize before maybe overriding that with the pretrained weights.
+
+        _loaded_keys = loaded_keys
+        for module_name, module in cls.named_modules():
+            loaded_keys = [
+                k.replace(f"{module_name}.", "")
+                for k in _loaded_keys
+                if k.startswith(f"{module_name}.")
+            ]
+            if len(set(module.state_dict().keys()) - set(loaded_keys)) == 0:
+                module._is_hf_initialized = True
+
+        # Make sure we are able to load base models as well as derived models (with heads)
+        start_prefix = ""
+        model_to_load = cls
+        error_msgs = []
+        if len(resolved_archive_file) > 1:
+            resolved_archive_file = tqdm_lib.tqdm(
+                resolved_archive_file, desc="Loading checkpoint shards"
+            )
+        for shard_file in resolved_archive_file:
+            # Skip the load for shards that only contain disk-offloaded weights when using safetensors for the offload.
+            state_dict = torch.load(shard_file, map_location="cpu")
+            # Mistmatched keys contains tuples key/shape1/shape2 of weights in the checkpoint that have a shape not
+            # matching the weights in the model.
+            error_msgs += cls._load_state_dict_into_model(
+                model_to_load, state_dict, start_prefix
+            )
+            # force memory release
+            del state_dict
+            gc.collect()
+
+        print(
+            f"All model checkpoint weights were used when initializing {cls.__class__.__name__}.\n"
+        )
+        print(
+            f"All the weights of {cls.__class__.__name__} were initialized from the model checkpoint at"
+            f" {pretrained_model_name_or_path}.\nIf your task is similar to the task the model of the checkpoint"
+            f" was trained on, you can already use {cls.__class__.__name__} for predictions without further"
+            " training."
+        )
+        return cls
+
+    @torch.inference_mode()
+    def chat(
+        self,
+        tokenizer,
+        query: str,
+        history: List[Tuple[str, str]] = None,
+        role: str = "user",
+    ):
+        if history is None:
+            history = []
+        inputs = tokenizer.build_chat_input(query, history=history, role=role)
+        inputs = inputs.to(next(self.parameters()).device)
+
+        generation_config = copy.deepcopy(self.generation_config)
+        inputs_tensor = inputs["input_ids"]
+        input_ids = inputs_tensor.repeat_interleave(
+            generation_config.num_return_sequences, dim=0
+        )
+
+        outputs = self.sample(
+            input_ids,
+            pad_token_id=generation_config.pad_token_id,
+            eos_token_id=generation_config.eos_token_id,
+            output_hidden_states = generation_config.output_hidden_states,
+            use_cache = generation_config.use_cache
+        )
+    
+        outputs = outputs.tolist()[0][len(inputs["input_ids"][0]) : -1]
+        response = tokenizer.decode(outputs)
+        history.append({"role": role, "content": query})
+        return response, history
+
+    def sample(
+        self,
+        input_ids: torch.LongTensor,
+        pad_token_id: Optional[int] = None,
+        eos_token_id: Optional[Union[int, List[int]]] = None,
+        output_hidden_states: Optional[bool] = None,
+        use_cache: Optional[bool] = None
+    ):
+        if isinstance(eos_token_id, int):
+            eos_token_id = [eos_token_id]
+        eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device)
+
+        unfinished_sequences = torch.ones(
+            input_ids.shape[0], dtype=torch.long, device=input_ids.device
+        )
+
+        this_peer_finished = False  # used by synced_gpus only
+        while True:
+            input_ids_in = input_ids
+            batch_size, seq_length = input_ids_in.shape
+            position_ids_in = (
+                torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
+                .unsqueeze(0)
+                .repeat(batch_size, 1)
+            )
+            use_cache = use_cache if use_cache is not None else self.config.use_cache
+            model_inputs = {
+                "input_ids": input_ids_in,
+                "past_key_values": None,
+                "position_ids": position_ids_in,
+                "return_last_logit": True,
+                "use_cache": use_cache,
+            }
+
+            logits = self.transformer(
+                **model_inputs,
+                return_dict=True,
+                output_hidden_states=output_hidden_states,
+            )
+            next_token_logits = logits[:, -1, :]
+            next_token_scores = next_token_logits
+            probs = nn.functional.softmax(next_token_scores, dim=-1)
+            next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
+
+            # finished sentences should have their next token be a padding token
+            if eos_token_id is not None:
+                next_tokens = next_tokens * unfinished_sequences + pad_token_id * (
+                    1 - unfinished_sequences
+                )
+
+            # update generated ids, model inputs, and length for next step
+            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
+
+            # if eos_token was found in one sentence, set sentence to finished
+            if eos_token_id_tensor is not None:
+                unfinished_sequences = unfinished_sequences.mul(
+                    next_tokens.tile(eos_token_id_tensor.shape[0], 1)
+                    .ne(eos_token_id_tensor.unsqueeze(1))
+                    .prod(dim=0)
+                )
+                if unfinished_sequences.max() == 0:
+                    this_peer_finished = True
+            if this_peer_finished:
+                break
+
+        return input_ids

chatglm/tokenization_chatglm.py

@@ -0,0 +1,161 @@
+import json
+import os
+import torch
+from typing import List, Optional, Union, Dict
+from sentencepiece import SentencePieceProcessor
+from transformers import PreTrainedTokenizer
+
+
+class SPTokenizer:
+    def __init__(self, model_path: str):
+        # reload tokenizer
+        assert os.path.isfile(model_path), model_path
+        self.sp_model = SentencePieceProcessor(model_file=model_path)
+
+        # BOS / EOS token IDs
+        self.n_words: int = self.sp_model.vocab_size()
+        self.bos_id: int = self.sp_model.bos_id()
+        self.eos_id: int = self.sp_model.eos_id()
+        self.pad_id: int = self.sp_model.unk_id()
+        assert self.sp_model.vocab_size() == self.sp_model.get_piece_size()
+
+        special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop", "<|system|>", "<|user|>", "<|assistant|>",
+                          "<|observation|>"]
+        self.special_tokens = {}
+        self.index_special_tokens = {}
+        for token in special_tokens:
+            self.special_tokens[token] = self.n_words
+            self.index_special_tokens[self.n_words] = token
+            self.n_words += 1
+
+    def tokenize(self, s: str):
+        return self.sp_model.EncodeAsPieces(s)
+
+    def encode(self, s: str, bos: bool = False, eos: bool = False) -> List[int]:
+        assert type(s) is str
+        t = self.sp_model.encode(s)
+        if bos:
+            t = [self.bos_id] + t
+        if eos:
+            t = t + [self.eos_id]
+        return t
+
+    def decode(self, t: List[int]) -> str:
+        text, buffer = "", []
+        for token in t:
+            if token in self.index_special_tokens:
+                if buffer:
+                    text += self.sp_model.decode(buffer)
+                    buffer = []
+                text += self.index_special_tokens[token]
+            else:
+                buffer.append(token)
+        if buffer:
+            text += self.sp_model.decode(buffer)
+        return text
+
+    def decode_tokens(self, tokens: List[str]) -> str:
+        text = self.sp_model.DecodePieces(tokens)
+        return text
+
+    def convert_token_to_id(self, token):
+        """ Converts a token (str) in an id using the vocab. """
+        if token in self.special_tokens:
+            return self.special_tokens[token]
+        return self.sp_model.PieceToId(token)
+
+    def convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        if index in self.index_special_tokens:
+            return self.index_special_tokens[index]
+        if index in [self.eos_id, self.bos_id, self.pad_id] or index < 0:
+            return ""
+        return self.sp_model.IdToPiece(index)
+
+
+class ChatGLMTokenizer(PreTrainedTokenizer):
+    vocab_files_names = {"vocab_file": "tokenizer.model"}
+
+    model_input_names = ["input_ids", "attention_mask", "position_ids"]
+
+    def __init__(self, vocab_file, padding_side="left", clean_up_tokenization_spaces=False, **kwargs):
+        self.name = "GLMTokenizer"
+
+        self.vocab_file = vocab_file
+        self.tokenizer = SPTokenizer(vocab_file)
+        self.special_tokens = {
+            "<bos>": self.tokenizer.bos_id,
+            "<eos>": self.tokenizer.eos_id,
+            "<pad>": self.tokenizer.pad_id
+        }
+        super().__init__(padding_side=padding_side, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs)
+
+    def get_command(self, token):
+        if token in self.special_tokens:
+            return self.special_tokens[token]
+        assert token in self.tokenizer.special_tokens, f"{token} is not a special token for {self.name}"
+        return self.tokenizer.special_tokens[token]
+
+    @property
+    def unk_token(self) -> str:
+        return "<unk>"
+
+    @property
+    def pad_token(self) -> str:
+        return "<unk>"
+
+    @property
+    def pad_token_id(self):
+        return self.get_command("<pad>")
+
+    @property
+    def eos_token(self) -> str:
+        return "</s>"
+
+    @property
+    def eos_token_id(self):
+        return self.get_command("<eos>")
+
+    @property
+    def vocab_size(self):
+        return self.tokenizer.n_words
+
+    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 _tokenize(self, text, **kwargs):
+        return self.tokenizer.tokenize(text)
+
+    def _convert_token_to_id(self, token):
+        """ Converts a token (str) in an id using the vocab. """
+        return self.tokenizer.convert_token_to_id(token)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.tokenizer.convert_id_to_token(index)
+
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        return self.tokenizer.decode_tokens(tokens)
+
+    def build_single_message(self, role, metadata, message):
+        assert role in ["system", "user", "assistant", "observation"], role
+        role_tokens = [self.get_command(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n")
+        message_tokens = self.tokenizer.encode(message)
+        tokens = role_tokens + message_tokens
+        return tokens
+
+    def build_chat_input(self, query, history=None, role="user"):
+        if history is None:
+            history = []
+        input_ids = []
+        for item in history:
+            content = item["content"]
+            if item["role"] == "system" and "tools" in item:
+                content = content + "\n" + json.dumps(item["tools"], indent=4, ensure_ascii=False)
+            input_ids.extend(self.build_single_message(item["role"], item.get("metadata", ""), content))
+        input_ids.extend(self.build_single_message(role, "", query))
+        input_ids.extend([self.get_command("<|assistant|>")])
+        return self.batch_encode_plus([input_ids], return_tensors="pt", is_split_into_words=True)

chatglm/tokenizer_config.json

@@ -0,0 +1,12 @@
+{
+  "name_or_path": "THUDM/chatglm3-6b",
+  "remove_space": false,
+  "do_lower_case": false,
+  "tokenizer_class": "ChatGLMTokenizer",
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_chatglm.ChatGLMTokenizer",
+      null
+      ]
+  }
+}

demo.py

@@ -0,0 +1,64 @@
+import json
+
+
+from chatglm import ChatGLMForConditionalGeneration
+from chatglm import ChatGLMTokenizer
+
+from transformers import AutoConfig
+
+pretrained_model_name_or_path = "../ZhipuAI/chatglm3-6b"
+config, kwargs = AutoConfig.from_pretrained(
+    pretrained_model_name_or_path,
+    return_unused_kwargs=True,
+    trust_remote_code=True,
+    code_revision=None,
+    _commit_hash=None,
+)
+glm = ChatGLMForConditionalGeneration(config)
+
+
+tokenizer_config_file = "./chatglm/tokenizer_config.json"
+if tokenizer_config_file is not None:
+    with open(tokenizer_config_file, encoding="utf-8") as tokenizer_config_handle:
+        init_kwargs = json.load(tokenizer_config_handle)
+    init_kwargs.pop("tokenizer_class", None)
+    init_kwargs.pop("tokenizer_file", None)
+    saved_init_inputs = init_kwargs.pop("init_inputs", ())
+    init_inputs = saved_init_inputs
+init_kwargs["vocab_file"] = './chatglm/tokenizer.model'
+init_kwargs["added_tokens_file"] = None
+init_kwargs["special_tokens_map_file"] = None
+init_kwargs["tokenizer_file"] = None
+init_kwargs["name_or_path"] = pretrained_model_name_or_path
+tokenizer = ChatGLMTokenizer(*init_inputs, **init_kwargs)
+
+
+glm = glm.from_pretrained(pretrained_model_name_or_path, config=config).half().cuda()
+glm = glm.eval()
+response, history = glm.chat(tokenizer, "colin", history=[])
+print(response)
+response, history = glm.chat(tokenizer, "你好", history=history)
+print(response)
+# response, history = glm.chat(tokenizer, "你是一个心理学专家，请问晚上睡不着应该怎么办", history=history)
+# print(response)
+
+
+# import plotly_express as px
+# px.imshow(ron)
+# gapminder = px.data.gapminder()
+# gapminder2007 = gapminder.query('year == 2007')
+# px.scatter(gapminder2007, x='gdpPercap', y='lifeExp')
+
+
+
+# from modelscope import AutoTokenizer, AutoModel, snapshot_download
+# model_dir = snapshot_download("ZhipuAI/chatglm3-6b", cache_dir="./chatglm", revision="v1.0.0")
+# model = AutoModel.from_pretrained(model_dir, trust_remote_code=True).half().cuda()
+# tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
+# model = model.eval()
+# response, history = model.chat(tokenizer, "colin", history=[])
+# print(response)
+# response, history = model.chat(tokenizer, "你好", history=history)
+# print(response)
+# # response, history = model.chat(tokenizer, "你是一个心理学专家，请问晚上睡不着应该怎么办", history=history)
+# # print(response)