Python files for modeling

configuration_glm2.py

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+"""gLM2 model configuration
+
+Copied straight from https://huggingface.co/tattabio/gLM2_650M/blob/main/configuration_glm2.py
+"""
+
+from typing import Optional
+from transformers import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+
+class gLM2Config(PretrainedConfig):
+    model_type = "gLM2"
+
+    def __init__(
+        self,
+        dim: int = 640,
+        depth: int = 30,
+        heads: int = 10,
+        vocab_size: int = 37,
+        swiglu_multiple_of: int = 256,
+        ffn_dim_multiplier: Optional[float] = None,
+        norm_eps: float = 1e-5,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.dim = dim
+        self.depth = depth
+        self.heads = heads
+        self.vocab_size = vocab_size
+        self.swiglu_multiple_of = swiglu_multiple_of
+        self.ffn_dim_multiplier = ffn_dim_multiplier
+        self.norm_eps = norm_eps
+
+        self.auto_map = {
+            "AutoConfig": "configuration_glm2.gLM2Config",
+            "AutoModel": "modeling_glm2.gLM2Model",
+            "AutoModelForMaskedLM": "modeling_glm2.gLM2ForMaskedLM"
+        }

glm_tokenizer.py

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+"""gLM tokenizer
+
+Copied straight from https://huggingface.co/tattabio/gLM2_650M/blob/main/glm_tokenizer.py
+"""
+
+from tokenizers import Tokenizer
+from tokenizers.models import BPE
+from transformers import PreTrainedTokenizerFast
+
+
+class gLM2Tokenizer(PreTrainedTokenizerFast):
+
+    VOCAB = [
+        "<cls>", "<pad>", "<eos>", "<unk>",
+        "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K",
+        "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z",
+        "O", "a", "t", "c", "g", "<+>", "<->", "<mask>", "<sep>",
+    ]
+
+    def __init__(
+        self,
+        unk_token="<unk>",
+        cls_token="<cls>",
+        pad_token="<pad>",
+        mask_token="<mask>",
+        eos_token="<eos>",
+        sep_token="<sep>",
+        pos_token="<+>",
+        neg_token="<->",
+        **kwargs,
+    ):
+        all_tokens = self.VOCAB
+        token_to_id = {tok: ind for ind, tok in enumerate(all_tokens)}
+
+        bpe = BPE(token_to_id, merges=[], unk_token=str(unk_token))
+        tokenizer = Tokenizer(bpe)
+        special_tokens = [cls_token, pad_token,
+                          mask_token, eos_token, sep_token, pos_token, neg_token]
+
+        tokenizer.add_special_tokens(
+            special_tokens,
+        )
+
+        super().__init__(
+            tokenizer_object=tokenizer,
+            unk_token=unk_token,
+            cls_token=cls_token,
+            pad_token=pad_token,
+            mask_token=mask_token,
+            eos_token=eos_token,
+            sep_token=sep_token,
+            **kwargs,
+        )

modeling_glm2.py

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+"""PyTorch gLM2 model.
+
+Copied straight from https://huggingface.co/tattabio/gLM2_650M/blob/main/modeling_glm2.py
+"""
+
+import torch
+from einops import rearrange, repeat
+from typing import Optional, Tuple, Union
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from transformers.modeling_outputs import (
+    BaseModelOutput,
+    MaskedLMOutput,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+from .configuration_glm2 import gLM2Config
+
+logger = logging.get_logger(__name__)
+
+
+def rotate_half(x, interleaved=False):
+    if not interleaved:
+        x1, x2 = x.chunk(2, dim=-1)
+        return torch.cat((-x2, x1), dim=-1)
+    else:
+        x1, x2 = x[..., ::2], x[..., 1::2]
+        return rearrange(
+            torch.stack((-x2, x1), dim=-1), "... d two -> ... (d two)", two=2
+        )
+
+
+def apply_rotary_emb_torch(x, cos, sin, interleaved=False):
+    """
+    x: (batch_size, seqlen, nheads, headdim)
+    cos, sin: (seqlen, rotary_dim / 2) or (batch_size, seqlen, rotary_dim / 2)
+    """
+    ro_dim = cos.shape[-1] * 2
+    assert ro_dim <= x.shape[-1]
+    seqlen = x.shape[1]
+    cos, sin = cos[:seqlen], sin[:seqlen]
+    cos = repeat(
+        cos, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)"
+    )
+    sin = repeat(
+        sin, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)"
+    )
+    return torch.cat(
+        [
+            x[..., :ro_dim] * cos +
+            rotate_half(x[..., :ro_dim], interleaved) * sin,
+            x[..., ro_dim:],
+        ],
+        dim=-1,
+    )
+
+
+class RotaryEmbedding(torch.nn.Module):
+    """
+    Copied from https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/layers/rotary.py.
+    Changed to use the torch version of apply_rotary_emb_func.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        base=10000.0,
+        interleaved=False,
+        scale_base=None,
+        pos_idx_in_fp32=True,
+        device=None,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.base = float(base)
+        self.pos_idx_in_fp32 = pos_idx_in_fp32
+        # Generate and save the inverse frequency buffer (non trainable)
+        inv_freq = self._compute_inv_freq(device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.interleaved = interleaved
+        self.scale_base = scale_base
+        scale = (
+            (torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim)
+            / (1.4 * dim)
+            if scale_base is not None
+            else None
+        )
+        self.register_buffer("scale", scale, persistent=False)
+
+        self._seq_len_cached = 0
+        self._cos_cached = None
+        self._sin_cached = None
+        self._cos_k_cached = None
+        self._sin_k_cached = None
+
+    def _compute_inv_freq(self, device=None):
+        return 1.0 / (
+            self.base
+            ** (
+                torch.arange(0, self.dim, 2, device=device,
+                             dtype=torch.float32)
+                / self.dim
+            )
+        )
+
+    def _update_cos_sin_cache(self, seqlen, device=None, dtype=None):
+        # Reset the tables if the sequence length has changed,
+        # if we're on a new device (possibly due to tracing for instance),
+        # or if we're switching from inference mode to training
+        if (
+            seqlen > self._seq_len_cached
+            or self._cos_cached is None
+            or self._cos_cached.device != device
+            or self._cos_cached.dtype != dtype
+            or (self.training and self._cos_cached.is_inference())
+        ):
+            self._seq_len_cached = seqlen
+            # We want fp32 here, not self.inv_freq.dtype, since the model could be loaded in bf16
+            # And the output of arange can be quite large, so bf16 would lose a lot of precision.
+            # However, for compatibility reason, we add an option to use the dtype of self.inv_freq.
+            if self.pos_idx_in_fp32:
+                t = torch.arange(seqlen, device=device, dtype=torch.float32)
+                # We want fp32 here as well since inv_freq will be multiplied with t, and the output
+                # will be large. Having it in bf16 will lose a lot of precision and cause the
+                # cos & sin output to change significantly.
+                # We want to recompute self.inv_freq if it was not loaded in fp32
+                if self.inv_freq.dtype != torch.float32:
+                    inv_freq = self._compute_inv_freq(device=device)
+                else:
+                    inv_freq = self.inv_freq
+            else:
+                t = torch.arange(seqlen, device=device,
+                                 dtype=self.inv_freq.dtype)
+                inv_freq = self.inv_freq
+            # Don't do einsum, it converts fp32 to fp16 under AMP
+            # freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+            freqs = torch.outer(t, inv_freq)
+            if self.scale is None:
+                self._cos_cached = torch.cos(freqs).to(dtype)
+                self._sin_cached = torch.sin(freqs).to(dtype)
+            else:
+                power = (
+                    torch.arange(
+                        seqlen, dtype=self.scale.dtype, device=self.scale.device
+                    )
+                    - seqlen // 2
+                ) / self.scale_base
+                scale = self.scale.to(device=power.device) ** rearrange(
+                    power, "s -> s 1"
+                )
+                # We want the multiplication by scale to happen in fp32
+                self._cos_cached = (torch.cos(freqs) * scale).to(dtype)
+                self._sin_cached = (torch.sin(freqs) * scale).to(dtype)
+                self._cos_k_cached = (torch.cos(freqs) / scale).to(dtype)
+                self._sin_k_cached = (torch.sin(freqs) / scale).to(dtype)
+
+    def forward(
+        self,
+        qkv: torch.Tensor,
+        max_seqlen: Optional[int] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        """
+        qkv: (batch, seqlen, 3, nheads, headdim)
+        """
+        seqlen = qkv.shape[1]
+        if seqlen > self._seq_len_cached:
+            self._update_cos_sin_cache(
+                seqlen, device=qkv.device, dtype=qkv.dtype)
+        elif max_seqlen is not None:
+            self._update_cos_sin_cache(
+                max_seqlen, device=qkv.device, dtype=qkv.dtype)
+        q_rot = apply_rotary_emb_torch(
+            qkv[:, :, 0], self._cos_cached, self._sin_cached, self.interleaved
+        )
+        k_rot = apply_rotary_emb_torch(
+            qkv[:, :, 1], self._cos_cached, self._sin_cached, self.interleaved
+        )
+        return torch.stack((q_rot, k_rot, qkv[:, :, 2]), dim=2)
+
+
+# @torch.jit.script
+def rmsnorm_func(hidden_states, weight, variance_epsilon):
+    """Apply the root mean square normalization."""
+    input_dtype = hidden_states.dtype
+    hidden_states = hidden_states.to(torch.float32)
+    variance = hidden_states.pow(2).mean(-1, keepdim=True)
+    hidden_states = hidden_states * torch.rsqrt(variance + variance_epsilon)
+    return (weight * hidden_states).to(input_dtype)
+
+
+class RMSNorm(nn.Module):
+    """Root mean square normalization."""
+
+    def __init__(self, dim, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(dim))
+        self.register_buffer(
+            "variance_epsilon",
+            torch.tensor(eps),
+            persistent=False,
+        )
+
+    def forward(self, hidden_states):
+        return rmsnorm_func(hidden_states, self.weight, self.variance_epsilon)
+
+
+class Attention(nn.Module):
+    """Multi-head attention module."""
+
+    def __init__(self, config: gLM2Config):
+        super().__init__()
+        self.n_heads = config.heads
+        self.head_dim = config.dim // config.heads
+
+        self.wqkv = nn.Linear(config.dim, self.n_heads *
+                              self.head_dim * 3, bias=False)
+        self.wo = nn.Linear(config.heads * self.head_dim,
+                            config.dim, bias=False)
+
+        self.rotary_emb = RotaryEmbedding(self.head_dim)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        bsz, seqlen, h_size = x.shape
+        qkv = self.wqkv(x)
+
+        qkv = qkv.view(bsz, seqlen, 3, self.n_heads, self.head_dim)
+        qkv = self.rotary_emb(qkv)
+
+        # (batch, nheads, 3, seqlen, headdim)
+        qkv = torch.transpose(qkv, 3, 1)
+        q = qkv[:, :, 0]
+        k = qkv[:, :, 1]
+        v = qkv[:, :, 2]
+        if attention_mask is not None:
+            attention_mask = attention_mask[:, None, None, :]
+            attention_mask = attention_mask.expand(
+                bsz, self.n_heads, seqlen, seqlen
+            ).bool()
+        # [B, heads, seq, D]
+        output = torch.nn.functional.scaled_dot_product_attention(
+            q, k, v, attn_mask=attention_mask
+        )
+        output = output.permute(0, 2, 1, 3).contiguous()
+
+        output = output.view(bsz, seqlen, h_size)
+        return self.wo(output)
+
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        hidden_dim: int,
+        multiple_of: int,
+        ffn_dim_multiplier: Optional[float],
+    ):
+        """
+        SwiGLU FeedForward module.
+
+        Args:
+            dim (int): Input dimension.
+            hidden_dim (int): Hidden dimension of the feedforward layer.
+            multiple_of (int): Value to ensure hidden dimension is a multiple of this value.
+            ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None.
+        """
+        super().__init__()
+        hidden_dim = int(2 * hidden_dim / 3)
+        # custom dim factor multiplier
+        if ffn_dim_multiplier is not None:
+            hidden_dim = int(ffn_dim_multiplier * hidden_dim)
+        hidden_dim = multiple_of * \
+            ((hidden_dim + multiple_of - 1) // multiple_of)
+
+        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
+        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
+        self.w3 = nn.Linear(dim, hidden_dim, bias=False)
+
+    def forward(self, x):
+        return self.w2(nn.functional.silu(self.w1(x)) * self.w3(x))
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, config: gLM2Config):
+        super().__init__()
+        self.n_heads = config.heads
+        self.dim = config.dim
+        self.head_dim = config.dim // config.heads
+        self.attention = Attention(config)
+        self.feed_forward = FeedForward(
+            dim=config.dim,
+            hidden_dim=4 * config.dim,
+            multiple_of=config.swiglu_multiple_of,
+            ffn_dim_multiplier=config.ffn_dim_multiplier,
+        )
+        self.attention_norm = RMSNorm(config.dim, eps=config.norm_eps)
+        self.ffn_norm = RMSNorm(config.dim, eps=config.norm_eps)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        r = self.attention(self.attention_norm(
+            x), attention_mask=attention_mask)
+        h = x + r
+        r = self.feed_forward(self.ffn_norm(h))
+        out = h + r
+        return out
+
+
+class TransformerLayers(nn.Module):
+    def __init__(self, config: gLM2Config):
+        super().__init__()
+        self.config = config
+        self.layers = torch.nn.ModuleList(
+            [TransformerBlock(config=config) for _ in range(config.depth)]
+        )
+
+    def forward(
+        self,
+        x: torch.FloatTensor,
+        attention_mask: Optional[torch.BoolTensor] = None,
+        return_all_hiddens: bool = False,
+    ):
+        if x.shape[-1] != self.config.dim:
+            raise ValueError(
+                f"Input feature dim should be {self.config.dim}, but input has shape {x.shape}"
+            )
+        hiddens = []
+        for layer in self.layers:
+            x = layer(x, attention_mask=attention_mask)
+            if return_all_hiddens:
+                hiddens.append(x)
+
+        if return_all_hiddens:
+            return x, hiddens
+        return x
+
+
+class gLM2PreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+    config_class = gLM2Config
+    base_model_prefix = "glm2"
+    supports_gradient_checkpointing = False
+
+    # https://github.com/huggingface/transformers/blob/7032e0203262ebb2ebf55da8d2e01f873973e835/src/transformers/models/bert/modeling_bert.py#L748
+    def _init_weights(module, initializer_range=0.02):
+        if isinstance(module, nn.Linear):
+            nn.init.normal_(module.weight, std=initializer_range)
+            if module.bias is not None:
+                nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            nn.init.normal_(module.weight, std=initializer_range)
+            if module.padding_idx is not None:
+                nn.init.zeros_(module.weight[module.padding_idx])
+
+
+class gLM2Model(gLM2PreTrainedModel):
+    """gLM2 Model."""
+
+    def __init__(self, config: gLM2Config):
+        super().__init__(config)
+        self.config = config
+
+        self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
+        self.encoder = TransformerLayers(config)
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutput]:
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        h = self.tok_embeddings(input_ids)
+        if output_hidden_states:
+            sequence_output, all_hidden_states = self.encoder(
+                h, attention_mask, return_all_hiddens=True)
+        else:
+            sequence_output = self.encoder(h, attention_mask)
+            all_hidden_states = None
+
+        if not return_dict:
+            return (sequence_output, all_hidden_states)
+
+        return BaseModelOutput(
+            last_hidden_state=sequence_output,
+            hidden_states=all_hidden_states,
+
+        )
+
+
+class gLM2ForMaskedLM(gLM2PreTrainedModel):
+
+    def __init__(self, config: gLM2Config):
+        super().__init__(config)
+
+        self.glm2 = gLM2Model(config)
+        self.lm_head = gLM2LMHead(config)
+        self.init_weights()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, MaskedLMOutput]:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.glm2(
+            input_ids,
+            attention_mask=attention_mask,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = outputs[0]
+        prediction_scores = self.lm_head(sequence_output)
+
+        masked_lm_loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+
+            labels = labels.to(prediction_scores.device)
+            masked_lm_loss = loss_fct(
+                prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+
+        if not return_dict:
+            output = (prediction_scores,) + outputs[2:]
+            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+        return MaskedLMOutput(
+            loss=masked_lm_loss,
+            logits=prediction_scores,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class gLM2LMHead(nn.Module):
+    """gLM2 head for masked language modeling."""
+
+    def __init__(self, config):
+        super().__init__()
+
+        self.norm = RMSNorm(config.dim, eps=config.norm_eps)
+        self.proj_output = nn.Linear(
+            config.dim, config.vocab_size, bias=False)
+
+    def forward(self, features):
+        return self.proj_output(self.norm(features))