Upload gLM2ForMaskedLM

config.json

@@ -0,0 +1,20 @@
+{
+  "architectures": [
+    "gLM2ForMaskedLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_glm2.gLM2Config",
+    "AutoModel": "modeling_glm2.gLM2Model",
+    "AutoModelForMaskedLM": "modeling_glm2.gLM2ForMaskedLM"
+  },
+  "depth": 30,
+  "dim": 640,
+  "ffn_dim_multiplier": null,
+  "heads": 10,
+  "model_type": "gLM2",
+  "norm_eps": 1e-05,
+  "swiglu_multiple_of": 256,
+  "torch_dtype": "float32",
+  "transformers_version": "4.36.0",
+  "vocab_size": 37
+}

configuration_glm2.py

@@ -0,0 +1,37 @@
+"""gLM2 model configuration"""
+
+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"
+        }

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:047facd39472afe082c985058fa042f43a7b10d4ffb2ab51b9a3e6c63e9f3834
+size 609855088

modeling_glm2.py

@@ -0,0 +1,565 @@
+"""PyTorch gLM2 model.
+
+Requires flash attention.
+Some modules adapted from:
+https://github.com/meta-llama/llama/blob/main/llama/model.py
+"""
+import math
+import torch
+from einops import rearrange
+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
+
+try:
+    from flash_attn.ops.activations import swiglu
+    from flash_attn.layers.rotary import apply_rotary_emb_func
+    from flash_attn import (
+        flash_attn_kvpacked_func,
+        flash_attn_varlen_kvpacked_func,
+    )
+    from flash_attn.bert_padding import pad_input, unpad_input
+    from flash_attn.ops.triton.layer_norm import RMSNorm
+except ImportError:
+    raise ImportError(
+        "gLM2 requires flash attention: `pip install flash-attn --no-build-isolation`")
+
+from .configuration_glm2 import gLM2Config
+
+
+logger = logging.get_logger(__name__)
+
+
+class RotaryEmbedding(torch.nn.Module):
+    """
+    Copied from https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/layers/rotary.py.
+    Changed to only support passing in q or k individually, so that we can use varlen rotary.
+    """
+
+    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,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        seqlen_offset: Union[int, torch.Tensor] = 0,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seqlen: Optional[int] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        """
+        q: (batch, seqlen, nheads, headdim). If cu_seqlens is not None,
+            shape (total_seqlen, nheads, headdim).
+        k: (batch, seqlen, nheads, headdim). If cu_seqlens is not None,
+            shape (total_seqlen, nheads, headdim).
+        seqlen_offset: (batch_size,) or int. Each sequence in x is shifted by this amount.
+            Most commonly used in inference when we have KV cache.
+            If it's a tensor of shape (batch_size,), then to update the cos / sin cache, one
+            should pass in max_seqlen, which will update the cos / sin cache up to that length.
+        Apply rotary embedding *inplace* to qkv and / or kv.
+        """
+        if cu_seqlens is not None:
+            assert max_seqlen is not None
+        seqlen = q.shape[1] if max_seqlen is None else max_seqlen
+        if max_seqlen is not None:
+            self._update_cos_sin_cache(
+                max_seqlen, device=q.device, dtype=q.dtype)
+        elif isinstance(seqlen_offset, int):
+            self._update_cos_sin_cache(
+                seqlen + seqlen_offset, device=q.device, dtype=q.dtype
+            )
+        q = apply_rotary_emb_func(
+            q,
+            self._cos_cached,
+            self._sin_cached,
+            interleaved=self.interleaved,
+            inplace=True,
+            seqlen_offsets=seqlen_offset,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+        )
+        if self.scale is None:
+            k = apply_rotary_emb_func(
+                k,
+                self._cos_cached,
+                self._sin_cached,
+                interleaved=self.interleaved,
+                inplace=True,
+                seqlen_offsets=seqlen_offset,
+                cu_seqlens=cu_seqlens,
+                max_seqlen=max_seqlen,
+            )
+        else:
+            k = apply_rotary_emb_func(
+                k,
+                self._cos_k_cached,
+                self._sin_k_cached,
+                interleaved=self.interleaved,
+                inplace=True,
+                seqlen_offsets=seqlen_offset,
+                cu_seqlens=cu_seqlens,
+                max_seqlen=max_seqlen,
+            )
+        return q, k
+
+
+# @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_varlen(
+        self,
+        x: torch.Tensor,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seq_len: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        total_seqlen, h_size = x.shape
+        qkv = self.wqkv(x)
+        q, k, v = torch.split(qkv, self.n_heads * self.head_dim, dim=-1)
+
+        q = q.view(total_seqlen, self.n_heads, self.head_dim)
+        k = k.view(total_seqlen, self.n_heads, self.head_dim)
+        v = v.view(total_seqlen, self.n_heads, self.head_dim)
+
+        q, k = self.rotary_emb(
+            q, k, cu_seqlens=cu_seqlens, max_seqlen=max_seq_len)
+
+        # (seqlen, 2, n_heads, head_dim)
+        kv = torch.stack([k, v], 1)
+
+        # (seqlen, n_heads, head_dim)
+        output = flash_attn_varlen_kvpacked_func(
+            q,
+            kv,
+            cu_seqlens_q=cu_seqlens,
+            cu_seqlens_k=cu_seqlens,
+            max_seqlen_q=max_seq_len,
+            max_seqlen_k=max_seq_len,
+            dropout_p=0.0,
+            causal=False,
+        )
+        output = output.view(total_seqlen, h_size)
+        return self.wo(output)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seq_len: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if cu_seqlens is not None:
+            assert max_seq_len is not None
+            return self._forward_varlen(x, cu_seqlens, max_seq_len)
+
+        bsz, seqlen, h_size = x.shape
+        qkv = self.wqkv(x)
+        q, k, v = torch.split(qkv, self.n_heads * self.head_dim, dim=-1)
+        q = q.view(bsz, seqlen, self.n_heads, self.head_dim)
+        k = k.view(bsz, seqlen, self.n_heads, self.head_dim)
+        v = v.view(bsz, seqlen, self.n_heads, self.head_dim)
+
+        q, k = self.rotary_emb(q, k)
+        # (bs, seqlen, 2, n_heads, head_dim)
+        kv = torch.stack([k, v], 2)
+
+        output = flash_attn_kvpacked_func(
+            q,
+            kv,
+            dropout_p=0.0,
+            causal=False,
+        )
+        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(swiglu(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,
+        cu_seqlens: Optional[torch.Tensor] = None,
+        max_seq_len: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        r = self.attention(
+            self.attention_norm(x), cu_seqlens, max_seq_len
+        )
+        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)]
+        )
+        self.apply(self._init_weights)
+        # Apply special scaled init to the residual projections, per GPT-2 paper.
+        # Weight w2 is output of FeedForward. Weight wo is output of Attention.
+        for pn, p in self.named_parameters():
+            if pn.endswith('w2.weight') or pn.endswith('wo.weight'):
+                torch.nn.init.normal_(
+                    p, mean=0.0, std=0.02/math.sqrt(2 * self.config.depth))
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+
+    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}"
+            )
+        batch_size, seq_len = x.shape[:2]
+        should_unpad = attention_mask is not None and not attention_mask.all()
+        if should_unpad:
+            x, indices, cu_seqlens, max_seq_len_in_batch = unpad_input(
+                x, attention_mask
+            )
+        else:
+            indices, cu_seqlens, max_seq_len_in_batch = None, None, None
+        hiddens = []
+        for layer in self.layers:
+            x = layer(x, cu_seqlens, max_seq_len_in_batch)
+            if return_all_hiddens:
+                hiddens.append(x)
+
+        if should_unpad:
+            x = pad_input(x, indices, batch_size, seq_len)
+            if return_all_hiddens:
+                hiddens = [pad_input(h, indices, batch_size, seq_len)
+                           for h in hiddens]
+
+        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._init_weights(self.tok_embeddings)
+        self.encoder = TransformerLayers(config)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+    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(self.lm_head)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+    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))