Upload TransformerForMaskedLM

config.json

@@ -7,7 +7,8 @@
   "attention_probs_dropout_prob": 0.1,
   "attn_impl": "sdpa",
   "auto_map": {
-    "AutoConfig": "configuration_transformer.TransformerConfig"
+    "AutoConfig": "configuration_transformer.TransformerConfig",
+    "AutoModelForMaskedLM": "modeling_transformer.TransformerForMaskedLM"
   },
   "decoder_start_token_id": 1,
   "decoder_vocab_size": 36,

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:96a62f322c3c4c8399ab9432c38bb392a175bd82dfdd246de5de4a4bf43d1616
+size 1208482648

modeling_transformer.py

@@ -0,0 +1,600 @@
+from .configuration_transformer import TransformerConfig
+import torch
+from torch import nn
+from torch.nn import init
+import torch.nn.functional as F
+from torch.nn.parameter import Parameter
+from torch import nn
+from typing import Tuple, List
+from itertools import chain
+from transformers.modeling_outputs import (
+    BaseModelOutput,
+    MaskedLMOutput,
+    CausalLMOutput,
+)
+from torch.utils.checkpoint import checkpoint
+from transformers.modeling_utils import PreTrainedModel
+import math
+try:
+    from flash_attn import flash_attn_varlen_func, flash_attn_func
+except ImportError:
+    pass
+
+
+A_LARGE_NEGATIVE_NUMER = -1e10
+
+
+def create_4d_mask(attn_mask, return_type="bool", x=None, causal=False):
+    B, L = attn_mask.shape
+    device = attn_mask.device
+    mask_4d = torch.eq(attn_mask[:, None, :, None], attn_mask[:, None, None, :])
+    if causal:
+        causal_mask = torch.tril(torch.ones(L, L, device=device)).unsqueeze(0).unsqueeze(0)
+        mask_4d = mask_4d & causal_mask
+    if return_type == "bool":
+        return mask_4d.to(torch.bool)
+    elif return_type == "float":
+        mask_4d = mask_4d.to(x.dtype)
+        return mask_4d * 0 + (1 - mask_4d) * A_LARGE_NEGATIVE_NUMER
+
+
+def rotate_half(x):
+    return torch.cat((-x[..., x.shape[-1] // 2 :], x[..., : x.shape[-1] // 2]), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+def apply_rotary_pos_emb_1(x, cos, sin, unsqueeze_dim=1):
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    return (x * cos) + (rotate_half(x) * sin)
+
+
+class RMSNorm(nn.Module):
+    
+    def __init__(self, dim: int, eps: float = 1e-5):
+        super().__init__()
+        self.eps = eps
+        init_device = None
+        self.weight = Parameter(
+            torch.empty(dim, device=init_device, dtype=torch.float32)
+        )
+        init.ones_(self.weight)
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x : torch.Tensor) -> torch.Tensor:
+        output = self._norm(x.float()).type_as(x)
+        return output * self.weight
+
+
+class TokenEmbedding(nn.Module):
+
+    def __init__(self, config: TransformerConfig):
+        super().__init__()
+        self.config = config
+        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
+        if config.embedding_layer_norm:
+            if config.layernorm_type == "layernorm":
+                self.rms_norm = nn.LayerNorm(
+                    config.hidden_size, eps=config.layer_norm_eps, bias=False
+                )  # For name compatibility
+            elif config.layernorm_type == "rmsnorm":
+                self.rms_norm = RMSNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.position_embedding_type = config.position_embedding_type
+        self.padding_idx = config.pad_token_id
+        self.token_dropout = config.token_dropout
+        self.mask_token_id = config.mask_token_id
+
+    def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
+        embeddings = self.word_embeddings(input_ids)
+        if self.config.embedding_shrinking and self.training:
+            # Embedding shrinking (https://keg.cs.tsinghua.edu.cn/jietang/publications/ICLR23-GLM-130B.pdf)
+            embeddings = embeddings * 0.1 + embeddings.detach() * 0.9
+        if self.config.embedding_layer_norm:
+            embeddings = self.rms_norm(embeddings)
+        return embeddings
+
+
+class RotaryEmbedding(nn.Module):
+    
+    def __init__(self, dim: int, b: int = 10000):
+        super().__init__()
+        inv_freq = 1.0 / (
+            b ** (torch.arange(0, dim, 2, dtype=torch.int64).float() / dim)
+        )
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    @torch.no_grad()
+    def forward(self, x: torch.Tensor, position_ids: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        with torch.autocast(device_type=x.device.type, enabled=False):
+            freqs = inv_freq_expanded.float() @ position_ids_expanded.float()
+            freqs = freqs.transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+        return cos.to(x.dtype), sin.to(x.dtype)
+
+
+class SelfAttention(nn.Module):
+    
+    def __init__(self, config: TransformerConfig, causal: bool=False):
+        super().__init__()
+        if config.hidden_size % config.num_attention_heads != 0:
+            raise ValueError(
+                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+                f"heads ({config.num_attention_heads})"
+            )
+        self.config = config
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = config.hidden_size // config.num_attention_heads
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+        self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+        self.output = nn.Linear(config.hidden_size, self.all_head_size, bias=False)
+        self.output_dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.config = config
+        self.causal = causal
+
+    def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
+        new_x_shape = x.size()[:-1] + (
+            self.num_attention_heads,
+            self.attention_head_size,
+        )  # [B, L, D] -> [B, L, num_heads, head_size]
+        x = x.view(new_x_shape)
+        return x.permute(
+            0, 2, 1, 3
+        )  # [B, L, num_heads, head_size] -> [B, num_heads, L, head_size] for broadcasting in the future
+
+    def naive_forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor = None,
+        rotary_embeddings: torch.Tensor = None,
+        output_attentions: bool = False,
+    ) -> Tuple[torch.Tensor]:
+        B, L, D = hidden_states.size()
+        query_states = self.query(hidden_states)
+        key_states = self.key(hidden_states)
+        value_states = self.value(hidden_states)
+        key_states = self.transpose_for_scores(key_states).contiguous()  # [B, L, D] -> [B, num_heads, L, head_size]
+        query_states = self.transpose_for_scores(query_states).contiguous()  # [B, L, D] -> [B, num_heads, L, head_size]
+        value_states = self.transpose_for_scores(value_states).contiguous()  # [B, L, D] -> [B, num_heads, L, head_size]
+        if rotary_embeddings is not None:
+            cos, sin = rotary_embeddings
+            query_states, key_states = apply_rotary_pos_emb(
+                query_states, key_states, cos, sin
+            )
+        scale_factor = self.attention_head_size**-0.5
+        attention_scores = torch.matmul(
+            query_states, key_states.transpose(-1, -2)
+        )  # [B, num_heads, L, L]
+        attention_scores = attention_scores * scale_factor
+        if attention_mask is not None:
+            attention_scores = attention_scores + attention_mask
+        attention_probs = nn.functional.softmax(
+            attention_scores, dim=-1, dtype=torch.float32
+        ).to(query_states.dtype)
+        attention_probs = self.dropout(attention_probs)
+        context = torch.matmul(
+            attention_probs, value_states
+        )  # [B, num_heads, L, head_size]
+        context = context.permute(
+            0, 2, 1, 3
+        ).contiguous()  # [B, L, num_heads, head_size]
+        context = context.view(B, L, D)
+        context = self.output(context)
+        context = self.output_dropout(context)
+        return_attention_probs = attention_probs.detach() if output_attentions else None
+        return context, return_attention_probs
+
+    def sdpa_forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor = None,
+        rotary_embeddings: torch.Tensor = None,
+    ) -> Tuple[torch.Tensor]:
+        B, L, D = hidden_states.size()
+        query_states = self.query(hidden_states)
+        key_states = self.key(hidden_states)
+        value_states = self.value(hidden_states)
+        key_states = self.transpose_for_scores(key_states).contiguous()
+        query_states = self.transpose_for_scores(query_states).contiguous()
+        value_states = self.transpose_for_scores(value_states).contiguous()
+        if rotary_embeddings is not None:
+            cos, sin = rotary_embeddings
+            query_states, key_states = apply_rotary_pos_emb(
+                query_states, key_states, cos, sin
+            )
+        scale_factor = self.attention_head_size**-0.5
+        dropout_p = self.config.attention_probs_dropout_prob if self.training else 0
+        context = F.scaled_dot_product_attention(
+            query=query_states,
+            key=key_states,
+            value=value_states,
+            attn_mask=attention_mask,
+            dropout_p=dropout_p,
+            scale=scale_factor,
+        )  # [B, num_heads, L, head_size]
+        context = context.permute(
+            0, 2, 1, 3
+        ).contiguous()  # [B, L, num_heads, head_size]
+        context = context.view(B, L, D)
+        context = self.output(context)
+        context = self.output_dropout(context)
+        return_attention_probs = None
+        return context, return_attention_probs
+
+    def flash_attn_forward(
+        self,
+        hidden_states: torch.Tensor,
+        rotary_embeddings: torch.Tensor = None,
+        lengths: List[List[int]] = None,
+    ) -> Tuple[torch.Tensor]:
+        B, L, D = hidden_states.size()
+        NH = self.num_attention_heads
+        H = self.attention_head_size
+
+        scale_factor = self.attention_head_size**-0.5
+        query_states = self.query(hidden_states)
+        key_states = self.key(hidden_states)
+        value_states = self.value(hidden_states)
+
+        if lengths is not None:
+            # flash_attn_varlen_func
+            query_states = query_states.view(B * L, NH, H).contiguous()
+            key_states = key_states.view(B * L, NH, H).contiguous()
+            value_states = value_states.view(B * L, NH, H).contiguous()
+            if rotary_embeddings is not None:
+                cos, sin = rotary_embeddings
+                cos = cos.view(B * L, 1, H)
+                sin = sin.view(B * L, 1, H)
+                query_states = (query_states * cos) + (rotate_half(query_states) * sin)
+                key_states = (key_states * cos) + (rotate_half(key_states) * sin)
+            lengths = [0, ] + list(chain(*lengths))
+            lengths = torch.tensor(lengths, dtype=torch.int, device=query_states.device)
+            max_seqlen = torch.max(lengths)
+            cum_seqlen = torch.cumsum(lengths, dim=0, dtype=torch.int)
+            context = flash_attn_varlen_func(
+                q=query_states,
+                k=key_states,
+                v=value_states,
+                cu_seqlens_q=cum_seqlen,
+                cu_seqlens_k=cum_seqlen,
+                max_seqlen_q=max_seqlen,
+                max_seqlen_k=max_seqlen,
+                causal=self.causal,
+                return_attn_probs=False,
+                softmax_scale=scale_factor,
+            )
+        else:
+            query_states = query_states.view(B, L, NH, H).contiguous()
+            key_states = key_states.view(B, L, NH, H).contiguous()
+            value_states = value_states.view(B, L, NH, H).contiguous()
+            if rotary_embeddings is not None:
+                cos, sin = rotary_embeddings
+                query_states, key_states = apply_rotary_pos_emb(
+                    query_states, key_states, cos, sin, unsqueeze_dim=2
+                )
+            context = flash_attn_func(
+                q=query_states,
+                k=key_states,
+                v=value_states,
+                softmax_scale=scale_factor,
+                causal=self.causal,
+            )
+        context = context.view(B, L, D).contiguous()
+        context = self.output(context)
+        context = self.output_dropout(context)
+        return_attention_probs = None
+        return context, return_attention_probs
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor = None,
+        lengths: List[torch.Tensor] = None,
+        rotary_embeddings: torch.Tensor = None,
+        output_attentions: bool = False,
+    ):
+        if self.config.attn_impl == "naive":
+            return self.naive_forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                rotary_embeddings=rotary_embeddings,
+                output_attentions=output_attentions,
+            )
+        elif self.config.attn_impl == "sdpa":
+            return self.sdpa_forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                rotary_embeddings=rotary_embeddings,
+            )
+        elif self.config.attn_impl == "flash_attn":
+            return self.flash_attn_forward(
+                hidden_states=hidden_states,
+                rotary_embeddings=rotary_embeddings,
+                lengths=lengths,
+            )
+
+
+class FeedForwardNetwork(nn.Module):
+    
+    def __init__(self, config: TransformerConfig):
+        super().__init__()
+        self.w1 = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
+        self.w2 = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
+        if config.act_fn == "gelu":
+            self.act_fn = nn.GELU()
+        elif config.act_fn == "silu":
+            self.act_fn = nn.SiLU()
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.w2(self.act_fn(self.w1(hidden_states)))
+
+
+class TransFormerLayer(nn.Module):
+
+    def __init__(self, config: TransformerConfig, causal=False):
+        super().__init__()
+        self.config = config
+        self.causal = causal
+        self.attention = SelfAttention(config, causal=causal)
+        self.ffn = FeedForwardNetwork(config)
+        if config.layernorm_type == "layernorm":
+            self.pre_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, bias=False)
+            self.post_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, bias=False)
+        else:
+            self.pre_norm = RMSNorm(config.hidden_size, eps=config.layer_norm_eps)
+            self.post_norm = RMSNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor = None,
+        lengths: List[torch.Tensor] = None,
+        rotary_embeddings: torch.Tensor = None,
+        output_attentions: bool = False,
+    ):
+        residual = hidden_states
+        hidden_states = self.pre_norm(hidden_states)
+        hidden_states, attn_probs = self.attention(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            lengths=lengths,
+            rotary_embeddings=rotary_embeddings,
+            output_attentions=output_attentions
+        )
+        hidden_states = residual + hidden_states
+        residual = hidden_states
+        hidden_states = self.post_norm(hidden_states)
+        hidden_states = self.ffn(hidden_states)
+        hidden_states = residual + hidden_states
+        return (hidden_states, attn_probs)
+
+
+class TransformerCore(nn.Module):
+
+    def __init__(self, config: TransformerConfig, causal=False):
+        super().__init__()
+        self.config = config
+        self.layer = []
+        for _ in range(config.num_hidden_layers):
+            sub_layer = TransFormerLayer(config, causal=causal)
+            self.layer.append(sub_layer)
+        self.layer = nn.ModuleList(self.layer)
+        if self.config.layernorm_type == "layernorm":
+            self.norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, bias=False)
+        else:
+            self.norm = RMSNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor = None,
+        lengths: List[torch.Tensor] = None,
+        rotary_embeddings: torch.Tensor = None,
+        output_attentions: bool = False,
+        output_hidden_states=False,
+    ):
+        all_hidden_states = []
+        all_self_attentions = []
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states.append(hidden_states.detach().cpu())
+            if torch.is_grad_enabled() and hidden_states.requires_grad and self.gradient_checkpointing:
+                hidden_states, attn_probs = checkpoint(
+                    layer_module,
+                    hidden_states,
+                    attention_mask,
+                    rotary_embeddings,
+                    lengths,
+                    output_attentions,
+                    use_reentrant=False,
+                )
+            else:
+                hidden_states, attn_probs = layer_module(
+                    hidden_states=hidden_states,
+                    attention_mask=attention_mask,
+                    lengths=lengths,
+                    rotary_embeddings=rotary_embeddings,
+                    output_attentions=output_attentions
+                )
+            if output_attentions:
+                all_self_attentions.append(attn_probs.detach().cpu() if attn_probs is not None else None)
+        hidden_states = self.norm(hidden_states)
+        if output_hidden_states:
+            all_hidden_states.append(hidden_states.detach().cpu(), )
+        return BaseModelOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+        )
+
+
+class BaseTransformerModel(PreTrainedModel):
+
+    config_class = TransformerConfig
+    base_model_prefix = "transformer"
+
+
+class TransformerModel(BaseTransformerModel):
+
+    def __init__(self, config: TransformerConfig, causal=False):
+        super().__init__(config)
+        self.config = config
+        self.rotary_embedding = RotaryEmbedding(dim=config.hidden_size // config.num_attention_heads)
+        self.token_embedding = TokenEmbedding(config)
+        self.transformer = TransformerCore(config, causal=causal)
+        self.causal = causal
+
+    def enable_gradient_checkpointing(self):
+        self.transformer.gradient_checkpointing = True
+
+    def disable_gradient_checkpointing(self):
+        self.transformer.gradient_checkpointing = False
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor=None,
+        lengths: torch.Tensor=None,
+        position_ids: torch.Tensor=None,
+        output_attentions=False,
+        output_hidden_states=False,
+    ) -> BaseModelOutput:
+        embeddings = self.token_embedding(input_ids)
+        if position_ids is None:
+            position_ids = torch.arange(input_ids.size(1)).to(input_ids.device)
+            position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+        if position_ids.shape != input_ids.shape:
+            raise ValueError("Position IDs must have the same shape as input_ids")
+        rotary_embeddings = self.rotary_embedding(embeddings, position_ids)
+    
+        if attention_mask is not None:
+            if self.config.attn_impl == "flash_attn":
+                raise ValueError("Flash attention does not support specifying attention mask")
+            attention_mask = create_4d_mask(
+                attention_mask,
+                return_type="float",
+                x=embeddings,
+                causal=self.causal,
+            )
+        
+        outputs = self.transformer(
+            hidden_states=embeddings,
+            attention_mask=attention_mask,
+            lengths=lengths,
+            rotary_embeddings=rotary_embeddings,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states
+        )
+        
+        return BaseModelOutput(
+            last_hidden_state=outputs.last_hidden_state,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class TransformerForMaskedLM(BaseTransformerModel):
+    
+    def __init__(self, config: TransformerConfig):
+        super().__init__(config)
+        self.model = TransformerModel(config, causal=False)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor=None,
+        lengths: torch.Tensor=None,
+        position_ids: torch.Tensor=None,
+        labels: torch.Tensor=None,
+        output_attentions=False,
+        output_hidden_states=False,
+    ) -> MaskedLMOutput:
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            lengths=lengths,
+            position_ids=position_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states
+        )
+        sequence_output = outputs.last_hidden_state
+        prediction_scores = self.lm_head(sequence_output)
+        loss = None
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            labels = labels.to(prediction_scores.device)
+            loss = loss_fct(
+                prediction_scores.view(-1, self.config.vocab_size).float(), labels.view(-1)
+            )
+        return MaskedLMOutput(
+            loss=loss,
+            logits=prediction_scores,
+            hidden_states=sequence_output,
+            attentions=outputs.attentions,
+        )
+
+
+class TransformerForCausalLM(BaseTransformerModel):
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = TransformerModel(config, causal=True)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.init_weights()
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor=None,
+        lengths: torch.Tensor=None,
+        position_ids: torch.Tensor=None,
+        labels: torch.Tensor=None,
+        output_attentions=False,
+        output_hidden_states=False,
+        reduction="mean",
+    ) -> CausalLMOutput:
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            lengths=lengths,
+            position_ids=position_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states
+        )
+        sequence_output = outputs.last_hidden_state
+        prediction_scores = self.lm_head(sequence_output)
+        loss = None
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss(reduction=reduction)
+            labels = labels.to(prediction_scores.device)
+            loss = loss_fct(
+                prediction_scores.view(-1, self.config.vocab_size).to(torch.float32),
+                labels.view(-1),
+            )
+        return CausalLMOutput(
+            loss=loss,
+            logits=prediction_scores,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+        
+TransformerForMaskedLM.register_for_auto_class("AutoModelForMaskedLM")