add remote code + model files

__init__.py

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+# for HF remote code

configuration_alibi.py

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+# -*- coding: utf-8 -*-
+
+from typing import Optional
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class AlibiConfig(PretrainedConfig):
+
+    model_type = 'transformer-project_fox'
+    keys_to_ignore_at_inference = ['past_key_values']
+
+    def __init__(
+        self,
+        vocab_size: int = 32000,
+        hidden_size: int = 2048,
+        hidden_ratio: Optional[int] = 4,
+        intermediate_size: Optional[int] = None,
+        num_hidden_layers: int = 24,
+        num_heads: int = 32,
+        num_kv_heads: int = None,
+        hidden_act: str = "swish",
+        window_size: Optional[int] = None,
+        max_position_embeddings: int = 2048,
+        initializer_range: float = 0.02,
+        elementwise_affine: Optional[bool] = True,
+        norm_eps: float = 1e-6,
+        use_cache: bool = True,
+        pad_token_id: int = None,
+        bos_token_id: int = 1,
+        eos_token_id: int = 2,
+        tie_word_embeddings: bool = False,
+        attention_bias: bool = False,
+        fuse_norm: bool = True,
+        fuse_cross_entropy: bool = True,
+        rope_base: float = 500000.0,
+        use_rope: bool = False,
+        use_alibi: bool = True,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.hidden_ratio = hidden_ratio
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_heads = num_heads
+        self.num_kv_heads = num_kv_heads
+        self.window_size = window_size
+        self.max_position_embeddings = max_position_embeddings
+
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.elementwise_affine = elementwise_affine
+        self.norm_eps = norm_eps
+        self.use_cache = use_cache
+        self.attention_bias = attention_bias
+        self.fuse_cross_entropy = fuse_cross_entropy
+        self.fuse_norm = fuse_norm
+        self.rope_base = rope_base
+        self.use_rope = use_rope
+        self.use_alibi = use_alibi
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )

modeling_alibi.py

@@ -0,0 +1,567 @@
+# -*- coding: utf-8 -*-
+
+from __future__ import annotations
+
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+from fla.modules import FusedCrossEntropyLoss, RMSNorm,RotaryEmbedding
+from jedi.inference.lazy_value import AbstractLazyValue
+from torch.nn import functional as F
+from fla.modules.activations import swiglu_linear
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_outputs import (BaseModelOutputWithPast,
+                                           CausalLMOutputWithPast)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+from einops import rearrange
+
+from forgetting_transformer.model.alibi.configuration_alibi import AlibiConfig
+
+from functools import partial
+
+logger = logging.get_logger(__name__)
+
+class Attention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int = 2048,
+        num_heads: int = 32,
+        num_kv_heads: Optional[int] = None,
+        window_size: Optional[int] = None,
+        max_position_embeddings: Optional[int] = None,
+        rope_base: float = 500000.0,
+        use_rope: bool = False,
+        use_alibi: bool = True,
+        layer_idx: int = None,
+    ):
+        super().__init__()
+
+        self.num_heads = num_heads
+        if num_kv_heads is None:
+            self.num_kv_heads = self.num_heads
+        else:
+            self.num_kv_heads = num_kv_heads
+        self.num_kv_groups = num_heads // self.num_kv_heads
+        self.hidden_size = hidden_size
+        self.head_dim = self.hidden_size // self.num_heads
+        self.kv_dim = self.num_kv_heads * self.head_dim
+        self.window_size = window_size
+        self.max_position_embeddings = max_position_embeddings
+        self.layer_idx = layer_idx
+
+        self.q_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.kv_dim, bias=False)
+        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
+
+        if use_rope:
+            self.rotary = RotaryEmbedding(self.head_dim, base=rope_base)
+        else:
+            self.rotary = None
+
+        if use_alibi:
+            slopes = torch.tensor(self._get_slopes(self.num_heads), dtype=torch.float32)
+            self.register_buffer("alibi_slopes", slopes.view(1, -1, 1, 1), persistent=False)
+
+        self.apply(self._initialize_weights)
+
+    def _initialize_weights(self, module: nn.Module):
+        pass
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            attention_mask: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[Cache] = None,
+            output_attentions: bool = False,
+            use_cache: bool = False,
+            **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+
+        B, T, _ = hidden_states.size()
+        q = rearrange(self.q_proj(hidden_states), 'b t (h d) -> b t h d', h=self.num_heads)
+        k = rearrange(self.k_proj(hidden_states), 'b t (h d) -> b t h d', h=self.num_kv_heads)
+        v = rearrange(self.v_proj(hidden_states), 'b t (h d) -> b t h d', h=self.num_kv_heads)
+
+        seqlen_offset = 0
+        max_seqlen = q.shape[1]
+        if past_key_values is not None:
+            seqlen_offset = past_key_values.get_seq_length(self.layer_idx)
+            max_seqlen = q.shape[1] + seqlen_offset
+        if self.max_position_embeddings is not None:
+            max_seqlen = max(max_seqlen, self.max_position_embeddings)
+
+        if self.rotary is not None:
+            q, k = self.rotary(q, k, seqlen_offset, max_seqlen)
+
+        q = rearrange(q, 'b t h d -> b h t d')
+        k = rearrange(k, 'b t h d -> b h t d')
+        v = rearrange(v, 'b t h d -> b h t d')
+
+
+        if past_key_values is not None:
+            k, v = past_key_values.update(k, v, self.layer_idx)
+
+
+        if self.num_kv_groups > 1:
+            k = k.repeat_interleave(self.num_kv_groups, dim=1)  # [B, H, Tk, D]
+            v = v.repeat_interleave(self.num_kv_groups, dim=1)  # [B, H, Tk, D]
+
+        B, H, Tq, Dh = q.shape
+        Tk = k.size(2)
+
+        scale = 1.0 / math.sqrt(Dh)
+        scores = torch.matmul(q, k.transpose(-2, -1)) * scale
+
+        pos_q = (seqlen_offset + torch.arange(Tq, device=scores.device))
+        pos_k = torch.arange(Tk, device=scores.device)
+        causal_mask = (pos_k.unsqueeze(0) > pos_q.unsqueeze(1))  # [Tq, Tk]
+        scores = scores.masked_fill(causal_mask.view(1, 1, Tq, Tk), float('-inf'))
+
+        if hasattr(self, "alibi_slopes"):
+
+            rel = (pos_q.unsqueeze(1) - pos_k.unsqueeze(0)).to(torch.float32)  # [Tq, Tk]
+            alibi_bias = -self.alibi_slopes.to(scores.device) * rel.view(1, 1, Tq, Tk)  # [1, H, Tq, Tk]
+            scores = scores + alibi_bias.to(scores.dtype)
+
+
+        if attention_mask is not None and attention_mask.shape[-1] == Tk:
+            pad_mask = (attention_mask == 0).view(B, 1, 1, Tk)
+            scores = scores.masked_fill(pad_mask, float('-inf'))
+
+        if self.window_size is not None:
+            past_too_far = (pos_k.view(1, Tk) < (pos_q.view(Tq, 1) - (self.window_size - 1)))
+            scores = scores.masked_fill(past_too_far.view(1, 1, Tq, Tk), float('-inf'))
+
+        attn = torch.softmax(scores, dim=-1)  # [B, H, Tq, Tk]
+        o = torch.matmul(attn, v)  # [B, H, Tq, Dh]
+        o = rearrange(o, 'b h t d -> b t (h d)')  # [B, Tq, H*Dh] = [B, Tq, hidden_size]
+        o = self.o_proj(o)
+
+        attentions = attn if output_attentions else None
+        return o, attentions, past_key_values
+
+    def _get_slopes(self, n):
+        """
+        Get slopes for Alibi positional embedding
+        n : int = number of heads.
+        For best performance, restrict n to a power of 2.
+        """
+
+        def get_slopes_power_of_2(n):
+            start = 2 ** (-(2 ** -(math.log2(n) - 3)))
+            ratio = start
+            return [start * ratio**i for i in range(n)]
+
+        if math.log2(n).is_integer():
+            return get_slopes_power_of_2(n)
+        else:
+            closest_power_of_2 = 2 ** math.floor(math.log2(n))
+            return (
+                get_slopes_power_of_2(closest_power_of_2)
+                + self._get_slopes(2 * closest_power_of_2)[0::2][
+                    : n - closest_power_of_2
+                ]
+            )
+
+
+class TransformerMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        hidden_ratio: Optional[int] = None,
+        intermediate_size: Optional[int] = None,
+        hidden_act: str = 'swish'
+    ) -> TransformerMLP:
+        super().__init__()
+
+        self.hidden_size = hidden_size
+        # the final number of params is `hidden_ratio * hidden_size^2`
+        # `intermediate_size` is chosen to be a multiple of 256 closest to `2/3 * hidden_size * hidden_ratio`
+        if hidden_ratio is None:
+            hidden_ratio = 4
+        if intermediate_size is None:
+            intermediate_size = int(hidden_size * hidden_ratio * 2 / 3)
+            intermediate_size = 256 * ((intermediate_size + 256 - 1) // 256)
+        self.hidden_ratio = hidden_ratio
+        self.intermediate_size = intermediate_size
+
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size * 2, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[hidden_act]
+
+    def forward(self, x):
+        y = self.gate_proj(x)
+        gate, y = y.chunk(2, -1)
+        # TODO: maybe wrap swiglu_linear in custom_fwd/custom_bwd
+        return swiglu_linear(
+            gate, y,
+            self.down_proj.weight.to(y.dtype),
+            self.down_proj.bias.to(y.dtype) if self.down_proj.bias is not None else self.down_proj.bias
+        )
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, config: AlibiConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.attn_norm = RMSNorm(hidden_size=config.hidden_size, eps=config.norm_eps)
+        self.attn = Attention(
+            hidden_size=config.hidden_size,
+            num_heads=config.num_heads,
+            num_kv_heads=config.num_kv_heads,
+            window_size=config.window_size,
+            use_alibi=config.use_alibi,
+            max_position_embeddings=config.max_position_embeddings,
+            rope_base=config.rope_base,
+            use_rope=config.use_rope,
+            layer_idx=layer_idx
+        )
+        self.mlp_norm = RMSNorm(hidden_size=config.hidden_size, eps=config.norm_eps)
+        self.mlp = TransformerMLP(
+            hidden_size=config.hidden_size,
+            hidden_ratio=config.hidden_ratio,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act
+        )
+
+    def forward_attn(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        **kwargs,
+    ):
+        # reisual handled outside
+        # residual = hidden_states
+        hidden_states = self.attn_norm(hidden_states)
+        hidden_states, attentions, past_key_values = self.attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions
+        )
+        return hidden_states, attentions, past_key_values
+
+    def forward_mlp(
+        self,
+        hidden_states: torch.Tensor,
+        residual: torch.Tensor,
+    ):
+        hidden_states, residual = self.mlp_norm(hidden_states, residual, True)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        gradient_checkpointing: bool = False
+        # **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+
+        residual = hidden_states
+
+
+        if gradient_checkpointing:
+            forward_attn = partial(torch.utils.checkpoint.checkpoint, self.forward_attn, use_reentrant=False)
+            forward_mlp = partial(torch.utils.checkpoint.checkpoint, self.forward_mlp, use_reentrant=False)
+        else:
+            forward_attn = self.forward_attn
+            forward_mlp = self.forward_mlp
+
+        hidden_states, attentions, past_key_values = forward_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions
+        )
+
+        hidden_states = forward_mlp(
+            hidden_states,
+            residual,
+        )
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (attentions,)
+
+        if use_cache:
+            outputs += (past_key_values,)
+
+        return outputs
+
+
+
+class TransformerPreTrainedModel(PreTrainedModel):
+
+    config_class = AlibiConfig
+    supports_gradient_checkpointing = True
+    _no_split_modules = ['TransformerBlock']
+
+    def __init__(self, *inputs, **kwargs):
+        super().__init__(*inputs, **kwargs)
+
+    def _init_weights(
+        self,
+        module: nn.Module,
+    ):
+        if isinstance(module, (nn.Linear, nn.Conv1d)):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+class AlibiModel(TransformerPreTrainedModel):
+
+    def __init__(self, config: AlibiConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList([TransformerBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)])
+        self.norm = RMSNorm(config.hidden_size, eps=config.norm_eps)
+
+        self.gradient_checkpointing = False
+
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings = value
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        if output_attentions:
+            warnings.warn(
+                "`TransformerModel` does not support output attention weights now, so `output_attentions` is set to `False`."
+            )
+            output_attentions = False
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False)
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is None and inputs_embeds is None:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        if use_cache:
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            if use_legacy_cache:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embeddings(input_ids)
+
+        # embed positions
+        hidden_states = inputs_embeds
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        all_hidden_states = () if output_hidden_states else None
+        all_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            layer_outputs = layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                past_key_values=past_key_values,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                gradient_checkpointing=self.gradient_checkpointing and self.training
+            )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_attns] if v is not None)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_attns
+        )
+
+
+class AlibiForCausalLM(TransformerPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = AlibiModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embeddings
+
+    def set_input_embeddings(self, value):
+        self.model.embeddings = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: torch.LongTensor = None,
+        past_key_values: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs
+    ):
+        # only last token for `inputs_ids` if the `past_key_values` is passed along.
+        if past_key_values is not None:
+            input_ids = input_ids[:, -1:]
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {'inputs_embeds': inputs_embeds}
+        else:
+            # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
+            # recompiles graphs as the stride of the inputs is a guard.
+            # Ref: https://github.com/huggingface/transformers/pull/29114
+            # TODO: use `next_tokens` directly instead.
+            model_inputs = {'input_ids': input_ids.contiguous()}
+
+        model_inputs.update({
+            'past_key_values': past_key_values,
+            'use_cache': kwargs.get('use_cache'),
+            'attention_mask': attention_mask,
+        })
+        return model_inputs
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict
+        )
+
+        hidden_states = outputs[0]
+
+        loss = None
+        if labels is not None:
+            if self.config.fuse_cross_entropy:
+                loss_fct = FusedCrossEntropyLoss(inplace_backward=True, reduction='none')
+            else:
+                loss_fct = nn.CrossEntropyLoss(reduction='none')
+            logits = self.lm_head(hidden_states)
+            # Enable model parallelism
+            labels = labels.to(logits.device)
+            # labels = torch.cat((labels[..., 1:], torch.full_like(labels[:, :1], loss_fct.ignore_index)), 1)
+            loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
+            loss = loss.view(*labels.size())
+            del logits
+            logits = None
+        else:
+            logits = self.lm_head(hidden_states)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )