added the model file

model.py

@@ -0,0 +1,1090 @@
+# Copyright 2021 The EleutherAI and HuggingFace Teams. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Modified forward-pass implementation based on https://github.com/huggingface/transformers/blob/main/src/transformers/models/gptj/modeling_gptj.py
+import math
+from dataclasses import dataclass
+from typing import Optional, Tuple, Union, Dict
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch.nn import CrossEntropyLoss
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast as _BaseModelOutputWithPast,
+)
+from transformers.modeling_outputs import (
+    CausalLMOutputWithPast as _CausalLMOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
+
+from .adapter import ParallelAdapterLayer, ProjectionMLP
+from .config import ProGenConfig, ProGenConditionalConfig
+from ..utils import exists
+
+logger = logging.get_logger(__name__)
+
+@dataclass
+class BaseModelOutputWithPast(_BaseModelOutputWithPast):
+    inputs: Optional[Union[torch.LongTensor, torch.FloatTensor]] = None
+
+
+@dataclass
+class CausalLMOutputWithPast(_CausalLMOutputWithPast):
+    all_losses: Optional[torch.FloatTensor] = None
+    inputs: Optional[Union[torch.LongTensor, torch.FloatTensor]] = None
+
+def fixed_pos_embedding(x, seq_dim=1, seq_len=None):
+    dim = x.shape[-1]
+    if seq_len is None:
+        seq_len = x.shape[seq_dim]
+    inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2) / dim))
+    sinusoid_inp = (
+        torch.einsum("i , j -> i j", torch.arange(seq_len), inv_freq).to(x.device).float()
+    )
+    return torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)
+
+
+def rotate_every_two(x):
+    x1 = x[:, :, :, ::2]
+    x2 = x[:, :, :, 1::2]
+    x = torch.stack((-x2, x1), axis=-1)
+    return x.flatten(-2)  # in einsum notation: rearrange(x, '... d j -> ... (d j)')
+
+
+def apply_rotary_pos_emb(x, sincos, offset=0):
+    sin, cos = map(
+        lambda t: t[None, offset : x.shape[1] + offset, None, :].repeat_interleave(2, 3), sincos
+    )
+    # einsum notation for lambda t: repeat(t[offset:x.shape[1]+offset,:], "n d -> () n () (d j)", j=2)
+    return (x * cos) + (rotate_every_two(x) * sin)
+
+
+class ProGenAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+
+        max_positions = config.max_position_embeddings
+        self.register_buffer(
+            "bias",
+            torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
+                1, 1, max_positions, max_positions
+            ),
+        )
+        self.register_buffer("masked_bias", torch.tensor(-1e9))
+
+        self.attn_dropout = nn.Dropout(config.attn_pdrop)
+        self.attn_pdrop = config.attn_pdrop
+        self.resid_dropout = nn.Dropout(config.resid_pdrop)
+
+        self.embed_dim = config.hidden_size
+        self.num_attention_heads = config.num_attention_heads
+        self.head_dim = self.embed_dim // self.num_attention_heads
+        if self.head_dim * self.num_attention_heads != self.embed_dim:
+            raise ValueError(
+                f"embed_dim must be divisible by num_attention_heads (got `embed_dim`: {self.embed_dim} and `num_attention_heads`: {self.num_attention_heads})."
+            )
+        self.scale_attn = math.sqrt(self.head_dim)
+        self.qkv_proj = nn.Linear(self.embed_dim, self.embed_dim * 3, bias=False)
+
+        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False)
+        self.rotary_dim = None
+        if config.rotary_dim is not None:
+            self.rotary_dim = config.rotary_dim
+
+    def _split_heads(self, x, n_head, dim_head, mp_num):
+        reshaped = x.reshape(x.shape[:-1] + (n_head // mp_num, dim_head))
+        reshaped = reshaped.reshape(x.shape[:-2] + (-1,) + reshaped.shape[-1:])
+        return reshaped
+
+    def _naive_attn(
+        self,
+        query,
+        key,
+        value,
+        attention_mask=None,
+    ):
+        # compute causal mask from causal mask buffer
+        batch_size, query_length, key_length = query.size(0), query.size(-2), key.size(-2)
+        causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length]
+
+        attn_weights = torch.matmul(query, key.transpose(-1, -2)) / self.scale_attn
+        attn_weights = torch.where(
+            causal_mask, attn_weights, self.masked_bias.to(attn_weights.dtype)
+        )
+
+        if attention_mask is not None:
+            # Apply the attention mask
+            attn_weights = attn_weights + attention_mask
+
+        attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+        attn_weights = self.attn_dropout(attn_weights)
+        attn_output = torch.matmul(attn_weights, value)
+
+        expected_size = (batch_size, self.num_attention_heads, query_length, self.head_dim)
+        if attn_output.size() != expected_size:
+            raise ValueError(
+                f"`attn_output` should be of size {expected_size}, but is  {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(batch_size, query_length, self.embed_dim)
+        return attn_output, attn_weights
+
+    def _sdpa_attn(
+        self,
+        query,
+        key,
+        value,
+        attention_mask=None,
+    ):
+        bsz, q_len = query.shape[0], query.shape[2]
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query.device.type == "cuda" and attention_mask is not None:
+            query = query.contiguous()
+            key = key.contiguous()
+            value = value.contiguous()
+
+        attn_output = F.scaled_dot_product_attention(
+            query,
+            key,
+            value,
+            attn_mask=attention_mask,
+            dropout_p=self.attn_pdrop if self.training else 0.0,
+            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+            is_causal=q_len > 1,
+            scale=1 / self.scale_attn,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.embed_dim)
+        return attn_output, None
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        layer_past=None,
+        use_cache=False,
+        output_attentions=False,
+    ):
+        qkv = self.qkv_proj(hidden_states)
+        # TODO(enijkamp): factor out number of logical TPU-v3/v4 cores or make forward pass agnostic
+        # mp_num = 4
+        mp_num = 8
+        qkv_split = qkv.reshape(qkv.shape[:-1] + (mp_num, -1))
+
+        local_dim = self.head_dim * self.num_attention_heads // mp_num
+        query, value, key = torch.split(qkv_split, local_dim, dim=-1)
+        query = self._split_heads(query, self.num_attention_heads, self.head_dim, mp_num=mp_num)
+        key = self._split_heads(key, self.num_attention_heads, self.head_dim, mp_num=mp_num)
+
+        value = self._split_heads(value, self.num_attention_heads, self.head_dim, mp_num=mp_num)
+        value = value.permute(0, 2, 1, 3)
+
+        seq_len = key.shape[1]
+        offset = 0
+
+        if layer_past is not None:
+            offset = layer_past[0].shape[-2]
+            seq_len += offset
+
+        if self.rotary_dim is not None:
+            k_rot = key[:, :, :, : self.rotary_dim]
+            k_pass = key[:, :, :, self.rotary_dim :]
+
+            q_rot = query[:, :, :, : self.rotary_dim]
+            q_pass = query[:, :, :, self.rotary_dim :]
+
+            sincos = fixed_pos_embedding(k_rot, 1, seq_len=seq_len)
+            k_rot = apply_rotary_pos_emb(k_rot, sincos, offset=offset)
+            q_rot = apply_rotary_pos_emb(q_rot, sincos, offset=offset)
+
+            key = torch.cat([k_rot, k_pass], dim=-1)
+            query = torch.cat([q_rot, q_pass], dim=-1)
+        else:
+            sincos = fixed_pos_embedding(key, 1, seq_len=seq_len)
+            key = apply_rotary_pos_emb(key, sincos, offset=offset)
+            query = apply_rotary_pos_emb(query, sincos, offset=offset)
+
+        key = key.permute(0, 2, 1, 3)
+        query = query.permute(0, 2, 1, 3)
+
+        if layer_past is not None:
+            past_key = layer_past[0]
+            past_value = layer_past[1]
+            key = torch.cat((past_key, key), dim=-2)
+            value = torch.cat((past_value, value), dim=-2)
+
+        if use_cache is True:
+            present = (key, value)
+        else:
+            present = None
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in float16 just to be sure everything works as expected.
+
+        input_dtype = query.dtype
+        if torch.is_autocast_enabled():
+            target_dtype = torch.get_autocast_gpu_dtype()
+        # Handle the case where the model is quantized
+        elif hasattr(self.config, "_pre_quantization_dtype"):
+            target_dtype = self.config._pre_quantization_dtype
+        else:
+            target_dtype = self.qkv_proj.weight.dtype #this is giving an issue, but it usually isn't called
+
+        if input_dtype != target_dtype:
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in {input_dtype}. "
+                f"This might be because you have upcasted embedding or layer norm layers "
+                f"in {input_dtype}. We will cast back the input in {target_dtype}."
+            )
+            query = query.to(target_dtype)
+            key = key.to(target_dtype)
+            value = value.to(target_dtype)
+
+        # compute self-attention: V x Softmax(QK^T)
+        if output_attentions:
+            attn_output, attn_weights = self._naive_attn(query, key, value, attention_mask)
+        else:
+            attn_output, attn_weights = self._sdpa_attn(query, key, value, None)
+        attn_output = self.out_proj(attn_output)
+        attn_output = self.resid_dropout(attn_output)
+
+        outputs = (attn_output, present)
+        if output_attentions:
+            outputs += (attn_weights,)
+
+        return outputs
+
+
+class ProGenMLP(nn.Module):
+    def __init__(self, intermediate_size, config):  # in MLP: intermediate_size= 4 * embed_dim
+        super().__init__()
+        embed_dim = config.n_embd
+
+        self.fc_in = nn.Linear(embed_dim, intermediate_size)
+        self.fc_out = nn.Linear(intermediate_size, embed_dim)
+
+        self.act = ACT2FN[config.activation_function]
+        self.dropout = nn.Dropout(config.resid_pdrop)
+
+    def forward(self, hidden_states):
+        hidden_states = self.fc_in(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.fc_out(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        return hidden_states
+
+
+class ProGenBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        inner_dim = config.n_inner if config.n_inner is not None else 4 * config.n_embd
+        self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.attn = ProGenAttention(config)
+        self.mlp = ProGenMLP(inner_dim, config)
+
+    def forward(
+        self,
+        hidden_states,
+        layer_past=None,
+        attention_mask=None,
+        head_mask=None,
+        adapter_layer=None,
+        adapter_dropout=None,
+        adapter_input=None,
+        use_cache=False,
+        output_attentions=False,
+    ):
+        residual = hidden_states
+        hidden_states = self.ln_1(hidden_states) 
+        attn_outputs = self.attn(
+            hidden_states,
+            layer_past=layer_past,
+            attention_mask=attention_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+        )
+        attn_output = attn_outputs[0] 
+        outputs = attn_outputs[1:]
+
+        feed_forward_hidden_states = self.mlp(hidden_states)
+        
+        ### addition of adapter layer ###
+        if exists(adapter_layer) and exists(adapter_dropout) and exists(
+                adapter_input):
+            
+            hidden_states_update = attn_output + feed_forward_hidden_states
+            adapter_out = adapter_layer(hidden_states_update, adapter_input)
+            adapter_out = adapter_dropout(adapter_out)
+            hidden_states_update = hidden_states_update + adapter_out
+
+            hidden_states = hidden_states_update + residual
+        else:
+            hidden_states = attn_output + feed_forward_hidden_states + residual
+        ### end of addition of adapter layer ###
+
+        if use_cache:
+            outputs = (hidden_states,) + outputs
+        else:
+            outputs = (hidden_states,) + outputs[1:]
+        
+        return outputs 
+
+
+class ProGenPreTrainedModel(PreTrainedModel):
+    """An abstract class to handle weights initialization and a simple interface for downloading
+    and loading pretrained models."""
+
+    config_class = ProGenConfig
+    base_model_prefix = "transformer"
+    is_parallelizable = True
+    _no_split_modules = ["ProGenBlock"]
+
+    def __init__(self, *inputs, **kwargs):
+        super().__init__(*inputs, **kwargs)
+
+    def _init_weights(self, module):
+        """Initialize the weights."""
+        if isinstance(module, (nn.Linear,)):
+            # Slightly different from Mesh Transformer JAX which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+class ModularProGenModel(ProGenPreTrainedModel):
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.embed_dim = config.n_embd
+        self.vocab_size = config.vocab_size
+        self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
+        self.drop = nn.Dropout(config.embd_pdrop)
+        self.h = nn.ModuleList(
+            [ProGenBlock(config) for _ in range(config.n_layer)])
+        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
+        self.rotary_dim = min(config.rotary_dim,
+                              config.n_ctx // config.num_attention_heads)
+        self.init_weights()
+
+    def get_input_embeddings(self):
+        return self.wte
+
+    def set_input_embeddings(self, new_embeddings):
+        self.wte = new_embeddings
+
+    def forward_prep(
+        self,
+        input_ids=None,
+        past_key_values=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        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 getattr(self.config, "gradient_checkpointing",
+                   False) and self.training:
+            #print('using gradient checkpointing')
+            if use_cache:
+                use_cache = False
+            
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        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 not None:
+            input_shape = input_ids.size()
+            input_ids = input_ids.view(-1, input_shape[-1])
+            batch_size = input_ids.shape[0]
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+            batch_size = inputs_embeds.shape[0]
+        else:
+            raise ValueError(
+                "You have to specify either input_ids or inputs_embeds")
+
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        if token_type_ids is not None:
+            token_type_ids = token_type_ids.view(-1, input_shape[-1])
+
+        if position_ids is not None:
+            position_ids = position_ids.view(-1, input_shape[-1])
+
+        if past_key_values is None:
+            past_length = 0
+            past_key_values = tuple([None] * len(self.h))
+        else:
+            past_length = past_key_values[0][0].size(-2)
+
+        if position_ids is None:
+            position_ids = torch.arange(past_length,
+                                        input_shape[-1] + past_length,
+                                        dtype=torch.long,
+                                        device=device)
+            position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
+
+        # Attention mask.
+        if attention_mask is not None:
+            assert batch_size > 0, "batch_size has to be defined and > 0"
+            attention_mask = attention_mask.view(batch_size, -1)
+            # We create a 3D attention mask from a 2D tensor mask.
+            # Sizes are [batch_size, 1, 1, to_seq_length]
+            # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+            # this attention mask is more simple than the triangular masking of causal attention
+            # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+            attention_mask = attention_mask[:, None, None, :]
+
+            # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+            # masked positions, this operation will create a tensor which is 0.0 for
+            # positions we want to attend and -10000.0 for masked positions.
+            # Since we are adding it to the raw scores before the softmax, this is
+            # effectively the same as removing these entirely.
+            attention_mask = attention_mask.to(
+                dtype=self.dtype)  # fp16 compatibility
+            attention_mask = (1.0 - attention_mask) * -10000.0
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x num_attention_heads x N x N
+        # head_mask has shape n_layer x batch x num_attention_heads x N x N
+        head_mask = self.get_head_mask(head_mask, self.config.n_layer)
+
+        return input_ids, attention_mask, head_mask, position_ids, token_type_ids, inputs_embeds, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict
+
+    def forward_embed(
+        self,
+        input_ids=None,
+        token_type_ids=None,
+        inputs_embeds=None,
+    ):
+        if inputs_embeds is None:
+            inputs_embeds = self.wte(input_ids)
+
+        hidden_states = inputs_embeds
+
+        if token_type_ids is not None:
+            token_type_embeds = self.wte(token_type_ids)
+            hidden_states = hidden_states + token_type_embeds
+
+        hidden_states = self.drop(hidden_states)
+
+        return hidden_states
+
+    def forward_layer(
+        self,
+        hidden_states,
+        layer_i,
+        layer_past=None,
+        attention_mask=None,
+        head_mask=None,
+        adapter_layer=None,
+        adapter_dropout=None,
+        adapter_input=None,
+        use_cache=None,
+        output_attentions=None,
+    ):
+        if getattr(self.config, "gradient_checkpointing",
+                   False) and self.training:
+            if use_cache:
+                logger.warning(
+                    "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
+                    "`use_cache=False`...")
+                use_cache = False
+
+            def create_custom_forward(module):
+
+                def custom_forward(*inputs):
+                    # None for past_key_value
+                    return module(*inputs, use_cache, output_attentions)
+
+                return custom_forward
+
+            outputs = torch.utils.checkpoint.checkpoint(
+                create_custom_forward(self.h[layer_i]),
+                hidden_states,
+                None,
+                attention_mask,
+                head_mask[layer_i],
+                adapter_layer, 
+                adapter_dropout,
+                adapter_input,
+            )
+        else:
+            outputs = self.h[layer_i](
+                hidden_states,
+                layer_past=layer_past,
+                attention_mask=attention_mask,
+                head_mask=head_mask[layer_i],
+                adapter_layer=adapter_layer,
+                adapter_dropout=adapter_dropout,
+                adapter_input=adapter_input,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+            )
+
+        hidden_states = outputs[0]
+
+        if use_cache:
+            presents = (outputs[1], )
+        else:
+            presents = None
+
+        if output_attentions:
+            self_attentions = outputs[2 if use_cache else 1]
+        else:
+            self_attentions = None
+
+        return hidden_states, presents, self_attentions
+
+    def forward_layers(
+        self,
+        hidden_states,
+        past_key_values=None,
+        attention_mask=None,
+        head_mask=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+    ):
+        all_presents = () if use_cache else None
+        all_self_attentions = () if output_attentions else None
+        all_hidden_states = () if output_hidden_states else None
+        for i in range(self.config.n_layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states, )
+
+            hidden_states, presents, self_attentions = self.forward_layer(
+                hidden_states,
+                i,
+                layer_past=past_key_values[i]
+                if past_key_values is not None else None,
+                attention_mask=attention_mask,
+                head_mask=head_mask,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+            )
+
+            if use_cache is True:
+                all_presents = all_presents + presents
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (self_attentions, )
+
+        return hidden_states, all_presents, all_self_attentions, all_hidden_states
+
+    def forward(
+        self,
+        input_ids=None,
+        past_key_values=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        input_shape = input_ids.size()
+        input_ids, attention_mask, head_mask, position_ids, token_type_ids, inputs_embeds, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict = self.forward_prep(
+            input_ids=input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = self.forward_embed(
+            input_ids=input_ids,
+            token_type_ids=token_type_ids,
+            inputs_embeds=inputs_embeds,
+        )
+
+        hidden_states, all_presents, all_self_attentions, all_hidden_states = self.forward_layers(
+            hidden_states=hidden_states,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            head_mask=head_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        hidden_states = self(hidden_states)
+
+        output_shape = input_shape + (hidden_states.size(-1), )
+        hidden_states = hidden_states.view(*output_shape)
+        # Add last hidden state
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states, )
+
+        if not return_dict:
+            return tuple(v for v in [
+                hidden_states, all_presents, all_hidden_states,
+                all_self_attentions
+            ] if v is not None)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=all_presents,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+        )
+
+class ModularProGenForCausalLM(ProGenPreTrainedModel):
+    _keys_to_ignore_on_load_missing = [
+        r"h\.\d+\.attn\.masked_bias", r"h\.\d+\.attn\.bias", r"lm_head\.weight"
+    ]
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.transformer = ModularProGenModel(config)
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
+        self.init_weights()
+
+    def get_output_embeddings(self):
+        return None
+
+    def set_output_embeddings(self, new_embeddings):
+        return
+
+    def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
+        token_type_ids = kwargs.get("token_type_ids", None)
+        # only last token for inputs_ids if past is defined in kwargs
+        if past:
+            input_ids = input_ids[:, -1].unsqueeze(-1)
+            if token_type_ids is not None:
+                token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
+
+        attention_mask = kwargs.get("attention_mask", None)
+        position_ids = kwargs.get("position_ids", None)
+
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+        else:
+            position_ids = None
+        return {
+            "input_ids": input_ids,
+            "past_key_values": past,
+            "use_cache": kwargs.get("use_cache"),
+            "position_ids": position_ids,
+            "attention_mask": attention_mask,
+            "token_type_ids": token_type_ids,
+        }
+
+    def forward(
+        self,
+        input_ids=None,
+        past_key_values=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+            ``labels = input_ids`` Indices are selected in ``[-100, 0, ..., config.vocab_size]`` All labels set to
+            ``-100`` are ignored (masked), the loss is only computed for labels in ``[0, ..., config.vocab_size]``
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.transformer(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+
+        # make sure sampling in fp16 works correctly and
+        # compute loss in fp32 to match with mesh-tf version
+        # https://github.com/EleutherAI/gpt-neo/blob/89ce74164da2fb16179106f54e2269b5da8db333/models/gpt2/gpt2.py#L179
+        lm_logits = self.lm_head(hidden_states).to(torch.float32)
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = lm_logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss() 
+            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
+                            shift_labels.view(-1))
+
+            loss = loss.to(hidden_states.dtype)
+
+        if not return_dict:
+            output = (lm_logits, ) + transformer_outputs[1:]
+            return ((loss, ) + output) if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=lm_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+    @staticmethod
+    def _reorder_cache(past: Tuple[Tuple[torch.Tensor]],
+                       beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
+        """
+        This function is used to re-order the :obj:`past_key_values` cache if
+        :meth:`~transformers.PretrainedModel.beam_search` or :meth:`~transformers.PretrainedModel.beam_sample` is
+        called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step.
+        """
+        return tuple(
+            tuple(
+                past_state.index_select(0, beam_idx.to(past_state.device))
+                for past_state in layer_past) for layer_past in past)
+
+    
+class ProgenConditional(ProGenPreTrainedModel): #nn.Module
+    def __init__(self, config: ProGenConditionalConfig):
+        super().__init__(config)
+
+        #self.model = ModularProGenForCausalLM.from_pretrained(pretrained_model_name_or_path=config.pretrained_model_dir, config=config)
+        self.model = ModularProGenForCausalLM.from_pretrained("jsunn-y/ProCALM", subfolder="progen2-base", config=config, cache_dir=config.pretrained_model_dir)
+        self.model.requires_grad_(False) #freeze the pretrained model by default
+
+        self.config = config
+
+        self.projection_mlps = torch.nn.ModuleDict() #conditioning encoders
+        if config.adapter_shared_projection == True:
+            n_projection_mlps = 1 #sharing a projector
+        else:
+            n_projection_mlps = len(self.model.transformer.h) #having a projector for every layer
+
+        for key, input_dim in config.encoding_dimensions.items():
+            adapter_projection_layers = nn.ModuleList()
+            for i in range(n_projection_mlps):
+                if config.adapter_projection_nlayers == None:
+                    projection_mlp = torch.nn.Linear(input_dim, config.adapter_c_s)
+                else:
+                    projection_mlp = ProjectionMLP(input_dim=input_dim, c_s=config.adapter_c_s, num_layers=config.adapter_projection_nlayers)
+                adapter_projection_layers.append(projection_mlp)
+
+            self.projection_mlps[key] = adapter_projection_layers
+        
+        #if using a shared adapter, append an extra MLP to process the summed input
+        #not necessary if you have a separate adapter for each layer
+        #this one is always nonlinear and uses two layers
+        if (config.conditions_shared_adapter == True) and (len(config.encoding_dimensions.values()) >=2):
+            adapter_projection_layers = nn.ModuleList()
+            for i in range(n_projection_mlps):
+                projection_mlp = ProjectionMLP(input_dim=config.adapter_c_s, c_s=config.adapter_c_s, num_layers=2)
+                adapter_projection_layers.append(projection_mlp)
+
+            self.projection_mlps["combination"] = adapter_projection_layers
+
+        #initialize the adapter layers
+        self.adapter_layers = torch.nn.ModuleList()
+        if config.conditions_shared_adapter == False:
+            keys = config.encoding_dimensions.keys()
+        else:
+            keys = ["joint"]
+        n_parallel = len(keys)
+        
+        for i in range(len(self.model.transformer.h)):
+            parallel_adapter_layer = ParallelAdapterLayer(
+            n_parallel=n_parallel,
+            c_s=config.adapter_c_s, 
+            c_h=config.n_embd,
+            adapter_summation=config.adapter_summation,
+            weight_init=config.adapter_weight_init,
+            adapter_nlayers=config.adapter_nlayers,
+            )
+            adapter_dropout = torch.nn.Dropout(config.adapter_dropout)
+            self.adapter_layers.append(nn.ModuleList([parallel_adapter_layer, adapter_dropout]))
+
+    def prepare_inputs_for_generation(self, input_ids, condition_encodings: Dict[str, torch.tensor] = None, past=None, **kwargs):
+        """
+        Overides the prepare inputs for generation function (HF compatible) to allow for the addition of adapter input.
+        """
+        token_type_ids = kwargs.get("token_type_ids", None)
+        # only last token for inputs_ids if past is defined in kwargs
+        past = kwargs.get("past_key_values", past)
+        if past:
+            input_ids = input_ids[:, -1].unsqueeze(-1)
+            if token_type_ids is not None:
+                token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
+
+        attention_mask = kwargs.get("attention_mask", None)
+        position_ids = kwargs.get("position_ids", None)
+
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+        else:
+            position_ids = None
+        
+        adapter_input = {}
+        for key, condition_encoding in condition_encodings.items():
+            if condition_encoding is not None:
+                single_adapter_input = condition_encoding.repeat(input_ids.shape[0], input_ids.shape[1], 1)
+            else:
+                single_adapter_input = None
+            adapter_input[key] = single_adapter_input
+
+        return {
+            "input_ids": input_ids,
+            "past_key_values": past,
+            "position_ids": position_ids,
+            "attention_mask": attention_mask,
+            "token_type_ids": token_type_ids,
+            "adapter_input": adapter_input,
+        }
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        if isinstance(past_key_values, Cache):
+            return past_key_values.reorder_cache(beam_idx)
+
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return DynamicCache.from_legacy_cache(reordered_past)
+    
+    def forward(
+        self,
+        input_ids=None,
+        past_key_values=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+        adapter_input=None,
+    ):
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        input_shape = input_ids.size()
+
+        input_ids, attention_mask, head_mask, position_ids, token_type_ids, inputs_embeds, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict = self.model.transformer.forward_prep(
+            input_ids=input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = self.model.transformer.forward_embed(
+            input_ids=input_ids,
+            token_type_ids=token_type_ids,
+            inputs_embeds=inputs_embeds,
+        )
+
+        all_presents = () if use_cache else None
+        all_self_attentions = () if output_attentions else None
+        all_hidden_states = () if output_hidden_states else None
+
+        #project the condition to the dimension of the adapter
+        #if sharing a single projection layer
+        #else do nothing until we get into the loop
+        if self.config.adapter_shared_projection == True:
+            encoded_adapter_input = ()
+            #if you're sharing an adapter and doing joint conditioning
+            if len(adapter_input.keys()) >= 2  and self.config.conditions_shared_adapter == True:
+                summed_adapter_input = torch.zeros(input_shape[0], input_shape[1], self.config.adapter_c_s).to(input_ids.device)
+                for key, single_adapter_input in adapter_input.items():
+                    projected_adapter_input = self.projection_mlps[key][0](single_adapter_input)
+                    summed_adapter_input += projected_adapter_input
+                
+                #combine the inputs and pass through one
+                key = "combination"
+                summed_adapter_input = self.projection_mlps[key][0](summed_adapter_input)
+                encoded_adapter_input = (summed_adapter_input, )
+            
+            #if you're not sharing an adapter (with or without multiple conditions)
+            else:
+                for key, value in adapter_input.items():
+                    summed_adapter_input = self.projection_mlps[key][0](value)
+                    encoded_adapter_input = encoded_adapter_input + (summed_adapter_input, )                
+            encoded_adapter_input = torch.stack(encoded_adapter_input, dim=0)
+
+        for i in range(len(self.model.transformer.h)):
+            #if not sharing a projection layer
+            if self.config.adapter_shared_projection == False:
+                encoded_adapter_input = ()
+                #if you're sharing an adapter and doing joint conditioning
+                if len(adapter_input.keys()) >= 2 and self.config.conditions_shared_adapter == True:
+                    summed_adapter_input = torch.zeros(input_shape[0], input_shape[1], self.config.adapter_c_s).to(input_ids.device)
+                    for key, single_adapter_input in adapter_input.items():
+                        projected_adapter_input = self.projection_mlps[key][i](single_adapter_input)
+                        encoded_adapter_input += projected_adapter_input
+                    
+                    #combine the inputs and pass through one more mlp
+                    key = "combination"
+                    summed_adapter_input = self.projection_mlps[key][i](summed_adapter_input)
+                    encoded_adapter_input = (summed_adapter_input, )
+
+                #if you're not sharing an adapter (with or without multiple conditions)
+                else:
+                    for key, value in adapter_input.items():
+                        summed_adapter_input = self.projection_mlps[key][i](value)
+                        encoded_adapter_input = encoded_adapter_input + (summed_adapter_input, )
+                encoded_adapter_input = torch.stack(encoded_adapter_input, dim=0)
+
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states, )
+
+            hidden_states, presents, self_attentions = self.model.transformer.forward_layer(
+                hidden_states=hidden_states,
+                layer_i=i,
+                layer_past=past_key_values[i] if past_key_values[i] is not None else None,
+                attention_mask=attention_mask,
+                head_mask=head_mask,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                adapter_layer=self.adapter_layers[i][0],
+                adapter_dropout=self.adapter_layers[i][1],
+                adapter_input=encoded_adapter_input,
+            )
+
+            if use_cache is True:
+                all_presents = all_presents + presents
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (self_attentions, )
+            
+        hidden_states = self.model.transformer.ln_f(hidden_states)
+
+        output_shape = input_shape + (hidden_states.size(-1), )
+        hidden_states = hidden_states.view(*output_shape)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states, )
+
+        if not return_dict:
+            return tuple(v for v in [
+                hidden_states, all_presents, all_hidden_states,
+                all_self_attentions
+            ] if v is not None)
+
+        transformer_outputs = BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=all_presents,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+        )
+        
+        hidden_states = transformer_outputs[0]
+
+        # make sure sampling in fp16 works correctly and
+        # compute loss in fp32 to match with mesh-tf version
+        # https://github.com/EleutherAI/gpt-neo/blob/89ce74164da2fb16179106f54e2269b5da8db333/models/gpt2/gpt2.py#L179
+        lm_logits = self.model.lm_head(hidden_states).to(torch.float32)
+
+        loss = None
+        all_losses = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = lm_logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+
+            #added this so that the loss of each sample is outputted
+            loss_fct = CrossEntropyLoss(ignore_index=0, reduction='none')
+            all_losses = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
+                            shift_labels.view(-1))
+            all_losses = all_losses.to(hidden_states.dtype)
+            
+            #still output the mean reduced loss
+            loss_fct = CrossEntropyLoss(ignore_index=0)
+            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)),
+                            shift_labels.view(-1))
+
+        if not return_dict:
+            output = (lm_logits, ) + transformer_outputs[1:]
+            return ((loss, ) + output) if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            all_losses=all_losses,
+            logits=lm_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )