Uploaded custom arch

This file will tell Transformers that this model has custom arch so it will automatically use it on inference

modeling_gpt3dev.py

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+import math
+import torch
+import torch.nn as nn
+from transformers.models.gpt2.configuration_gpt2 import GPT2Config
+from transformers.models.gpt2.modeling_gpt2 import (
+    GPT2LMHeadModel,
+    GPT2Model,
+    GPT2Block,
+    GPT2Attention,
+    GPT2MLP,
+    CausalLMOutputWithCrossAttentions
+)
+
+from transformers import (
+    CONFIG_MAPPING,
+    AutoConfig,
+    AutoModel,
+    AutoModelForCausalLM,
+)
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+# Custom Configuration Class
+class GPT3DevConfig(GPT2Config):
+    model_type = "gpt3dev"
+
+    def __init__(self, use_pre_layernorm=True, **kwargs):
+        super().__init__(**kwargs)
+        self.use_pre_layernorm = use_pre_layernorm
+
+# Register the configuration with AutoConfig
+CONFIG_MAPPING.register("gpt3dev", GPT3DevConfig)
+AutoConfig.register("gpt3dev", GPT3DevConfig)
+
+# Custom Attention Module
+class GPT3DevAttention(GPT2Attention):
+    def __init__(self, config, is_cross_attention=False):
+        super().__init__(config, is_cross_attention)
+
+        # Ensure biases are included
+        self.c_attn = nn.Linear(config.hidden_size, 3 * config.hidden_size, bias=True)
+        self.c_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=True)
+
+# Custom MLP Module
+class GPT3DevMLP(GPT2MLP):
+    def __init__(self, intermediate_size, config):
+        super().__init__(intermediate_size, config)
+        self.c_fc = nn.Linear(config.hidden_size, intermediate_size, bias=True)
+        self.c_proj = nn.Linear(intermediate_size, config.hidden_size, bias=True)
+        self.act = nn.GELU()  # Use standard GeLU
+
+# Custom Transformer Block
+class GPT3DevBlock(GPT2Block):
+    def __init__(self, config):
+        super().__init__(config)
+        self.use_pre_layernorm = config.use_pre_layernorm
+        self.ln_1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+        self.attn = GPT3DevAttention(config)
+        self.mlp = GPT3DevMLP(4 * config.hidden_size, config)
+        self.ln_2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+
+    def forward(
+        self,
+        hidden_states,
+        layer_past=None,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        use_cache=None,
+        output_attentions=False,
+    ):
+        if self.use_pre_layernorm:
+            # Pre-LayerNorm
+            residual = hidden_states
+            hidden_states = self.ln_1(hidden_states)
+            attn_outputs = self.attn(
+                hidden_states,
+                layer_past=layer_past,
+                attention_mask=attention_mask,
+                head_mask=head_mask,
+                encoder_hidden_states=encoder_hidden_states,
+                encoder_attention_mask=encoder_attention_mask,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+            )
+            attn_output = attn_outputs[0]
+            outputs = attn_outputs[1:]  # present, (attentions)
+
+            hidden_states = residual + attn_output
+
+            residual = hidden_states
+            hidden_states = self.ln_2(hidden_states)
+            feed_forward_hidden_states = self.mlp(hidden_states)
+            hidden_states = residual + feed_forward_hidden_states
+        else:
+            # Original GPT-2 Post-LayerNorm
+            residual = hidden_states
+            attn_outputs = self.attn(
+                hidden_states,
+                layer_past=layer_past,
+                attention_mask=attention_mask,
+                head_mask=head_mask,
+                encoder_hidden_states=encoder_hidden_states,
+                encoder_attention_mask=encoder_attention_mask,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+            )
+            attn_output = attn_outputs[0]
+            outputs = attn_outputs[1:]  # present, (attentions)
+
+            hidden_states = residual + attn_output
+            hidden_states = self.ln_1(hidden_states)
+
+            residual = hidden_states
+            feed_forward_hidden_states = self.mlp(hidden_states)
+            hidden_states = residual + feed_forward_hidden_states
+            hidden_states = self.ln_2(hidden_states)
+
+        if use_cache:
+            outputs = (hidden_states,) + outputs
+        else:
+            outputs = (hidden_states,) + outputs[1:]
+
+        return outputs  # hidden_states, present, (attentions)
+
+# Custom Transformer Model
+class GPT3DevModel(GPT2Model):
+    config_class = GPT3DevConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.wte = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.wpe = nn.Embedding(config.n_positions, config.hidden_size)
+        self.drop = nn.Dropout(config.embd_pdrop)
+        self.h = nn.ModuleList(
+            [GPT3DevBlock(config) for _ in range(config.num_hidden_layers)]
+        )
+        self.ln_f = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+
+        # Initialize weights
+        self.post_init()
+
+# Custom LM Head Model
+class GPT3DevLMHeadModel(GPT2LMHeadModel):
+    config_class = GPT3DevConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.transformer = GPT3DevModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights
+        self.post_init()
+
+    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,
+    ):
+        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]
+
+        lm_logits = self.lm_head(hidden_states)
+
+        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()
+            loss_fct = nn.CrossEntropyLoss()
+            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 CausalLMOutputWithCrossAttentions(
+            loss=loss,
+            logits=lm_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+            cross_attentions=transformer_outputs.cross_attentions,
+        )
+
+# Register the custom model with AutoModel and AutoModelForCausalLM
+AutoConfig.register("gpt3dev", GPT3DevConfig)
+AutoModel.register(GPT3DevConfig, GPT3DevModel)
+AutoModelForCausalLM.register(GPT3DevConfig, GPT3DevLMHeadModel)