Add tmlt model with 3 layers

config.json

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+{
+  "architectures": [
+    "TMLTGPTForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "modeling_tmlt.GPTConfig",
+    "AutoModelForCausalLM": "modeling_tmlt.TMLTGPTForCausalLM"
+  },
+  "dtype": "bfloat16",
+  "model_type": "tmlt",
+  "transformers_version": "4.57.0"
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "transformers_version": "4.57.0"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1ce799d14f7d0243078dc932ed0d887d26be3d18f6dcef51acdd112307e03b07
+size 546896752

modeling_tmlt.py

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+import math
+from dataclasses import dataclass
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import PreTrainedModel, PretrainedConfig
+from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
+
+class CausalSelfAttention(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+        # key, query, value projections for all heads, but in a batch
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
+        # output projection
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
+        # regularization
+        self.attn_dropout = nn.Dropout(config.dropout)
+        self.resid_dropout = nn.Dropout(config.dropout)
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.dropout = config.dropout
+        # flash attention make GPU go brrrrr but support is only in PyTorch >= 2.0
+        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
+        if not self.flash:
+            print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
+            # causal mask to ensure that attention is only applied to the left in the input sequence
+            self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
+                                        .view(1, 1, config.block_size, config.block_size))
+
+    def forward(self, x):
+        B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd)
+
+        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
+        q, k, v  = self.c_attn(x).split(self.n_embd, dim=2)
+        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
+        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
+        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
+
+        # causal self-attention; Self-attend: (B, nh, T, hs) x (B, nh, hs, T) -> (B, nh, T, T)
+        if self.flash:
+            # efficient attention using Flash Attention CUDA kernels
+            y = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=self.dropout if self.training else 0, is_causal=True)
+        else:
+            # manual implementation of attention
+            att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+            att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))
+            att = F.softmax(att, dim=-1)
+            att = self.attn_dropout(att)
+            y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
+        y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
+
+        # output projection
+        y = self.resid_dropout(self.c_proj(y))
+        return y
+
+class MLP(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.c_fc    = nn.Linear(config.n_embd, config.intermediate_dim, bias=config.bias)
+        self.gelu    = nn.GELU()
+        self.c_proj  = nn.Linear(config.intermediate_dim, config.n_embd, bias=config.bias)
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        x = self.c_fc(x)
+        x = self.gelu(x)
+        x = self.c_proj(x)
+        x = self.dropout(x)
+        return x
+
+class LoopFormerBlock(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.norm_1 = nn.RMSNorm(config.n_embd, elementwise_affine=False)
+        self.attn = CausalSelfAttention(config)
+        self.norm_2 = nn.RMSNorm(config.n_embd, elementwise_affine=False)
+        self.mlp  = MLP(config)
+
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(config.n_embd, 4 * config.n_embd, bias=True),
+        )
+
+        nn.init.zeros_(self.adaLN_modulation[1].weight)
+        nn.init.zeros_(self.adaLN_modulation[1].bias)
+
+    def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
+        gate_msa, gate_mlp, scale_msa, scale_mlp = self.adaLN_modulation(c).chunk(4, dim=1)
+
+        x = x + gate_msa.unsqueeze(1) * self.attn(
+            self.norm_1(x) * (1 + scale_msa.unsqueeze(1))
+        )
+        x = x + gate_mlp.unsqueeze(1) * self.mlp(
+            self.norm_2(x) * (1 + scale_mlp.unsqueeze(1))
+        )
+        return x
+
+class TimestepEmbedder(nn.Module):
+    def __init__(self, hidden_size, frequency_embedding_size=256):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
+            nn.SiLU(),
+            nn.Linear(hidden_size, hidden_size, bias=True),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        half = dim // 2
+        freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(
+            device=t.device
+        )
+        args = t[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+        return embedding
+
+    def forward(self, t):
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
+        t_freq = t_freq.to(dtype=self.mlp[0].weight.dtype)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+class SharedBlock(nn.Module):
+    def __init__(self, depth, config):
+        super().__init__()
+        self.blocks = nn.ModuleList([
+            LoopFormerBlock(config) for _ in range(depth)
+        ])
+
+    def forward(self, x, c):
+        for block in self.blocks:
+            x = block(x, c)
+        return x
+
+@dataclass
+class GPTConfig(PretrainedConfig):
+    model_type: str = 'tmlt'
+    block_size: int = 1024
+    vocab_size: int = 50304 # GPT-2 vocab_size of 50257, padded up to nearest multiple of 64 for efficiency
+    n_layer: int = 3
+    n_head: int = 32
+    n_embd: int = 2048
+    dropout: float = 0.0
+    bias: bool = False # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
+    intermediate_dim: int = 5120
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+class GPT(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        assert config.vocab_size is not None
+        assert config.block_size is not None
+        self.config = config
+
+        self.transformer = nn.ModuleDict(dict(
+            wte = nn.Embedding(config.vocab_size, config.n_embd),
+            wpe = nn.Embedding(config.block_size, config.n_embd),
+            drop = nn.Dropout(config.dropout),
+            h = SharedBlock(config.n_layer, config),
+            norm_f = nn.RMSNorm(config.n_embd),
+        ))
+
+        self.time_embedder = TimestepEmbedder(config.n_embd)
+
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        # with weight tying when using torch.compile() some warnings get generated:
+        # "UserWarning: functional_call was passed multiple values for tied weights.
+        # This behavior is deprecated and will be an error in future versions"
+        # not 100% sure what this is, so far seems to be harmless. TODO investigate
+        self.transformer.wte.weight = self.lm_head.weight # https://paperswithcode.com/method/weight-tying
+
+        # init all weights
+        self.apply(self._init_weights)
+        # apply special scaled init to the residual projections, per GPT-2 paper
+        for pn, p in self.named_parameters():
+            if pn.endswith('c_proj.weight'):
+                torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * config.n_layer))
+
+        # report number of parameters
+        print("number of parameters: %.2fM" % (self.get_num_params()/1e6,))
+
+    def get_num_params(self, non_embedding=True):
+        """
+        Return the number of parameters in the model.
+        For non-embedding count (default), the position embeddings get subtracted.
+        The token embeddings would too, except due to the parameter sharing these
+        params are actually used as weights in the final layer, so we include them.
+        """
+        n_params = sum(p.numel() for p in self.parameters())
+        if non_embedding:
+            n_params -= self.transformer.wpe.weight.numel()
+        return n_params
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+    def forward(self, idx, targets=None, steps=8, **kwargs):
+        device = idx.device
+        b, t = idx.size()
+        assert t <= self.config.block_size, f"Cannot forward sequence of length {t}, block size is only {self.config.block_size}"
+        pos = torch.arange(0, t, dtype=torch.long, device=device) # shape (t)
+
+        # forward the GPT model itself
+        tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
+        pos_emb = self.transformer.wpe(pos) # position embeddings of shape (t, n_embd)
+        x = self.transformer.drop(tok_emb + pos_emb)
+
+        for step_i in range(steps):
+            ti = torch.full((b,), step_i, dtype=torch.long, device=x.device)
+            te = self.time_embedder(ti)
+            x = self.transformer.h(x, te)
+
+        x = self.transformer.norm_f(x)
+
+        logits = self.lm_head(x)
+        
+        loss = None
+        if targets is not None:
+            loss = F.cross_entropy(
+                logits.view(-1, logits.size(-1)),
+                targets.view(-1),
+                ignore_index=-1,
+            )
+
+        return logits, loss
+
+
+# ---- HF wrapper -------------------------------------------------------------
+
+from transformers.generation.utils import GenerationMixin
+
+class TMLTGPTForCausalLM(PreTrainedModel, GenerationMixin):
+    config_class = GPTConfig
+    main_input_name = "input_ids"
+    _tied_weights_keys = ["gpt.transformer.wte.weight", "gpt.lm_head.weight"]
+
+    def __init__(self, config: GPTConfig, **kwargs):
+        super().__init__(config)
+        self.gpt = GPT(config)
+        self.post_init()
+
+    # expose embeddings/heads for HF utilities
+    def get_input_embeddings(self):
+        return self.gpt.transformer.wte
+
+    def set_input_embeddings(self, new_emb):
+        self.gpt.transformer.wte = new_emb
+        self.gpt.lm_head.weight = new_emb.weight  # keep tied
+
+    def get_output_embeddings(self):
+        return self.gpt.lm_head
+
+    def set_output_embeddings(self, new_out):
+        self.gpt.lm_head = new_out
+
+    def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **kwargs):
+        return {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            # no labels during generation
+        }
+
+    def forward(self, input_ids=None, attention_mask=None, labels=None, **kwargs):
+        # pass the mask all the way through
+        logits, loss = self.gpt(
+            input_ids, targets=labels, attention_mask=attention_mask
+        )
+        return CausalLMOutputWithCrossAttentions(loss=loss, logits=logits)
+

special_tokens_map.json

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+{
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "pad_token": "<|endoftext|>",
+  "unk_token": "<|endoftext|>"
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "50256": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "extra_special_tokens": {},
+  "model_max_length": 1024,
+  "pad_token": "<|endoftext|>",
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}