add crate-gpt support

README.md

@@ -3,6 +3,7 @@ This repository is the official PyTorch implementation of the papers:
 
 - **White-Box Transformers via Sparse Rate Reduction** [**NeurIPS-2023**, [paper link](https://openreview.net/forum?id=THfl8hdVxH#)]. By [Yaodong Yu](https://yaodongyu.github.io) (UC Berkeley), [Sam Buchanan](https://sdbuchanan.com) (TTIC), [Druv Pai](https://druvpai.github.io) (UC Berkeley), [Tianzhe Chu](https://tianzhechu.com/) (UC Berkeley), [Ziyang Wu](https://robinwu218.github.io/) (UC Berkeley), [Shengbang Tong](https://tsb0601.github.io/petertongsb/) (UC Berkeley), [Benjamin D Haeffele](https://www.cis.jhu.edu/~haeffele/#about) (Johns Hopkins University), and [Yi Ma](http://people.eecs.berkeley.edu/~yima/) (UC Berkeley). 
 - **Emergence of Segmentation with Minimalistic White-Box Transformers** [[paper link](https://arxiv.org/abs/2308.16271)]. By [Yaodong Yu](https://yaodongyu.github.io)* (UC Berkeley),  [Tianzhe Chu](https://tianzhechu.com/)* (UC Berkeley & ShanghaiTech U), [Shengbang Tong](https://tsb0601.github.io/petertongsb/) (UC Berkeley & NYU), [Ziyang Wu](https://robinwu218.github.io/) (UC Berkeley),  [Druv Pai](https://druvpai.github.io) (UC Berkeley),  [Sam Buchanan](https://sdbuchanan.com) (TTIC), and [Yi Ma](http://people.eecs.berkeley.edu/~yima/) (UC Berkeley & HKU). 2023. (* equal contribution)
+- **White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is?** [**NeurIPS-2023**, [paper link](https://openreview.net/forum?id=THfl8hdVxH#)]. By [Yaodong Yu](https://yaodongyu.github.io) (UC Berkeley), [Sam Buchanan](https://sdbuchanan.com) (TTIC), [Druv Pai](https://druvpai.github.io) (UC Berkeley), [Tianzhe Chu](https://tianzhechu.com/) (UC Berkeley), [Ziyang Wu](https://robinwu218.github.io/) (UC Berkeley), [Shengbang Tong](https://tsb0601.github.io/petertongsb/) (UC Berkeley), [Hao Bai](https://jackgethome.com/) (UIUC), [Yuexiang Zhai](https://yx-s-z.github.io/) (UC Berkeley), [Benjamin D Haeffele](https://www.cis.jhu.edu/~haeffele/#about) (Johns Hopkins University), and [Yi Ma](http://people.eecs.berkeley.edu/~yima/) (UC Berkeley). 
 
 ## What is CRATE?
 CRATE (Coding RAte reduction TransformEr) is a white-box (mathematically interpretable) transformer architecture, where each layer performs a single step of an alternating minimization algorithm to optimize the **sparse rate reduction objective**
@@ -119,6 +120,15 @@ Link: [crate-emergence.ipynb](https://colab.research.google.com/drive/1rYn_Nlepy
 </p>
 <p align="center">
 
+## CRATE On NLP Tasks
+
+### Pre-training CRATE-GPT
+
+We use the [NanoGPT](https://github.com/karpathy/nanoGPT) framework from Andrew Karpathy for pre-training. The CRATE arch file is at [`model/crate_gpt.py`](https://github.com/Ma-Lab-Berkeley/CRATE/tree/main/model/crate_gpt.py). To adapt CRATE to the NanoGPT framework, simply move `model/crare_gpt.py` to the `nanoGPT/` repo, and change the dependency of `nanoGPT/model.py` to `nanoGPT/crate_gpt.py` in the `nanoGPT/train,py` script. The pretrained checkpoint is released on [HuggingFace](https://huggingface.co/JackBAI/CRATE-GPT2-Base).
+
+### Pre-training CRATE-BERT
+
+To be completed soon.
 
 ## Reference
 For technical details and full experimental results, please check the [crate paper](https://arxiv.org/abs/2306.01129) and [crate segmentation paper](https://arxiv.org/abs/2308.16271). Please consider citing our work if you find it helpful to yours:

model/crate_gpt.py

@@ -0,0 +1,299 @@
+import math
+import inspect
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+import torch.nn.init as init
+
+class LayerNorm(nn.Module):
+    """ LayerNorm but with an optional bias. PyTorch doesn't support simply bias=False """
+
+    def __init__(self, ndim, bias=True):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(ndim))
+        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
+
+    def forward(self, input):
+        return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)
+
+class CausalSelfAttention(nn.Module):
+    """Masked MSSA Attention"""
+
+    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)
+        self.c_attn = nn.Linear(config.n_embd, config.n_embd, bias=False)
+        # output projection
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=True)
+        # 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')
+        self.flash = False
+        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
+        qkv = self.c_attn(x)
+        qkv = qkv.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(qkv, qkv, qkv, attn_mask=None, dropout_p=self.dropout if self.training else 0, is_causal=True)
+        else:
+            # manual implementation of attention
+            att = (qkv @ qkv.transpose(-2, -1)) * (1.0 / math.sqrt(qkv.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 @ qkv # (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):
+    """ISTA Block"""
+
+    def __init__(self, config):
+        super().__init__()
+        self.weight = nn.Parameter(torch.Tensor(config.n_embd, config.n_embd))
+        with torch.no_grad():
+            init.kaiming_uniform_(self.weight)
+        self.step_size = 0.1
+        self.lambd = 0.1
+
+    def forward(self, x):
+        # compute D^T * D * x
+        x1 = F.linear(x, self.weight, bias=None)
+        grad_1 = F.linear(x1, self.weight.t(), bias=None)
+        # compute D^T * x
+        grad_2 = F.linear(x, self.weight.t(), bias=None)
+        # compute negative gradient update: step_size * (D^T * x - D^T * D * x)
+        grad_update = self.step_size * (grad_2 - grad_1) - self.step_size * self.lambd
+
+        output = F.relu(x + grad_update)
+        return output
+
+class Block(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.prenorm_1 = LayerNorm(config.n_embd)
+        self.attn = CausalSelfAttention(config)
+        self.prenorm_2 = LayerNorm(config.n_embd)
+        self.ista = MLP(config)
+
+    def forward(self, x):
+        x = x + self.attn(self.prenorm_1(x))
+        x = self.ista(self.prenorm_2(x))
+        return x
+
+@dataclass
+class CRATEConfig:
+    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 = 12
+    n_head: int = 12
+    n_embd: int = 768
+    dropout: float = 0.0
+    bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster
+
+class CRATE(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 = nn.ModuleList([Block(config) for _ in range(config.n_layer)])
+        ))
+        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):
+        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).unsqueeze(0) # shape (1, t)
+
+        # forward the CRATE 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 (1, t, n_embd)
+        x = self.transformer.drop(tok_emb + pos_emb)
+        for block in self.transformer.h:
+            x = block(x)
+
+        if targets is not None:
+            # if we are given some desired targets also calculate the loss
+            logits = self.lm_head(x)
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
+        else:
+            # inference-time mini-optimization: only forward the lm_head on the very last position
+            logits = self.lm_head(x[:, [-1], :]) # note: using list [-1] to preserve the time dim
+            loss = None
+
+        return logits, loss
+
+    def crop_block_size(self, block_size):
+        # model surgery to decrease the block size if necessary
+        # e.g. we may load the GPT2 pretrained model checkpoint (block size 1024)
+        # but want to use a smaller block size for some smaller, simpler model
+        assert block_size <= self.config.block_size
+        self.config.block_size = block_size
+        self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:block_size])
+        for block in self.transformer.h:
+            if hasattr(block.attn, 'bias'):
+                block.attn.bias = block.attn.bias[:,:,:block_size,:block_size]
+
+    @classmethod
+    def from_pretrained(cls, model_type, override_args=None):
+        assert model_type in {'crate', 'crate-medium', 'crate-large', 'crate-xl'}
+        override_args = override_args or {} # default to empty dict
+        # only dropout can be overridden see more notes below
+        assert all(k == 'dropout' for k in override_args)
+
+        # n_layer, n_head and n_embd are determined from model_type
+        config_args = {
+            'crate':         dict(n_layer=12, n_head=12, n_embd=768),  # 124M params
+            'crate-medium':  dict(n_layer=24, n_head=16, n_embd=1024), # 350M params
+            'crate-large':   dict(n_layer=36, n_head=20, n_embd=1280), # 774M params
+            'crate-xl':      dict(n_layer=48, n_head=25, n_embd=1600), # 1558M params
+        }[model_type]
+        print("forcing vocab_size=50257, block_size=1024, bias=True")
+        config_args['vocab_size'] = 50257 # always 50257 for GPT model checkpoints
+        config_args['block_size'] = 1024 # always 1024 for GPT model checkpoints
+        config_args['bias'] = True # always True for GPT model checkpoints
+        # we can override the dropout rate, if desired
+        if 'dropout' in override_args:
+            print(f"overriding dropout rate to {override_args['dropout']}")
+            config_args['dropout'] = override_args['dropout']
+        # create a from-scratch initialized minGPT model
+        config = CRATEConfig(**config_args)
+        model = CRATE(config)
+
+        return model
+
+    def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
+        # start with all of the candidate parameters
+        param_dict = {pn: p for pn, p in self.named_parameters()}
+        # filter out those that do not require grad
+        param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad}
+        # create optim groups. Any parameters that is 2D will be weight decayed, otherwise no.
+        # i.e. all weight tensors in matmuls + embeddings decay, all biases and layernorms don't.
+        decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
+        nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]
+        optim_groups = [
+            {'params': decay_params, 'weight_decay': weight_decay},
+            {'params': nodecay_params, 'weight_decay': 0.0}
+        ]
+        num_decay_params = sum(p.numel() for p in decay_params)
+        num_nodecay_params = sum(p.numel() for p in nodecay_params)
+        print(f"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters")
+        print(f"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters")
+        # Create AdamW optimizer and use the fused version if it is available
+        fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters
+        use_fused = fused_available and device_type == 'cuda'
+        extra_args = dict(fused=True) if use_fused else dict()
+        optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas, **extra_args)
+        print(f"using fused AdamW: {use_fused}")
+
+        return optimizer
+
+    def estimate_mfu(self, fwdbwd_per_iter, dt):
+        """ estimate model flops utilization (MFU) in units of A100 bfloat16 peak FLOPS """
+        # first estimate the number of flops we do per iteration.
+        # see PaLM paper Appendix B as ref: https://arxiv.org/abs/2204.02311
+        N = self.get_num_params()
+        cfg = self.config
+        L, H, Q, T = cfg.n_layer, cfg.n_head, cfg.n_embd//cfg.n_head, cfg.block_size
+        flops_per_token = 6*N + 12*L*H*Q*T
+        flops_per_fwdbwd = flops_per_token * T
+        flops_per_iter = flops_per_fwdbwd * fwdbwd_per_iter
+        # express our flops throughput as ratio of A100 bfloat16 peak flops
+        flops_achieved = flops_per_iter * (1.0/dt) # per second
+        flops_promised = 312e12 # A100 GPU bfloat16 peak flops is 312 TFLOPS
+        mfu = flops_achieved / flops_promised
+        return mfu
+
+    @torch.no_grad()
+    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
+        """
+        Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and complete
+        the sequence max_new_tokens times, feeding the predictions back into the model each time.
+        Most likely you'll want to make sure to be in model.eval() mode of operation for this.
+        """
+        for _ in range(max_new_tokens):
+            # if the sequence context is growing too long we must crop it at block_size
+            idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
+            # forward the model to get the logits for the index in the sequence
+            logits, _ = self(idx_cond)
+            # pluck the logits at the final step and scale by desired temperature
+            logits = logits[:, -1, :] / temperature
+            # optionally crop the logits to only the top k options
+            if top_k is not None:
+                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+                logits[logits < v[:, [-1]]] = -float('Inf')
+            # apply softmax to convert logits to (normalized) probabilities
+            probs = F.softmax(logits, dim=-1)
+            # sample from the distribution
+            idx_next = torch.multinomial(probs, num_samples=1)
+            # append sampled index to the running sequence and continue
+            idx = torch.cat((idx, idx_next), dim=1)
+
+        return idx