Models + tokenizer

model.py

@@ -0,0 +1,271 @@
+# Copyright (c) 2023, Albert Gu, Tri Dao.
+
+import math
+from functools import partial
+import json
+import os
+
+from collections import namedtuple
+
+import torch
+import torch.nn as nn
+
+from mamba_ssm.models.config_mamba import MambaConfig
+from mamba_ssm.modules.mamba_simple import Mamba, Block
+from mamba_ssm.utils.generation import GenerationMixin
+from mamba_ssm.utils.hf import load_config_hf, load_state_dict_hf
+import torch.nn.functional as F
+try:
+    from mamba_ssm.ops.triton.layernorm import RMSNorm, layer_norm_fn, rms_norm_fn
+except ImportError:
+    RMSNorm, layer_norm_fn, rms_norm_fn = None, None, None
+
+
+def create_block(
+    d_model,
+    ssm_cfg=None,
+    norm_epsilon=1e-5,
+    rms_norm=False,
+    residual_in_fp32=False,
+    fused_add_norm=False,
+    layer_idx=None,
+    device=None,
+    dtype=None,
+):
+    if ssm_cfg is None:
+        ssm_cfg = {}
+    factory_kwargs = {"device": device, "dtype": dtype}
+    mixer_cls = partial(Mamba, layer_idx=layer_idx, **ssm_cfg, **factory_kwargs)
+    norm_cls = partial(
+        nn.LayerNorm if not rms_norm else RMSNorm, eps=norm_epsilon, **factory_kwargs
+    )
+    block = Block(
+        d_model,
+        mixer_cls,
+        norm_cls=norm_cls,
+        fused_add_norm=fused_add_norm,
+        residual_in_fp32=residual_in_fp32,
+    )
+    block.layer_idx = layer_idx
+    return block
+
+
+# https://github.com/huggingface/transformers/blob/c28d04e9e252a1a099944e325685f14d242ecdcd/src/transformers/models/gpt2/modeling_gpt2.py#L454
+def _init_weights(
+    module,
+    n_layer,
+    initializer_range=0.02,  # Now only used for embedding layer.
+    rescale_prenorm_residual=True,
+    n_residuals_per_layer=1,  # Change to 2 if we have MLP
+):
+    if isinstance(module, nn.Linear):
+        if module.bias is not None:
+            if not getattr(module.bias, "_no_reinit", False):
+                nn.init.zeros_(module.bias)
+    elif isinstance(module, nn.Embedding):
+        nn.init.normal_(module.weight, std=initializer_range)
+
+    if rescale_prenorm_residual:
+        # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
+        #   > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
+        #   > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
+        #   >   -- GPT-2 :: https://openai.com/blog/better-language-models/
+        #
+        # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
+        for name, p in module.named_parameters():
+            if name in ["out_proj.weight", "fc2.weight"]:
+                # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
+                # Following Pytorch init, except scale by 1/sqrt(2 * n_layer)
+                # We need to reinit p since this code could be called multiple times
+                # Having just p *= scale would repeatedly scale it down
+                nn.init.kaiming_uniform_(p, a=math.sqrt(5))
+                with torch.no_grad():
+                    p /= math.sqrt(n_residuals_per_layer * n_layer)
+
+
+class MixerModel(nn.Module):
+    def __init__(
+        self,
+        d_model: int,
+        n_layer: int,
+        vocab_size: int,
+        ssm_cfg=None,
+        norm_epsilon: float = 1e-5,
+        rms_norm: bool = False,
+        initializer_cfg=None,
+        fused_add_norm=False,
+        residual_in_fp32=False,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.residual_in_fp32 = residual_in_fp32
+
+        self.embedding = nn.Embedding(vocab_size, d_model, **factory_kwargs)
+
+        # We change the order of residual and layer norm:
+        # Instead of LN -> Attn / MLP -> Add, we do:
+        # Add -> LN -> Attn / MLP / Mixer, returning both the residual branch (output of Add) and
+        # the main branch (output of MLP / Mixer). The model definition is unchanged.
+        # This is for performance reason: we can fuse add + layer_norm.
+        self.fused_add_norm = fused_add_norm
+        if self.fused_add_norm:
+            if layer_norm_fn is None or rms_norm_fn is None:
+                raise ImportError("Failed to import Triton LayerNorm / RMSNorm kernels")
+
+        self.layers = nn.ModuleList(
+            [
+                create_block(
+                    d_model,
+                    ssm_cfg=ssm_cfg,
+                    norm_epsilon=norm_epsilon,
+                    rms_norm=rms_norm,
+                    residual_in_fp32=residual_in_fp32,
+                    fused_add_norm=fused_add_norm,
+                    layer_idx=i,
+                    **factory_kwargs,
+                )
+                for i in range(n_layer)
+            ]
+        )
+
+        self.norm_f = (nn.LayerNorm if not rms_norm else RMSNorm)(
+            d_model, eps=norm_epsilon, **factory_kwargs
+        )
+
+        self.apply(
+            partial(
+                _init_weights,
+                n_layer=n_layer,
+                **(initializer_cfg if initializer_cfg is not None else {}),
+            )
+        )
+
+    def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):
+        return {
+            i: layer.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype, **kwargs)
+            for i, layer in enumerate(self.layers)
+        }
+
+    def forward(self, input_ids, inference_params=None):
+        hidden_states = self.embedding(input_ids)
+        residual = None
+        for layer in self.layers:
+            hidden_states, residual = layer(
+                hidden_states, residual, inference_params=inference_params
+            )
+        if not self.fused_add_norm:
+            residual = (hidden_states + residual) if residual is not None else hidden_states
+            hidden_states = self.norm_f(residual.to(dtype=self.norm_f.weight.dtype))
+        else:
+            # Set prenorm=False here since we don't need the residual
+            fused_add_norm_fn = rms_norm_fn if isinstance(self.norm_f, RMSNorm) else layer_norm_fn
+            hidden_states = fused_add_norm_fn(
+                hidden_states,
+                self.norm_f.weight,
+                self.norm_f.bias,
+                eps=self.norm_f.eps,
+                residual=residual,
+                prenorm=False,
+                residual_in_fp32=self.residual_in_fp32,
+            )
+        return hidden_states
+
+
+class MambaLMHeadModel(nn.Module, GenerationMixin):
+
+    def __init__(
+        self,
+        config: MambaConfig,
+        initializer_cfg=None,
+        device=None,
+        dtype=None,
+    ) -> None:
+        self.config = config
+        d_model = config.d_model
+        n_layer = config.n_layer
+        vocab_size = config.vocab_size
+        ssm_cfg = config.ssm_cfg
+        rms_norm = config.rms_norm
+        residual_in_fp32 = config.residual_in_fp32
+        fused_add_norm = config.fused_add_norm
+        pad_vocab_size_multiple = config.pad_vocab_size_multiple
+        factory_kwargs = {"device": device, "dtype": dtype}
+
+        super().__init__()
+        if vocab_size % pad_vocab_size_multiple != 0:
+            vocab_size += pad_vocab_size_multiple - (vocab_size % pad_vocab_size_multiple)
+        self.backbone = MixerModel(
+            d_model=d_model,
+            n_layer=n_layer,
+            vocab_size=vocab_size,
+            ssm_cfg=ssm_cfg,
+            rms_norm=rms_norm,
+            initializer_cfg=initializer_cfg,
+            fused_add_norm=fused_add_norm,
+            residual_in_fp32=residual_in_fp32,
+            **factory_kwargs,
+        )
+        self.lm_head = nn.Linear(d_model, vocab_size, bias=False, **factory_kwargs)
+
+        # Initialize weights and apply final processing
+        self.apply(
+            partial(
+                _init_weights,
+                n_layer=n_layer,
+                **(initializer_cfg if initializer_cfg is not None else {}),
+            )
+        )
+        self.tie_weights()
+
+    def tie_weights(self):
+        self.lm_head.weight = self.backbone.embedding.weight
+
+    def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):
+        return self.backbone.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype, **kwargs)
+
+    def forward(self, input_ids, target_ids=None, inference_params=None, num_last_tokens=0):
+        """
+        "position_ids" is just to be compatible with Transformer generation. We don't use it.
+        num_last_tokens: if > 0, only return the logits for the last n tokens
+        """
+        hidden_states = self.backbone(input_ids, inference_params=inference_params)
+        if num_last_tokens > 0:
+            hidden_states = hidden_states[:, -num_last_tokens:]
+        if target_ids is not None:
+            # if we are given some desired targets also calculate the loss
+            logits = self.lm_head(hidden_states)
+            self.last_loss = F.cross_entropy(logits.view(-1, logits.size(-1)), target_ids.view(-1), ignore_index=-1)
+        else:
+            # inference-time mini-optimization: only forward the output on the very last position
+            logits = self.output(hidden_states[:, [-1], :]) # note: using list [-1] to preserve the time dim
+            self.last_loss = None
+        
+        return logits
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name, device=None, dtype=None, **kwargs):
+        config_data = load_config_hf(pretrained_model_name)
+        config = MambaConfig(**config_data)
+        model = cls(config, device=device, dtype=dtype, **kwargs)
+        model.load_state_dict(load_state_dict_hf(pretrained_model_name, device=device, dtype=dtype))
+        return model
+
+    def save_pretrained(self, save_directory):
+        """
+        Minimal implementation of save_pretrained for MambaLMHeadModel.
+        Save the model and its configuration file to a directory.
+        """
+        # Ensure save_directory exists
+        if not os.path.exists(save_directory):
+            os.makedirs(save_directory)
+
+        # Save the model's state_dict
+        model_path = os.path.join(save_directory, 'pytorch_model.bin')
+        torch.save(self.state_dict(), model_path)
+
+        # Save the configuration of the model
+        config_path = os.path.join(save_directory, 'config.json')
+        with open(config_path, 'w') as f:
+            json.dump(self.config.__dict__, f)

models/300-1/ckpt.pt

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models/768-1/ckpt.pt

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models/768-4/ckpt.pt

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models/768-8/ckpt.pt

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models/old/300-1/ckpt.pt

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tok4096.model

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train_snakes.py

@@ -0,0 +1,338 @@
+"""
+This training script can be run both on a single gpu in debug mode,
+and also in a larger training run with distributed data parallel (ddp).
+
+To run on a single GPU small debug run, example:
+$ python -m train.py --compile=False --eval_iters=10 --batch_size=8
+
+To run with DDP on 4 gpus on 1 node, example:
+$ torchrun --standalone --nproc_per_node=4 train.py
+
+To run with DDP on 4 gpus across 2 nodes, example:
+- Run on the first (master) node with example IP 123.456.123.456:
+$ torchrun --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr=123.456.123.456 --master_port=1234 train.py
+- Run on the worker node:
+$ torchrun --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr=123.456.123.456 --master_port=1234 train.py
+(If your cluster does not have Infiniband interconnect prepend NCCL_IB_DISABLE=1)
+"""
+
+import math
+import os
+import time
+from contextlib import nullcontext
+from datetime import datetime
+from functools import partial
+import inspect
+import torch
+from torch.distributed import destroy_process_group, init_process_group
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+from tinystories import Task
+
+from model import MambaLMHeadModel
+
+
+# -----------------------------------------------------------------------------
+# I/O
+out_dir = "out/768-8"
+eval_interval = 2000
+log_interval = 1
+eval_iters = 100
+eval_only = False  # if True, script exits right after the first eval
+always_save_checkpoint = True  # if True, always save a checkpoint after each eval
+init_from = "resume"  # 'scratch' or 'resume'
+# wandb logging
+wandb_log = True  # disabled by default
+wandb_project = "tiny-mambas"
+wandb_run_name = "run" + datetime.now().strftime("%Y_%m_%d_%H_%M_%S")
+# data
+batch_size = 128  # if gradient_accumulation_steps > 1, tshis is the micro-batch size
+max_seq_len = 256
+vocab_size = 4096 # the Llama 2 tokenizer has 32K tokens
+vocab_source = "custom"
+# model
+d_model = 768
+n_layer = 8
+vocab_size = 4096
+# adamw optimizer
+gradient_accumulation_steps = 4  # used to simulate larger batch sizes
+learning_rate = 5e-4  # max learning rate
+max_iters = 100000  # total number of training iterations
+weight_decay = 1e-1
+beta1 = 0.9
+beta2 = 0.95
+grad_clip = 1.0  # clip gradients at this value, or disable if == 0.0
+# learning rate decay settings
+decay_lr = True  # whether to decay the learning rate
+warmup_iters = 1000  # how many steps to warm up for
+# system
+device = "cuda"  # examples: 'cpu', 'cuda', 'cuda:0', 'cuda:1' etc., or try 'mps' on macbooks
+dtype = "float16"  # float32|bfloat16|float16
+compile = False  # use PyTorch 2.0 to compile the model to be faster
+
+
+class mambaConfig:
+
+    d_model: int = d_model
+    n_layer: int = n_layer
+    vocab_size: int = vocab_size
+    ssm_cfg: dict = None
+    rms_norm: bool = True
+    residual_in_fp32: bool = True
+    fused_add_norm: bool = True
+    pad_vocab_size_multiple: int = 8
+
+
+
+
+config_keys = [
+    k
+    for k, v in globals().items()
+    if not k.startswith("_") and isinstance(v, (int, float, bool, str))
+]
+config = {k: globals()[k] for k in config_keys}  # will be useful for logging
+
+# fixing some hyperparams to sensible defaults
+lr_decay_iters = max_iters  # should be ~= max_iters per Chinchilla
+min_lr = 5e-5  # minimum learning rate, should be ~= learning_rate/10 per Chinchilla
+
+
+torch.cuda.set_device(0)
+
+# -----------------------------------------------------------------------------
+torch.manual_seed(1337)
+torch.backends.cuda.matmul.allow_tf32 = True  # allow tf32 on matmul
+torch.backends.cudnn.allow_tf32 = True  # allow tf32 on cudnn
+device_type = "cuda" if "cuda" in device else "cpu"  # for later use in torch.autocast
+# note: float16 data type will automatically use a GradScaler
+ptdtype = {"float32": torch.float32, "bfloat16": torch.bfloat16, "float16": torch.float16}[dtype]
+ctx = (
+    nullcontext()
+    if device_type == "cpu"
+    else torch.amp.autocast(device_type=device_type, dtype=ptdtype)
+)
+
+# task-specific setup
+iter_batches = partial(
+    Task.iter_batches,
+    batch_size=batch_size,
+    max_seq_len=max_seq_len,
+    vocab_size=vocab_size,
+    vocab_source=vocab_source,
+    device=device,
+    num_workers=0,
+)
+
+# init these up here, can override if init_from='resume' (i.e. from a checkpoint)
+iter_num = 0
+best_val_loss = 1e9
+
+# model init
+model_args = dict(
+    d_model=d_model,
+    n_layer=n_layer,
+    vocab_size=vocab_size,
+    max_seq_len=max_seq_len,
+) 
+tokens_per_iter = gradient_accumulation_steps * 1 * batch_size * max_seq_len
+
+# start with model_args from command line
+if init_from == "scratch":
+    # init a new model from scratch
+    print("Initializing a new model from scratch")
+    
+    model = MambaLMHeadModel(mambaConfig)
+    model.last_loss = None
+elif init_from == "resume":
+    print(f"Resuming training from {out_dir}")
+    # resume training from a checkpoint.
+    ckpt_path = os.path.join(out_dir, "ckpt.pt")
+    checkpoint = torch.load(ckpt_path, map_location=device)
+    checkpoint_model_args = checkpoint["model_args"]
+    # force these config attributes to be equal otherwise we can't even resume training
+    # the rest of the attributes (e.g. dropout) can stay as desired from command line
+    for k in ["d_model", "n_layer", "vocab_size", "max_seq_len"]:
+        model_args[k] = checkpoint_model_args[k]
+    # create the model
+    model = MambaLMHeadModel(mambaConfig)
+    model.last_loss = None
+    state_dict = checkpoint["model"]
+    # fix the keys of the state dictionary :(
+    # honestly no idea how checkpoints sometimes get this prefix, have to debug more
+    unwanted_prefix = "_orig_mod."
+    for k, v in list(state_dict.items()):
+        if k.startswith(unwanted_prefix):
+            state_dict[k[len(unwanted_prefix) :]] = state_dict.pop(k)
+    model.load_state_dict(state_dict)
+    iter_num = checkpoint["iter_num"]
+    best_val_loss = checkpoint["best_val_loss"]
+model.to(device)
+
+# initialize a GradScaler. If enabled=False scaler is a no-op
+scaler = torch.cuda.amp.GradScaler(enabled=(dtype == "float16"))
+
+# optimizer
+# start with all of the candidate parameters
+param_dict = {pn: p for pn, p in model.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.
+betas = (beta1, beta2)
+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}")
+
+
+
+if init_from == "resume" and "optimizer" in checkpoint:
+    optimizer.load_state_dict(checkpoint["optimizer"])
+checkpoint = None  # free up memory
+
+# compile the model
+if compile:
+    print("compiling the model... (takes a ~minute)")
+    unoptimized_model = model
+    model = torch.compile(model)  # requires PyTorch 2.0
+
+# wrap model into DDP container
+
+
+# helps estimate an arbitrarily accurate loss over either split using many batches
+@torch.no_grad()
+def estimate_loss():
+    out = {}
+    model.eval()
+    for split in ["train", "val"]:
+        batch_iter = iter_batches(split=split)
+        losses = torch.zeros(eval_iters)  # keep on CPU
+        for k in range(eval_iters):
+            X, Y = next(batch_iter)
+            with ctx:
+                logits = model(X, Y)
+                loss = raw_model.last_loss
+            losses[k] = loss.item()
+        out[split] = losses.mean()
+    model.train()
+    return out
+
+# learning rate decay scheduler (cosine with warmup)
+def get_lr(it):
+    # 1) linear warmup for warmup_iters steps
+    if it < warmup_iters:
+        return learning_rate * it / warmup_iters
+    # 2) if it > lr_decay_iters, return min learning rate
+    if it > lr_decay_iters:
+        return min_lr
+    # 3) in between, use cosine decay down to min learning rate
+    decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters)
+    assert 0 <= decay_ratio <= 1
+    coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))  # coeff ranges 0..1
+    return min_lr + coeff * (learning_rate - min_lr)
+
+# logging
+if wandb_log:
+    import wandb
+    wandb.init(project=wandb_project, name=wandb_run_name, config=config)
+
+# training loop
+train_batch_iter = iter_batches(split="train")
+X, Y = next(train_batch_iter)  # fetch the very first batch
+t0 = time.time()
+local_iter_num = 0  # number of iterations in the lifetime of this process
+raw_model = model  # unwrap DDP container if needed
+running_mfu = -1.0
+while True:
+    # determine and set the learning rate for this iteration
+    lr = get_lr(iter_num) if decay_lr else learning_rate
+    for param_group in optimizer.param_groups:
+        param_group["lr"] = lr
+
+    # evaluate the loss on train/val sets and write checkpoints
+    if iter_num % eval_interval == 0:
+        losses = estimate_loss()
+        print(f"step {iter_num}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")
+        if wandb_log:
+            try:
+                wandb.log(
+                    {
+                        "iter": iter_num,
+                        "tokens": iter_num * tokens_per_iter,
+                        "loss/train": losses["train"],
+                        "loss/val": losses["val"],
+                        "lr": lr,
+                
+                    }, step = iter_num
+                )
+            except Exception as e:
+                print(f"logging to wandb failed: {e}")
+        if losses["val"] < best_val_loss or always_save_checkpoint:
+            best_val_loss = losses["val"]
+            if iter_num > 0:
+                checkpoint = {
+                    "model": raw_model.state_dict(),
+                    "optimizer": optimizer.state_dict(),
+                    "model_args": model_args,
+                    "iter_num": iter_num,
+                    "best_val_loss": best_val_loss,
+                    "config": config,
+                }
+                print(f"saving checkpoint to {out_dir}")
+                torch.save(checkpoint, os.path.join(out_dir, "ckpt.pt"))
+                #model_export(raw_model, os.path.join(out_dir, "model.bin"), version=0)
+    if iter_num == 0 and eval_only:
+        break
+
+    # forward backward update, with optional gradient accumulation to simulate larger batch size
+    # and using the GradScaler if data type is float16
+    for micro_step in range(gradient_accumulation_steps):
+        
+        with ctx:
+            logits = model(X, Y)
+            loss = raw_model.last_loss
+            loss = loss / gradient_accumulation_steps
+        # immediately async prefetch next batch while model is doing the forward pass on the GPU
+        X, Y = next(train_batch_iter)
+        # backward pass, with gradient scaling if training in fp16
+        scaler.scale(loss).backward()
+    # clip the gradient
+    if grad_clip != 0.0:
+        scaler.unscale_(optimizer)
+        torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
+    # step the optimizer and scaler if training in fp16
+    scaler.step(optimizer)
+    scaler.update()
+    # flush the gradients as soon as we can, no need for this memory anymore
+    optimizer.zero_grad(set_to_none=True)
+
+    # timing and logging
+    t1 = time.time()
+    dt = t1 - t0
+    t0 = t1
+    if iter_num % log_interval == 0:
+        # get loss as float, scale up due to the divide above. note: this is a CPU-GPU sync point
+        lossf = loss.item() * gradient_accumulation_steps
+        
+        print(
+            f"{iter_num} | loss {lossf:.4f} | lr {lr:e} | {dt*1000:.2f}ms |"
+        )
+    iter_num += 1
+    local_iter_num += 1
+
+    # termination conditions
+    if iter_num > max_iters:
+        break
+