Parallel Training Techniques


Parallel Training Techniques

AI 🤝 Compute

[…] since 2012, the amount of [AI] compute used has been increasing exponentially with a 3.4-month doubling time1, or [300,000x]. Source.

AI 🤝 Compute [Modern Era]

Figure 2: [300,000x since 2012] vs [7x for Moore’s Law]. Source.

Single GPU Training

Figure 3: SLOW !! model size limited by GPU memory

Collective Communication

Typically, we assign 1 rank to each GPU (or accelerator), i.e. rank \in [0, 1, ..., WORLD_SIZE-1].

  • Perform reductions on data (e.g. sum, min, max) across ranks, send result back to everyone
Figure 4: All-Reduce operation: each rank receives the reduction of input values across ranks.
  • Perform a reduction on data across ranks, send to individual
Figure 5: Reduce operation: one rank receives the reduction of input values across ranks
  • broadcast (send) a tensor x from one rank to all ranks
Figure 6
  • Gathers tensors from the whole group in a list.
Figure 7
  • Scatters a list of tensors to the whole group
Figure 8

Collective Operations

⌛ Timeouts

  • Collective operations have to be called for each rank to form a complete collective operation.
    • Failure to do so will result in other ranks waiting indefinitely

Why Distributed Training?

  • Splitting data across workers \longrightarrow larger batch size1
    • [micro_batch_size = 1] \times [N GPUs] \rightarrow [global_batch_size = N]
  • Smooth loss landscape
  • Improved gradient estimators
  • Less iterations needed for same number of epochs
    • May need to train for more epochs if another change is not made
    • e.g. scaling learning rate
  • See Large Batch Training of Convolutional Networks

Recent Progress

Table 1: Batch-Size-Scaling
Year Author Batch Size Processor # Processors Time Accuracy
2016 He 256 Tesla P100 8 29 Hour 75.30%
2019 Yamazaki 81,920 Tesla V100 2048 1.2 Min 75.08%

Data Parallel Training

Figure 9

Data Parallel Training

Data Parallel Training

Figure 10

Data Parallel Training

  • Typically easier to implement
  • Existing frameworks (Horovod, DeepSpeed, DDP, etc)
    • Relatively simple to get up and running (minor modifications to code)
  • Recent presentation on data-parallel training available on YouTube


Data Parallel Training

  • Each worker has copy of complete model
  • Global batch of data split into multiple mini-batches
    • Each worker computes the corresponding loss and gradients from local data
  • Before updating parameters, loss and gradients averaged across workers


Data Parallel Training

Figure 11

Deal with Data

  • At each training step, we want to ensure that each worker receives unique data

  • This can be done in one of two ways:

    1. Manually partition data (ahead of time) and assign different sections to different workers
      1. Each worker can only see their local portion of the data
    2. From each worker, randomly select a mini-batch
      1. Each worker can see the full dataset

    ⚠️ Warning

    Don’t forget your seed!

    When randomly selecting, it is important that each worker uses different seeds to ensure they receive unique data

Broadcast Initial State

  • At the start of training (or when loading from a checkpoint), we want all of our workers to be initialized consistently
    • Broadcast the model and optimizer states from rank() == 0 worker

  flowchart TD
    0["GPU0"] --> 1["GPU 1"]
    0 --> 2["GPU 2"]
    0 -->|Model + Optimizer State| 3["GPU 3"]
    0 --> ...
    0 --> N["GPU N"]

Best Practices

  • Use parallel IO whenever possible
    • Feed each rank from different files
    • Use MPI IO to have each rank read its own batch from a file
    • Use several ranks to read data, MPI to scatter to remaining ranks
      • Most practical in big at-scale training

    🤝 Keeping things in Sync

    Computation stalls during communication !!

    Keeping the communication to computation ratio small is important for effective scaling.

  • Take advantage of data storage
  • Preload data when possible
    • Offloading to a GPU frees CPU cycles for loading the next batch of data
      • minimize IO latency this way

Model Parallel Training

  • Split up network over multiple workers

    • Each receives disjoint subset
    • All communication associated with subsets are distributed
  • Communication whenever dataflow between two subsets

  • Typically more complicated to implement than data parallel training

  • Suitable when the model is too large to fit onto a single device (CPU / GPU)

  • argonne-lcf/Megatron-DeepSpeed

  • 🤗 huggingface/nanotron

Figure 12:

Model Parallel Training: Example

y = \sum_{i} w_{i} * x_{i} = w_0 * x_0 + w_1 * x_1 + w_2 * x_2

  1. Compute y_{0} = w_{0} * x_{0} and send to \longrightarrow GPU1
  2. Compute y_{1} = y_{0} + w_{1} * x_{1} and send to \longrightarrow GPU2
  3. Compute y = y_{1} * w_{2} * x_{2}

flowchart LR
  subgraph X0["GPU0"]
    direction LR
  subgraph X1["GPU1"]
    direction LR
  subgraph X2["GPU2"]
    direction LR
  X1 & X0 <--> X2
  X0 <--> X1
  x["x0, x1, x2"] --> X0


Thank you!

  • Organizers

  • ALCF Data Science & Operations

  • Feel free to reach out!

This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.


Forward Pass

  • Each worker has identical copy of model
  • Global batch of data split across workers
  • Loss + Grads averaged across workers before updating parameters

    flowchart TD
      D["dataset"] --> S1["subset_1"]
      D --> S2["subset_2"]
      D --> S3["subset_3"]
      D --> S4["subset_4"]
      S1 --> W1["Worker 1"]
      S2 --> W2["Worker 2"]
      S3 --> W3["Worker 3"]
      S4 --> W4["Worker 4"]


flowchart TD
  subgraph identifier[" "]
    direction LR
  subgraph Network
    direction LR
  Network -.-> GPU1
  Network -.-> GPU2
  Network -.-> GPU3
  Network -.-> GPU4
  subset1 --> GPU1
  subset2 --> GPU2
  subset3 --> GPU3
  subset4 --> GPU4
  subgraph Dataset
    direction LR
  subgraph Communication
    direction LR
    GPU1 <-.-> AR[Allreduce]
    GPU2 <-.-> AR
    GPU3 <-.-> AR
    GPU4 <-.-> AR
  AR ==>|Broadcast| Network

Data Parallel Training

  • Each worker receives identical copy of model and unique subset of data

flowchart TD
    subgraph identifier[" "]
        direction LR
        data --> subset1
        data --> subset2
        data --> subset3
        data --> subset4
    subgraph Workers
        direction LR
        subset1 --> GPX1["GPU1"]
        subset2 --> GPX2["GPU2"]
        subset3 --> GPX3["GPU3"]
        subset4 --> GPX4["GPU4"]
    GPX1 <.-> Communication["Avg + Distribute Gradients"]
    GPX2 <.-> Communication
    GPX3 <.-> Communication
    GPX4 <.-> Communication

Emergent Abilities

Emergent abilities of Large Language Models Yao et al. (2023)

Training LLMs

Figure 13: Visualization from Yang et al. (2023)

Life-Cycle of the LLM

  1. Data collection + preprocessing

  2. Pre-training

    • Architecture decisions:
      {model_size, hyperparameters,
      parallelism, lr_schedule, ...}
  3. Supervised Fine-Tuning

    • Instruction Tuning
    • Alignment
  4. Deploy (+ monitor, re-evaluate, etc.)

Pre-training: Virtually all of the compute used during pretraining phase.

Figure 14: Figure from The Illustrated Transformer

Forward Pass

Figure 15: Language Model trained for causal language modeling. Video from: 🤗 Generation with LLMs

Generating Text

Figure 16: Language Model trained for causal language modeling. Video from: 🤗 Generation with LLMs

Life-Cycle of the LLM: Pre-training

Figure 17: Pre-training: Virtually all of the compute used during pretraining phase

Life-Cycle of the LLM: Fine-Tuning

Figure 18: Fine-tuning1: Fine-tuning actually updates the model’s weights to make the model better at a certain task.

Assistant Models

saforem2/wordplay 🎮💬

  • Fork of Andrej Karpathy’s nanoGPT

Figure 19: The simplest, fastest repository for training / finetuning GPT based models.

saforem2/wordplay 🎮💬

(a) nanoGPT
(b) wordplay 🎮 💬
Figure 20: nanoGPT, transformed.


python3 -m pip install "git+"
python3 -c 'import wordplay; print(wordplay.__file__)'
# ./wordplay/src/wordplay/


  • transformers for transformers (to load GPT-2 checkpoints)
  • datasets for datasets (if you want to use OpenWebText)
  • tiktoken for OpenAI’s fast BPE code
  • wandb for optional logging
  • tqdm for progress bars

Quick Start

  • We start with training a character-level GPT on the works of Shakespeare.

    1. Downloading the data (~ 1MB) file
    2. Convert raw text to one large stream of integers
    python3 data/shakespeare_char/

    This will create data/shakespeare_char/{train.bin, val.bin}.


class CausalSelfAttention(nn.Module):
    def __init__(self, config: GPTModelConfig):
        assert config.n_embd % config.n_head == 0
        # key, query, value projections for all heads, but in a batch
        self.c_attn = nn.Linear(
            3 * config.n_embd,
        # output projection
        self.c_proj = nn.Linear(
        # 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(
        # if self.flash and RANK == 0:
        #     log.warning(
        #         f'Using torch.nn.functional.scaled_dot_product_attention'
        #         '(Flash Attn)'
        #     )
        if not self.flash:
                "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
                ).view(1, 1, config.block_size, config.block_size)

    def forward(self, x):
        # batch size, sequence length, embedding dimensionality (n_embd)
        B, T, C = x.size()

        # 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)
        # (B, nh, T, hs)
        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)
        # 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(
                dropout_p=(self.dropout if else 0),
            # 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,  # type:ignore
            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)
        # re-assemble all head outputs side by side
        y = y.transpose(1, 2).contiguous().view(B, T, C)

        # output projection
        y = self.resid_dropout(self.c_proj(y))
        return y
class LayerNorm(nn.Module):
    LayerNorm but with an optional bias.

    (PyTorch doesn't support simply bias=False)

    def __init__(self, ndim, bias):
        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(
class MLP(nn.Module):

    def __init__(
            config: GPTModelConfig,
            activation: str = 'gelu',
        self.c_fc = nn.Linear(
            4 * config.n_embd,
        if activation.lower() in ACTIVATIONS:
            self.act_fn = ACTIVATIONS[activation.lower()]
                act_fn = getattr(nn, activation)
                assert callable(act_fn)
                self.act_fn = act_fn()
            except Exception as exc:
                log.error(f'{activation} not yet supported!')
                raise exc
        # self.gelu = nn.GELU()
        self.c_proj = nn.Linear(
            4 * config.n_embd,
        self.dropout = nn.Dropout(config.dropout)

    def forward(self, x):
        x = self.c_fc(x)
        # x = self.gelu(x)
        x = self.act_fn(x)
        x = self.c_proj(x)
        x = self.dropout(x)
        return x
class Block(nn.Module):

    def __init__(self, config: GPTModelConfig):
        self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
        self.attn = CausalSelfAttention(config)
        self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
        self.mlp = MLP(config)

    def forward(self, x):
        x = x + self.attn(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x
class GPT(nn.Module):
    def __init__(self, config: GPTModelConfig):
        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),
            h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
            ln_f=LayerNorm(config.n_embd, bias=config.bias),
        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  # type:ignore

        # init all weights
        # apply special scaled init to the residual projections, per GPT-2
        for pn, p in self.named_parameters():
            if pn.endswith('c_proj.weight'):
                    std=0.02/math.sqrt(2 * config.n_layer)

        # report number of parameters"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

        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()  # type:ignore
        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:
        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(
        )  # shape (t)

        # forward the GPT model itself
        # token embeddings of shape (b, t, n_embd)
        tok_emb = self.transformer.wte(idx)  # type:ignore
        # position embeddings of shape (t, n_embd)
        pos_emb = self.transformer.wpe(pos)  # type:ignore
        x = self.transformer.drop(tok_emb + pos_emb)  # type:ignore
        for block in self.transformer.h:  # type:ignore
            x = block(x)
        x = self.transformer.ln_f(x)  # type:ignore
        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(
            # inference-time mini-optimization: only forward the lm_head on the
            # very last position
            # note: using list [-1] to preserve the time dim
            logits = self.lm_head(x[:, [-1], :])
            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 = (  # type:ignore
                self.transformer.wpe.weight[:block_size]  # type:ignore
        for block in self.transformer.h:   # type:ignore
            if hasattr(block.attn, 'bias'):
                block.attn.bias = (
                    block.attn.bias[:, :, :block_size, :block_size]

    def from_pretrained(cls, model_type, override_args=None):
        assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-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)
        from transformers import GPT2LMHeadModel"loading weights from pretrained gpt: {model_type=}")
        # n_layer, n_head and n_embd are determined from model_type
        # gpt2: 124M params
        # gpt2-medium: 350M params
        # gpt2-large: 774M params
        # gpt2-xl: 1558M params
        config_args = {
            # 'baby-llama2': dict(n_layer=16, n_head=16, n_embed=1024),
            # 'llama2-7b': dict(n_layer=32, n_head=32, n_embd=4096),
            'gpt2': dict(n_layer=12, n_head=12, n_embd=768),
            'gpt2-medium': dict(n_layer=24, n_head=16, n_embd=1024),
            'gpt2-large': dict(n_layer=36, n_head=20, n_embd=1280),
            'gpt2-xl': dict(n_layer=48, n_head=25, n_embd=1600),
        # we can override the dropout rate, if desired
        if 'dropout' in override_args:
  "overriding dropout rate to {override_args['dropout']}")
            config_args['dropout'] = override_args['dropout']
        # create a from-scratch initialized minGPT model"forcing vocab_size=50257, block_size=1024, bias=True")
        config = GPTModelConfig(
            block_size=1024,   # always 1024 for GPT model checkpoints
            vocab_size=50257,  # always 50257 for GPT model checkpoints
            bias=True,         # always True for GPT model checkpoints
        model = GPT(config)
        sd = model.state_dict()
        sd_keys = sd.keys()
        sd_keys = [
            k for k in sd_keys if not k.endswith('.attn.bias')
        ]  # discard this mask / buffer, not a param

        # init a huggingface/transformers model
        model_hf = GPT2LMHeadModel.from_pretrained(model_type)
        sd_hf = model_hf.state_dict()

        # copy while ensuring all of the parameters are aligned and match in
        # names and shapes
        sd_keys_hf = sd_hf.keys()
        sd_keys_hf = [
            k for k in sd_keys_hf if not k.endswith('.attn.masked_bias')
        ]  # ignore these, just a buffer
        sd_keys_hf = [
            k for k in sd_keys_hf if not k.endswith('.attn.bias')
        ]  # same, just the mask (buffer)
        transposed = [
        # basically the openai checkpoints use a "Conv1D" module, but we only
        # want to use a vanilla Linear this means that we have to transpose
        # these weights when we import them
        assert len(sd_keys_hf) == len(sd_keys), (
            f"mismatched keys: {len(sd_keys_hf)} != {len(sd_keys)}"
        for k in sd_keys_hf:
            if any(k.endswith(w) for w in transposed):
                # special treatment for the Conv1D weights we need to transpose
                assert sd_hf[k].shape[::-1] == sd[k].shape
                with torch.no_grad():
                # vanilla copy over the other parameters
                assert sd_hf[k].shape == sd[k].shape
                with torch.no_grad():

        return model

    def configure_optimizers(
        # start with all of the candidate parameters
        # filter out those that do not require grad
        # param_dict = {
        #     pn: p for pn, p in param_dict.items() if p.requires_grad
        # }
        param_dict = {
            pn: p for pn, p in self.named_parameters() 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 _, p in param_dict.items() if p.dim() >= 2]
        nodecay_params = [p for _, 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)
            f"num decayed parameter tensors: {len(decay_params)}, "
            f"with {num_decay_params:,} parameters"
            f"num non-decayed parameter tensors: {len(nodecay_params)}, "
            f"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 {}
        optimizer = torch.optim.AdamW(
        )"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:
        N = self.get_num_params()
        cfg = self.config
        L, H, Q, T = (
        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
        return flops_achieved / flops_promised

    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 =, idx_next), dim=1)

        return idx


    def get_batch(self, split: str) -> tuple[torch.Tensor, torch.Tensor]:
        # data = self.config.train_data if split == 'train'
        # else self.config.val_data
        data =, None)
        assert data is not None
        ix = torch.randint(
            len(data) - self.config.model.block_size,
        block_size = self.config.model.block_size
        x = torch.stack(
                for i in ix
        y = torch.stack(
                for i in ix
        if self.config.device_type == 'cuda':
            x = x.pin_memory().to(self.config.device_type, non_blocking=True)
            y = y.pin_memory().to(self.config.device_type, non_blocking=True)
            x =
            y =
        return x, y
    def _forward_step(self, x: torch.Tensor, y: torch.Tensor) -> dict:
        t0 = time.perf_counter()
        with self.config.ctx:
            logits, loss = self.model_engine(x, y)
        return {
            'logits': logits,
            'loss': loss,
            'dt': time.perf_counter() - t0
    def _backward_step(
            loss: torch.Tensor,
            propagate_grads: bool = False,
    ) -> float:
        t0 = time.perf_counter()
        if self.config.train.backend.lower() in ['ds', 'deepspeed']:
            self.model_engine.backward(loss)  # type:ignore
            self.model_engine.step(loss)      # type:ignore
            if self.grad_scaler is not None:
                self.grad_scaler.scale(loss).backward()  # type:ignore
            if propagate_grads:
                if self.config.optimizer.grad_clip != 0.0:
                    if self.grad_scaler is not None:
                    torch.nn.utils.clip_grad_norm_(  # pyright: ignore
                if self.grad_scaler is not None:

        return time.perf_counter() - t0
    def train_step(
            x: torch.Tensor,
            y: torch.Tensor,
    ) -> dict:
        lr = (
            if self.config.optimizer.decay_lr
            else self._lr
        for param_group in self.optimizer.param_groups:
            param_group['lr'] = lr
        dtf = []
        dtb = []
        dt = []
        loss = torch.tensor(0.0)
        for micro_step in range(self._gas):
            is_last_micro_step = (micro_step == self._gas - 1)
            # NOTE: -----------------------------------------------------------
            # In DDP training we only need to sync gradients at the last micro
            # step. the official way to do this is with model.no_sync() context
            # manager, but I really dislike that this bloats the code and
            # forces us to repeat code looking at the source of that context
            # manager, it just toggles this variable
            # -----------------------------------------------------------------
            if self.config.train.backend.lower() == 'ddp':
                _ = (
                    if (is_last_micro_step and self.world_size > 1)
                    else None
            fout = self._forward_step(x, y)
            # immediately async prefetch next batch while model is doing the
            # forward pass on the GPU
            x, y = self.get_batch('train')
            loss = fout['loss'] / self._gas
            dtb_ = self._backward_step(
            dt.append(dtf + dtb)
        timers = {
            'iter': self.config.iter_num,
            'dt': np.array(dt),
            'dt_tot': np.sum(dt),
            'dt_avg': np.mean(dt),
            'dtf': np.array(dtf),
            'dtf_tot': np.sum(dtf),
            'dtf_avg': np.mean(dtf),
            'dtb': np.array(dtb),
            'dtb_tot': np.sum(dtb),
            'dtb_avg': np.mean(dtb)
        metrics = {
            'iter': self.config.iter_num,
            'loss': loss,
            'lr': lr,
        self.config.iter_num += 1
        return {
            'metrics': metrics,
            'timers': timers,
            'x': x,
            'y': y,
    def estimate_loss(self):
        out = {}
        for split in
            losses = torch.zeros(self.config.train.eval_iters)
            for k in range(self.config.train.eval_iters):
                x, y = self.get_batch(split)
                with self.config.ctx:
                    _, loss = self.model_engine(x, y)
                losses[k] = loss.item()
            out[split] = losses.mean()
        return out

Self-Contained Shakespeare Example


  1. DeepSpeed: Extreme-scale model training for everyone - Microsoft Research

  2. NVIDIA / NCCL / Collective Operations

Yang, Jingfeng, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Bing Yin, and Xia Hu. 2023. “Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond.”
Yao, Shunyu, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan. 2023. “Tree of Thoughts: Deliberate Problem Solving with Large Language Models.”