diff --git a/prepare.py b/prepare.py index 06bea9165..a52f6d457 100644 --- a/prepare.py +++ b/prepare.py @@ -27,9 +27,9 @@ # Constants (fixed, do not modify) # --------------------------------------------------------------------------- -MAX_SEQ_LEN = 2048 # context length +MAX_SEQ_LEN = int(os.environ.get("AUTORESEARCH_SEQ_LEN", 2048)) # context length (auto-set by train.py) TIME_BUDGET = 300 # training time budget in seconds (5 minutes) -EVAL_TOKENS = 40 * 524288 # number of tokens for val eval +EVAL_TOKENS = int(os.environ.get("AUTORESEARCH_EVAL_TOKENS", str(40 * 524288))) # tokens for val eval (auto-set by train.py) # --------------------------------------------------------------------------- # Configuration diff --git a/pyproject.toml b/pyproject.toml index 94ae32989..7081a5ffb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,6 @@ description = "Autonomous pretraining research swarm" readme = "README.md" requires-python = ">=3.10" dependencies = [ - "kernels>=0.11.7", "matplotlib>=3.10.8", "numpy>=2.2.6", "pandas>=2.3.3", @@ -13,15 +12,19 @@ dependencies = [ "requests>=2.32.0", "rustbpe>=0.1.0", "tiktoken>=0.11.0", - "torch==2.9.1", + "torch==2.5.1", ] +[project.optional-dependencies] +# FA3 requires sm_80+ (Ampere/Hopper); install with: uv sync --extra fa3 +fa3 = ["kernels>=0.11.7"] + [tool.uv.sources] torch = [ - { index = "pytorch-cu128" }, + { index = "pytorch-cu121" }, ] [[tool.uv.index]] -name = "pytorch-cu128" -url = "https://download.pytorch.org/whl/cu128" +name = "pytorch-cu121" +url = "https://download.pytorch.org/whl/cu121" explicit = true diff --git a/train.py b/train.py index 2e743974c..6d07cf0bd 100644 --- a/train.py +++ b/train.py @@ -1,6 +1,7 @@ """ Autoresearch pretraining script. Single-GPU, single-file. Cherry-picked and simplified from nanochat. +Auto-detects GPU hardware and configures optimally. Usage: uv run train.py """ @@ -17,13 +18,106 @@ import torch.nn as nn import torch.nn.functional as F -from kernels import get_kernel -cap = torch.cuda.get_device_capability() -# varunneal's FA3 is Hopper only, use kernels-community on non-Hopper GPUs -repo = "varunneal/flash-attention-3" if cap == (9, 0) else "kernels-community/flash-attn3" -fa3 = get_kernel(repo).flash_attn_interface +# --------------------------------------------------------------------------- +# Hardware detection and configuration +# --------------------------------------------------------------------------- -from prepare import MAX_SEQ_LEN, TIME_BUDGET, Tokenizer, make_dataloader, evaluate_bpb +def detect_gpu_config(): + """Detect GPU and return optimal configuration dict.""" + assert torch.cuda.is_available(), "No CUDA GPU detected" + props = torch.cuda.get_device_properties(0) + cap = (props.major, props.minor) + vram_gb = props.total_mem / (1024**3) + gpu_name = props.name + + print(f"GPU detected: {gpu_name}") + print(f" Compute capability: sm_{cap[0]}{cap[1]}") + print(f" VRAM: {vram_gb:.1f} GB") + + cfg = {} + + # --- Attention backend --- + # FA3 requires sm_80+ (Ampere/Hopper); use SDPA for older GPUs + if cap >= (8, 0): + try: + from kernels import get_kernel + repo = "varunneal/flash-attention-3" if cap == (9, 0) else "kernels-community/flash-attn3" + cfg["fa3"] = get_kernel(repo).flash_attn_interface + cfg["attn_backend"] = "fa3" + print(" Attention: Flash Attention 3") + except (ImportError, Exception) as e: + print(f" Attention: SDPA (FA3 unavailable: {e})") + cfg["fa3"] = None + cfg["attn_backend"] = "sdpa" + else: + cfg["fa3"] = None + cfg["attn_backend"] = "sdpa" + print(" Attention: SDPA") + + # --- Dtype --- + # bf16 requires sm_80+ (Ampere); older GPUs use fp16 + if cap >= (8, 0): + cfg["dtype"] = torch.bfloat16 + cfg["half_fn"] = "bfloat16" + print(" Dtype: bfloat16") + else: + cfg["dtype"] = torch.float16 + cfg["half_fn"] = "half" + print(" Dtype: float16") + + # --- torch.compile --- + # Triton requires sm_70+ (Volta) + cfg["use_compile"] = cap >= (7, 0) + print(f" torch.compile: {'enabled' if cfg['use_compile'] else 'disabled (requires sm_70+)'}") + + # --- Model/training hyperparameters based on VRAM --- + if vram_gb >= 40: # H100 80GB, A100 40/80GB + cfg["depth"] = 8 + cfg["device_batch_size"] = 128 + cfg["total_batch_size"] = 2**19 # 524K + cfg["window_pattern"] = "SSSL" + cfg["seq_len"] = 2048 + cfg["eval_tokens"] = 40 * 524288 + cfg["peak_flops"] = 989.5e12 if "H100" in gpu_name else 312e12 + tier = "high" + elif vram_gb >= 16: # RTX 4090 24GB, RTX 3090 24GB, A5000 24GB, V100 16GB + cfg["depth"] = 8 + cfg["device_batch_size"] = 64 + cfg["total_batch_size"] = 2**18 # 262K + cfg["window_pattern"] = "SSSL" if cap >= (8, 0) else "L" + cfg["seq_len"] = 2048 + cfg["eval_tokens"] = 20 * 524288 + cfg["peak_flops"] = 165e12 if cap >= (8, 0) else 125e12 + tier = "mid-high" + elif vram_gb >= 8: # RTX 3070 8GB, RTX 2080 8GB, GTX 1080 8GB + cfg["depth"] = 6 + cfg["device_batch_size"] = 16 + cfg["total_batch_size"] = 2**17 # 131K + cfg["window_pattern"] = "L" + cfg["seq_len"] = 1024 + cfg["eval_tokens"] = 10 * 524288 + cfg["peak_flops"] = 80e12 + tier = "mid" + else: # GTX 1060 6GB, GTX 1070 8GB, etc. + cfg["depth"] = 4 + cfg["device_batch_size"] = 8 + cfg["total_batch_size"] = 2**15 # 32K + cfg["window_pattern"] = "L" + cfg["seq_len"] = 512 + cfg["eval_tokens"] = 4 * 524288 + cfg["peak_flops"] = 45e12 + tier = "low" + + print(f" Config tier: {tier} (DEPTH={cfg['depth']}, BATCH={cfg['device_batch_size']}, SEQ={cfg['seq_len']})") + return cfg + +HW = detect_gpu_config() + +# Set env vars so prepare.py picks up the detected config +os.environ["AUTORESEARCH_SEQ_LEN"] = str(HW["seq_len"]) +os.environ["AUTORESEARCH_EVAL_TOKENS"] = str(HW["eval_tokens"]) + +from prepare import TIME_BUDGET, Tokenizer, make_dataloader, evaluate_bpb # --------------------------------------------------------------------------- # GPT Model @@ -90,8 +184,16 @@ def forward(self, x, ve, cos_sin, window_size): q, k = apply_rotary_emb(q, cos, sin), apply_rotary_emb(k, cos, sin) q, k = norm(q), norm(k) - y = fa3.flash_attn_func(q, k, v, causal=True, window_size=window_size) - y = y.contiguous().view(B, T, -1) + if HW["attn_backend"] == "fa3": + y = HW["fa3"].flash_attn_func(q, k, v, causal=True, window_size=window_size) + y = y.contiguous().view(B, T, -1) + else: + # SDPA expects (B, H, T, D) + q = q.transpose(1, 2) + k = k.transpose(1, 2) + v = v.transpose(1, 2) + y = F.scaled_dot_product_attention(q, k, v, is_causal=True) + y = y.transpose(1, 2).contiguous().view(B, T, -1) y = self.c_proj(y) return y @@ -175,10 +277,10 @@ def init_weights(self): head_dim = self.config.n_embd // self.config.n_head cos, sin = self._precompute_rotary_embeddings(self.rotary_seq_len, head_dim) self.cos, self.sin = cos, sin - # Cast embeddings to bf16 - self.transformer.wte.to(dtype=torch.bfloat16) + # Cast embeddings to detected dtype + self.transformer.wte.to(dtype=HW["dtype"]) for ve in self.value_embeds.values(): - ve.to(dtype=torch.bfloat16) + ve.to(dtype=HW["dtype"]) def _precompute_rotary_embeddings(self, seq_len, head_dim, base=10000, device=None): if device is None: @@ -188,7 +290,8 @@ def _precompute_rotary_embeddings(self, seq_len, head_dim, base=10000, device=No t = torch.arange(seq_len, dtype=torch.float32, device=device) freqs = torch.outer(t, inv_freq) cos, sin = freqs.cos(), freqs.sin() - cos, sin = cos.bfloat16(), sin.bfloat16() + cos = cos.to(HW["dtype"]) + sin = sin.to(HW["dtype"]) cos, sin = cos[None, :, None, :], sin[None, :, None, :] return cos, sin @@ -302,26 +405,69 @@ def forward(self, idx, targets=None, reduction='mean'): (2.3465413258596377, -1.7097828382687081, 0.42323551169305323), ] -@torch.compile(dynamic=False, fullgraph=True) -def adamw_step_fused(p, grad, exp_avg, exp_avg_sq, step_t, lr_t, beta1_t, beta2_t, eps_t, wd_t): - p.mul_(1 - lr_t * wd_t) - exp_avg.lerp_(grad, 1 - beta1_t) - exp_avg_sq.lerp_(grad.square(), 1 - beta2_t) - bias1 = 1 - beta1_t ** step_t - bias2 = 1 - beta2_t ** step_t - denom = (exp_avg_sq / bias2).sqrt() + eps_t - step_size = lr_t / bias1 - p.add_(exp_avg / denom, alpha=-step_size) - -@torch.compile(dynamic=False, fullgraph=True) -def muon_step_fused(stacked_grads, stacked_params, momentum_buffer, second_momentum_buffer, - momentum_t, lr_t, wd_t, beta2_t, ns_steps, red_dim): - # Nesterov momentum - momentum = momentum_t.to(stacked_grads.dtype) - momentum_buffer.lerp_(stacked_grads, 1 - momentum) - g = stacked_grads.lerp_(momentum_buffer, momentum) - # Polar express orthogonalization - X = g.bfloat16() +if HW["use_compile"]: + @torch.compile(dynamic=False, fullgraph=True) + def adamw_step_compiled(p, grad, exp_avg, exp_avg_sq, step_t, lr_t, beta1_t, beta2_t, eps_t, wd_t): + p.mul_(1 - lr_t * wd_t) + exp_avg.lerp_(grad, 1 - beta1_t) + exp_avg_sq.lerp_(grad.square(), 1 - beta2_t) + bias1 = 1 - beta1_t ** step_t + bias2 = 1 - beta2_t ** step_t + denom = (exp_avg_sq / bias2).sqrt() + eps_t + step_size = lr_t / bias1 + p.add_(exp_avg / denom, alpha=-step_size) + + @torch.compile(dynamic=False, fullgraph=True) + def muon_step_compiled(stacked_grads, stacked_params, momentum_buffer, second_momentum_buffer, + momentum_t, lr_t, wd_t, beta2_t, ns_steps, red_dim): + momentum = momentum_t.to(stacked_grads.dtype) + momentum_buffer.lerp_(stacked_grads, 1 - momentum) + g = stacked_grads.lerp_(momentum_buffer, momentum) + X = g.to(HW["dtype"]) + X = X / (X.norm(dim=(-2, -1), keepdim=True) * 1.02 + 1e-6) + if g.size(-2) > g.size(-1): + for a, b, c in polar_express_coeffs[:ns_steps]: + A = X.mT @ X + B = b * A + c * (A @ A) + X = a * X + X @ B + else: + for a, b, c in polar_express_coeffs[:ns_steps]: + A = X @ X.mT + B = b * A + c * (A @ A) + X = a * X + B @ X + g = X + beta2 = beta2_t.to(g.dtype) + v_mean = g.float().square().mean(dim=red_dim, keepdim=True) + red_dim_size = g.size(red_dim) + v_norm_sq = v_mean.sum(dim=(-2, -1), keepdim=True) * red_dim_size + v_norm = v_norm_sq.sqrt() + second_momentum_buffer.lerp_(v_mean.to(dtype=second_momentum_buffer.dtype), 1 - beta2) + step_size = second_momentum_buffer.clamp_min(1e-10).rsqrt() + scaled_sq_sum = (v_mean * red_dim_size) * step_size.float().square() + v_norm_new = scaled_sq_sum.sum(dim=(-2, -1), keepdim=True).sqrt() + final_scale = step_size * (v_norm / v_norm_new.clamp_min(1e-10)) + g = g * final_scale.to(g.dtype) + lr = lr_t.to(g.dtype) + wd = wd_t.to(g.dtype) + mask = (g * stacked_params) >= 0 + stacked_params.sub_(lr * g + lr * wd * stacked_params * mask) + + +def adamw_step_eager(p, grad, exp_avg, exp_avg_sq, step, lr, beta1, beta2, eps, wd): + p.data.mul_(1 - lr * wd) + exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) + exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) + bias1 = 1 - beta1 ** step + bias2 = 1 - beta2 ** step + denom = (exp_avg_sq / bias2).sqrt().add_(eps) + step_size = lr / bias1 + p.data.addcdiv_(exp_avg, denom, value=-step_size) + +def muon_step_eager(stacked_grads, stacked_params, momentum_buffer, second_momentum_buffer, + momentum, lr, wd, beta2, ns_steps, red_dim): + momentum_buffer.mul_(momentum).add_(stacked_grads, alpha=1 - momentum) + g = stacked_grads.mul_(1 - momentum).add_(momentum_buffer, alpha=momentum) + X = g.to(HW["dtype"]) X = X / (X.norm(dim=(-2, -1), keepdim=True) * 1.02 + 1e-6) if g.size(-2) > g.size(-1): for a, b, c in polar_express_coeffs[:ns_steps]: @@ -334,21 +480,16 @@ def muon_step_fused(stacked_grads, stacked_params, momentum_buffer, second_momen B = b * A + c * (A @ A) X = a * X + B @ X g = X - # NorMuon variance reduction - beta2 = beta2_t.to(g.dtype) v_mean = g.float().square().mean(dim=red_dim, keepdim=True) red_dim_size = g.size(red_dim) v_norm_sq = v_mean.sum(dim=(-2, -1), keepdim=True) * red_dim_size v_norm = v_norm_sq.sqrt() - second_momentum_buffer.lerp_(v_mean.to(dtype=second_momentum_buffer.dtype), 1 - beta2) + second_momentum_buffer.mul_(beta2).add_(v_mean.to(dtype=second_momentum_buffer.dtype), alpha=1 - beta2) step_size = second_momentum_buffer.clamp_min(1e-10).rsqrt() scaled_sq_sum = (v_mean * red_dim_size) * step_size.float().square() v_norm_new = scaled_sq_sum.sum(dim=(-2, -1), keepdim=True).sqrt() final_scale = step_size * (v_norm / v_norm_new.clamp_min(1e-10)) g = g * final_scale.to(g.dtype) - # Cautious weight decay + parameter update - lr = lr_t.to(g.dtype) - wd = wd_t.to(g.dtype) mask = (g * stacked_params) >= 0 stacked_params.sub_(lr * g + lr * wd * stacked_params * mask) @@ -358,38 +499,56 @@ class MuonAdamW(torch.optim.Optimizer): def __init__(self, param_groups): super().__init__(param_groups, defaults={}) - # 0-D CPU tensors to avoid torch.compile recompilation when values change - self._adamw_step_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_beta1_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_eps_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._adamw_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_momentum_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") - self._muon_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + if HW["use_compile"]: + # 0-D CPU tensors to avoid torch.compile recompilation when values change + self._adamw_step_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_beta1_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_eps_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._adamw_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_momentum_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_lr_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_wd_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") + self._muon_beta2_t = torch.tensor(0.0, dtype=torch.float32, device="cpu") def _step_adamw(self, group): - for p in group['params']: - if p.grad is None: - continue - grad = p.grad - state = self.state[p] - if not state: - state['step'] = 0 - state['exp_avg'] = torch.zeros_like(p) - state['exp_avg_sq'] = torch.zeros_like(p) - state['step'] += 1 - self._adamw_step_t.fill_(state['step']) - self._adamw_lr_t.fill_(group['lr']) - self._adamw_beta1_t.fill_(group['betas'][0]) - self._adamw_beta2_t.fill_(group['betas'][1]) - self._adamw_eps_t.fill_(group['eps']) - self._adamw_wd_t.fill_(group['weight_decay']) - adamw_step_fused(p, grad, state['exp_avg'], state['exp_avg_sq'], - self._adamw_step_t, self._adamw_lr_t, self._adamw_beta1_t, - self._adamw_beta2_t, self._adamw_eps_t, self._adamw_wd_t) + if HW["use_compile"]: + for p in group['params']: + if p.grad is None: + continue + grad = p.grad + state = self.state[p] + if not state: + state['step'] = 0 + state['exp_avg'] = torch.zeros_like(p) + state['exp_avg_sq'] = torch.zeros_like(p) + state['step'] += 1 + self._adamw_step_t.fill_(state['step']) + self._adamw_lr_t.fill_(group['lr']) + self._adamw_beta1_t.fill_(group['betas'][0]) + self._adamw_beta2_t.fill_(group['betas'][1]) + self._adamw_eps_t.fill_(group['eps']) + self._adamw_wd_t.fill_(group['weight_decay']) + adamw_step_compiled(p, grad, state['exp_avg'], state['exp_avg_sq'], + self._adamw_step_t, self._adamw_lr_t, self._adamw_beta1_t, + self._adamw_beta2_t, self._adamw_eps_t, self._adamw_wd_t) + else: + for p in group['params']: + if p.grad is None: + continue + grad = p.grad.float() + p_float = p.data.float() + state = self.state[p] + if not state: + state['step'] = 0 + state['exp_avg'] = torch.zeros_like(p_float) + state['exp_avg_sq'] = torch.zeros_like(p_float) + state['step'] += 1 + adamw_step_eager(p_float, grad, state['exp_avg'], state['exp_avg_sq'], + state['step'], group['lr'], group['betas'][0], + group['betas'][1], group['eps'], group['weight_decay']) + p.data.copy_(p_float) def _step_muon(self, group): params = group['params'] @@ -405,17 +564,31 @@ def _step_muon(self, group): state_shape = (num_params, shape[-2], 1) if shape[-2] >= shape[-1] else (num_params, 1, shape[-1]) state["second_momentum_buffer"] = torch.zeros(state_shape, dtype=dtype, device=device) red_dim = -1 if shape[-2] >= shape[-1] else -2 - stacked_grads = torch.stack([p.grad for p in params]) - stacked_params = torch.stack(params) - self._muon_momentum_t.fill_(group["momentum"]) - self._muon_beta2_t.fill_(group["beta2"] if group["beta2"] is not None else 0.0) - self._muon_lr_t.fill_(group["lr"] * max(1.0, shape[-2] / shape[-1])**0.5) - self._muon_wd_t.fill_(group["weight_decay"]) - muon_step_fused(stacked_grads, stacked_params, - state["momentum_buffer"], state["second_momentum_buffer"], - self._muon_momentum_t, self._muon_lr_t, self._muon_wd_t, - self._muon_beta2_t, group["ns_steps"], red_dim) - torch._foreach_copy_(params, list(stacked_params.unbind(0))) + if HW["use_compile"]: + stacked_grads = torch.stack([p.grad for p in params]) + stacked_params = torch.stack(params) + self._muon_momentum_t.fill_(group["momentum"]) + self._muon_beta2_t.fill_(group["beta2"] if group["beta2"] is not None else 0.0) + self._muon_lr_t.fill_(group["lr"] * max(1.0, shape[-2] / shape[-1])**0.5) + self._muon_wd_t.fill_(group["weight_decay"]) + muon_step_compiled(stacked_grads, stacked_params, + state["momentum_buffer"], state["second_momentum_buffer"], + self._muon_momentum_t, self._muon_lr_t, self._muon_wd_t, + self._muon_beta2_t, group["ns_steps"], red_dim) + torch._foreach_copy_(params, list(stacked_params.unbind(0))) + else: + stacked_grads = torch.stack([p.grad.float() for p in params]) + stacked_params = torch.stack([p.data.float() for p in params]) + momentum = group["momentum"] + beta2_val = group["beta2"] if group["beta2"] is not None else 0.0 + lr_val = group["lr"] * max(1.0, shape[-2] / shape[-1])**0.5 + wd_val = group["weight_decay"] + muon_step_eager(stacked_grads, stacked_params, + state["momentum_buffer"], state["second_momentum_buffer"], + momentum, lr_val, wd_val, + beta2_val, group["ns_steps"], red_dim) + for i, p in enumerate(params): + p.data.copy_(stacked_params[i]) @torch.no_grad() def step(self): @@ -432,10 +605,10 @@ def step(self): # Model architecture ASPECT_RATIO = 64 # model_dim = depth * ASPECT_RATIO HEAD_DIM = 128 # target head dimension for attention -WINDOW_PATTERN = "SSSL" # sliding window pattern: L=full, S=half context +WINDOW_PATTERN = HW["window_pattern"] # Optimization -TOTAL_BATCH_SIZE = 2**19 # ~524K tokens per optimizer step +TOTAL_BATCH_SIZE = HW["total_batch_size"] EMBEDDING_LR = 0.6 # learning rate for token embeddings (Adam) UNEMBEDDING_LR = 0.004 # learning rate for lm_head (Adam) MATRIX_LR = 0.04 # learning rate for matrix parameters (Muon) @@ -446,9 +619,11 @@ def step(self): WARMDOWN_RATIO = 0.5 # fraction of time budget for LR warmdown FINAL_LR_FRAC = 0.0 # final LR as fraction of initial -# Model size -DEPTH = 8 # number of transformer layers -DEVICE_BATCH_SIZE = 128 # per-device batch size (reduce if OOM) +# Model size (auto-detected from GPU) +MAX_SEQ_LEN = HW["seq_len"] +DEPTH = HW["depth"] +DEVICE_BATCH_SIZE = HW["device_batch_size"] +EVAL_TOKENS = HW["eval_tokens"] # --------------------------------------------------------------------------- # Setup: tokenizer, model, optimizer, dataloader @@ -459,8 +634,8 @@ def step(self): torch.cuda.manual_seed(42) torch.set_float32_matmul_precision("high") device = torch.device("cuda") -autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16) -H100_BF16_PEAK_FLOPS = 989.5e12 +autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=HW["dtype"]) +GPU_PEAK_FLOPS = HW["peak_flops"] tokenizer = Tokenizer.from_directory() vocab_size = tokenizer.get_vocab_size() @@ -505,7 +680,8 @@ def build_model_config(depth): weight_decay=WEIGHT_DECAY, ) -model = torch.compile(model, dynamic=False) +if HW["use_compile"]: + model = torch.compile(model, dynamic=False) train_loader = make_dataloader(tokenizer, DEVICE_BATCH_SIZE, MAX_SEQ_LEN, "train") x, y, epoch = next(train_loader) # prefetch first batch @@ -584,7 +760,7 @@ def get_weight_decay(progress): debiased_smooth_loss = smooth_train_loss / (1 - ema_beta**(step + 1)) pct_done = 100 * progress tok_per_sec = int(TOTAL_BATCH_SIZE / dt) - mfu = 100 * num_flops_per_token * TOTAL_BATCH_SIZE / dt / H100_BF16_PEAK_FLOPS + mfu = 100 * num_flops_per_token * TOTAL_BATCH_SIZE / dt / GPU_PEAK_FLOPS remaining = max(0, TIME_BUDGET - total_training_time) print(f"\rstep {step:05d} 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