diff --git a/prepare.py b/prepare.py index 62607b9af..a3c99d83c 100644 --- a/prepare.py +++ b/prepare.py @@ -340,7 +340,7 @@ def refill_buffer(): # --------------------------------------------------------------------------- @torch.no_grad() -def evaluate_bpb(model, tokenizer, batch_size): +def evaluate_bpb(model, tokenizer, batch_size, seq_len=None, max_steps=None): """ Bits per byte (BPB): vocab size-independent evaluation metric. Sums per-token cross-entropy (in nats), sums target byte lengths, @@ -348,9 +348,12 @@ def evaluate_bpb(model, tokenizer, batch_size): are excluded from both sums. Uses fixed MAX_SEQ_LEN so results are comparable across configs. """ + seq_len = seq_len or MAX_SEQ_LEN token_bytes = get_token_bytes(device="cuda") - val_loader = make_dataloader(tokenizer, batch_size, MAX_SEQ_LEN, "val") - steps = EVAL_TOKENS // (batch_size * MAX_SEQ_LEN) + val_loader = make_dataloader(tokenizer, batch_size, seq_len, "val") + steps = EVAL_TOKENS // (batch_size * seq_len) + if max_steps is not None: + steps = min(steps, max_steps) total_nats = 0.0 total_bytes = 0 for _ in range(steps): diff --git a/pyproject.toml b/pyproject.toml index 94ae32989..9f2630390 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,7 @@ description = "Autonomous pretraining research swarm" readme = "README.md" requires-python = ">=3.10" dependencies = [ - "kernels>=0.11.7", + "kernels>=0.12.2", "matplotlib>=3.10.8", "numpy>=2.2.6", "pandas>=2.3.3", @@ -13,7 +13,7 @@ dependencies = [ "requests>=2.32.0", "rustbpe>=0.1.0", "tiktoken>=0.11.0", - "torch==2.9.1", + "torch==2.8.0", ] [tool.uv.sources] diff --git a/run.log b/run.log new file mode 100644 index 000000000..7ea3ec15e Binary files /dev/null and b/run.log differ diff --git a/train.py b/train.py index 6994fb9bb..9b13600a2 100644 --- a/train.py +++ b/train.py @@ -16,14 +16,26 @@ 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 +# Try flash-attn3 (Linux only); fall back to SDPA on Windows (no prebuilt FA3 wheels) +fa3 = None +try: + from kernels import get_kernel + cap = torch.cuda.get_device_capability() + repo = "varunneal/flash-attention-3" if cap == (9, 0) else "kernels-community/flash-attn3" + fa3 = get_kernel(repo).flash_attn_interface +except (FileNotFoundError, ModuleNotFoundError): + pass # Use SDPA fallback from prepare import MAX_SEQ_LEN, TIME_BUDGET, Tokenizer, make_dataloader, evaluate_bpb +# Triton/torch.compile requires CUDA capability >= 7.0 (e.g. GTX 1050 Ti is 6.1) +USE_TORCH_COMPILE = torch.cuda.is_available() and torch.cuda.get_device_capability()[0] >= 7 + +def _maybe_compile(dynamic=False, fullgraph=False): + def decorator(fn): + return torch.compile(fn, dynamic=dynamic, fullgraph=fullgraph) if USE_TORCH_COMPILE else fn + return decorator + # --------------------------------------------------------------------------- # GPT Model # --------------------------------------------------------------------------- @@ -72,6 +84,22 @@ def __init__(self, config, layer_idx): self.c_proj = nn.Linear(self.n_embd, self.n_embd, bias=False) self.ve_gate_channels = 32 self.ve_gate = nn.Linear(self.ve_gate_channels, self.n_kv_head, bias=False) if has_ve(layer_idx, config.n_layer) else None + self._mask_cache = {} # for SDPA fallback + + def _get_sdpa_mask(self, seq_len, window_size, device): + """Build causal + sliding-window mask for SDPA (used when flash-attn3 unavailable, e.g. Windows).""" + window = window_size[0] if isinstance(window_size, tuple) else window_size + cache_key = (seq_len, int(window) if window is not None else -1, device.type, device.index) + mask = self._mask_cache.get(cache_key) + if mask is not None: + return mask + row = torch.arange(seq_len, device=device).unsqueeze(1) + col = torch.arange(seq_len, device=device).unsqueeze(0) + mask = col <= row # causal + if window is not None and window >= 0 and window < seq_len: + mask = mask & (col >= (row - window)) + self._mask_cache[cache_key] = mask + return mask def forward(self, x, ve, cos_sin, window_size): B, T, C = x.size() @@ -89,7 +117,20 @@ 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) + if fa3 is not None: + y = fa3.flash_attn_func(q, k, v, causal=True, window_size=window_size) + else: + # SDPA fallback (Windows; flash-attn3 has no prebuilt wheels) + q = q.transpose(1, 2) # (B, H, T, D) + k = k.transpose(1, 2) + v = v.transpose(1, 2) + attn_mask = self._get_sdpa_mask(T, window_size, q.device) + y = F.scaled_dot_product_attention( + q, k, v, attn_mask=attn_mask, is_causal=False, + enable_gqa=self.n_kv_head < self.n_head, + ) + y = y.transpose(1, 2) + y = y.contiguous().view(B, T, -1) y = self.c_proj(y) return y @@ -301,7 +342,7 @@ def forward(self, idx, targets=None, reduction='mean'): (2.3465413258596377, -1.7097828382687081, 0.42323551169305323), ] -@torch.compile(dynamic=False, fullgraph=True) +@_maybe_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) @@ -312,7 +353,7 @@ def adamw_step_fused(p, grad, exp_avg, exp_avg_sq, step_t, lr_t, beta1_t, beta2_ step_size = lr_t / bias1 p.add_(exp_avg / denom, alpha=-step_size) -@torch.compile(dynamic=False, fullgraph=True) +@_maybe_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 @@ -437,11 +478,11 @@ def step(self): TOTAL_BATCH_SIZE = 2**19 # ~524K tokens per optimizer step 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) +MATRIX_LR = 0.05 # learning rate for matrix parameters (Muon) SCALAR_LR = 0.5 # learning rate for per-layer scalars (Adam) WEIGHT_DECAY = 0.2 # cautious weight decay for Muon ADAM_BETAS = (0.8, 0.95) # Adam beta1, beta2 -WARMUP_RATIO = 0.0 # fraction of time budget for LR warmup +WARMUP_RATIO = 0.1 # 10% warmup WARMDOWN_RATIO = 0.5 # fraction of time budget for LR warmdown FINAL_LR_FRAC = 0.0 # final LR as fraction of initial @@ -458,6 +499,18 @@ def step(self): torch.cuda.manual_seed(42) torch.set_float32_matmul_precision("high") device = torch.device("cuda") + +# Low-VRAM preset for GPUs < 6GB (e.g. GTX 1050 Ti 4GB) - SDPA materializes full attn matrices +VRAM_GB = torch.cuda.get_device_properties(0).total_memory / 1e9 +if VRAM_GB < 6: + DEVICE_BATCH_SIZE = 32 + TOTAL_BATCH_SIZE = 2**14 # 16K tokens per step + WINDOW_PATTERN = "SSSL" # banded attention (half context in S layers) + TRAIN_SEQ_LEN = min(MAX_SEQ_LEN, 256) # SDPA OOMs on long seqs + DEPTH = 4 + print(f"Low-VRAM mode: {VRAM_GB:.1f}GB GPU detected, using seq_len={TRAIN_SEQ_LEN}, batch={DEVICE_BATCH_SIZE}, depth={DEPTH}") +else: + TRAIN_SEQ_LEN = MAX_SEQ_LEN autocast_ctx = torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16) H100_BF16_PEAK_FLOPS = 989.5e12 @@ -470,7 +523,7 @@ def build_model_config(depth): model_dim = ((base_dim + HEAD_DIM - 1) // HEAD_DIM) * HEAD_DIM num_heads = model_dim // HEAD_DIM return GPTConfig( - sequence_len=MAX_SEQ_LEN, vocab_size=vocab_size, + sequence_len=TRAIN_SEQ_LEN, vocab_size=vocab_size, n_layer=depth, n_head=num_heads, n_kv_head=num_heads, n_embd=model_dim, window_pattern=WINDOW_PATTERN, ) @@ -491,7 +544,7 @@ def build_model_config(depth): num_flops_per_token = model.estimate_flops() print(f"Estimated FLOPs per token: {num_flops_per_token:e}") -tokens_per_fwdbwd = DEVICE_BATCH_SIZE * MAX_SEQ_LEN +tokens_per_fwdbwd = DEVICE_BATCH_SIZE * TRAIN_SEQ_LEN assert TOTAL_BATCH_SIZE % tokens_per_fwdbwd == 0 grad_accum_steps = TOTAL_BATCH_SIZE // tokens_per_fwdbwd @@ -504,9 +557,10 @@ def build_model_config(depth): weight_decay=WEIGHT_DECAY, ) -model = torch.compile(model, dynamic=False) +if USE_TORCH_COMPILE: + model = torch.compile(model, dynamic=False) -train_loader = make_dataloader(tokenizer, DEVICE_BATCH_SIZE, MAX_SEQ_LEN, "train") +train_loader = make_dataloader(tokenizer, DEVICE_BATCH_SIZE, TRAIN_SEQ_LEN, "train") x, y, epoch = next(train_loader) # prefetch first batch print(f"Time budget: {TIME_BUDGET}s") @@ -609,7 +663,8 @@ def get_weight_decay(progress): # Final eval model.eval() with autocast_ctx: - val_bpb = evaluate_bpb(model, tokenizer, DEVICE_BATCH_SIZE) + val_bpb = evaluate_bpb(model, tokenizer, DEVICE_BATCH_SIZE, + seq_len=TRAIN_SEQ_LEN, max_steps=50 if VRAM_GB < 6 else None) # Final summary t_end = time.time() diff --git a/uv.lock b/uv.lock index c840d62f5..c97959958 100644 --- a/uv.lock +++ b/uv.lock @@ -61,7 +61,7 @@ dependencies = [ [package.metadata] requires-dist = [ - { name = "kernels", specifier = ">=0.11.7" }, + { name = "kernels", specifier = ">=0.12.2" }, { name = "matplotlib", specifier = ">=3.10.8" }, { name = "numpy", specifier = ">=2.2.6" }, { name = "pandas", specifier = ">=2.3.3" }, @@ -69,7 +69,7 @@ requires-dist = [ { name = "requests", specifier = ">=2.32.0" }, { name = "rustbpe", specifier = ">=0.1.0" }, { name = "tiktoken", specifier = ">=0.11.0" }, - { name = "torch", specifier = "==2.9.1", index = 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