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feat: add [Research] DPI v16.2 initialization (geometric priors for faster convergence)
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DPI_Research_Paper.pdf
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DPI_Research_Paper.pdf
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nanochat/initialize_dpi.py
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174
nanochat/initialize_dpi.py
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import torch
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import torch.nn as nn
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import numpy as np
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import math
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from sklearn.cluster import MiniBatchKMeans
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"""
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Deterministic Pipeline Initialization (DPI)
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Core Engine v16.2 - Optimized for nanochat/GPT-2 style architectures.
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DPI replaces random weight initialization with a data-driven approach:
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- Phase 0: Seeding Lexical Manifold via SVD on token co-occurrence matrix.
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- Phase 1: K-Means clustering of embedding activations to structure latent space.
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- Phase 2: Spectral Bootstrapping with orthogonality constraints to prevent rank collapse.
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This initialization typically yields a significantly lower starting loss (bpb) and
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faster convergence by placing the model in a more "mature" region of the loss landscape.
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"""
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from nanochat.common import COMPUTE_DTYPE
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def get_activations(model, dataloader, layer_idx, num_samples=2000):
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"""Samples model activations at a given layer index."""
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model.eval()
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activations = []
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device = next(model.parameters()).device
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with torch.no_grad():
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for i, (x_batch, _, _) in enumerate(dataloader):
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x_batch = x_batch.to(device)
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# 1. Forward pass through initial embedding
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x = model.transformer['wte'](x_batch)
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x = x.to(COMPUTE_DTYPE) # Ensure activations are in COMPUTE_DTYPE
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# 2. Sequential pass through blocks up to target layer
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if layer_idx >= 0:
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for j in range(layer_idx + 1):
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T = x.size(1)
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# Ensure cos/sin match the COMPUTE_DTYPE for the forward pass
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cos = model.cos[:, :T, :, :].to(COMPUTE_DTYPE)
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sin = model.sin[:, :T, :, :].to(COMPUTE_DTYPE)
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window_size = model.window_sizes[j]
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ve = model.value_embeds[str(j)](x_batch).to(COMPUTE_DTYPE) if str(j) in model.value_embeds else None
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x = model.transformer['h'][j](x, ve, (cos, sin), window_size, None)
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activations.append(x.view(-1, x.size(-1)))
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if len(activations) * x.size(1) >= num_samples: break
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return torch.cat(activations, dim=0)
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def normalize_weight(W, target_std=None):
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"""Rescales weights to a specific standard deviation for stability."""
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if target_std is None: target_std = math.sqrt(1.0 / W.size(1))
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curr_std = W.std()
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if curr_std > 1e-8: return W * (target_std / curr_std)
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return W
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def init_phase0_lexical(model, dataloader):
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"""Initializes embeddings using SVD of the token co-occurrence matrix."""
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vocab_size = model.transformer['wte'].num_embeddings
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d_model = model.config.n_embd
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device = next(model.parameters()).device
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model_dtype = COMPUTE_DTYPE # Use COMPUTE_DTYPE for embeddings
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print(f" [Phase 0] Seeding Lexical Manifold (Exact SVD on CUDA)...")
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# Co-occurrence matrix: U * vocab_size + V trick for fast GPU accumulation
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C = torch.zeros(vocab_size * vocab_size, device=device)
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for i, (x, _, _) in enumerate(dataloader):
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x = x.to(device)
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u, v = x[:, :-1].reshape(-1), x[:, 1:].reshape(-1)
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C.index_add_(0, u * vocab_size + v, torch.ones_like(u, dtype=torch.float, device=device))
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if i >= 100: break # Sufficient statistics for initialization
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C = C.view(vocab_size, vocab_size)
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# Perform SVD to get principal semantic directions
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U, S, V = torch.svd_lowrank(C.float(), q=d_model, niter=5)
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model.transformer['wte'].weight.data[:, :min(d_model, vocab_size)].copy_(U[:, :min(d_model, vocab_size)].to(model_dtype))
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model.transformer['wte'].weight.data.copy_(normalize_weight(model.transformer['wte'].weight.data, target_std=0.8).to(model_dtype))
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return U, V
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def initialize_dpi(model, dataloader, spectral_gamma=0.25, mode="v16.2"):
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"""
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Core DPI routine. Re-initializes model weights based on data-driven manifolds.
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- All matrix operations (SVD, dot products) are forced to float32 for stability and CUDA support.
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- KMeans uses a fixed random_state to ensure cross-rank consistency (if not broadcasting).
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"""
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device = next(model.parameters()).device
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model_dtype = COMPUTE_DTYPE # Use COMPUTE_DTYPE for consistency
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model.to(model_dtype) # Robustness: ensure model is in its intended dtype
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n_layers = len(model.transformer['h'])
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d_model = model.config.n_embd
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phase_shift_layer = n_layers // 2
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U_lex, V_lex = init_phase0_lexical(model, dataloader)
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print(f" [Phase 1] K-Means Clustering on Embeddings...")
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X_lex = get_activations(model, dataloader, -1, num_samples=max(4000, d_model))
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# Cast to float32 for sklearn compatibility and precision
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km = MiniBatchKMeans(n_clusters=d_model, n_init=3, batch_size=1024, random_state=42).fit(X_lex.float().cpu().numpy())
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centers = torch.from_numpy(km.cluster_centers_).float().to(device)
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print(f" [Phase 2] Bootstrapping Mode: {mode.upper()} (Genomic Ready)...")
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for l in range(n_layers):
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# We sample activations before the current layer to find the manifold basis
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X_curr = get_activations(model, dataloader, l-1, num_samples=max(2000, d_model))
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X_centered = X_curr - X_curr.mean(dim=0)
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# Manifold discovery via covariance SVD (float32 mandatory for CUDA SVD)
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cov = torch.matmul(X_centered.t().float(), X_centered.float()) / X_centered.size(0)
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U, S, V = torch.svd(cov)
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# Calculate current layer spectral gamma (depth-dependent scaling)
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progress = l / (n_layers - 1) if n_layers > 1 else 0
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current_gamma = spectral_gamma * (1.0 - 0.2 * math.sin(math.pi * progress))
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svd_basis = normalize_weight((U.t() * torch.pow(S + 1e-6, current_gamma).unsqueeze(1)).to(device))
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# Alignment with nanochat architectural priors (std targets)
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qkv_std = 1.0 / math.sqrt(d_model)
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mlp_fc_std = 0.4 / math.sqrt(d_model)
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ve_std = 1.0 / math.sqrt(d_model)
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layer = model.transformer['h'][l]
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attn = layer.attn
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mlp = layer.mlp
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# 1. MLP Initializers (Expansion/Contraction)
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# Use data-driven basis scaled to 0.4/sqrt(n_embd)
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mlp_basis = svd_basis.repeat(mlp.c_fc.weight.data.size(0) // d_model, 1)
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mlp.c_fc.weight.data.copy_(normalize_weight(mlp_basis + torch.randn_like(mlp_basis) * mlp_fc_std, target_std=mlp_fc_std).to(model_dtype))
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# Projections are set to exactly zero to match native init_weights
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torch.nn.init.zeros_(mlp.c_proj.weight)
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# 2. Value Embeddings (CRITICAL for nanochat)
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# If this block has value embeddings, they must be aligned with the semantic manifold
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if hasattr(model, 'value_embeds') and str(l) in model.value_embeds:
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ve_target = model.value_embeds[str(l)]
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# Align VE with svd_basis to ensure semantic consistency in the value stream
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# repeat basis if vocab size is larger (it is)
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ve_basis = svd_basis.repeat(ve_target.num_embeddings // svd_basis.size(0) + 1, 1)[:ve_target.num_embeddings]
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ve_target.weight.data.copy_(normalize_weight(ve_basis + torch.randn_like(ve_basis) * ve_std, target_std=ve_std).to(model_dtype))
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# 3. Attention Initializers (Alignment vs. Orthogonality)
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is_consolidated = (l >= phase_shift_layer)
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alignment = 0.40 * math.sin(math.pi * progress) if not is_consolidated else 0.0001
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if not is_consolidated:
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# Early layers: Align K/V with data centers
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attn.c_k.weight.data.copy_(normalize_weight(centers + 0.2 * svd_basis, target_std=qkv_std).to(model_dtype))
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attn.c_v.weight.data.copy_(normalize_weight(svd_basis, target_std=qkv_std).to(model_dtype))
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attn.c_q.weight.data.copy_(normalize_weight(alignment * attn.c_k.weight.data.float() + (1 - alignment) * svd_basis, target_std=qkv_std).to(model_dtype))
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else:
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# Deep layers: Force Query orthogonality to prevent rank collapse
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attn.c_k.weight.data.copy_(normalize_weight(svd_basis, target_std=qkv_std).to(model_dtype))
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attn.c_v.weight.data.copy_(normalize_weight(svd_basis, target_std=qkv_std).to(model_dtype))
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Q_rand = torch.randn_like(attn.c_k.weight.data)
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dot = (Q_rand.float() * attn.c_k.weight.data.float()).sum(dim=1, keepdim=True)
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norm_k = (attn.c_k.weight.data.float() * attn.c_k.weight.data.float()).sum(dim=1, keepdim=True)
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Q_ortho = Q_rand.float() - (dot / (norm_k + 1e-8)) * attn.c_k.weight.data.float()
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attn.c_q.weight.data.copy_(normalize_weight(alignment * attn.c_k.weight.data.float() + (1 - alignment) * Q_ortho, target_std=qkv_std).to(model_dtype))
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# 3. Final Attention Projection
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torch.nn.init.zeros_(attn.c_proj.weight)
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# 4. Phase 4: Zero-Wait Output Head
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if hasattr(model, 'lm_head'):
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model.lm_head.weight.data[:, :min(d_model, model.lm_head.out_features)].copy_(V_lex[:, :min(d_model, model.lm_head.out_features)].to(model_dtype))
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model.lm_head.weight.data.copy_(normalize_weight(model.lm_head.weight.data, target_std=0.001).to(model_dtype))
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# Final dtype sync to ensure all parameters and buffers are consistent
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model.to(model_dtype)
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# Explicitly cast rotary embeddings to COMPUTE_DTYPE to pass GPT assertion
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model.cos.data = model.cos.data.to(COMPUTE_DTYPE)
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model.sin.data = model.sin.data.to(COMPUTE_DTYPE)
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print(f"✓ DPI-V16.2 Initialization Complete.")
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@ -10,6 +10,7 @@ dependencies = [
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"kernels>=0.11.7",
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"psutil>=7.1.0",
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"rustbpe>=0.1.0",
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"scikit-learn>=1.7.2",
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"tiktoken>=0.11.0",
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"tokenizers>=0.22.0",
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"torch==2.9.1",
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@ -34,6 +34,7 @@ from nanochat.loss_eval import evaluate_bpb
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from nanochat.engine import Engine
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from nanochat.flash_attention import HAS_FA3
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from scripts.base_eval import evaluate_core
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from nanochat.initialize_dpi import initialize_dpi
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print_banner()
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# -----------------------------------------------------------------------------
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@ -48,6 +49,7 @@ parser.add_argument("--fp8", action="store_true", help="enable FP8 training (req
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parser.add_argument("--fp8-recipe", type=str, default="tensorwise", choices=["rowwise", "tensorwise"], help="FP8 scaling recipe: tensorwise (faster, recommended) or rowwise (more accurate but slower)")
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# Model architecture
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parser.add_argument("--depth", type=int, default=20, help="depth of the Transformer model")
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parser.add_argument("--dpi", action="store_true", help="enable Deterministic Pipeline Initialization (DPI)")
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parser.add_argument("--aspect-ratio", type=int, default=64, help="model_dim = depth * aspect_ratio")
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parser.add_argument("--head-dim", type=int, default=128, help="target head dimension for attention")
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parser.add_argument("--max-seq-len", type=int, default=2048, help="max context length")
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@ -123,6 +125,12 @@ token_bytes = get_token_bytes(device=device)
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vocab_size = tokenizer.get_vocab_size()
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print0(f"Vocab size: {vocab_size:,}")
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# Initialize the DataLoaders for train/val (needed for DPI if enabled)
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# For now we assume no resume if we use DPI, or handle it simply.
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dataloader_resume_state_dict = None if args.resume_from_step == -1 else None # placeholder
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train_loader = tokenizing_distributed_data_loader_with_state_bos_bestfit(tokenizer, args.device_batch_size, args.max_seq_len, split="train", device=device, resume_state_dict=dataloader_resume_state_dict)
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build_val_loader = lambda: tokenizing_distributed_data_loader_bos_bestfit(tokenizer, args.device_batch_size, args.max_seq_len, split="val", device=device)
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# -----------------------------------------------------------------------------
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# Initialize the Model
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@ -150,6 +158,22 @@ print0(f"Model config:\n{json.dumps(model_config_kwargs, indent=2)}")
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model.to_empty(device=device) # 2) All tensors get storage on target device but with uninitialized (garbage) data
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model.init_weights() # 3) All tensors get initialized
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# Deterministic Pipeline Initialization (DPI)
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if args.dpi:
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print0("✓ Deterministic Pipeline Initialization (DPI) enabled (v16.2). Initializing...")
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# In DDP, we perform initialization on rank 0 and broadcast the parameters to all ranks.
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# This ensures all GPUs start with the exact same weights.
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if master_process:
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initialize_dpi(model, train_loader)
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if ddp:
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print0(" Broadcasting DPI-initialized parameters to all ranks...")
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for param in model.parameters():
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dist.broadcast(param.data, src=0)
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for buffer in model.buffers():
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dist.broadcast(buffer.data, src=0)
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dist.barrier()
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# If we are resuming, overwrite the model parameters with those of the checkpoint
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base_dir = get_base_dir()
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output_dirname = args.model_tag if args.model_tag else f"d{args.depth}" # e.g. d12
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@ -327,9 +351,7 @@ if scaler is not None:
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# -----------------------------------------------------------------------------
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# Initialize the DataLoaders for train/val
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dataloader_resume_state_dict = None if not resuming else meta_data["dataloader_state_dict"]
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train_loader = tokenizing_distributed_data_loader_with_state_bos_bestfit(tokenizer, args.device_batch_size, args.max_seq_len, split="train", device=device, resume_state_dict=dataloader_resume_state_dict)
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build_val_loader = lambda: tokenizing_distributed_data_loader_bos_bestfit(tokenizer, args.device_batch_size, args.max_seq_len, split="val", device=device)
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# (already initialized earlier if DPI was used)
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x, y, dataloader_state_dict = next(train_loader) # kick off load of the very first batch of data
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# -----------------------------------------------------------------------------
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@ -358,7 +380,8 @@ print0(f"Total training FLOPs estimate: {num_flops_per_token * total_tokens:e}")
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# Learning rate schedule (linear warmup, constant, linear warmdown)
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def get_lr_multiplier(it):
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warmup_iters = args.warmup_steps
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# DPI v16.2 provides high initial conductivity; disable warmup to leverage immediate stability
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warmup_iters = 0 if args.dpi else args.warmup_steps
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warmdown_iters = round(args.warmdown_ratio * num_iterations)
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if it < warmup_iters:
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return (it + 1) / warmup_iters
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uv.lock
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uv.lock
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@ -1060,6 +1060,15 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/62/a1/3d680cbfd5f4b8f15abc1d571870c5fc3e594bb582bc3b64ea099db13e56/jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67", size = 134899, upload-time = "2025-03-05T20:05:00.369Z" },
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]
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[[package]]
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name = "joblib"
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version = "1.5.3"
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source = { registry = "https://pypi.org/simple" }
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sdist = { url = "https://files.pythonhosted.org/packages/41/f2/d34e8b3a08a9cc79a50b2208a93dce981fe615b64d5a4d4abee421d898df/joblib-1.5.3.tar.gz", hash = "sha256:8561a3269e6801106863fd0d6d84bb737be9e7631e33aaed3fb9ce5953688da3", size = 331603, upload-time = "2025-12-15T08:41:46.427Z" }
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wheels = [
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{ url = "https://files.pythonhosted.org/packages/7b/91/984aca2ec129e2757d1e4e3c81c3fcda9d0f85b74670a094cc443d9ee949/joblib-1.5.3-py3-none-any.whl", hash = "sha256:5fc3c5039fc5ca8c0276333a188bbd59d6b7ab37fe6632daa76bc7f9ec18e713", size = 309071, upload-time = "2025-12-15T08:41:44.973Z" },
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]
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[[package]]
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name = "jupyter-client"
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version = "8.7.0"
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@ -1495,6 +1504,8 @@ dependencies = [
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{ name = "kernels" },
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{ name = "psutil" },
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{ name = "rustbpe" },
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{ name = "scikit-learn", version = "1.7.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
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{ name = "scikit-learn", version = "1.8.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
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{ name = "tiktoken" },
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{ name = "tokenizers" },
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{ name = "torch", version = "2.9.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(sys_platform == 'darwin' and extra == 'extra-8-nanochat-cpu') or (extra == 'extra-8-nanochat-cpu' and extra == 'extra-8-nanochat-gpu')" },
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@ -1530,6 +1541,7 @@ requires-dist = [
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{ name = "kernels", specifier = ">=0.11.7" },
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{ name = "psutil", specifier = ">=7.1.0" },
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{ name = "rustbpe", specifier = ">=0.1.0" },
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{ name = "scikit-learn", specifier = ">=1.7.2" },
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{ name = "tiktoken", specifier = ">=0.11.0" },
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{ name = "tokenizers", specifier = ">=0.22.0" },
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{ name = "torch", specifier = "==2.9.1" },
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@ -2631,6 +2643,277 @@ wheels = [
|
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||||
{ url = "https://download-r2.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp312-cp312-win_amd64.whl", hash = "sha256:a4e06b4f441675d26b462123c8a83e77c55f1ec8ebc081203be2db1ea8054add" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp312-cp312-win_arm64.whl", hash = "sha256:1abe31f14b560c1f062699e966cb08ef5b67518a1cfac2d8547a3dbcd8387b06" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:3e532e553b37ee859205a9b2d1c7977fd6922f53bbb1b9bfdd5bdc00d1a60ed4" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:39b3dff6d8fba240ae0d1bede4ca11c2531ae3b47329206512d99e17907ff74b" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp313-cp313-win_amd64.whl", hash = "sha256:404a7ab2fffaf2ca069e662f331eb46313692b2f1630df2720094284f390ccef" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp313-cp313-win_arm64.whl", hash = "sha256:161decbff26a33f13cb5ba6d2c8f458bbf56193bcc32ecc70be6dd4c7a3ee79d" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:01b1884f724977a20c7da2f640f1c7b37f4a2c117a7f4a6c1c0424d14cb86322" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:031a597147fa81b1e6d79ccf1ad3ccc7fafa27941d6cf26ff5caaa384fb20e92" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp313-cp313t-win_amd64.whl", hash = "sha256:e586ab1363e3f86aa4cc133b7fdcf98deb1d2c13d43a7a6e5a6a18e9c5364893" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:65010ab4aacce6c9a1ddfc935f986c003ca8638ded04348fd326c3e74346237c" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:88adf5157db5da1d54b1c9fe4a6c1d20ceef00e75d854e206a87dbf69e3037dc" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp314-cp314-win_amd64.whl", hash = "sha256:f60e2565f261542efac07e25208fb3fc55c6fe82314a5a9cbee971edb5f27713" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:3ac2b8df2c55430e836dcda31940d47f1f5f94b8731057b6f20300ebea394dd9" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:5b688445f928f13563b7418b17c57e97bf955ab559cf73cd8f2b961f8572dbb3" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cpu/torch-2.9.1%2Bcpu-cp314-cp314t-win_amd64.whl", hash = "sha256:cf9c3e50b595721ca6b488bdcc326e0f1af73ed28b9b66eff504a96649bb5c96" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -2989,27 +3281,27 @@ dependencies = [
|
|||
{ name = "typing-extensions", marker = "extra == 'extra-8-nanochat-gpu'" },
|
||||
]
|
||||
wheels = [
|
||||
{ url = "https://download.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:72f0f096475e8095a6bea3fba75bd3b46cf42c761b29588f7599314e67a32661" },
|
||||
{ url = "https://download.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:c8d670aa0be6fbecd2b0e7b7d514a104dbdefcc3786ca446cf0c3415043ea40a" },
|
||||
{ url = "https://download.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp310-cp310-win_amd64.whl", hash = "sha256:64399adaa8ea0896d02cf844cba3c5dd77e769520a1af73572599e0eaa2cf551" },
|
||||
{ url = "https://download.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:cf4ad82430824a80a9f398e29369524ed26c152cf00c2c12002e5400b35e260d" },
|
||||
{ url = "https://download.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:2a1da940f0757621d098c9755f7504d791a72a40920ec85a4fd98b20253fca4e" },
|
||||
{ url = "https://download.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp311-cp311-win_amd64.whl", hash = "sha256:633005a3700e81b5be0df2a7d3c1d48aced23ed927653797a3bd2b144a3aeeb6" },
|
||||
{ url = "https://download.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:1176f250311fa95cc3bca8077af323e0d73ea385ba266e096af82e7e2b91f256" },
|
||||
{ url = "https://download.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:7cb4018f4ce68b61fd3ef87dc1c4ca520731c7b5b200e360ad47b612d7844063" },
|
||||
{ url = "https://download.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp312-cp312-win_amd64.whl", hash = "sha256:3a01f0b64c10a82d444d9fd06b3e8c567b1158b76b2764b8f51bfd8f535064b0" },
|
||||
{ url = "https://download.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:0b80b7555dcd0a75b7b06016991f01281a0bb078cf28fa2d1dfb949fad2fbd07" },
|
||||
{ url = "https://download.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:63381a109a569b280ed3319da89d3afe5cf9ab5c879936382a212affb5c90552" },
|
||||
{ url = "https://download.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp313-cp313-win_amd64.whl", hash = "sha256:ad9183864acdd99fc5143d7ca9d3d2e7ddfc9a9600ff43217825d4e5e9855ccc" },
|
||||
{ url = "https://download.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:2314521c74d76e513c53bb72c0ce3511ef0295ff657a432790df6c207e5d7962" },
|
||||
{ url = "https://download.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:4454a4faca31af81566e3a4208f10f20b8a6d9cfe42791b0ca7ff134326468fc" },
|
||||
{ url = "https://download.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp313-cp313t-win_amd64.whl", hash = "sha256:24420e430e77136f7079354134b34e7ba9d87e539f5ac84c33b08e5c13412ebe" },
|
||||
{ url = "https://download.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:32c036296c557f19a1537ce981c40533650097114e1720a321a39a3b08d9df56" },
|
||||
{ url = "https://download.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:7788d3d03d939cf00f93ac0da5ab520846f66411e339cfbf519a806e8facf519" },
|
||||
{ url = "https://download.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp314-cp314-win_amd64.whl", hash = "sha256:7bcd40cbffac475b478d6ce812f03da84e9a4894956efb89c3b7bcca5dbd4f91" },
|
||||
{ url = "https://download.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:e88c78e5b08ae9303aa15da43b68b44287ecbec16d898d9fad6998832fe626a5" },
|
||||
{ url = "https://download.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:7d8769bdf3200ca16a92f14df404c3370171ac3732996528a8973d753eac562f" },
|
||||
{ url = "https://download.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp314-cp314t-win_amd64.whl", hash = "sha256:0c784b600959ec70ee01cb23e8bc870a0e0475af30378ff5e39f4abed8b7c1cc" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:72f0f096475e8095a6bea3fba75bd3b46cf42c761b29588f7599314e67a32661" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:c8d670aa0be6fbecd2b0e7b7d514a104dbdefcc3786ca446cf0c3415043ea40a" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp310-cp310-win_amd64.whl", hash = "sha256:64399adaa8ea0896d02cf844cba3c5dd77e769520a1af73572599e0eaa2cf551" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:cf4ad82430824a80a9f398e29369524ed26c152cf00c2c12002e5400b35e260d" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:2a1da940f0757621d098c9755f7504d791a72a40920ec85a4fd98b20253fca4e" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp311-cp311-win_amd64.whl", hash = "sha256:633005a3700e81b5be0df2a7d3c1d48aced23ed927653797a3bd2b144a3aeeb6" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:1176f250311fa95cc3bca8077af323e0d73ea385ba266e096af82e7e2b91f256" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:7cb4018f4ce68b61fd3ef87dc1c4ca520731c7b5b200e360ad47b612d7844063" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp312-cp312-win_amd64.whl", hash = "sha256:3a01f0b64c10a82d444d9fd06b3e8c567b1158b76b2764b8f51bfd8f535064b0" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:0b80b7555dcd0a75b7b06016991f01281a0bb078cf28fa2d1dfb949fad2fbd07" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:63381a109a569b280ed3319da89d3afe5cf9ab5c879936382a212affb5c90552" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp313-cp313-win_amd64.whl", hash = "sha256:ad9183864acdd99fc5143d7ca9d3d2e7ddfc9a9600ff43217825d4e5e9855ccc" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:2314521c74d76e513c53bb72c0ce3511ef0295ff657a432790df6c207e5d7962" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:4454a4faca31af81566e3a4208f10f20b8a6d9cfe42791b0ca7ff134326468fc" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp313-cp313t-win_amd64.whl", hash = "sha256:24420e430e77136f7079354134b34e7ba9d87e539f5ac84c33b08e5c13412ebe" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp314-cp314-manylinux_2_28_aarch64.whl", hash = "sha256:32c036296c557f19a1537ce981c40533650097114e1720a321a39a3b08d9df56" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp314-cp314-manylinux_2_28_x86_64.whl", hash = "sha256:7788d3d03d939cf00f93ac0da5ab520846f66411e339cfbf519a806e8facf519" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp314-cp314-win_amd64.whl", hash = "sha256:7bcd40cbffac475b478d6ce812f03da84e9a4894956efb89c3b7bcca5dbd4f91" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp314-cp314t-manylinux_2_28_aarch64.whl", hash = "sha256:e88c78e5b08ae9303aa15da43b68b44287ecbec16d898d9fad6998832fe626a5" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp314-cp314t-manylinux_2_28_x86_64.whl", hash = "sha256:7d8769bdf3200ca16a92f14df404c3370171ac3732996528a8973d753eac562f" },
|
||||
{ url = "https://download-r2.pytorch.org/whl/cu128/torch-2.9.1%2Bcu128-cp314-cp314t-win_amd64.whl", hash = "sha256:0c784b600959ec70ee01cb23e8bc870a0e0475af30378ff5e39f4abed8b7c1cc" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user