Delete checkpoints/10b_ternary_ep5/JiRackTernaryPyTorch_10b.py

checkpoints/10b_ternary_ep5/JiRackTernaryPyTorch_10b.py

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-#%%writefile  JiRackTernaryPyTorch_10b.py
-# =============================================================================
-# COPYRIGHT © 2025 Konstantin Vladimirovich Grabko. ALL RIGHTS RESERVED.
-# =============================================================================
-# Rope fix and стабильного 99-го квантиля (torch.quantile).
-#
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.utils.checkpoint import checkpoint
-
-# ========================= CONFIG CONSTANTS =========================
-VOCAB_SIZE = 128256
-HIDDEN_SIZE = 4096
-INTERMEDIATE_SIZE = 18432
-NUM_LAYERS = 32
-NUM_HEADS = 32        # 4096 // 32 = 128 (HEAD_DIM)
-NUM_KV_HEADS = 8
-HEAD_DIM = 128
-MAX_SEQ_LEN = 8192
-ROPE_THETA = 500000.0
-RMS_EPS = 1e-5
-ROPE_SCALE_FACTOR = 1.0
-# =================================================================
-
-class JiRackConfig10B:
-    def __init__(self):
-        self.vocab_size = VOCAB_SIZE
-        self.hidden_size = HIDDEN_SIZE
-        self.intermediate_size = INTERMEDIATE_SIZE
-        self.num_hidden_layers = NUM_LAYERS
-        self.num_attention_heads = NUM_HEADS
-        self.num_key_value_heads = NUM_KV_HEADS
-        self.head_dim = HEAD_DIM
-        self.max_seq_len = MAX_SEQ_LEN
-        self.rope_theta = ROPE_THETA
-        self.rms_norm_eps = RMS_EPS
-        self.rope_scale_factor = ROPE_SCALE_FACTOR
-
-def precompute_freqs_cis(dim: int, end: int, theta: float = ROPE_THETA, scale_factor: float = ROPE_SCALE_FACTOR):
-    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2).float() / dim))
-    if scale_factor > 1.0:
-        freqs = freqs / scale_factor
-
-    t = torch.arange(end, dtype=torch.float32)
-    freqs = torch.outer(t, freqs)
-    return torch.cos(freqs), torch.sin(freqs)
-
-def apply_rotary_emb(xq, xk, freqs_cos, freqs_sin):
-    def rotate_interleaved(x):
-        x_even = x[..., 0::2]
-        x_odd = x[..., 1::2]
-        return torch.stack((-x_odd, x_even), dim=-1).flatten(-2)
-
-    cos = freqs_cos[None, None, :, :].repeat_interleave(2, dim=-1)
-    sin = freqs_sin[None, None, :, :].repeat_interleave(2, dim=-1)
-
-    xq_out = (xq * cos) + (rotate_interleaved(xq) * sin)
-    xk_out = (xk * cos) + (rotate_interleaved(xk) * sin)
-    return xq_out, xk_out
-
-class BitLinear(nn.Linear):
-    def __init__(self, in_features, out_features, bias=False, ternary=True):
-        super().__init__(in_features, out_features, bias=bias)
-        self.ternary = ternary
-        self.eps = 1e-5
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        if not self.ternary:
-            return F.linear(x, self.weight, self.bias)
-
-        # Троичное квантование весов (Ternary Weights)
-        w = self.weight
-        gamma = w.abs().mean().clamp(min=self.eps)
-        w_quant = torch.clamp(torch.round(w / gamma), -1.0, 1.0)
-        w_effective = w + (w_quant * gamma - w).detach()
-
-        # Нормализация активаций
-        x_mean = x.mean(dim=-1, keepdim=True)
-        x_variance = x.var(dim=-1, keepdim=True, unbiased=False)
-        x_norm = (x - x_mean) / torch.sqrt(x_variance + self.eps)
-
-        # Стабильное квантование активаций через 99-й квантиль вместо жесткого .max()
-        # Извлекаем квантиль по последней размерности (или можно по всей последовательности)
-        x_abs = x_norm.abs()
-        x_quantile = torch.quantile(x_abs.float(), 0.99, dim=-1, keepdim=True).to(x_norm.dtype)
-        x_scale_bound = x_quantile.clamp(min=self.eps)
-
-        x_scale = 127.0 / x_scale_bound
-        x_quant = torch.clamp(torch.round(x_norm * x_scale), -128.0, 127.0)
-        x_effective = x_norm + (x_quant / x_scale - x_norm).detach()
-
-        # Линейное преобразование
-        out = F.linear(x_effective, w_effective, self.bias)
-
-        # Декуантование обратно
-        x_std = torch.sqrt(x_variance + self.eps)
-        return out * (x_std * gamma / 127.0)
-
-class RMSNorm(nn.Module):
-    def __init__(self, dim, eps=RMS_EPS):
-        super().__init__()
-        self.eps = eps
-        self.weight = nn.Parameter(torch.ones(dim))
-
-    def forward(self, x):
-        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) * self.weight
-
-class TransformerBlock(nn.Module):
-    def __init__(self, config, use_checkpoint=False, bias=False , ternary=False):
-        super().__init__()
-        self.ternary=ternary
-        self.bias=bias
-        self.use_checkpoint = use_checkpoint
-        self.n_heads = config.num_attention_heads
-        self.n_kv_heads = config.num_key_value_heads
-        self.head_dim = config.head_dim
-        self.n_rep = self.n_heads // self.n_kv_heads
-
-        self.norm1 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.norm2 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-        self.q_proj = BitLinear(config.hidden_size, config.hidden_size, self.bias,self.ternary)
-        self.k_proj = BitLinear(config.hidden_size, self.n_kv_heads * self.head_dim, self.bias,self.ternary)
-        self.v_proj = BitLinear(config.hidden_size, self.n_kv_heads * self.head_dim, self.bias,self.ternary)
-        self.out_proj = BitLinear(config.hidden_size, config.hidden_size, self.bias,self.ternary)
-
-        self.ffn_w1 = BitLinear(config.hidden_size, config.intermediate_size, self.bias,self.ternary)
-        self.ffn_w3 = BitLinear(config.hidden_size, config.intermediate_size, self.bias,self.ternary)
-        self.ffn_w2 = BitLinear(config.intermediate_size, config.hidden_size, self.bias,self.ternary)
-
-    def forward(self, x, freqs_cos, freqs_sin):
-        if self.use_checkpoint and self.training:
-            return checkpoint(self._forward_impl, x, freqs_cos, freqs_sin, use_reentrant=False)
-        return self._forward_impl(x, freqs_cos, freqs_sin)
-
-    def _forward_impl(self, x, freqs_cos, freqs_sin):
-        h = self.norm1(x)
-        B, T, _ = h.shape
-
-        q = self.q_proj(h).view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
-        k = self.k_proj(h).view(B, T, self.n_kv_heads, self.head_dim).transpose(1, 2)
-        v = self.v_proj(h).view(B, T, self.n_kv_heads, self.head_dim).transpose(1, 2)
-
-        q, k = apply_rotary_emb(q, k, freqs_cos, freqs_sin)
-
-        if self.n_rep > 1:
-            k = k.repeat_interleave(self.n_rep, dim=1)
-            v = v.repeat_interleave(self.n_rep, dim=1)
-
-        attn_out = F.scaled_dot_product_attention(q, k, v, is_causal=True)
-        attn_out = attn_out.transpose(1, 2).contiguous().view(B, T, -1)
-
-        x = x + self.out_proj(attn_out)
-
-        m = self.norm2(x)
-        gate = F.silu(self.ffn_w1(m))
-        up = self.ffn_w3(m)
-        x = x + self.ffn_w2(gate * up)
-
-        return x
-
-class JiRackTransformer10B(nn.Module):
-    def __init__(self, config: JiRackConfig10B = None, use_checkpoint=False, bias=False , ternary=True):
-        super().__init__()
-        self.config = config if config is not None else JiRackConfig10B()
-        self.use_checkpoint = use_checkpoint
-        self.ternary=ternary
-        self.bias=bias
-
-        self.token_emb = nn.Embedding(self.config.vocab_size, self.config.hidden_size)
-        self.blocks = nn.ModuleList([
-            TransformerBlock(self.config, self.use_checkpoint, self.bias , self.ternary)
-            for _ in range(self.config.num_hidden_layers)
-        ])
-        self.ln_f = RMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
-        self.lm_head = nn.Linear(self.config.hidden_size, self.config.vocab_size, bias=False)
-
-        cos, sin = precompute_freqs_cis(
-            dim=self.config.head_dim,
-            end=self.config.max_seq_len,
-            theta=self.config.rope_theta,
-            scale_factor=self.config.rope_scale_factor
-        )
-        self.register_buffer("freqs_cos", cos, persistent=False)
-        self.register_buffer("freqs_sin", sin, persistent=False)
-
-    def _set_ternary(self, module):
-        if isinstance(module, BitLinear):
-            module.ternary = True
-
-    def forward(self, input_ids):
-        seq_len = input_ids.shape[1]
-        x = self.token_emb(input_ids)
-
-        # RoPE буферы приводятся к типу и девайсу входящих эмбеддингов
-        cos = self.freqs_cos[:seq_len].to(x)
-        sin = self.freqs_sin[:seq_len].to(x)
-
-        for block in self.blocks:
-            x = block(x, cos, sin)
-
-        return self.lm_head(self.ln_f(x))