modeling_grok2.py

"""
modeling_grok2.py — Grok 2 for transformers, full multi-GPU support.
Pure bf16 throughout. Device-aware at every operation.
"""

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel, PretrainedConfig, GenerationMixin
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers import AutoConfig, AutoModelForCausalLM


# ── Config ────────────────────────────────────────────────────────────────────
class Grok2Config(PretrainedConfig):
    model_type = "grok2"

    def __init__(
        self,
        vocab_size=131072,
        hidden_size=8192,
        num_hidden_layers=64,
        num_attention_heads=64,
        num_key_value_heads=8,
        intermediate_size=32768,
        moe_intermediate_size=16384,
        num_local_experts=8,
        num_experts_per_tok=2,
        max_position_embeddings=131072,
        rope_theta=208533496.0,
        rms_norm_eps=1e-5,
        embedding_multiplier_scale=90.50966799187809,
        output_multiplier_scale=0.5,
        final_logit_softcapping=50.0,
        attn_logit_softcapping=30.0,
        router_logit_softcapping=30.0,
        pad_token_id=0,
        bos_token_id=1,
        eos_token_id=2,
        tie_word_embeddings=False,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.num_key_value_heads = num_key_value_heads
        self.head_dim = hidden_size // num_attention_heads
        self.intermediate_size = intermediate_size
        self.moe_intermediate_size = moe_intermediate_size
        self.num_local_experts = num_local_experts
        self.num_experts_per_tok = num_experts_per_tok
        self.max_position_embeddings = max_position_embeddings
        self.rope_theta = rope_theta
        self.rms_norm_eps = rms_norm_eps
        self.embedding_multiplier_scale = embedding_multiplier_scale
        self.output_multiplier_scale = output_multiplier_scale
        self.final_logit_softcapping = final_logit_softcapping
        self.attn_logit_softcapping = attn_logit_softcapping
        self.router_logit_softcapping = router_logit_softcapping
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )


# ── RMSNorm ───────────────────────────────────────────────────────────────────
class Grok2RMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-5):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.eps = eps

    def forward(self, x):
        # Stay in input dtype throughout
        variance = x.pow(2).mean(-1, keepdim=True)
        x = x * torch.rsqrt(variance + self.eps)
        return self.weight.to(x.device, x.dtype) * x


# ── RoPE ──────────────────────────────────────────────────────────────────────
def rotate_half(x):
    x1, x2 = x[..., :x.shape[-1]//2], x[..., x.shape[-1]//2:]
    return torch.cat([-x2, x1], dim=-1)

def apply_rotary_emb(q, k, cos, sin):
    return (q * cos) + (rotate_half(q) * sin), \
           (k * cos) + (rotate_half(k) * sin)

class Grok2RotaryEmbedding(nn.Module):
    def __init__(self, dim, max_pos=131072, base=208533496.0, scaling_factor=16.0):
        super().__init__()
        base = base * scaling_factor
        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self._cached_len = 0

    def _build_cache(self, seq_len, device, dtype):
        t = torch.arange(seq_len, device=device).float()
        freqs = torch.outer(t, self.inv_freq.to(device))
        emb = torch.cat([freqs, freqs], dim=-1)
        self._cos = emb.cos().to(dtype)[None, None, :, :]
        self._sin = emb.sin().to(dtype)[None, None, :, :]
        self._cached_len = seq_len
        self._cached_device = device

    def forward(self, seq_len, device, dtype):
        if seq_len > self._cached_len or not hasattr(self, '_cached_device') or device != self._cached_device:
            self._build_cache(seq_len, device, dtype)
        return self._cos[:, :, :seq_len, :], self._sin[:, :, :seq_len, :]


# ── Attention ─────────────────────────────────────────────────────────────────
class Grok2Attention(nn.Module):
    def __init__(self, config: Grok2Config):
        super().__init__()
        self.num_heads = config.num_attention_heads
        self.num_kv_heads = config.num_key_value_heads
        self.head_dim = config.head_dim
        self.num_kv_groups = self.num_heads // self.num_kv_heads
        self.attn_softcap = config.attn_logit_softcapping

        self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * config.head_dim, bias=False)
        self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * config.head_dim, bias=False)
        self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * config.head_dim, bias=False)
        self.o_proj = nn.Linear(config.num_attention_heads * config.head_dim, config.hidden_size, bias=False)
        self.rotary_emb = Grok2RotaryEmbedding(config.head_dim, config.max_position_embeddings, config.rope_theta)

    def forward(self, hidden_states, attention_mask=None, **kwargs):
        B, T, _ = hidden_states.shape
        device = hidden_states.device
        dtype = hidden_states.dtype

        q = self.q_proj(hidden_states).view(B, T, self.num_heads, self.head_dim).transpose(1, 2)
        k = self.k_proj(hidden_states).view(B, T, self.num_kv_heads, self.head_dim).transpose(1, 2)
        v = self.v_proj(hidden_states).view(B, T, self.num_kv_heads, self.head_dim).transpose(1, 2)

        cos, sin = self.rotary_emb(T, device, dtype)
        cos = cos[:, :, :T, :self.head_dim]
        sin = sin[:, :, :T, :self.head_dim]
        q, k = apply_rotary_emb(q, k, cos, sin)

        # GQA expand
        k = k.repeat_interleave(self.num_kv_groups, dim=1)
        v = v.repeat_interleave(self.num_kv_groups, dim=1)

        scale = math.sqrt(self.head_dim)
        attn = torch.matmul(q, k.transpose(-2, -1)) / scale

        if self.attn_softcap > 0:
            attn = torch.tanh(attn / self.attn_softcap) * self.attn_softcap

        causal = torch.triu(
            torch.full((T, T), float("-inf"), device=device, dtype=dtype),
            diagonal=1
        )
        attn = attn + causal.unsqueeze(0).unsqueeze(0)

        if attention_mask is not None:
            attn = attn + attention_mask.to(device=device, dtype=dtype)

        attn = F.softmax(attn, dim=-1).to(dtype)
        out = torch.matmul(attn, v)
        out = out.transpose(1, 2).contiguous().view(B, T, -1)
        return self.o_proj(out)


# ── MoE Expert ────────────────────────────────────────────────────────────────
class Grok2Expert(nn.Module):
    def __init__(self, hidden_size, moe_intermediate_size):
        super().__init__()
        self.w1 = nn.Linear(hidden_size, moe_intermediate_size, bias=False)
        self.w2 = nn.Linear(moe_intermediate_size, hidden_size, bias=False)
        self.w3 = nn.Linear(hidden_size, moe_intermediate_size, bias=False)

    def forward(self, x):
        device = self.w1.weight.device
        x = x.to(device)
        d1 = self.w1.weight.device
        d3 = self.w3.weight.device
        d2 = self.w2.weight.device
        gate = F.silu(self.w1(x.to(d1)))
        up = self.w3(x.to(d3))
        h = gate.to(d2) * up.to(d2)
        return self.w2(h)


# ── Sparse MoE ────────────────────────────────────────────────────────────────
class Grok2SparseMoE(nn.Module):
    def __init__(self, config: Grok2Config):
        super().__init__()
        self.num_experts = config.num_local_experts
        self.top_k = config.num_experts_per_tok
        self.router_softcap = config.router_logit_softcapping

        self.gate = nn.Linear(config.hidden_size, config.num_local_experts, bias=False)
        self.experts = nn.ModuleList([
            Grok2Expert(config.hidden_size, config.moe_intermediate_size)
            for _ in range(config.num_local_experts)
        ])

    def forward(self, x):
        B, T, H = x.shape
        device = x.device
        dtype = x.dtype
        x_flat = x.view(-1, H)

        router_logits = self.gate(x_flat)
        if self.router_softcap > 0:
            router_logits = torch.tanh(router_logits / self.router_softcap) * self.router_softcap

        router_weights = F.softmax(router_logits, dim=-1)
        top_weights, top_indices = router_weights.topk(self.top_k, dim=-1)
        top_weights = top_weights / top_weights.sum(dim=-1, keepdim=True)

        out = torch.zeros_like(x_flat)
        for k in range(self.top_k):
            expert_ids = top_indices[:, k]
            weights = top_weights[:, k].unsqueeze(-1)
            for e in range(self.num_experts):
                mask = (expert_ids == e)
                if not mask.any():
                    continue
                # Move tokens to expert's device, compute, move result back
                expert_device = next(self.experts[e].parameters()).device
                x_masked = x_flat[mask].to(device=expert_device, dtype=dtype)
                expert_out = self.experts[e](x_masked).to(device=device, dtype=dtype)
                out[mask] += weights[mask] * expert_out

        return out.view(B, T, H)


# ── Dense MLP ─────────────────────────────────────────────────────────────────
class Grok2MLP(nn.Module):
    def __init__(self, config: Grok2Config):
        super().__init__()
        self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
        self.up_proj   = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)

    def forward(self, x):
        return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))


# ── Decoder Layer ─────────────────────────────────────────────────────────────
class Grok2DecoderLayer(nn.Module):
    def __init__(self, config: Grok2Config, layer_idx: int):
        super().__init__()
        self.layer_idx = layer_idx
        self.pre_attn_norm    = Grok2RMSNorm(config.hidden_size, config.rms_norm_eps)
        self.self_attn        = Grok2Attention(config)
        self.post_attn_norm   = Grok2RMSNorm(config.hidden_size, config.rms_norm_eps)
        self.pre_moe_norm     = Grok2RMSNorm(config.hidden_size, config.rms_norm_eps)
        self.block_sparse_moe = Grok2SparseMoE(config)
        self.mlp              = Grok2MLP(config)
        self.post_moe_norm    = Grok2RMSNorm(config.hidden_size, config.rms_norm_eps)

    def forward(self, hidden_states, attention_mask=None, **kwargs):
        device = hidden_states.device
        dtype  = hidden_states.dtype

        # Attention block
        residual = hidden_states
        hidden_states = self.pre_attn_norm(hidden_states)
        hidden_states = self.self_attn(hidden_states, attention_mask=attention_mask)
        hidden_states = self.post_attn_norm(hidden_states.to(device=device, dtype=dtype))
        hidden_states = residual + hidden_states.to(device=device, dtype=dtype)

        # MoE + dense residual block
        residual = hidden_states
        normed = self.pre_moe_norm(hidden_states)
        moe_out = self.block_sparse_moe(normed)
        mlp_out = self.mlp(normed)
        combined = moe_out.to(device=device, dtype=dtype) + mlp_out.to(device=device, dtype=dtype)
        hidden_states = self.post_moe_norm(combined)
        hidden_states = residual + hidden_states.to(device=device, dtype=dtype)

        return hidden_states


# ── Model ─────────────────────────────────────────────────────────────────────
class Grok2Model(nn.Module):
    def __init__(self, config: Grok2Config):
        super().__init__()
        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
        self.embedding_multiplier_scale = config.embedding_multiplier_scale
        self.layers = nn.ModuleList([
            Grok2DecoderLayer(config, i) for i in range(config.num_hidden_layers)
        ])
        self.norm = Grok2RMSNorm(config.hidden_size, config.rms_norm_eps)

    def forward(self, input_ids, attention_mask=None, **kwargs):
        hidden_states = self.embed_tokens(input_ids) * self.embedding_multiplier_scale
        for layer in self.layers:
            hidden_states = layer(hidden_states, attention_mask=attention_mask)
        return self.norm(hidden_states)


# ── CausalLM ──────────────────────────────────────────────────────────────────
class Grok1ForCausalLM(PreTrainedModel, GenerationMixin):
    config_class = Grok2Config
    base_model_prefix = "model"
    supports_gradient_checkpointing = False

    def __init__(self, config: Grok2Config):
        super().__init__(config)
        self.model   = Grok2Model(config)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.output_multiplier_scale = config.output_multiplier_scale
        self.final_logit_softcapping = config.final_logit_softcapping
        self.post_init()

    def get_input_embeddings(self):  return self.model.embed_tokens
    def get_output_embeddings(self): return self.lm_head

    def forward(
        self,
        input_ids=None,
        attention_mask=None,
        past_key_values=None,
        inputs_embeds=None,
        labels=None,
        use_cache=None,
        **kwargs,
    ):
        hidden_states = self.model(input_ids, attention_mask=attention_mask)
        # Move to lm_head device
        hidden_states = hidden_states.to(self.lm_head.weight.device)
        logits = self.lm_head(hidden_states) * self.output_multiplier_scale

        if self.final_logit_softcapping > 0:
            logits = torch.tanh(logits / self.final_logit_softcapping) * self.final_logit_softcapping

        loss = None
        if labels is not None:
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            loss = F.cross_entropy(
                shift_logits.view(-1, shift_logits.size(-1)),
                shift_labels.view(-1),
                ignore_index=-100,
            )

        return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=None)

    def prepare_inputs_for_generation(self, input_ids, **kwargs):
        return {"input_ids": input_ids}


# ── Register ──────────────────────────────────────────────────────────────────
AutoConfig.register("grok2", Grok2Config)
AutoModelForCausalLM.register(Grok2Config, Grok1ForCausalLM)