use std::collections::HashMap;
use half::bf16;
use xserv_kernels::*;
use xserv_tensor::{DType, Device, Tensor};

use crate::config::ModelConfig;
use crate::gpt2::KVCache;
use crate::kv_cache::GpuKVCache;
use crate::paged_kv_cache::PagedKVCache;

pub struct Qwen3 {
    pub config: ModelConfig,
    embed_tokens: Tensor,
    layers: Vec<Qwen3Block>,
    norm: Tensor,
    lm_head_t: Tensor,  // precomputed transpose
    rope_cache: RopeCache,
}

struct Qwen3Block {
    input_norm: Tensor,       // [hidden]
    q_proj_wt: Tensor,        // TRANSPOSED: [hidden, num_heads*head_dim]
    k_proj_wt: Tensor,        // TRANSPOSED: [hidden, num_kv_heads*head_dim]
    v_proj_wt: Tensor,
    o_proj_wt: Tensor,        // TRANSPOSED: [num_heads*head_dim, hidden]
    q_norm: Tensor,           // [head_dim]
    k_norm: Tensor,           // [head_dim]
    post_norm: Tensor,        // [hidden]
    gate_proj_wt: Tensor,     // TRANSPOSED: [hidden, intermediate]
    up_proj_wt: Tensor,
    down_proj_wt: Tensor,     // TRANSPOSED: [intermediate, hidden]
}

impl Qwen3 {
    pub fn from_weights(config: ModelConfig, mut w: HashMap<String, Tensor>) -> Self {
        crate::init_kernels();
        let take = |w: &mut HashMap<String, Tensor>, name: &str| -> Tensor {
            w.remove(name).unwrap_or_else(|| panic!("missing weight: {name}"))
        };

        let embed_tokens = take(&mut w, "model.embed_tokens.weight");
        let norm = take(&mut w, "model.norm.weight");
        let lm_head_raw = take(&mut w, "lm_head.weight");

        let rope_cache = RopeCache::new(
            config.max_seq_len(),
            config.head_dim(),
            config.rope_theta.unwrap_or(1_000_000.0) as f32,
        );

        // Precompute transposed weights: [out, in] → [in, out] so we can do x @ wt directly
        let transpose_w = |t: Tensor| -> Tensor {
            t.transpose(0, 1).contiguous()
        };

        let num_layers = config.num_layers();
        let mut layers = Vec::with_capacity(num_layers);
        eprintln!("Transposing weights for {} layers...", num_layers);
        for i in 0..num_layers {
            let p = format!("model.layers.{i}");
            layers.push(Qwen3Block {
                input_norm: take(&mut w, &format!("{p}.input_layernorm.weight")),
                q_proj_wt: transpose_w(take(&mut w, &format!("{p}.self_attn.q_proj.weight"))),
                k_proj_wt: transpose_w(take(&mut w, &format!("{p}.self_attn.k_proj.weight"))),
                v_proj_wt: transpose_w(take(&mut w, &format!("{p}.self_attn.v_proj.weight"))),
                o_proj_wt: transpose_w(take(&mut w, &format!("{p}.self_attn.o_proj.weight"))),
                q_norm: take(&mut w, &format!("{p}.self_attn.q_norm.weight")),
                k_norm: take(&mut w, &format!("{p}.self_attn.k_norm.weight")),
                post_norm: take(&mut w, &format!("{p}.post_attention_layernorm.weight")),
                gate_proj_wt: transpose_w(take(&mut w, &format!("{p}.mlp.gate_proj.weight"))),
                up_proj_wt: transpose_w(take(&mut w, &format!("{p}.mlp.up_proj.weight"))),
                down_proj_wt: transpose_w(take(&mut w, &format!("{p}.mlp.down_proj.weight"))),
            });
        }

        let lm_head_t = transpose_w(lm_head_raw);
        Self { config, embed_tokens, layers, norm, lm_head_t, rope_cache }
    }

    pub fn forward_with_cache(&self, token_ids: &[u32], cache: &mut KVCache) -> Tensor {
        let new_tokens = token_ids.len();
        let pos_offset = cache.seq_len();
        let hidden = self.config.hidden();
        let num_heads = self.config.num_heads();
        let num_kv_heads = self.config.num_kv_heads();
        let head_dim = self.config.head_dim();
        let eps = self.config.rms_norm_eps.unwrap_or(1e-6) as f32;

        let mut x = embedding(&self.embed_tokens, token_ids);
        let positions: Vec<u32> = (pos_offset..pos_offset + new_tokens).map(|p| p as u32).collect();

        for (layer_idx, layer) in self.layers.iter().enumerate() {
            let residual = x.clone();
            let normed = rmsnorm(&x, &layer.input_norm, eps);

            // Q/K/V projections (pre-transposed weights, x @ wt)
            let q = matmul_2d(&normed, &layer.q_proj_wt);
            let k = matmul_2d(&normed, &layer.k_proj_wt);
            let v = matmul_2d(&normed, &layer.v_proj_wt);

            // Reshape to [1, heads, seq, head_dim]
            let q = reshape_heads(&q, new_tokens, num_heads, head_dim);
            let k = reshape_heads(&k, new_tokens, num_kv_heads, head_dim);
            let v = reshape_heads(&v, new_tokens, num_kv_heads, head_dim);

            // QK normalization (per-head RMSNorm)
            let q = head_rmsnorm(&q, &layer.q_norm, eps);
            let k = head_rmsnorm(&k, &layer.k_norm, eps);

            // RoPE — kernel expects [S, H, D], our tensors are [1, H, S, D]
            // Transpose to [1, S, H, D] → reshape to [S, H, D] for RoPE
            let q = transpose_for_rope(&q, new_tokens, num_heads, head_dim);
            let k = transpose_for_rope(&k, new_tokens, num_kv_heads, head_dim);
            rope_inplace(&q, &self.rope_cache, &positions);
            rope_inplace(&k, &self.rope_cache, &positions);
            // Transpose back to [1, H, S, D]
            let q = transpose_from_rope(&q, new_tokens, num_heads, head_dim);
            let k = transpose_from_rope(&k, new_tokens, num_kv_heads, head_dim);

            // KV cache
            let k_cpu = k.to_device(Device::Cpu);
            let v_cpu = v.to_device(Device::Cpu);
            cache.append_kv_tensor(layer_idx, &k_cpu, &v_cpu, new_tokens);
            let (k_full, v_full) = cache.get_kv_tensors(layer_idx);

            // GQA: repeat K/V
            let n_rep = num_heads / num_kv_heads;
            let k_full = repeat_kv(&k_full, n_rep);
            let v_full = repeat_kv(&v_full, n_rep);

            // Attention
            let attn_out = attention(&q, &k_full, &v_full, true);
            let attn_merged = merge_heads_any(&attn_out, new_tokens, hidden);
            let attn_proj = matmul_2d(&attn_merged, &layer.o_proj_wt);
            x = add_any(&residual, &attn_proj);

            // SwiGLU FFN
            let residual = x.clone();
            let normed = rmsnorm(&x, &layer.post_norm, eps);
            let gate = matmul_2d(&normed, &layer.gate_proj_wt);
            let up = matmul_2d(&normed, &layer.up_proj_wt);
            let gate_activated = silu(&gate);
            let hidden_states = mul_any(&gate_activated, &up);
            let down = matmul_2d(&hidden_states, &layer.down_proj_wt);
            x = add_any(&residual, &down);
        }

        let x = rmsnorm(&x, &self.norm, eps);
        matmul_2d(&x, &self.lm_head_t)
    }

    /// Batched decode: process one token per sequence simultaneously.
    /// All compute-heavy ops (projections, FFN) operate on [B, hidden] tensors.
    /// Per-sequence ops (RoPE, KV cache, attention) are handled individually.
    ///
    /// tokens: one token per sequence (len = batch_size)
    /// positions: position offset for each sequence (len = batch_size)
    /// caches: one mutable KV cache per sequence (len = batch_size)
    ///
    /// Returns logits: [batch_size, vocab_size]
    pub fn forward_decode_batch(
        &self,
        tokens: &[u32],
        positions: &[usize],
        caches: &mut [&mut GpuKVCache],
    ) -> Tensor {
        let batch = tokens.len();
        assert_eq!(positions.len(), batch);
        assert_eq!(caches.len(), batch);
        assert!(batch > 0);

        let num_heads = self.config.num_heads();
        let num_kv_heads = self.config.num_kv_heads();
        let head_dim = self.config.head_dim();
        let eps = self.config.rms_norm_eps.unwrap_or(1e-6) as f32;

        // Batched embedding: [B, hidden]
        let mut x = embedding(&self.embed_tokens, tokens);

        for (layer_idx, layer) in self.layers.iter().enumerate() {
            let residual = x.clone();
            let normed = rmsnorm(&x, &layer.input_norm, eps); // [B, hidden]

            // Batched projections: [B, hidden] × [hidden, X] = [B, X]
            let q_all = matmul_2d(&normed, &layer.q_proj_wt); // [B, num_heads*head_dim]
            let k_all = matmul_2d(&normed, &layer.k_proj_wt); // [B, num_kv_heads*head_dim]
            let v_all = matmul_2d(&normed, &layer.v_proj_wt); // [B, num_kv_heads*head_dim]

            // Per-sequence: reshape, qk-norm, RoPE, KV cache, attention, merge
            let mut attn_outputs: Vec<Tensor> = Vec::with_capacity(batch);
            for b in 0..batch {
                // Extract row b: [1, X] — view into contiguous [B, X]
                let q_row = row_view(&q_all, b); // [1, num_heads*head_dim]
                let k_row = row_view(&k_all, b); // [1, num_kv_heads*head_dim]
                let v_row = row_view(&v_all, b); // [1, num_kv_heads*head_dim]

                // GPU reshape: [1, H*D] → [1, H, 1, D]
                let q = xserv_kernels::reshape_heads_gpu(&q_row, 1, num_heads, head_dim);
                let k = xserv_kernels::reshape_heads_gpu(&k_row, 1, num_kv_heads, head_dim);
                let v = xserv_kernels::reshape_heads_gpu(&v_row, 1, num_kv_heads, head_dim);

                // QK norm
                let q = head_rmsnorm(&q, &layer.q_norm, eps);
                let k = head_rmsnorm(&k, &layer.k_norm, eps);

                // GPU transpose for RoPE: [1, H, 1, D] → [1, H, D]
                let q = xserv_kernels::transpose_for_rope_gpu(&q, 1, num_heads, head_dim);
                let k = xserv_kernels::transpose_for_rope_gpu(&k, 1, num_kv_heads, head_dim);

                // RoPE with per-sequence position
                let pos = [positions[b] as u32];
                rope_inplace(&q, &self.rope_cache, &pos);
                rope_inplace(&k, &self.rope_cache, &pos);

                // Transpose back: [1, H, D] → [1, H, 1, D]
                let q = xserv_kernels::transpose_from_rope_gpu(&q, 1, num_heads, head_dim);
                let k = xserv_kernels::transpose_from_rope_gpu(&k, 1, num_kv_heads, head_dim);

                // KV cache: append and get full cache
                let pos_b = positions[b];
                caches[b].append(layer_idx, &k, &v, 1, pos_b);
                let (k_full, v_full) = caches[b].get_kv_len(layer_idx, pos_b + 1);

                // Decode attention (uses native GQA, no repeat_kv needed)
                let attn_out = flash_attention(&q, &k_full, &v_full, true);

                // Merge heads: [1, H, 1, D] → [1, hidden]
                let merged = xserv_kernels::merge_heads_gpu(&attn_out, 1, num_heads, head_dim);
                attn_outputs.push(merged);
            }

            // Concat attention outputs: [B, hidden]
            let attn_merged = concat_rows(&attn_outputs);

            // Batched O projection: [B, hidden] × [hidden, hidden] = [B, hidden]
            let attn_proj = matmul_2d(&attn_merged, &layer.o_proj_wt);

            // Fused add + rmsnorm
            let (normed, x_new) = xserv_kernels::add_rmsnorm(&attn_proj, &residual, &layer.post_norm, eps);
            let residual = x_new.clone();

            // Batched FFN: all projections on [B, hidden]
            let gate = matmul_2d(&normed, &layer.gate_proj_wt);
            let up = matmul_2d(&normed, &layer.up_proj_wt);
            let hidden_states = xserv_kernels::silu_mul(&gate, &up);
            let down = matmul_2d(&hidden_states, &layer.down_proj_wt);
            x = add_any(&residual, &down);
        }

        // Advance KV cache seq_len for each sequence
        for b in 0..batch {
            caches[b].advance_seq_len(1);
        }

        let x = rmsnorm(&x, &self.norm, eps);
        matmul_2d(&x, &self.lm_head_t) // [B, vocab_size]
    }

    /// Paged decode: process one token per sequence using a shared paged KV cache.
    ///
    /// tokens:    [B] one token per sequence
    /// positions: [B] current logical position (BEFORE this step) per sequence
    /// seq_slots: [B] slot ids in `paged_cache`
    pub fn forward_decode_paged(
        &self,
        tokens: &[u32],
        positions: &[usize],
        seq_slots: &[usize],
        paged_cache: &mut PagedKVCache,
    ) -> Tensor {
        let batch = tokens.len();
        assert_eq!(positions.len(), batch);
        assert_eq!(seq_slots.len(), batch);
        assert!(batch > 0);

        let num_heads = self.config.num_heads();
        let num_kv_heads = self.config.num_kv_heads();
        let head_dim = self.config.head_dim();
        let eps = self.config.rms_norm_eps.unwrap_or(1e-6) as f32;

        // Ensure all slots have enough physical blocks for this token, then
        // upload block tables + context_lens once for the whole forward (the
        // tables are identical across layers; only the layer's K/V pool changes).
        let kv_lens: Vec<i32> = positions.iter().map(|&p| (p + 1) as i32).collect();
        for (b, &slot) in seq_slots.iter().enumerate() {
            paged_cache.ensure_capacity(slot, positions[b] + 1);
        }
        paged_cache.sync_active_batch_with_lens(seq_slots, &kv_lens);

        let bt_ptr = paged_cache.block_table_gpu().as_ptr() as *const i32;
        let cl_ptr = paged_cache.context_lens_gpu().as_ptr() as *const i32;
        let max_blocks = paged_cache.max_blocks_per_seq();

        // Batched embedding: [B, hidden]
        let mut x = embedding(&self.embed_tokens, tokens);

        for (layer_idx, layer) in self.layers.iter().enumerate() {
            let residual = x.clone();
            let normed = rmsnorm(&x, &layer.input_norm, eps);

            let q_all = matmul_2d(&normed, &layer.q_proj_wt);
            let k_all = matmul_2d(&normed, &layer.k_proj_wt);
            let v_all = matmul_2d(&normed, &layer.v_proj_wt);

            let mut q_rows: Vec<Tensor> = Vec::with_capacity(batch);
            for b in 0..batch {
                let q_row = row_view(&q_all, b);
                let k_row = row_view(&k_all, b);
                let v_row = row_view(&v_all, b);

                let q = xserv_kernels::reshape_heads_gpu(&q_row, 1, num_heads, head_dim);
                let k = xserv_kernels::reshape_heads_gpu(&k_row, 1, num_kv_heads, head_dim);
                let v = xserv_kernels::reshape_heads_gpu(&v_row, 1, num_kv_heads, head_dim);

                let q = head_rmsnorm(&q, &layer.q_norm, eps);
                let k = head_rmsnorm(&k, &layer.k_norm, eps);

                let q = xserv_kernels::transpose_for_rope_gpu(&q, 1, num_heads, head_dim);
                let k = xserv_kernels::transpose_for_rope_gpu(&k, 1, num_kv_heads, head_dim);

                let pos = [positions[b] as u32];
                rope_inplace(&q, &self.rope_cache, &pos);
                rope_inplace(&k, &self.rope_cache, &pos);

                let q = xserv_kernels::transpose_from_rope_gpu(&q, 1, num_heads, head_dim);
                let k = xserv_kernels::transpose_from_rope_gpu(&k, 1, num_kv_heads, head_dim);

                paged_cache.append_tokens(seq_slots[b], layer_idx, &k, &v, 1, positions[b]);

                let q_flat = xserv_kernels::merge_heads_gpu(&q, 1, num_heads, head_dim);
                q_rows.push(q_flat);
            }

            let q_batched_2d = concat_rows(&q_rows);
            // q_batched_2d: [B, num_heads * head_dim]. Memory is [B, H, D] —
            // a plain reshape view to [B, H, 1, D] is what the paged kernel expects.
            let q_4d = q_batched_2d.reshape(&[batch, num_heads, 1, head_dim]);

            let k_pool_ptr = paged_cache.k_pool(layer_idx).as_ptr() as *const std::ffi::c_void;
            let v_pool_ptr = paged_cache.v_pool(layer_idx).as_ptr() as *const std::ffi::c_void;

            let attn_out = xserv_kernels::paged_decode_attention(
                &q_4d,
                k_pool_ptr,
                v_pool_ptr,
                bt_ptr,
                cl_ptr,
                batch,
                num_heads,
                num_kv_heads,
                head_dim,
                max_blocks,
            );

            // attn_out shape [B, H, 1, D] is contiguous-equivalent to [B, H*D].
            // Plain reshape is a view; merge_heads_gpu would incorrectly swap B<->H.
            let attn_merged = attn_out.reshape(&[batch, num_heads * head_dim]);
            let attn_proj = matmul_2d(&attn_merged, &layer.o_proj_wt);

            let (normed, x_new) = xserv_kernels::add_rmsnorm(&attn_proj, &residual, &layer.post_norm, eps);
            let residual = x_new.clone();

            let gate = matmul_2d(&normed, &layer.gate_proj_wt);
            let up = matmul_2d(&normed, &layer.up_proj_wt);
            let hidden_states = xserv_kernels::silu_mul(&gate, &up);
            let down = matmul_2d(&hidden_states, &layer.down_proj_wt);
            x = add_any(&residual, &down);
        }

        // Advance logical seq_len now that all layers have been written.
        for &slot in seq_slots {
            paged_cache.advance_seq_len(slot, 1);
        }

        let x = rmsnorm(&x, &self.norm, eps);
        matmul_2d(&x, &self.lm_head_t)
    }

    /// Paged prefill: write a sequence of `new_tokens` K/V into the paged
    /// cache for `slot`, run flash attention via gathered contiguous K/V.
    /// Returns logits [new_tokens, vocab_size].
    pub fn forward_prefill_paged(
        &self,
        token_ids: &[u32],
        slot: usize,
        paged_cache: &mut PagedKVCache,
    ) -> Tensor {
        let new_tokens = token_ids.len();
        let pos_offset = paged_cache.seq_len(slot);
        let num_heads = self.config.num_heads();
        let num_kv_heads = self.config.num_kv_heads();
        let head_dim = self.config.head_dim();
        let eps = self.config.rms_norm_eps.unwrap_or(1e-6) as f32;

        // Pre-allocate enough blocks and bump seq_len up-front so per-layer
        // gather_kv_contiguous returns the freshly written K/V range.
        paged_cache.ensure_capacity(slot, pos_offset + new_tokens);
        paged_cache.advance_seq_len(slot, new_tokens);

        let mut x = embedding(&self.embed_tokens, token_ids);
        let positions: Vec<u32> = (pos_offset..pos_offset + new_tokens).map(|p| p as u32).collect();

        for (layer_idx, layer) in self.layers.iter().enumerate() {
            let residual = x.clone();
            let normed = rmsnorm(&x, &layer.input_norm, eps);

            let q = matmul_2d(&normed, &layer.q_proj_wt);
            let k = matmul_2d(&normed, &layer.k_proj_wt);
            let v = matmul_2d(&normed, &layer.v_proj_wt);

            let q = xserv_kernels::reshape_heads_gpu(&q, new_tokens, num_heads, head_dim);
            let k = xserv_kernels::reshape_heads_gpu(&k, new_tokens, num_kv_heads, head_dim);
            let v = xserv_kernels::reshape_heads_gpu(&v, new_tokens, num_kv_heads, head_dim);

            let q = head_rmsnorm(&q, &layer.q_norm, eps);
            let k = head_rmsnorm(&k, &layer.k_norm, eps);

            let q = xserv_kernels::transpose_for_rope_gpu(&q, new_tokens, num_heads, head_dim);
            let k = xserv_kernels::transpose_for_rope_gpu(&k, new_tokens, num_kv_heads, head_dim);
            rope_inplace(&q, &self.rope_cache, &positions);
            rope_inplace(&k, &self.rope_cache, &positions);
            let q = xserv_kernels::transpose_from_rope_gpu(&q, new_tokens, num_heads, head_dim);
            let k = xserv_kernels::transpose_from_rope_gpu(&k, new_tokens, num_kv_heads, head_dim);

            // Write into paged pool at the original (pre-advance) position.
            paged_cache.append_tokens(slot, layer_idx, &k, &v, new_tokens, pos_offset);

            // Gather contiguous K/V for the full sequence (seq_len already includes new_tokens).
            let (k_full, v_full) = paged_cache.gather_kv_contiguous(slot, layer_idx);
            let attn_out = flash_attention(&q, &k_full, &v_full, true);

            let attn_merged = xserv_kernels::merge_heads_gpu(&attn_out, new_tokens, num_heads, head_dim);
            let attn_proj = matmul_2d(&attn_merged, &layer.o_proj_wt);

            let (normed, x_new) = xserv_kernels::add_rmsnorm(&attn_proj, &residual, &layer.post_norm, eps);
            let residual = x_new.clone();

            let gate = matmul_2d(&normed, &layer.gate_proj_wt);
            let up = matmul_2d(&normed, &layer.up_proj_wt);
            let hidden_states = xserv_kernels::silu_mul(&gate, &up);
            let down = matmul_2d(&hidden_states, &layer.down_proj_wt);
            x = add_any(&residual, &down);
        }

        let x = rmsnorm(&x, &self.norm, eps);
        matmul_2d(&x, &self.lm_head_t)
    }

    /// Forward with GPU-resident KV cache and GPU transpose/reshape kernels.
    pub fn forward_gpu_cache(&self, token_ids: &[u32], cache: &mut GpuKVCache) -> Tensor {
        let new_tokens = token_ids.len();
        let pos_offset = cache.seq_len();
        let hidden = self.config.hidden();
        let num_heads = self.config.num_heads();
        let num_kv_heads = self.config.num_kv_heads();
        let head_dim = self.config.head_dim();
        let eps = self.config.rms_norm_eps.unwrap_or(1e-6) as f32;

        let mut x = embedding(&self.embed_tokens, token_ids);
        let positions: Vec<u32> = (pos_offset..pos_offset + new_tokens).map(|p| p as u32).collect();

        for (layer_idx, layer) in self.layers.iter().enumerate() {
            let residual = x.clone();
            let normed = rmsnorm(&x, &layer.input_norm, eps);

            let q = matmul_2d(&normed, &layer.q_proj_wt);
            let k = matmul_2d(&normed, &layer.k_proj_wt);
            let v = matmul_2d(&normed, &layer.v_proj_wt);

            // GPU reshape: [S, H*D] → [1, H, S, D]
            let q = xserv_kernels::reshape_heads_gpu(&q, new_tokens, num_heads, head_dim);
            let k = xserv_kernels::reshape_heads_gpu(&k, new_tokens, num_kv_heads, head_dim);
            let v = xserv_kernels::reshape_heads_gpu(&v, new_tokens, num_kv_heads, head_dim);

            // QK norm (reshape to [H*S, D], rmsnorm, reshape back — stays on GPU)
            let q = head_rmsnorm(&q, &layer.q_norm, eps);
            let k = head_rmsnorm(&k, &layer.k_norm, eps);

            // GPU transpose for RoPE: [1, H, S, D] → [S, H, D]
            let q = xserv_kernels::transpose_for_rope_gpu(&q, new_tokens, num_heads, head_dim);
            let k = xserv_kernels::transpose_for_rope_gpu(&k, new_tokens, num_kv_heads, head_dim);
            rope_inplace(&q, &self.rope_cache, &positions);
            rope_inplace(&k, &self.rope_cache, &positions);
            // GPU transpose back: [S, H, D] → [1, H, S, D]
            let q = xserv_kernels::transpose_from_rope_gpu(&q, new_tokens, num_heads, head_dim);
            let k = xserv_kernels::transpose_from_rope_gpu(&k, new_tokens, num_kv_heads, head_dim);

            // GPU KV cache
            cache.append(layer_idx, &k, &v, new_tokens, pos_offset);
            let (k_full, v_full) = cache.get_kv_len(layer_idx, pos_offset + new_tokens);

            // Flash Attention with native GQA (no repeat_kv needed)
            let attn_out = flash_attention(&q, &k_full, &v_full, true);
            // GPU merge_heads: [1, H, S, D] → [S, H*D]
            let attn_merged = xserv_kernels::merge_heads_gpu(&attn_out, new_tokens, num_heads, head_dim);
            let attn_proj = matmul_2d(&attn_merged, &layer.o_proj_wt);

            // Fused add + rmsnorm: (normed, x) where x = residual + attn_proj
            let (normed, x_new) = xserv_kernels::add_rmsnorm(&attn_proj, &residual, &layer.post_norm, eps);
            let residual = x_new.clone();

            // Fused SiLU×Mul
            let gate = matmul_2d(&normed, &layer.gate_proj_wt);
            let up = matmul_2d(&normed, &layer.up_proj_wt);
            let hidden_states = xserv_kernels::silu_mul(&gate, &up);
            let down = matmul_2d(&hidden_states, &layer.down_proj_wt);
            x = add_any(&residual, &down);
        }

        cache.advance_seq_len(new_tokens);
        let x = rmsnorm(&x, &self.norm, eps);
        matmul_2d(&x, &self.lm_head_t)
    }

    /// Extract weight pointers for CUDA Graph capture.
    pub fn layer_weight_ptrs(&self) -> Vec<crate::decode_graph::LayerWeightPtrs> {
        self.layers.iter().map(|l| crate::decode_graph::LayerWeightPtrs {
            input_norm: l.input_norm.data_ptr() as *const std::ffi::c_void,
            q_proj_wt: l.q_proj_wt.data_ptr() as *const std::ffi::c_void,
            k_proj_wt: l.k_proj_wt.data_ptr() as *const std::ffi::c_void,
            v_proj_wt: l.v_proj_wt.data_ptr() as *const std::ffi::c_void,
            o_proj_wt: l.o_proj_wt.data_ptr() as *const std::ffi::c_void,
            q_norm: l.q_norm.data_ptr() as *const std::ffi::c_void,
            k_norm: l.k_norm.data_ptr() as *const std::ffi::c_void,
            post_norm: l.post_norm.data_ptr() as *const std::ffi::c_void,
            gate_proj_wt: l.gate_proj_wt.data_ptr() as *const std::ffi::c_void,
            up_proj_wt: l.up_proj_wt.data_ptr() as *const std::ffi::c_void,
            down_proj_wt: l.down_proj_wt.data_ptr() as *const std::ffi::c_void,
        }).collect()
    }

    /// Get pointers needed for CUDA Graph capture.
    pub fn graph_capture_ptrs(&self) -> (
        *const std::ffi::c_void,  // norm weight
        *const std::ffi::c_void,  // lm_head_t
        *const std::ffi::c_void,  // embed_tokens
        *const std::ffi::c_void,  // rope cos
        *const std::ffi::c_void,  // rope sin
    ) {
        (
            self.norm.data_ptr() as *const std::ffi::c_void,
            self.lm_head_t.data_ptr() as *const std::ffi::c_void,
            self.embed_tokens.data_ptr() as *const std::ffi::c_void,
            self.rope_cache.cos.as_ptr() as *const std::ffi::c_void,
            self.rope_cache.sin.as_ptr() as *const std::ffi::c_void,
        )
    }
}

// --- Helpers ---

fn matmul_2d(a: &Tensor, b: &Tensor) -> Tensor {
    assert_eq!(a.ndim(), 2);
    assert_eq!(b.ndim(), 2);
    matmul(a, b, GemmBackend::CuBlas)
}

fn reshape_heads(x: &Tensor, seq_len: usize, num_heads: usize, head_dim: usize) -> Tensor {
    let x_cpu = x.to_device(Device::Cpu);
    let hidden = num_heads * head_dim;
    let src = x_cpu.as_slice::<bf16>();
    let mut out = vec![bf16::ZERO; num_heads * seq_len * head_dim];
    for s in 0..seq_len {
        for h in 0..num_heads {
            let si = s * hidden + h * head_dim;
            let di = (h * seq_len + s) * head_dim;
            out[di..di + head_dim].copy_from_slice(&src[si..si + head_dim]);
        }
    }
    Tensor::from_slice(&out, &[1, num_heads, seq_len, head_dim]).to_device(x.device())
}

fn merge_heads_any(x: &Tensor, seq_len: usize, hidden: usize) -> Tensor {
    let num_heads = x.shape()[1];
    let head_dim = x.shape()[3];
    let x_cpu = x.to_device(Device::Cpu);
    let src = x_cpu.as_slice::<bf16>();
    let mut out = vec![bf16::ZERO; seq_len * hidden];
    for s in 0..seq_len {
        for h in 0..num_heads {
            let si = (h * seq_len + s) * head_dim;
            let di = s * hidden + h * head_dim;
            out[di..di + head_dim].copy_from_slice(&src[si..si + head_dim]);
        }
    }
    Tensor::from_slice(&out, &[seq_len, hidden]).to_device(x.device())
}

/// Per-head RMSNorm: apply RMSNorm to each [head_dim] slice independently.
/// x: [1, H, S, D], norm_weight: [D]
fn head_rmsnorm(x: &Tensor, norm_weight: &Tensor, eps: f32) -> Tensor {
    let num_heads = x.shape()[1];
    let seq_len = x.shape()[2];
    let head_dim = x.shape()[3];
    // Reshape to [H*S, D], apply rmsnorm, reshape back
    let total_rows = num_heads * seq_len;
    let flat = x.reshape(&[total_rows, head_dim]);
    let normed = rmsnorm(&flat, norm_weight, eps);
    normed.reshape(&[1, num_heads, seq_len, head_dim])
}

/// [1, H, S, D] → [S, H, D] for RoPE kernel
fn transpose_for_rope(x: &Tensor, seq_len: usize, num_heads: usize, head_dim: usize) -> Tensor {
    let x_cpu = x.to_device(Device::Cpu);
    let src = x_cpu.as_slice::<bf16>();
    let mut out = vec![bf16::ZERO; seq_len * num_heads * head_dim];
    for h in 0..num_heads {
        for s in 0..seq_len {
            let si = (h * seq_len + s) * head_dim;
            let di = (s * num_heads + h) * head_dim;
            out[di..di + head_dim].copy_from_slice(&src[si..si + head_dim]);
        }
    }
    Tensor::from_slice(&out, &[seq_len, num_heads, head_dim]).to_device(x.device())
}

/// [S, H, D] → [1, H, S, D] after RoPE
fn transpose_from_rope(x: &Tensor, seq_len: usize, num_heads: usize, head_dim: usize) -> Tensor {
    let x_cpu = x.to_device(Device::Cpu);
    let src = x_cpu.as_slice::<bf16>();
    let mut out = vec![bf16::ZERO; num_heads * seq_len * head_dim];
    for s in 0..seq_len {
        for h in 0..num_heads {
            let si = (s * num_heads + h) * head_dim;
            let di = (h * seq_len + s) * head_dim;
            out[di..di + head_dim].copy_from_slice(&src[si..si + head_dim]);
        }
    }
    Tensor::from_slice(&out, &[1, num_heads, seq_len, head_dim]).to_device(x.device())
}

fn repeat_kv(x: &Tensor, n_rep: usize) -> Tensor {
    if n_rep == 1 { return x.clone(); }
    let kv_heads = x.shape()[1];
    let seq_len = x.shape()[2];
    let head_dim = x.shape()[3];
    let x_cpu = x.to_device(Device::Cpu);
    let src = x_cpu.as_slice::<bf16>();
    let new_heads = kv_heads * n_rep;
    let mut out = vec![bf16::ZERO; new_heads * seq_len * head_dim];
    let chunk = seq_len * head_dim;
    for kv_h in 0..kv_heads {
        for r in 0..n_rep {
            let dst_h = kv_h * n_rep + r;
            out[dst_h * chunk..(dst_h + 1) * chunk]
                .copy_from_slice(&src[kv_h * chunk..(kv_h + 1) * chunk]);
        }
    }
    Tensor::from_slice(&out, &[1, new_heads, seq_len, head_dim]).to_device(x.device())
}

/// Extract row `b` from a contiguous 2D tensor [B, cols] as a [1, cols] view.
/// Zero-copy: shares storage with the original tensor.
fn row_view(t: &Tensor, row: usize) -> Tensor {
    assert_eq!(t.ndim(), 2);
    assert!(t.is_contiguous());
    let cols = t.shape()[1];
    assert!(row < t.shape()[0]);
    let new_offset = t.offset() + row * cols;
    Tensor::from_storage(
        t.storage().clone(),
        smallvec::SmallVec::from_slice(&[1, cols]),
        xserv_tensor::shape::contiguous_strides(&[1, cols]),
        new_offset,
        t.dtype(),
    )
}

/// Concatenate row tensors [1, cols] into a single [B, cols] tensor via D2D memcpy.
fn concat_rows(rows: &[Tensor]) -> Tensor {
    assert!(!rows.is_empty());
    let batch = rows.len();
    let cols = rows[0].shape()[1];
    let dtype = rows[0].dtype();
    let device = rows[0].device();
    let elem_size = dtype.size_bytes();
    let row_bytes = cols * elem_size;

    // Allocate output [B, cols] and copy each row into it
    let total_bytes = batch * row_bytes;
    let mut out_buf = xserv_cuda::allocator::cached_alloc(total_bytes).expect("alloc concat_rows");

    for (b, row) in rows.iter().enumerate() {
        assert_eq!(row.shape(), &[1, cols]);
        assert!(row.is_contiguous());
        let src_buf = row.storage().gpu_buffer();
        let src_offset = row.offset() * elem_size;
        let dst_offset = b * row_bytes;
        out_buf.copy_from_device_at(src_buf, src_offset, dst_offset, row_bytes).unwrap();
    }

    // Wrap in a Tensor
    let device_id = match device { Device::Cuda(id) => id, _ => panic!("expected CUDA device") };
    unsafe {
        crate::kv_cache::tensor_from_gpu_buffer_pub(out_buf, &[batch, cols], dtype, device_id)
    }
}

fn add_any(a: &Tensor, b: &Tensor) -> Tensor {
    xserv_kernels::add(a, b)
}

fn mul_any(a: &Tensor, b: &Tensor) -> Tensor {
    xserv_kernels::mul(a, b)
}

pub fn sample_greedy(logits: &Tensor) -> u32 {
    assert_eq!(logits.ndim(), 2);
    let logits_cpu = logits.to_device(Device::Cpu);
    let vocab_size = logits.shape()[1];
    let seq_len = logits.shape()[0];
    let data = logits_cpu.as_slice::<bf16>();
    let last = &data[(seq_len - 1) * vocab_size..seq_len * vocab_size];
    last.iter().enumerate()
        .max_by(|a, b| a.1.to_f32().partial_cmp(&b.1.to_f32()).unwrap())
        .map(|(i, _)| i as u32).unwrap()
}