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// src/granite_conformer/shaw_attn.cpp - shared Shaw block-local attention.
//
// See shaw_attn.h for the contract.
#include "shaw_attn.h"
#include "ggml.h"
#include "transcribe-log.h"
#include <cmath>
#include <cstdio>
namespace transcribe::granite_conformer {
namespace {
ggml_tensor * layer_norm(ggml_context * ctx, ggml_tensor * x, ggml_tensor * gamma, ggml_tensor * beta, float eps) {
ggml_tensor * y = ggml_norm(ctx, x, eps);
y = ggml_mul(ctx, y, gamma);
if (beta != nullptr) {
y = ggml_add(ctx, y, beta);
}
return y;
}
} // namespace
ggml_tensor * shaw_block_attn(ggml_context * ctx,
ggml_tensor * x,
ggml_tensor * zero_pad,
ggml_tensor * dists,
ggml_tensor * pad_mask_3d,
const ShawAttnWeights & w,
int n_heads,
int head_dim,
int context_size,
int num_blocks,
int T_enc,
float layer_norm_eps) {
const int64_t inner_dim = static_cast<int64_t>(n_heads) * head_dim;
const int64_t T_pad = static_cast<int64_t>(context_size) * num_blocks;
const float scale = 1.0f / std::sqrt(static_cast<float>(head_dim));
// Offline batch: x ne=[d_model, T_enc, B]. Block-local attention treats
// each time-block independently, so B utterances fold into the block
// axis: num_blocks_eff = num_blocks * B (memory order [.., T_pad, B]
// splits cleanly into (ctx, num_blocks*B)). The caller tiles the per-block
// pad mask across B and sizes zero_pad with the batch.
const int64_t B = x->ne[2];
const int64_t num_blocks_eff = static_cast<int64_t>(num_blocks) * B;
// Pre-LayerNorm on the T_enc input.
ggml_tensor * h = layer_norm(ctx, x, w.norm_attn_w, w.norm_attn_b, layer_norm_eps);
// Zero-pad along the time axis when T_enc is not block-aligned. The
// pad buffer is a caller-owned graph input; nullptr is a programmer
// error.
if (T_pad > T_enc) {
if (zero_pad == nullptr) {
log_msg(TRANSCRIBE_LOG_LEVEL_ERROR,
"granite_conformer::shaw_block_attn: "
"zero_pad is null but T_pad > T_enc");
return nullptr;
}
h = ggml_concat(ctx, h, zero_pad, /*dim=*/1);
}
// h ne = [d_model, T_pad]
// QKV projections. q has no bias; kv is fused (2*inner_dim) with no
// bias; out has bias.
ggml_tensor * q = ggml_mul_mat(ctx, w.attn_q_w, h); // [inner_dim, T_pad]
ggml_tensor * kv = ggml_mul_mat(ctx, w.attn_kv_w, h); // [2*inner_dim, T_pad]
// Split kv into k, v along ne[0]. The mul_mat output is contiguous, so
// views with a strided ne[0] are safe. ne[2] carries the utterance batch
// (1 for single-shot, == B otherwise).
ggml_tensor * k = ggml_view_3d(ctx, kv, inner_dim, kv->ne[1], kv->ne[2], kv->nb[1], kv->nb[2], /*offset=*/0);
ggml_tensor * v =
ggml_view_3d(ctx, kv, inner_dim, kv->ne[1], kv->ne[2], kv->nb[1], kv->nb[2], inner_dim * ggml_element_size(kv));
k = ggml_cont(ctx, k);
v = ggml_cont(ctx, v);
// Reshape into block-local form. Target ne layout
// [head_dim, context_size, n_heads, num_blocks_eff] so that
// ggml_mul_mat(k, q) does per-(head, block, utterance) batched attention.
auto reshape_qkv = [&](ggml_tensor * t) -> ggml_tensor * {
ggml_tensor * r = ggml_reshape_4d(ctx, t, head_dim, n_heads, context_size, num_blocks_eff);
r = ggml_cont(ctx, ggml_permute(ctx, r, 0, 2, 1, 3));
return r;
};
q = reshape_qkv(q);
k = reshape_qkv(k);
v = reshape_qkv(v);
// Shaw positional bias. rel_pos_emb [head_dim, 2*max_pos_emb+1], dists
// [context_size, context_size] int32. Materialise the per-(c, r) lookup
// as [head_dim, context_size*context_size], then reshape to
// [head_dim, r=context_size, c=context_size].
ggml_tensor * dists_flat = ggml_reshape_1d(ctx, dists, static_cast<int64_t>(context_size) * context_size);
ggml_tensor * rel_lookup = ggml_get_rows(ctx, w.attn_rel_pos_emb, dists_flat);
rel_lookup = ggml_reshape_3d(ctx, rel_lookup, head_dim, context_size, context_size);
// pos_attn[h, b, c, r] = sum_d q[h, b, c, d] * rel_lookup[c, r, d].
// Permute q to put c on ne[2] for batched mul_mat against
// rel_lookup; rel_lookup's ne[3]=1 broadcasts across num_blocks.
ggml_tensor * q_perm = ggml_cont(ctx, ggml_permute(ctx, q, 0, 2, 1, 3));
ggml_tensor * pos_attn = ggml_mul_mat(ctx, rel_lookup, q_perm);
// QK^T: ggml_mul_mat(K, Q), both [head_dim, context_size, n_heads,
// num_blocks] -> [k=context_size, q=context_size, n_heads, num_blocks].
ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
// Align pos_attn's axis order to kq's before the add.
pos_attn = ggml_cont(ctx, ggml_permute(ctx, pos_attn, 0, 2, 1, 3));
// scores = (kq + pos_attn) * scale + pad_mask
ggml_tensor * scores = ggml_add(ctx, kq, pos_attn);
scores = ggml_scale(ctx, scores, scale);
// Broadcast the per-block additive mask across n_heads by giving it
// ne[2]=1 in a 4D view. pad_mask_3d is [ctx, ctx, num_blocks_eff] — the
// caller tiles the per-block pattern across the batch.
ggml_tensor * pad_mask_4d = ggml_reshape_4d(ctx, pad_mask_3d, context_size, context_size, 1, num_blocks_eff);
scores = ggml_add(ctx, scores, pad_mask_4d);
// Softmax over the k axis (ne[0]).
ggml_tensor * attn = ggml_soft_max(ctx, scores);
// out = V^T @ attn → [head_dim, q, n_heads, num_blocks].
ggml_tensor * v_t = ggml_cont(ctx, ggml_permute(ctx, v, 1, 0, 2, 3));
ggml_tensor * out = ggml_mul_mat(ctx, v_t, attn);
// Reshape back to [inner_dim, T_pad, B].
out = ggml_cont(ctx, ggml_permute(ctx, out, 0, 2, 1, 3));
out = ggml_reshape_3d(ctx, out, inner_dim, T_pad, B);
// Slice off pad rows BEFORE out_proj. This mirrors the reference's
// `out = self.to_out(out[:, :num_features, :])` order; it keeps
// pad-row garbage from leaking through the post-attention depthwise
// conv.
if (T_pad > T_enc) {
out = ggml_view_3d(ctx, out, inner_dim, T_enc, B, out->nb[1], out->nb[2], 0);
out = ggml_cont(ctx, out);
}
// Final out_proj (with bias). Operates at T_enc.
out = ggml_mul_mat(ctx, w.attn_out_w, out);
if (w.attn_out_b != nullptr) {
out = ggml_add(ctx, out, w.attn_out_b);
}
// out ne = [d_model, T_enc]
return out;
}
} // namespace transcribe::granite_conformer