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fix: use backend-agnostic APIs for KV cache on GPU backends #5

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fix: use backend-agnostic APIs for KV cache on GPU backends #5

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48 changes: 18 additions & 30 deletions src/voxtral.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1076,14 +1076,8 @@ static void clear_kv_cache(voxtral_context * ctx) {
if (!ctx || !ctx->kv_self_k || !ctx->kv_self_v) {
return;
}
void * k_data = ggml_get_data(ctx->kv_self_k);
void * v_data = ggml_get_data(ctx->kv_self_v);
if (k_data) {
memset(k_data, 0, ggml_nbytes(ctx->kv_self_k));
}
if (v_data) {
memset(v_data, 0, ggml_nbytes(ctx->kv_self_v));
}
ggml_backend_tensor_memset(ctx->kv_self_k, 0, 0, ggml_nbytes(ctx->kv_self_k));
ggml_backend_tensor_memset(ctx->kv_self_v, 0, 0, ggml_nbytes(ctx->kv_self_v));
ctx->kv_used = 0;
}

Expand All @@ -1097,24 +1091,23 @@ static void kv_cache_shift_left(voxtral_context * ctx, int32_t shift) {
return;
}

uint8_t * k_data = (uint8_t *) ggml_get_data(ctx->kv_self_k);
uint8_t * v_data = (uint8_t *) ggml_get_data(ctx->kv_self_v);
if (!k_data || !v_data) {
return;
}

const size_t row_bytes = ctx->kv_self_k->nb[1];
const size_t row_bytes = ctx->kv_self_k->nb[1];
const size_t layer_stride = ctx->kv_self_k->nb[2];
const size_t keep_bytes = (size_t)(window - shift) * row_bytes;
const size_t shift_offset = (size_t)shift * row_bytes;

std::vector<uint8_t> tmp(keep_bytes);

for (int32_t l = 0; l < VOXTRAL_DEC_LAYERS; ++l) {
uint8_t * k_base = k_data + (size_t) l * layer_stride;
uint8_t * v_base = v_data + (size_t) l * layer_stride;
const size_t base = (size_t)l * layer_stride;

memmove(k_base, k_base + (size_t) shift * row_bytes, (size_t) (window - shift) * row_bytes);
memmove(v_base, v_base + (size_t) shift * row_bytes, (size_t) (window - shift) * row_bytes);
ggml_backend_tensor_get(ctx->kv_self_k, tmp.data(), base + shift_offset, keep_bytes);
ggml_backend_tensor_set(ctx->kv_self_k, tmp.data(), base, keep_bytes);
ggml_backend_tensor_memset(ctx->kv_self_k, 0, base + keep_bytes, shift_offset);

memset(k_base + (size_t) (window - shift) * row_bytes, 0, (size_t) shift * row_bytes);
memset(v_base + (size_t) (window - shift) * row_bytes, 0, (size_t) shift * row_bytes);
ggml_backend_tensor_get(ctx->kv_self_v, tmp.data(), base + shift_offset, keep_bytes);
ggml_backend_tensor_set(ctx->kv_self_v, tmp.data(), base, keep_bytes);
ggml_backend_tensor_memset(ctx->kv_self_v, 0, base + keep_bytes, shift_offset);
}
}

Expand Down Expand Up @@ -1592,26 +1585,21 @@ static ggml_tensor * build_decoder_layer(
ctx->kv_self_v->nb[1],
layer_idx * ctx->kv_self_v->nb[2]); // [kv_dim, n_kv]

// Flash attention with GQA
// Q: [n_heads*head_dim, n_tokens] -> [head_dim, n_heads, n_tokens] -> [head_dim, n_tokens, n_heads]
// Reshape for flash attention: [head_dim, n_tokens/n_kv, n_heads/n_kv_heads]
ggml_tensor * q3 = ggml_reshape_3d(gctx, q, VOXTRAL_DEC_HEAD_DIM, VOXTRAL_DEC_HEADS, n_tokens);
q3 = ggml_permute(gctx, q3, 0, 2, 1, 3); // [head_dim, n_tokens, n_heads]

// K: [kv_dim, n_kv] -> [head_dim, n_kv_heads, n_kv] -> [head_dim, n_kv, n_kv_heads]
ggml_tensor * k3 = ggml_reshape_3d(gctx, k_full, VOXTRAL_DEC_HEAD_DIM, VOXTRAL_DEC_KV_HEADS, n_kv);
k3 = ggml_permute(gctx, k3, 0, 2, 1, 3); // [head_dim, n_kv, n_kv_heads]

// V: [kv_dim, n_kv] -> [head_dim, n_kv_heads, n_kv] -> [head_dim, n_kv, n_kv_heads]
ggml_tensor * v3 = ggml_reshape_3d(gctx, v_full, VOXTRAL_DEC_HEAD_DIM, VOXTRAL_DEC_KV_HEADS, n_kv);
v3 = ggml_permute(gctx, v3, 0, 2, 1, 3); // [head_dim, n_kv, n_kv_heads]

const float scale = 1.0f / sqrtf((float)VOXTRAL_DEC_HEAD_DIM);

// ggml_flash_attn_ext fuses Q@K^T, scale, mask, softmax, @V in one op
// GQA broadcast is built-in (n_heads % n_kv_heads == 0)
// Mask is cast to F16 inside the graph if provided
ggml_tensor * attn_mask_f16 = attn_mask ? ggml_cast(gctx, attn_mask, GGML_TYPE_F16) : nullptr;
ggml_tensor * attn_out = ggml_flash_attn_ext(gctx, q3, k3, v3, attn_mask_f16, scale, 0.0f, 0.0f);
ggml_tensor * mask_f16 = attn_mask ? ggml_cast(gctx, attn_mask, GGML_TYPE_F16) : nullptr;

ggml_tensor * attn_out = ggml_flash_attn_ext(gctx, q3, k3, v3, mask_f16, scale, 0.0f, 0.0f);
// Output: [head_dim, n_heads, n_tokens] (already permuted by flash_attn_ext)
attn_out = ggml_cont(gctx, attn_out);
attn_out = ggml_reshape_2d(gctx, attn_out, VOXTRAL_DEC_HEADS * VOXTRAL_DEC_HEAD_DIM, n_tokens);
Expand Down