diff --git a/src/models/models.h b/src/models/models.h index 6d11a6ceef..e23918f55f 100644 --- a/src/models/models.h +++ b/src/models/models.h @@ -541,8 +541,7 @@ private: const llama_model & model; }; -// TODO: derive llm_build_delta_net_base instead -struct llm_build_qwen35 : public llm_graph_context { +struct llm_build_qwen35 : public llm_build_delta_net_base { llm_build_qwen35(const llama_model & model, const llm_graph_params & params); private: ggml_tensor * build_layer_attn( @@ -555,39 +554,12 @@ private: ggml_tensor * build_layer_attn_linear( llm_graph_input_rs * inp, ggml_tensor * cur, - ggml_tensor * causal_mask, - ggml_tensor * identity, - ggml_tensor * diag_mask, int il); - ggml_tensor * build_layer_ffn( ggml_tensor * cur, int il); - // returns pair of output and new state - std::pair build_delta_net_chunking( - ggml_tensor * q, - ggml_tensor * k, - ggml_tensor * v, - ggml_tensor * g, - ggml_tensor * beta, - ggml_tensor * state, - ggml_tensor * causal_mask, - ggml_tensor * identity, - ggml_tensor * diag_mask, - int il); - - // returns pair of output and new state - std::pair build_delta_net_autoregressive( - ggml_tensor * q, - ggml_tensor * k, - ggml_tensor * v, - ggml_tensor * g, - ggml_tensor * beta, - ggml_tensor * state, - int il); - ggml_tensor * build_norm_gated( ggml_tensor * input, ggml_tensor * weights, @@ -603,7 +575,7 @@ private: }; // TODO: derive llm_build_delta_net_base instead -struct llm_build_qwen35moe : public llm_graph_context { +struct llm_build_qwen35moe : public llm_build_delta_net_base { llm_build_qwen35moe(const llama_model & model, const llm_graph_params & params); private: ggml_tensor * build_layer_attn( @@ -616,38 +588,12 @@ private: ggml_tensor * build_layer_attn_linear( llm_graph_input_rs * inp, ggml_tensor * cur, - ggml_tensor * causal_mask, - ggml_tensor * identity, - ggml_tensor * diag_mask, int il); ggml_tensor * build_layer_ffn( ggml_tensor * cur, int il); - // returns pair of output and new state - std::pair build_delta_net_chunking( - ggml_tensor * q, - ggml_tensor * k, - ggml_tensor * v, - ggml_tensor * g, - ggml_tensor * beta, - ggml_tensor * state, - ggml_tensor * causal_mask, - ggml_tensor * identity, - ggml_tensor * diag_mask, - int il); - - // returns pair of output and new state - std::pair build_delta_net_autoregressive( - ggml_tensor * q, - ggml_tensor * k, - ggml_tensor * v, - ggml_tensor * g, - ggml_tensor * beta, - ggml_tensor * state, - int il); - ggml_tensor * build_norm_gated( ggml_tensor * input, ggml_tensor * weights, diff --git a/src/models/qwen35.cpp b/src/models/qwen35.cpp index 94c68dbb26..7e1749b2c8 100644 --- a/src/models/qwen35.cpp +++ b/src/models/qwen35.cpp @@ -2,10 +2,8 @@ #include "llama-memory-recurrent.h" -#define CHUNK_SIZE 64 - llm_build_qwen35::llm_build_qwen35(const llama_model & model, const llm_graph_params & params) : - llm_graph_context(params), model(model) { + llm_build_delta_net_base(params), model(model) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -25,17 +23,6 @@ llm_build_qwen35::llm_build_qwen35(const llama_model & model, const llm_graph_pa ggml_tensor * inp_pos = build_inp_pos(); ggml_tensor * inp_out_ids = build_inp_out_ids(); - ggml_tensor * causal_mask = - ggml_tri(ctx0, ggml_fill(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f), - GGML_TRI_TYPE_LOWER); - - ggml_tensor * identity = ggml_diag(ctx0, ggml_fill(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f)); - ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity); - - ggml_build_forward_expand(gf, causal_mask); - ggml_build_forward_expand(gf, identity); - ggml_build_forward_expand(gf, diag_mask); - for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -45,7 +32,7 @@ llm_build_qwen35::llm_build_qwen35(const llama_model & model, const llm_graph_pa // Determine layer type and build appropriate attention mechanism if (hparams.is_recurrent(il)) { // Linear attention layer (gated delta net) - cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il); + cur = build_layer_attn_linear(inp->get_recr(), cur, il); } else { // Full attention layer cur = build_layer_attn(inp->get_attn(), cur, inp_pos, sections, il); @@ -95,361 +82,6 @@ llm_build_qwen35::llm_build_qwen35(const llama_model & model, const llm_graph_pa ggml_build_forward_expand(gf, cur); } -// utility to get one slice from the third dimension -// input dim: [x, y, c, b] -// output dim: [x, y, 1, b] -static ggml_tensor * get_slice_2d(ggml_context * ctx0, ggml_tensor * t, int64_t c) { - return ggml_view_4d(ctx0, t, t->ne[0], t->ne[1], 1, t->ne[3], - t->nb[1], t->nb[2], t->nb[3], t->nb[2] * c); -} - -std::pair llm_build_qwen35::build_delta_net_chunking( - ggml_tensor * q, - ggml_tensor * k, - ggml_tensor * v, - ggml_tensor * g, - ggml_tensor * beta, - ggml_tensor * state, - ggml_tensor * causal_mask, - ggml_tensor * identity, - ggml_tensor * diag_mask, - int il) { - const int64_t S_k = q->ne[0]; - const int64_t H_k = q->ne[1]; - const int64_t n_tokens = q->ne[2]; - const int64_t n_seqs = q->ne[3]; - - const int64_t S_v = v->ne[0]; - const int64_t H_v = v->ne[1]; - - GGML_ASSERT(v->ne[2] == n_tokens); - GGML_ASSERT(k->ne[2] == n_tokens); - GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs); - GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs); - GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs); - - GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs); - GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs); - - GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case - - const float eps_norm = hparams.f_norm_rms_eps; - - q = ggml_l2_norm(ctx0, q, eps_norm); - k = ggml_l2_norm(ctx0, k, eps_norm); - - const float scale = 1.0f / sqrtf(S_v); - - q = ggml_scale(ctx0, q, scale); - - beta = ggml_sigmoid(ctx0, beta); - - cb(q, "q_in", il); - cb(k, "k_in", il); - cb(v, "v_in", il); - cb(beta, "beta_in", il); - cb(g, "g_in", il); - - q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs); - k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs); - v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs); - g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs); - - beta = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3)); - state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs); - - cb(q, "q_perm", il); - cb(k, "k_perm", il); - cb(v, "v_perm", il); - cb(beta, "beta_perm", il); - cb(g, "g_perm", il); - cb(state, "state_in", il); - - GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs); - GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs); - GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs); - GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs); - - // Do padding - const int64_t chunk_size = CHUNK_SIZE; - - const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size; - const int64_t n_chunks = (n_tokens + pad) / chunk_size; - - q = ggml_pad(ctx0, q, 0, pad, 0, 0); - k = ggml_pad(ctx0, k, 0, pad, 0, 0); - v = ggml_pad(ctx0, v, 0, pad, 0, 0); - g = ggml_pad(ctx0, g, pad, 0, 0, 0); - beta = ggml_pad(ctx0, beta, 0, pad, 0, 0); - - cb(q, "q_pad", il); - cb(k, "k_pad", il); - cb(v, "v_pad", il); - cb(beta, "beta_pad", il); - cb(g, "g_pad", il); - - ggml_tensor * v_beta = ggml_mul(ctx0, v, beta); - ggml_tensor * k_beta = ggml_mul(ctx0, k, beta); - - cb(v_beta, "v_beta", il); - cb(k_beta, "k_beta", il); - - q = ggml_reshape_4d(ctx0, q, S_k, chunk_size, n_chunks, H_k * n_seqs); - k = ggml_reshape_4d(ctx0, k, S_k, chunk_size, n_chunks, H_k * n_seqs); - k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs); - v = ggml_reshape_4d(ctx0, v, S_v, chunk_size, n_chunks, H_v * n_seqs); - v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs); - - g = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs); - beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs); - - ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g); - cb(g_cumsum, "g_cumsum", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs) - - ggml_tensor * gcs_i = g_cumsum; // ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs); - ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs); - - ggml_tensor * gcs_j_broadcast = - ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs); - - ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i); - cb(decay_mask, "decay_mask", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs) - - decay_mask = ggml_mul(ctx0, decay_mask, diag_mask); - decay_mask = ggml_exp(ctx0, decay_mask); - decay_mask = ggml_mul(ctx0, decay_mask, diag_mask); - - ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta); - - ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask); - ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask)); - cb(attn, "attn_pre_solve", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs) - - ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask); - ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower); - - ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false); - attn = ggml_mul(ctx0, lin_solve, causal_mask); - attn = ggml_add(ctx0, attn, identity); - cb(attn, "attn_solved", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs) - - v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn); - - ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum)); - ggml_tensor * gexp = ggml_exp(ctx0, g_cumsum_t); - - ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp); - cb(kbeta_gexp, "kbeta_gexp", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs) - - ggml_tensor * k_cumdecay = - ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp))))); - cb(k_cumdecay, "k_cumdecay", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs) - - ggml_tensor * attn_kq = ggml_mul_mat(ctx0, k, q); - attn_kq = ggml_mul(ctx0, attn_kq, decay_mask); - attn_kq = ggml_mul(ctx0, attn_kq, diag_mask); - cb(attn_kq, "attn_kq", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs) - - - // vectorized calculation of key_gdiff - // improved from the chunked version: - // g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1) - // g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp() - // key_gdiff = key * g_diff.unsqueeze(-1) - // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new - // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew - - // get last element in g_cumsum along chunk_size dimension (ne0) - // example: [[x, y, z, ..., last], ...] -> [[last], ...] - ggml_tensor * g_last = ggml_view_4d(ctx0, g_cumsum, 1, 1, g_cumsum->ne[2], g_cumsum->ne[3], - g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3], - (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum)); - g_last = ggml_cont(ctx0, g_last); - cb(g_last, "g_last", il); // shape: (1, 1, n_chunks, H_v * n_seqs) - - ggml_tensor * g_last_exp = ggml_exp(ctx0, g_last); - cb(g_last_exp, "g_last_exp", il); // shape: (1, 1, n_chunks, H_v * n_seqs) - - ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last)); - cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs) - - ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff); - ggml_tensor * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp, - 1, chunk_size, n_chunks, g_diff_exp->ne[3]); - - ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp_t); - cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs) - - ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff)); - cb(key_gdiff_t, "key_gdiff_t", il); // shape: (chunk_size, S_k, n_chunks, H_v * n_seqs) - - // state to be updated per chunk - ggml_tensor * new_state = state; // ggml_dup(ctx0, state); - cb(new_state, "new_state", il); // shape: (S_v, S_v, H_v, n_seqs) - - // shape after loop of chunks: (S_v, chunk_size, n_chunks, H_v * n_seqs) - ggml_tensor * core_attn_out = nullptr; - - for (int64_t chunk = 0; chunk < n_chunks; chunk++) { - // shape: (S_k, chunk_size, 1, H_k * n_seqs) - ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk); // (no cont), next op: ggml_mul - - // shape: (S_v, chunk_size, 1, H_v * n_seqs) - ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk); // (no cont), next op: ggml_repeat - - // shape: (chunk_size, 1, n_chunks, H_v * n_seqs) - ggml_tensor * gexp_chunk = get_slice_2d(ctx0, gexp, chunk); // (no cont), next op: ggml_mul - - // shape: (chunk_size, 1, H_v * n_seqs) - ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk); // (no cont), next op: ggml_mul_mat - - // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0) - // replaced by precomputed attn_kq - ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk); - cb(attn_chunk, "attn_chunk", il); - - ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs); - - // v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state - ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk); - cb(v_prime, "v_prime_chunk", il); // shape: (S_v, 1, H_v * n_seqs) - - // v_new = v_i - v_prime - ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime); - ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new)); - cb(v_new, "v_new_chunk", il); - - // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state - ggml_tensor * q_g_exp = ggml_mul(ctx0, q_chunk, gexp_chunk); - ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp); - cb(attn_inter, "attn_inter_chunk", il); - - // core_attn_out[:, :, i] = attn_inter + attn @ v_new - ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk); - cb(v_attn, "v_attn_chunk", il); - - ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn); - cb(core_attn_out_chunk, "core_attn_out_chunk", il); // shape: (S_v, chunk_size, 1, H_v * n_seqs) - - core_attn_out = core_attn_out == nullptr - ? core_attn_out_chunk - : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2); - - // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new - ggml_tensor * k_gdiff_t = get_slice_2d(ctx0, key_gdiff_t, chunk); - //ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, k_gdiff, v_new); // this is slower on metal, why? - ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, k_gdiff_t); - - // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew - ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk)); - new_state = ggml_add(ctx0, - ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gexp_last_chunk, gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs)), - ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs)); - } - - // truncate padded tokens - ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out, - S_v, n_tokens, H_v, n_seqs, - ggml_row_size(core_attn_out->type, S_v), - ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks), - ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0); - output_tokens = ggml_cont(ctx0, output_tokens); - cb(output_tokens, "output_tokens", il); - - // permute back to (S_v, H_v, n_tokens, n_seqs) - output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3); - output_tokens = ggml_cont(ctx0, output_tokens); - - return {output_tokens, new_state}; -} - -std::pair llm_build_qwen35::build_delta_net_autoregressive( - ggml_tensor * q, - ggml_tensor * k, - ggml_tensor * v, - ggml_tensor * g, - ggml_tensor * beta, - ggml_tensor * state, - int il) { - const int64_t S_k = q->ne[0]; - const int64_t H_k = q->ne[1]; - const int64_t n_tokens = q->ne[2]; - const int64_t n_seqs = q->ne[3]; - - const int64_t S_v = v->ne[0]; - const int64_t H_v = v->ne[1]; - - GGML_ASSERT(n_tokens == 1); // This function is optimized for single token processing - GGML_ASSERT(v->ne[2] == n_tokens); - GGML_ASSERT(k->ne[2] == n_tokens); - GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs); - GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs); - GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs); - - GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs); - GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs); - - GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case - - const float eps_norm = hparams.f_norm_rms_eps; - - q = ggml_l2_norm(ctx0, q, eps_norm); - k = ggml_l2_norm(ctx0, k, eps_norm); - - const float scale = 1.0f / sqrtf(S_v); - - q = ggml_scale(ctx0, q, scale); - beta = ggml_sigmoid(ctx0, beta); - - cb(q, "q_in", il); - cb(k, "k_in", il); - cb(v, "v_in", il); - cb(beta, "beta_in", il); - cb(g, "g_in", il); - - state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs); - - ggml_tensor * g_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs); - ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs); - - // Apply exponential to g_t - g_t = ggml_exp(ctx0, g_t); - - // Apply the gated delta rule for the single timestep - // last_recurrent_state = last_recurrent_state * g_t - state = ggml_mul(ctx0, state, g_t); - - // kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2) - ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs); - ggml_tensor * kv_mem = ggml_mul(ctx0, state, k_t_unsqueezed); - // we need to sum over dim=-2, so we transpose, sum, then transpose again - kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem)))); - - // v_t = v.unsqueeze(2) (we insert the singleton dimension after n_seqs and H_v) - ggml_tensor * v_t = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs); - // delta = (v_t - kv_mem) * beta_t - ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem); // both should be [S_v, 1, H_v, n_seqs] - ggml_tensor * delta = ggml_mul(ctx0, v_diff, beta_t); - - // last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta - ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta); - state = ggml_add(ctx0, state, k_t_delta); - - // Compute the attention output - // core_attn_out = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2) - ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs); // unsqueeze q_t - ggml_tensor * state_q = ggml_mul(ctx0, state, q_t_unsqueezed); - // again, since it's over dim = -2, transpose, sum, transpose back - ggml_tensor * core_attn_out = - ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, state_q)))); - - // core_attn_out should be [S_v, 1, H_v, n_seqs] after this - cb(core_attn_out, "output_tokens", il); - cb(state, "new_state", il); - - return {core_attn_out, state}; -} - std::pair llm_build_qwen35::build_qkvz( ggml_tensor * input, int il) { @@ -561,9 +193,6 @@ ggml_tensor * llm_build_qwen35::build_layer_attn( ggml_tensor * llm_build_qwen35::build_layer_attn_linear( llm_graph_input_rs * inp, ggml_tensor * cur, - ggml_tensor * causal_mask, - ggml_tensor * identity, - ggml_tensor * diag_mask, int il) { const auto * mctx_cur = inp->mctx; @@ -589,6 +218,9 @@ ggml_tensor * llm_build_qwen35::build_layer_attn_linear( ggml_tensor * beta = build_lora_mm(model.layers[il].ssm_beta, cur); beta = ggml_reshape_4d(ctx0, beta, num_v_heads, 1, n_seq_tokens, n_seqs); cb(beta, "beta", il); + + beta = ggml_sigmoid(ctx0, beta); + ggml_tensor * alpha = build_lora_mm(model.layers[il].ssm_alpha, cur); alpha = ggml_cont_3d(ctx0, alpha, num_v_heads, n_seq_tokens, n_seqs); cb(alpha, "alpha", il); @@ -596,6 +228,7 @@ ggml_tensor * llm_build_qwen35::build_layer_attn_linear( ggml_tensor * alpha_biased = ggml_add(ctx0, alpha, model.layers[il].ssm_dt); ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased); cb(alpha_softplus, "a_softplus", il); + ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a); // -A_log.exp() * softplus cb(gate, "gate", il); @@ -603,8 +236,6 @@ ggml_tensor * llm_build_qwen35::build_layer_attn_linear( ggml_tensor * conv_states_all = mctx_cur->get_r_l(il); ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il); - // bool use_precomputed_states = n_seq_tokens == 1 && mctx_cur->has_previous_state(); - // Build the convolution states tensor ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs); cb(conv_states, "conv_states", il); @@ -613,11 +244,12 @@ ggml_tensor * llm_build_qwen35::build_layer_attn_linear( ggml_tensor * conv_kernel = model.layers[il].ssm_conv1d; const int64_t conv_kernel_size = conv_kernel->ne[0]; const int64_t conv_channels = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state; - conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs); + + conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs); cb(conv_states, "conv_states_reshaped", il); - qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3); - cb(qkv_mixed, "qkv_mixed_permuted", il); + qkv_mixed = ggml_transpose(ctx0, qkv_mixed); + cb(qkv_mixed, "qkv_mixed_transposed", il); ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0); cb(conv_input, "conv_input", il); @@ -637,7 +269,10 @@ ggml_tensor * llm_build_qwen35::build_layer_attn_linear( ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target)); cb(conv_states_all, "conv_states_updated", il); - // Apply SSM convolution + ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs); + state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim, num_v_heads, n_seqs); + cb(state, "state_predelta", il); + ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel); cb(conv_output_proper, "conv_output_raw", il); @@ -651,31 +286,41 @@ ggml_tensor * llm_build_qwen35::build_layer_attn_linear( int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim); // Extract the convolved Q, K, V from conv_output - ggml_tensor * q_conv = - ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, 0); + ggml_tensor * q_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_k_dim, num_k_heads, n_seq_tokens, n_seqs, + ggml_row_size(conv_qkv_mix->type, head_k_dim), + nb1_qkv, + nb1_qkv * n_seq_tokens, + 0); + + ggml_tensor * k_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_k_dim, num_k_heads, n_seq_tokens, n_seqs, + ggml_row_size(conv_qkv_mix->type, head_k_dim), + nb1_qkv, + nb1_qkv * n_seq_tokens, + head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix)); + + ggml_tensor * v_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_v_dim, num_v_heads, n_seq_tokens, n_seqs, + ggml_row_size(conv_qkv_mix->type, head_v_dim), + nb1_qkv, + nb1_qkv * n_seq_tokens, + ggml_row_size(conv_qkv_mix->type, 2 * head_k_dim * num_k_heads)); + cb(q_conv, "q_conv", il); - ggml_tensor * k_conv = - ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, - head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix)); cb(k_conv, "k_conv", il); - ggml_tensor * v_conv = - ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, nb1_qkv, - 2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix)); cb(v_conv, "v_conv", il); - // Unsqueeze them - q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs); - k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs); - v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs); + const float eps_norm = hparams.f_norm_rms_eps; - ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs); - state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim * num_v_heads, 1, n_seqs); - cb(state, "state_predelta", il); + q_conv = ggml_l2_norm(ctx0, q_conv, eps_norm); + k_conv = ggml_l2_norm(ctx0, k_conv, eps_norm); - // if head keys and value keys are different, repeat Q/K to match V's head count - // V heads are in tiled order (from conversion), so simple tiled repeat works + //q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs); + //k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs); + //v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs); + + // if head keys and value keys are different, repeat to force tensors into matching shapes if (num_k_heads != num_v_heads) { GGML_ASSERT(num_v_heads % num_k_heads == 0); + // TODO: try to avoid these explicit repeats by utilizing op broadcast q_conv = ggml_repeat_4d(ctx0, q_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs); k_conv = ggml_repeat_4d(ctx0, k_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs); } @@ -689,7 +334,7 @@ ggml_tensor * llm_build_qwen35::build_layer_attn_linear( if (n_seq_tokens == 1) { attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il); } else { - attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il); + attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, il); } ggml_tensor * output = attn_out.first; ggml_tensor * new_state = attn_out.second; @@ -698,19 +343,15 @@ ggml_tensor * llm_build_qwen35::build_layer_attn_linear( // Update the recurrent states ggml_build_forward_expand(gf, - ggml_cpy(ctx0, new_state, - ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs, - kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all)))); - - // Reshape both attn_out_final and z to 2D tensors for normalization - // attn_out_final: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim] - ggml_tensor * attn_out_2d_final = ggml_reshape_2d(ctx0, output, head_v_dim, num_v_heads * n_seq_tokens * n_seqs); + ggml_cpy(ctx0, new_state, + ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs, + kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all)))); // z: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim] - ggml_tensor * z_2d = ggml_reshape_2d(ctx0, z, head_v_dim, num_v_heads * n_seq_tokens * n_seqs); + ggml_tensor * z_2d = ggml_reshape_4d(ctx0, z, head_v_dim, num_v_heads, n_seq_tokens, n_seqs); // Apply gated normalization: self.norm(core_attn_out, z) - ggml_tensor * attn_out_norm = build_norm_gated(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il); + ggml_tensor * attn_out_norm = build_norm_gated(output, model.layers[il].ssm_norm, z_2d, il); // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_tokens, n_seqs, n_heads * head_dim] ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs); @@ -721,7 +362,8 @@ ggml_tensor * llm_build_qwen35::build_layer_attn_linear( cb(cur, "linear_attn_out", il); // Reshape back to original dimensions - cur = ggml_cont_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs); + cur = ggml_reshape_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs); + return cur; } diff --git a/src/models/qwen35moe.cpp b/src/models/qwen35moe.cpp index 93da7ea628..e12a5dea73 100644 --- a/src/models/qwen35moe.cpp +++ b/src/models/qwen35moe.cpp @@ -2,10 +2,8 @@ #include "llama-memory-recurrent.h" -#define CHUNK_SIZE 64 - llm_build_qwen35moe::llm_build_qwen35moe(const llama_model & model, const llm_graph_params & params) : - llm_graph_context(params), model(model) { + llm_build_delta_net_base(params), model(model) { const int64_t n_embd_head = hparams.n_embd_head_v; GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); @@ -25,17 +23,6 @@ llm_build_qwen35moe::llm_build_qwen35moe(const llama_model & model, const llm_gr ggml_tensor * inp_pos = build_inp_pos(); ggml_tensor * inp_out_ids = build_inp_out_ids(); - ggml_tensor * causal_mask = - ggml_tri(ctx0, ggml_fill(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f), - GGML_TRI_TYPE_LOWER); - - ggml_tensor * identity = ggml_diag(ctx0, ggml_fill(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f)); - ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity); - - ggml_build_forward_expand(gf, causal_mask); - ggml_build_forward_expand(gf, identity); - ggml_build_forward_expand(gf, diag_mask); - for (int il = 0; il < n_layer; ++il) { ggml_tensor * inpSA = inpL; @@ -45,7 +32,7 @@ llm_build_qwen35moe::llm_build_qwen35moe(const llama_model & model, const llm_gr // Determine layer type and build appropriate attention mechanism if (hparams.is_recurrent(il)) { // Linear attention layer (gated delta net) - cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il); + cur = build_layer_attn_linear(inp->get_recr(), cur, il); } else { // Full attention layer cur = build_layer_attn(inp->get_attn(), cur, inp_pos, sections, il); @@ -95,362 +82,6 @@ llm_build_qwen35moe::llm_build_qwen35moe(const llama_model & model, const llm_gr ggml_build_forward_expand(gf, cur); } -// utility to get one slice from the third dimension -// input dim: [x, y, c, b] -// output dim: [x, y, 1, b] -static ggml_tensor * get_slice_2d(ggml_context * ctx0, ggml_tensor * t, int64_t c) { - return ggml_view_4d(ctx0, t, t->ne[0], t->ne[1], 1, t->ne[3], - t->nb[1], t->nb[2], t->nb[3], t->nb[2] * c); -} - -std::pair llm_build_qwen35moe::build_delta_net_chunking( - ggml_tensor * q, - ggml_tensor * k, - ggml_tensor * v, - ggml_tensor * g, - ggml_tensor * beta, - ggml_tensor * state, - ggml_tensor * causal_mask, - ggml_tensor * identity, - ggml_tensor * diag_mask, - int il) { - const int64_t S_k = q->ne[0]; - const int64_t H_k = q->ne[1]; - const int64_t n_tokens = q->ne[2]; - const int64_t n_seqs = q->ne[3]; - - const int64_t S_v = v->ne[0]; - const int64_t H_v = v->ne[1]; - - GGML_ASSERT(v->ne[2] == n_tokens); - GGML_ASSERT(k->ne[2] == n_tokens); - GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs); - GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs); - GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs); - - GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs); - GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs); - - GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case - - const float eps_norm = hparams.f_norm_rms_eps; - - q = ggml_l2_norm(ctx0, q, eps_norm); - k = ggml_l2_norm(ctx0, k, eps_norm); - - const float scale = 1.0f / sqrtf(S_v); - - q = ggml_scale(ctx0, q, scale); - - beta = ggml_sigmoid(ctx0, beta); - - cb(q, "q_in", il); - cb(k, "k_in", il); - cb(v, "v_in", il); - cb(beta, "beta_in", il); - cb(g, "g_in", il); - - q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs); - k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs); - v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs); - g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs); - - beta = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3)); - state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs); - - cb(q, "q_perm", il); - cb(k, "k_perm", il); - cb(v, "v_perm", il); - cb(beta, "beta_perm", il); - cb(g, "g_perm", il); - cb(state, "state_in", il); - - GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs); - GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs); - GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs); - GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs); - - // Do padding - const int64_t chunk_size = CHUNK_SIZE; - - const int64_t pad = (chunk_size - n_tokens % chunk_size) % chunk_size; - const int64_t n_chunks = (n_tokens + pad) / chunk_size; - - q = ggml_pad(ctx0, q, 0, pad, 0, 0); - k = ggml_pad(ctx0, k, 0, pad, 0, 0); - v = ggml_pad(ctx0, v, 0, pad, 0, 0); - g = ggml_pad(ctx0, g, pad, 0, 0, 0); - beta = ggml_pad(ctx0, beta, 0, pad, 0, 0); - - cb(q, "q_pad", il); - cb(k, "k_pad", il); - cb(v, "v_pad", il); - cb(beta, "beta_pad", il); - cb(g, "g_pad", il); - - ggml_tensor * v_beta = ggml_mul(ctx0, v, beta); - ggml_tensor * k_beta = ggml_mul(ctx0, k, beta); - - cb(v_beta, "v_beta", il); - cb(k_beta, "k_beta", il); - - q = ggml_reshape_4d(ctx0, q, S_k, chunk_size, n_chunks, H_k * n_seqs); - k = ggml_reshape_4d(ctx0, k, S_k, chunk_size, n_chunks, H_k * n_seqs); - k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs); - v = ggml_reshape_4d(ctx0, v, S_v, chunk_size, n_chunks, H_v * n_seqs); - v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs); - - g = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs); - beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs); - - ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g); - cb(g_cumsum, "g_cumsum", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs) - - ggml_tensor * gcs_i = g_cumsum; // ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs); - ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs); - - ggml_tensor * gcs_j_broadcast = - ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs); - - ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i); - cb(decay_mask, "decay_mask", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs) - - decay_mask = ggml_mul(ctx0, decay_mask, diag_mask); - decay_mask = ggml_exp(ctx0, decay_mask); - decay_mask = ggml_mul(ctx0, decay_mask, diag_mask); - - ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta); - - ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask); - ggml_tensor * attn = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask)); - cb(attn, "attn_pre_solve", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs) - - ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask); - ggml_tensor * lhs = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower); - - ggml_tensor * lin_solve = ggml_solve_tri(ctx0, lhs, attn, true, true, false); - attn = ggml_mul(ctx0, lin_solve, causal_mask); - attn = ggml_add(ctx0, attn, identity); - cb(attn, "attn_solved", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs) - - v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn); - - ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum)); - ggml_tensor * gexp = ggml_exp(ctx0, g_cumsum_t); - - ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp); - cb(kbeta_gexp, "kbeta_gexp", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs) - - ggml_tensor * k_cumdecay = - ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp))))); - cb(k_cumdecay, "k_cumdecay", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs) - - ggml_tensor * attn_kq = ggml_mul_mat(ctx0, k, q); - attn_kq = ggml_mul(ctx0, attn_kq, decay_mask); - attn_kq = ggml_mul(ctx0, attn_kq, diag_mask); - cb(attn_kq, "attn_kq", il); // shape: (chunk_size, chunk_size, n_chunks, H_v * n_seqs) - - - // vectorized calculation of key_gdiff - // improved from the chunked version: - // g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1) - // g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp() - // key_gdiff = key * g_diff.unsqueeze(-1) - // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new - // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew - - // get last element in g_cumsum along chunk_size dimension (ne0) - // example: [[x, y, z, ..., last], ...] -> [[last], ...] - ggml_tensor * g_last = ggml_view_4d(ctx0, g_cumsum, 1, 1, g_cumsum->ne[2], g_cumsum->ne[3], - g_cumsum->nb[1], g_cumsum->nb[2], g_cumsum->nb[3], - (g_cumsum->ne[0] - 1) * ggml_element_size(g_cumsum)); - g_last = ggml_cont(ctx0, g_last); - cb(g_last, "g_last", il); // shape: (1, 1, n_chunks, H_v * n_seqs) - - ggml_tensor * g_last_exp = ggml_exp(ctx0, g_last); - cb(g_last_exp, "g_last_exp", il); // shape: (1, 1, n_chunks, H_v * n_seqs) - - ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum, g_last)); - cb(g_diff, "g_diff", il); // shape: (chunk_size, 1, n_chunks, H_v * n_seqs) - - ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff); - ggml_tensor * g_diff_exp_t = ggml_reshape_4d(ctx0, g_diff_exp, - 1, chunk_size, n_chunks, g_diff_exp->ne[3]); - - ggml_tensor * key_gdiff = ggml_mul(ctx0, k, g_diff_exp_t); - cb(key_gdiff, "key_gdiff", il); // shape: (S_k, chunk_size, n_chunks, H_v * n_seqs) - - ggml_tensor * key_gdiff_t = ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff)); - cb(key_gdiff_t, "key_gdiff_t", il); // shape: (chunk_size, S_k, n_chunks, H_v * n_seqs) - - - // state to be updated per chunk - ggml_tensor * new_state = state; // ggml_dup(ctx0, state); - cb(new_state, "new_state", il); // shape: (S_v, S_v, H_v, n_seqs) - - // shape after loop of chunks: (S_v, chunk_size, n_chunks, H_v * n_seqs) - ggml_tensor * core_attn_out = nullptr; - - for (int64_t chunk = 0; chunk < n_chunks; chunk++) { - // shape: (S_k, chunk_size, 1, H_k * n_seqs) - ggml_tensor * q_chunk = get_slice_2d(ctx0, q, chunk); // (no cont), next op: ggml_mul - - // shape: (S_v, chunk_size, 1, H_v * n_seqs) - ggml_tensor * v_chunk = get_slice_2d(ctx0, v, chunk); // (no cont), next op: ggml_repeat - - // shape: (chunk_size, 1, n_chunks, H_v * n_seqs) - ggml_tensor * gexp_chunk = get_slice_2d(ctx0, gexp, chunk); // (no cont), next op: ggml_mul - - // shape: (chunk_size, 1, H_v * n_seqs) - ggml_tensor * k_cumdecay_chunk = get_slice_2d(ctx0, k_cumdecay, chunk); // (no cont), next op: ggml_mul_mat - - // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0) - // replaced by precomputed attn_kq - ggml_tensor * attn_chunk = get_slice_2d(ctx0, attn_kq, chunk); - cb(attn_chunk, "attn_chunk", il); - - ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs); - - // v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state - ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay_chunk); - cb(v_prime, "v_prime_chunk", il); // shape: (S_v, 1, H_v * n_seqs) - - // v_new = v_i - v_prime - ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v_chunk, v_prime), v_prime); - ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new)); - cb(v_new, "v_new_chunk", il); - - // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state - ggml_tensor * q_g_exp = ggml_mul(ctx0, q_chunk, gexp_chunk); - ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp); - cb(attn_inter, "attn_inter_chunk", il); - - // core_attn_out[:, :, i] = attn_inter + attn @ v_new - ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn_chunk); - cb(v_attn, "v_attn_chunk", il); - - ggml_tensor * core_attn_out_chunk = ggml_add(ctx0, attn_inter, v_attn); - cb(core_attn_out_chunk, "core_attn_out_chunk", il); // shape: (S_v, chunk_size, 1, H_v * n_seqs) - - core_attn_out = core_attn_out == nullptr - ? core_attn_out_chunk - : ggml_concat(ctx0, core_attn_out, core_attn_out_chunk, 2); - - // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new - ggml_tensor * k_gdiff_t = get_slice_2d(ctx0, key_gdiff_t, chunk); - //ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, k_gdiff, v_new); // this is slower on metal, why? - ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, k_gdiff_t); - - // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew - ggml_tensor * gexp_last_chunk = ggml_cont(ctx0, get_slice_2d(ctx0, g_last_exp, chunk)); - new_state = ggml_add(ctx0, - ggml_mul(ctx0, new_state, ggml_reshape_4d(ctx0, gexp_last_chunk, gexp_last_chunk->ne[0], gexp_last_chunk->ne[1], H_v, n_seqs)), - ggml_reshape_4d(ctx0, kgdmulvnew, kgdmulvnew->ne[0], kgdmulvnew->ne[1], H_v, n_seqs)); - } - - // truncate padded tokens - ggml_tensor * output_tokens = ggml_view_4d(ctx0, core_attn_out, - S_v, n_tokens, H_v, n_seqs, - ggml_row_size(core_attn_out->type, S_v), - ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks), - ggml_row_size(core_attn_out->type, S_v * chunk_size * n_chunks * H_v), 0); - output_tokens = ggml_cont(ctx0, output_tokens); - cb(output_tokens, "output_tokens", il); - - // permute back to (S_v, H_v, n_tokens, n_seqs) - output_tokens = ggml_permute(ctx0, output_tokens, 0, 2, 1, 3); - output_tokens = ggml_cont(ctx0, output_tokens); - - return {output_tokens, new_state}; -} - -std::pair llm_build_qwen35moe::build_delta_net_autoregressive( - ggml_tensor * q, - ggml_tensor * k, - ggml_tensor * v, - ggml_tensor * g, - ggml_tensor * beta, - ggml_tensor * state, - int il) { - const int64_t S_k = q->ne[0]; - const int64_t H_k = q->ne[1]; - const int64_t n_tokens = q->ne[2]; - const int64_t n_seqs = q->ne[3]; - - const int64_t S_v = v->ne[0]; - const int64_t H_v = v->ne[1]; - - GGML_ASSERT(n_tokens == 1); // This function is optimized for single token processing - GGML_ASSERT(v->ne[2] == n_tokens); - GGML_ASSERT(k->ne[2] == n_tokens); - GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs); - GGML_ASSERT(beta->ne[0] == H_v && beta->ne[2] == n_tokens && beta->ne[3] == n_seqs); - GGML_ASSERT(state->ne[0] == S_v && state->ne[1] == S_v * H_v && state->ne[2] == 1 && state->ne[3] == n_seqs); - - GGML_ASSERT(q->ne[0] == S_k && q->ne[1] == H_k && q->ne[2] == n_tokens && q->ne[3] == n_seqs); - GGML_ASSERT(k->ne[0] == S_k && k->ne[1] == H_k && k->ne[2] == n_tokens && k->ne[3] == n_seqs); - - GGML_ASSERT(H_k == H_v); // we did a repeat to make sure this is the case - - const float eps_norm = hparams.f_norm_rms_eps; - - q = ggml_l2_norm(ctx0, q, eps_norm); - k = ggml_l2_norm(ctx0, k, eps_norm); - - const float scale = 1.0f / sqrtf(S_v); - - q = ggml_scale(ctx0, q, scale); - beta = ggml_sigmoid(ctx0, beta); - - cb(q, "q_in", il); - cb(k, "k_in", il); - cb(v, "v_in", il); - cb(beta, "beta_in", il); - cb(g, "g_in", il); - - state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs); - - ggml_tensor * g_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs); - ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs); - - // Apply exponential to g_t - g_t = ggml_exp(ctx0, g_t); - - // Apply the gated delta rule for the single timestep - // last_recurrent_state = last_recurrent_state * g_t - state = ggml_mul(ctx0, state, g_t); - - // kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2) - ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs); - ggml_tensor * kv_mem = ggml_mul(ctx0, state, k_t_unsqueezed); - // we need to sum over dim=-2, so we transpose, sum, then transpose again - kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem)))); - - // v_t = v.unsqueeze(2) (we insert the singleton dimension after n_seqs and H_v) - ggml_tensor * v_t = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs); - // delta = (v_t - kv_mem) * beta_t - ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem); // both should be [S_v, 1, H_v, n_seqs] - ggml_tensor * delta = ggml_mul(ctx0, v_diff, beta_t); - - // last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta - ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta); - state = ggml_add(ctx0, state, k_t_delta); - - // Compute the attention output - // core_attn_out = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2) - ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs); // unsqueeze q_t - ggml_tensor * state_q = ggml_mul(ctx0, state, q_t_unsqueezed); - // again, since it's over dim = -2, transpose, sum, transpose back - ggml_tensor * core_attn_out = - ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, state_q)))); - - // core_attn_out should be [S_v, 1, H_v, n_seqs] after this - cb(core_attn_out, "output_tokens", il); - cb(state, "new_state", il); - - return {core_attn_out, state}; -} - std::pair llm_build_qwen35moe::build_qkvz( ggml_tensor * input, int il) { @@ -562,9 +193,6 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn( ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear( llm_graph_input_rs * inp, ggml_tensor * cur, - ggml_tensor * causal_mask, - ggml_tensor * identity, - ggml_tensor * diag_mask, int il) { const auto * mctx_cur = inp->mctx; @@ -590,6 +218,9 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear( ggml_tensor * beta = build_lora_mm(model.layers[il].ssm_beta, cur); beta = ggml_reshape_4d(ctx0, beta, num_v_heads, 1, n_seq_tokens, n_seqs); cb(beta, "beta", il); + + beta = ggml_sigmoid(ctx0, beta); + ggml_tensor * alpha = build_lora_mm(model.layers[il].ssm_alpha, cur); alpha = ggml_cont_3d(ctx0, alpha, num_v_heads, n_seq_tokens, n_seqs); cb(alpha, "alpha", il); @@ -597,6 +228,7 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear( ggml_tensor * alpha_biased = ggml_add(ctx0, alpha, model.layers[il].ssm_dt); ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased); cb(alpha_softplus, "a_softplus", il); + ggml_tensor * gate = ggml_mul(ctx0, alpha_softplus, model.layers[il].ssm_a); // -A_log.exp() * softplus cb(gate, "gate", il); @@ -604,8 +236,6 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear( ggml_tensor * conv_states_all = mctx_cur->get_r_l(il); ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il); - // bool use_precomputed_states = n_seq_tokens == 1 && mctx_cur->has_previous_state(); - // Build the convolution states tensor ggml_tensor * conv_states = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs); cb(conv_states, "conv_states", il); @@ -614,11 +244,12 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear( ggml_tensor * conv_kernel = model.layers[il].ssm_conv1d; const int64_t conv_kernel_size = conv_kernel->ne[0]; const int64_t conv_channels = d_inner + 2 * hparams.ssm_n_group * hparams.ssm_d_state; - conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs); + + conv_states = ggml_reshape_3d(ctx0, conv_states, conv_kernel_size - 1, conv_channels, n_seqs); cb(conv_states, "conv_states_reshaped", il); - qkv_mixed = ggml_permute(ctx0, qkv_mixed, 1, 0, 2, 3); - cb(qkv_mixed, "qkv_mixed_permuted", il); + qkv_mixed = ggml_transpose(ctx0, qkv_mixed); + cb(qkv_mixed, "qkv_mixed_transposed", il); ggml_tensor * conv_input = ggml_concat(ctx0, conv_states, qkv_mixed, 0); cb(conv_input, "conv_input", il); @@ -638,7 +269,10 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear( ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv_states, state_update_target)); cb(conv_states_all, "conv_states_updated", il); - // Apply SSM convolution + ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs); + state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim, num_v_heads, n_seqs); + cb(state, "state_predelta", il); + ggml_tensor * conv_output_proper = ggml_ssm_conv(ctx0, conv_input, conv_kernel); cb(conv_output_proper, "conv_output_raw", il); @@ -652,31 +286,41 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear( int64_t nb1_qkv = ggml_row_size(conv_qkv_mix->type, qkv_dim); // Extract the convolved Q, K, V from conv_output - ggml_tensor * q_conv = - ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, 0); + ggml_tensor * q_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_k_dim, num_k_heads, n_seq_tokens, n_seqs, + ggml_row_size(conv_qkv_mix->type, head_k_dim), + nb1_qkv, + nb1_qkv * n_seq_tokens, + 0); + + ggml_tensor * k_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_k_dim, num_k_heads, n_seq_tokens, n_seqs, + ggml_row_size(conv_qkv_mix->type, head_k_dim), + nb1_qkv, + nb1_qkv * n_seq_tokens, + head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix)); + + ggml_tensor * v_conv = ggml_view_4d(ctx0, conv_qkv_mix, head_v_dim, num_v_heads, n_seq_tokens, n_seqs, + ggml_row_size(conv_qkv_mix->type, head_v_dim), + nb1_qkv, + nb1_qkv * n_seq_tokens, + ggml_row_size(conv_qkv_mix->type, 2 * head_k_dim * num_k_heads)); + cb(q_conv, "q_conv", il); - ggml_tensor * k_conv = - ggml_view_2d(ctx0, conv_qkv_mix, head_k_dim * num_k_heads, n_seq_tokens * n_seqs, nb1_qkv, - head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix)); cb(k_conv, "k_conv", il); - ggml_tensor * v_conv = - ggml_view_2d(ctx0, conv_qkv_mix, head_v_dim * num_v_heads, n_seq_tokens * n_seqs, nb1_qkv, - 2 * head_k_dim * num_k_heads * ggml_element_size(conv_qkv_mix)); cb(v_conv, "v_conv", il); - // Unsqueeze them - q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs); - k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs); - v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs); + const float eps_norm = hparams.f_norm_rms_eps; - ggml_tensor * state = build_rs(inp, ssm_states_all, hparams.n_embd_s(), n_seqs); - state = ggml_reshape_4d(ctx0, state, head_v_dim, head_v_dim * num_v_heads, 1, n_seqs); - cb(state, "state_predelta", il); + q_conv = ggml_l2_norm(ctx0, q_conv, eps_norm); + k_conv = ggml_l2_norm(ctx0, k_conv, eps_norm); - // if head keys and value keys are different, repeat Q/K to match V's head count - // V heads are in tiled order (from conversion), so simple tiled repeat works + //q_conv = ggml_cont_4d(ctx0, q_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs); + //k_conv = ggml_cont_4d(ctx0, k_conv, head_k_dim, num_k_heads, n_seq_tokens, n_seqs); + //v_conv = ggml_cont_4d(ctx0, v_conv, head_v_dim, num_v_heads, n_seq_tokens, n_seqs); + + // if head keys and value keys are different, repeat to force tensors into matching shapes if (num_k_heads != num_v_heads) { GGML_ASSERT(num_v_heads % num_k_heads == 0); + // TODO: try to avoid these explicit repeats by utilizing op broadcast q_conv = ggml_repeat_4d(ctx0, q_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs); k_conv = ggml_repeat_4d(ctx0, k_conv, head_k_dim, num_v_heads, n_seq_tokens, n_seqs); } @@ -690,7 +334,7 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear( if (n_seq_tokens == 1) { attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il); } else { - attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il); + attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, il); } ggml_tensor * output = attn_out.first; ggml_tensor * new_state = attn_out.second; @@ -699,19 +343,15 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear( // Update the recurrent states ggml_build_forward_expand(gf, - ggml_cpy(ctx0, new_state, - ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs, - kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all)))); - - // Reshape both attn_out_final and z to 2D tensors for normalization - // attn_out_final: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim] - ggml_tensor * attn_out_2d_final = ggml_reshape_2d(ctx0, output, head_v_dim, num_v_heads * n_seq_tokens * n_seqs); + ggml_cpy(ctx0, new_state, + ggml_view_1d(ctx0, ssm_states_all, hparams.n_embd_s() * n_seqs, + kv_head * hparams.n_embd_s() * ggml_element_size(ssm_states_all)))); // z: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * n_tokens * n_seqs, head_dim] - ggml_tensor * z_2d = ggml_reshape_2d(ctx0, z, head_v_dim, num_v_heads * n_seq_tokens * n_seqs); + ggml_tensor * z_2d = ggml_reshape_4d(ctx0, z, head_v_dim, num_v_heads, n_seq_tokens, n_seqs); // Apply gated normalization: self.norm(core_attn_out, z) - ggml_tensor * attn_out_norm = build_norm_gated(attn_out_2d_final, model.layers[il].ssm_norm, z_2d, il); + ggml_tensor * attn_out_norm = build_norm_gated(output, model.layers[il].ssm_norm, z_2d, il); // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_tokens, n_seqs, n_heads * head_dim] ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs); @@ -722,7 +362,8 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_attn_linear( cb(cur, "linear_attn_out", il); // Reshape back to original dimensions - cur = ggml_cont_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs); + cur = ggml_reshape_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs); + return cur; }