diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index 1d2a354024675..b8a21a2ccc3f0 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -1777,10 +1777,8 @@ extern "C" { GGML_API struct ggml_tensor * ggml_ssm_conv( struct ggml_context * ctx, - struct ggml_tensor * s, - struct ggml_tensor * x, - struct ggml_tensor * c, - struct ggml_tensor * sq); + struct ggml_tensor * sx, + struct ggml_tensor * c); GGML_API struct ggml_tensor * ggml_ssm_scan( struct ggml_context * ctx, @@ -1789,8 +1787,7 @@ extern "C" { struct ggml_tensor * dt, struct ggml_tensor * A, struct ggml_tensor * B, - struct ggml_tensor * C, - struct ggml_tensor * sq); + struct ggml_tensor * C); // partition into non-overlapping windows with padding if needed // example: diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 88e4fb7325dd9..d63c917a5705a 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -7229,43 +7229,34 @@ struct ggml_tensor * ggml_flash_attn_back( struct ggml_tensor * ggml_ssm_conv( struct ggml_context * ctx, - struct ggml_tensor * s, - struct ggml_tensor * x, - struct ggml_tensor * c, - struct ggml_tensor * sq) { - GGML_ASSERT(ggml_is_3d(s)); - GGML_ASSERT(ggml_is_matrix(x)); + struct ggml_tensor * sx, + struct ggml_tensor * c) { + GGML_ASSERT(ggml_is_3d(sx)); GGML_ASSERT(ggml_is_matrix(c)); - GGML_ASSERT(ggml_is_matrix(sq)); - GGML_ASSERT(sq->type == GGML_TYPE_I32); - const int64_t d_conv = c->ne[0]; - const int64_t d_inner = c->ne[1]; - const int64_t n_tokens = x->ne[1]; - const int64_t n_kv = s->ne[2]; + const int64_t d_conv = c->ne[0]; + const int64_t d_inner = c->ne[1]; + const int64_t n_t = sx->ne[0] - d_conv + 1; // tokens per sequence + const int64_t n_s = sx->ne[2]; - GGML_ASSERT( s->ne[0] == d_conv - 1); - GGML_ASSERT( s->ne[1] == d_inner); - GGML_ASSERT( x->ne[0] == d_inner); - GGML_ASSERT(sq->ne[0] == n_kv); - GGML_ASSERT(sq->ne[1] == n_tokens); + // TODO: maybe support other strides than 1? + GGML_ASSERT(sx->ne[0] == d_conv - 1 + n_t); + GGML_ASSERT(sx->ne[1] == d_inner); + GGML_ASSERT(n_t >= 0); bool is_node = false; - if (s->grad || x->grad || c->grad || sq->grad) { + if (sx->grad || c->grad) { GGML_ABORT("fatal error"); // TODO: implement is_node = true; } - // 2-in-1 concatenated x and conv_states, {d_inner, n_tokens} with {d_conv, d_inner, n_kv} - struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, (d_inner*n_tokens) + (d_conv*d_inner*n_kv)); + struct ggml_tensor * result = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_t, n_s); result->op = GGML_OP_SSM_CONV; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; - result->src[0] = s; - result->src[1] = x; - result->src[2] = c; - result->src[3] = sq; + result->src[0] = sx; + result->src[1] = c; return result; } @@ -7279,39 +7270,42 @@ struct ggml_tensor * ggml_ssm_scan( struct ggml_tensor * dt, struct ggml_tensor * A, struct ggml_tensor * B, - struct ggml_tensor * C, - struct ggml_tensor * sq) { + struct ggml_tensor * C) { GGML_ASSERT(ggml_is_contiguous(s)); GGML_ASSERT(ggml_is_contiguous(x)); GGML_ASSERT(ggml_is_contiguous(dt)); GGML_ASSERT(ggml_is_contiguous(A)); - GGML_ASSERT(sq->type == GGML_TYPE_I32); + GGML_ASSERT(ggml_is_matrix(A)); + GGML_ASSERT(ggml_is_3d(B)); + GGML_ASSERT(ggml_is_3d(s)); GGML_ASSERT(B->nb[0] == ggml_type_size(B->type)); GGML_ASSERT(C->nb[0] == ggml_type_size(C->type)); GGML_ASSERT(ggml_are_same_shape(x, dt)); + GGML_ASSERT(ggml_are_same_shape(B, C)); { - const int64_t d_state = s->ne[0]; - const int64_t d_inner = s->ne[1]; - const int64_t n_tokens = x->ne[1]; + const int64_t d_state = s->ne[0]; + const int64_t d_inner = s->ne[1]; + const int64_t n_seq_tokens = x->ne[1]; + const int64_t n_seqs = x->ne[2]; + GGML_ASSERT(s->ne[2] == n_seqs); GGML_ASSERT(x->ne[0] == d_inner); GGML_ASSERT(A->ne[0] == d_state); GGML_ASSERT(A->ne[1] == d_inner); GGML_ASSERT(B->ne[0] == d_state); - GGML_ASSERT(B->ne[1] == n_tokens); - GGML_ASSERT(C->ne[0] == d_state); - GGML_ASSERT(C->ne[1] == n_tokens); + GGML_ASSERT(B->ne[1] == n_seq_tokens); + GGML_ASSERT(B->ne[2] == n_seqs); } bool is_node = false; - if (s->grad || x->grad || dt->grad || A->grad || B->grad || C->grad || sq->grad) { + if (s->grad || x->grad || dt->grad || A->grad || B->grad || C->grad) { GGML_ABORT("fatal error"); // TODO: implement is_node = true; } - // 2-in-1 concatenated y and ssm_states, {d_inner, n_tokens} with {d_state, d_inner, n_kv} + // concatenated y + ssm_states struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, ggml_nelements(x) + ggml_nelements(s)); result->op = GGML_OP_SSM_SCAN; @@ -7322,7 +7316,6 @@ struct ggml_tensor * ggml_ssm_scan( result->src[3] = A; result->src[4] = B; result->src[5] = C; - result->src[6] = sq; return result; } @@ -10995,11 +10988,6 @@ static void ggml_compute_forward_concat_f32( GGML_TENSOR_BINARY_OP_LOCALS - // TODO: support for transposed / permuted tensors - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb00 == sizeof(float)); - GGML_ASSERT(nb10 == sizeof(float)); - const int32_t dim = ggml_get_op_params_i32(dst, 0); GGML_ASSERT(dim >= 0 && dim < 4); @@ -15782,27 +15770,22 @@ static void ggml_compute_forward_flash_attn_back( static void ggml_compute_forward_ssm_conv_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { - const struct ggml_tensor * src0 = dst->src[0]; // conv_state - const struct ggml_tensor * src1 = dst->src[1]; // x - const struct ggml_tensor * src2 = dst->src[2]; // conv1d.weight - const struct ggml_tensor * src3 = dst->src[3]; // state_seq + const struct ggml_tensor * src0 = dst->src[0]; // conv_x + const struct ggml_tensor * src1 = dst->src[1]; // conv1d.weight const int ith = params->ith; const int nth = params->nth; - const int nc = src2->ne[0]; // d_conv - const int nr = src0->ne[1]; // d_inner - const int n_t = src1->ne[1]; // n_tokens - const int n_kv = src0->ne[2]; // max number of sequences in the batch + const int nc = src1->ne[0]; // d_conv + const int ncs = src0->ne[0]; // d_conv - 1 + n_t + const int nr = src0->ne[1]; // d_inner + const int n_t = dst->ne[1]; // tokens per sequence + const int n_s = dst->ne[2]; // number of sequences in the batch - GGML_ASSERT((nr*n_t) + (nc*nr*n_kv) == ggml_nelements(dst)); + GGML_ASSERT( dst->ne[0] == nr); GGML_ASSERT(src0->nb[0] == sizeof(float)); GGML_ASSERT(src1->nb[0] == sizeof(float)); - GGML_ASSERT(src2->nb[0] == sizeof(float)); - GGML_ASSERT(src3->nb[0] == sizeof(int32_t)); GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); - // for use with the destination state offset between sequences - GGML_ASSERT(src2->nb[2] == src2->ne[1]*src2->ne[0]*sizeof(float)); // rows per thread const int dr = (nr + nth - 1)/nth; @@ -15812,74 +15795,27 @@ static void ggml_compute_forward_ssm_conv_f32( const int ir1 = MIN(ir0 + dr, nr); const int ir = ir1 - ir0; - if (n_kv > 1) { - // multiple sequences means it's hard to know when it's the first time a state is read, - // so copy them all over to the destination, just to be sure. - for (int i3 = 0; i3 < n_kv; ++i3) { - float * s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); - float * s = (float *) ((char *) dst->data + ir0*(src2->nb[1]) + i3*(src2->nb[2]) + nr*n_t*sizeof(float)); - // can't use memcpy because of d_conv vs d_conv - 1 - for (int i1 = 0; i1 < ir; ++i1) { - for (int i0 = 0; i0 < nc - 1; ++i0) { - // copy s0 to last (d_conv - 1) columns of s - s[1 + i0 + i1*nc] = s0[i0 + i1*(nc - 1)]; - } - } - } - } - - for (int i2 = 0; i2 < n_t; ++i2) { - int32_t * sq = (int32_t *) ((char *) src3->data + i2*(src3->nb[1])); // {n_kv, n_tokens} - float * x = (float *) ((char *) dst->data + ir0*sizeof(float) + i2*(nr*sizeof(float))); // {d_inner, n_tokens} - float * s = (float *) ((char *) dst->data + ir0*(src2->nb[1]) + sq[0]*(src2->nb[2]) + nr*n_t*sizeof(float)); // {d_conv, d_inner, n_kv} - float * s0; // {d_conv - 1, d_inner, n_kv} - float * x0 = (float *) ((char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1])); // {d_inner, n_tokens} - float * c = (float *) ((char *) src2->data + ir0*(src2->nb[1])); // {d_conv, d_inner} - int ne0s0; + for (int i3 = 0; i3 < n_s; ++i3) { + for (int i2 = 0; i2 < n_t; ++i2) { + // {d_conv - 1 + n_t, d_inner, n_seqs} + // sliding window + const float * s = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i2*(src0->nb[0]) + i3*(src0->nb[2])); // {d_conv, d_inner, n_s} + const float * c = (const float *) ((const char *) src1->data + ir0*(src1->nb[1])); // {d_conv, d_inner} + float * x = (float *) ((char *) dst->data + ir0*(dst->nb[0]) + i2*(dst->nb[1]) + i3*(dst->nb[2])); // {d_inner, n_t, n_s} - GGML_ASSERT(0 <= sq[0] && sq[0] < n_kv); - - // avoid needing to copy the state for the first token - if (i2 == 0) { - s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2])); // {d_conv - 1, d_inner, n_kv} - ne0s0 = src0->ne[0]; - } else { - // the source is the last (d_conv - 1) columns of the destination - s0 = s + 1; - ne0s0 = nc; - } - - // d_inner - for (int i1 = 0; i1 < ir; ++i1) { - // shift state left - for (int i0 = 0; i0 < nc - 1; ++i0) { - s[i0 + i1*nc] = s0[i0 + i1*ne0s0]; - } - // insert x on the last column - s[(nc - 1) + i1*nc] = x0[i1]; - } - - // handle copies when there are multiple output states - for (int i3 = 1; i3 < n_kv; ++i3) { - int32_t seq = sq[i3]; - if (0 <= seq && seq < n_kv) { - float * s1 = s + (seq - sq[0])*nc*nr; - memcpy(s1, s, nc*ir*sizeof(float)); - } else { - // stop at negative or too big seq_ids - break; - } - } + // TODO: transpose the output for smaller strides for big batches? + // d_inner + for (int i1 = 0; i1 < ir; ++i1) { + // rowwise dot product + // NOTE: not using ggml_vec_dot_f32, because its sum is in double precision + float sumf = 0.0f; - // it seems a little faster when this is separate from the state shift - for (int i1 = 0; i1 < ir; ++i1) { - // rowwise dot product - float sumf = 0.0f; - for (int i0 = 0; i0 < nc; ++i0) { - int i = i0 + i1*nc; - sumf += s[i] * c[i]; + // d_conv + for (int i0 = 0; i0 < nc; ++i0) { + sumf += s[i0 + i1*ncs] * c[i0 + i1*nc]; + } + x[i1] = sumf; } - x[i1] = sumf; } } } @@ -15910,15 +15846,14 @@ static void ggml_compute_forward_ssm_scan_f32( const struct ggml_tensor * src3 = dst->src[3]; // A const struct ggml_tensor * src4 = dst->src[4]; // B const struct ggml_tensor * src5 = dst->src[5]; // C - const struct ggml_tensor * src6 = dst->src[6]; // sq const int ith = params->ith; const int nth = params->nth; - const int64_t nc = src0->ne[0]; // d_state - const int64_t nr = src0->ne[1]; // d_inner - const int64_t n_t = src1->ne[1]; // number of tokens in the batch - const int64_t n_kv = src0->ne[2]; // max number of sequences in the batch + const int64_t nc = src0->ne[0]; // d_state + const int64_t nr = src0->ne[1]; // d_inner + const int64_t n_t = src1->ne[1]; // number of tokens per sequence + const int64_t n_s = src0->ne[2]; // number of sequences in the batch GGML_ASSERT(ggml_nelements(src1) + ggml_nelements(src0) == ggml_nelements(dst)); GGML_ASSERT(src0->nb[0] == sizeof(float)); @@ -15927,12 +15862,12 @@ static void ggml_compute_forward_ssm_scan_f32( GGML_ASSERT(src3->nb[0] == sizeof(float)); GGML_ASSERT(src4->nb[0] == sizeof(float)); GGML_ASSERT(src5->nb[0] == sizeof(float)); - // required for the dot product between s and C, and when copying the states + // required for the dot product between s and C GGML_ASSERT(src0->nb[1] == src0->ne[0]*sizeof(float)); // required for per-sequence offsets for states GGML_ASSERT(src0->nb[2] == src0->ne[0]*src0->ne[1]*sizeof(float)); - // required to get correct offset for state destination (i.e. src1->nb[2]) - GGML_ASSERT(src1->nb[2] == src1->ne[0]*src1->ne[1]*sizeof(float)); + // required to get correct offset for state destination (i.e. src1->nb[3]) + GGML_ASSERT(src1->nb[3] == src1->ne[0]*src1->ne[1]*src1->ne[2]*sizeof(float)); // rows per thread const int dr = (nr + nth - 1)/nth; @@ -15942,64 +15877,36 @@ static void ggml_compute_forward_ssm_scan_f32( const int ir1 = MIN(ir0 + dr, nr); const int ir = ir1 - ir0; - if (n_kv > 1) { - // it's hard to know if the source states have already been copied - // when there are multiple, so copy them already. - for (int i3 = 0; i3 < n_kv; ++i3) { - float * s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); - float * s = (float *) ((char *) dst->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]) + src1->nb[2]); - memcpy(s, s0, nc*ir*sizeof(float)); - } - } - - for (int i2 = 0; i2 < n_t; ++i2) { - int32_t * sq = (int32_t *) ((char *) src6->data + i2*(src6->nb[1])); // {n_kv, n_tokens} - float * y = (float *) ((char *) dst->data + ir0*(src1->nb[0]) + i2*(src1->nb[1])); // {d_inner, n_tokens} - float * s = (float *) ((char *) dst->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2]) + src1->nb[2]); // {d_state, d_inner, n_kv} - float * s0; - float * x = (float *) ((char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1])); // {d_inner, n_tokens} - float * dt = (float *) ((char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1])); // {d_inner, n_tokens} - float * A = (float *) ((char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner} - float * B = (float *) ((char *) src4->data + i2*(src4->nb[1])); // {d_state, n_tokens} - float * C = (float *) ((char *) src5->data + i2*(src5->nb[1])); // {d_state, n_tokens} - - GGML_ASSERT(0 <= sq[0] && sq[0] < n_kv); - - // avoid needing to copy the state for the first token - if (i2 == 0) { - s0 = (float *) ((char *) src0->data + ir0*(src0->nb[1]) + sq[0]*(src0->nb[2])); // {d_state, d_inner, n_kv} - } else { - // otherwise the source is the same as the destination - s0 = s; - } - - // d_inner - for (int i1 = 0; i1 < ir; ++i1) { - // ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78 - float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1]; - float x_dt = x[i1] * dt_soft_plus; - float sumf = 0.0f; - // d_state - for (int i0 = 0; i0 < nc; ++i0) { - int i = i0 + i1*nc; - // state = prev_state * dA + dB * x - float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt); - // y = rowwise_dotprod(state, C) - sumf += state * C[i0]; - s[i] = state; - } - y[i1] = sumf; - } - - // handle copies when there are multiple output states - for (int i3 = 1; i3 < n_kv; ++i3) { - int32_t seq = sq[i3]; - if (0 <= seq && seq < n_kv) { - float * s1 = s + (seq - sq[0])*nc*nr; - memcpy(s1, s, nc*ir*sizeof(float)); - } else { - // stop at negative or too big seq_ids - break; + for (int i3 = 0; i3 < n_s; ++i3) { + for (int i2 = 0; i2 < n_t; ++i2) { + const float * s0 = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); // {d_state, d_inner, n_s} + const float * x = (const float *) ((const char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s} + const float * dt = (const float *) ((const char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1]) + i3*(src2->nb[2])); // {d_inner, n_t, n_s} + const float * A = (const float *) ((const char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner} + const float * B = (const float *) ((const char *) src4->data + i2*(src4->nb[1]) + i3*(src4->nb[2])); // {d_state, n_t, n_s} + const float * C = (const float *) ((const char *) src5->data + i2*(src5->nb[1]) + i3*(src5->nb[2])); // {d_state, n_t, n_s} + float * y = (float *) ((char *) dst->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s} + float * s = (float *) ((char *) dst->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]) + src1->nb[3]); // {d_state, d_inner, n_s} + + // use the output as the source for the next token-wise iterations + if (i2 > 0) { s0 = s; } + + // d_inner + for (int i1 = 0; i1 < ir; ++i1) { + // ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78 + float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1]; + float x_dt = x[i1] * dt_soft_plus; + float sumf = 0.0f; + // d_state + for (int i0 = 0; i0 < nc; ++i0) { + int i = i0 + i1*nc; + // state = prev_state * dA + dB * x + float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt); + // y = rowwise_dotprod(state, C) + sumf += state * C[i0]; + s[i] = state; + } + y[i1] = sumf; } } } diff --git a/include/llama.h b/include/llama.h index 188ae76f8001e..6cca6320b347d 100644 --- a/include/llama.h +++ b/include/llama.h @@ -511,6 +511,9 @@ extern "C" { // to the decoder to start generating output sequence. For other models, it returns -1. LLAMA_API llama_token llama_model_decoder_start_token(const struct llama_model * model); + // Returns true if the model is recurrent (like Mamba, RWKV, etc.) + LLAMA_API bool llama_model_is_recurrent(const struct llama_model * model); + // Returns 0 on success LLAMA_API uint32_t llama_model_quantize( const char * fname_inp, diff --git a/src/llama.cpp b/src/llama.cpp index fe3c0db6f2931..bd7f1508b2644 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -2516,10 +2516,29 @@ struct llama_layer { struct ggml_tensor * ffn_down_scale; }; +// very similar to llama_batch, +// but has more metadata about sequences +struct llama_ubatch { + bool equal_seqs; + // TODO: whole_seqs for embeddings? + + uint32_t n_tokens; // total tokens (n_seq_tokens * n_seqs) + uint32_t n_seq_tokens; // tokens per sequence + uint32_t n_seqs; + + llama_token * token; // [n_tokens] + float * embd; // [n_embd, n_tokens] + llama_pos * pos; // [n_tokens] + int32_t * n_seq_id; // [n_seqs] + llama_seq_id ** seq_id; // [n_seqs] + int8_t * output; // [n_tokens] +}; + struct llama_kv_cell { llama_pos pos = -1; llama_pos delta = 0; - int32_t src = 0; // used by recurrent state models to copy states + int32_t src = -1; // used by recurrent state models to copy states + int32_t tail = -1; std::set seq_id; @@ -2540,7 +2559,6 @@ struct llama_kv_cell { struct llama_kv_cache { bool has_shift = false; bool do_defrag = false; - bool do_copy = false; bool recurrent = false; // with recurrent state models, a cell can hold the state for more than one past token bool v_trans = true; // the value tensor is transposed @@ -2703,6 +2721,340 @@ struct llama_model { } }; +struct llama_sbatch_seq { + int32_t n_seq_id; + llama_seq_id * seq_id; + size_t offset; + size_t length; + + // helper for smoother batch API transition -- can be deprecated in the future + llama_seq_id all_seq_id; // used if seq_id == NULL +}; + +// sequence-length-aware batch splitting +struct llama_sbatch { + // tokens left in this batch + size_t n_tokens; + + size_t n_embd; + + bool logits_all; // TODO: remove once lctx.logits_all is removed too + + // sorted indices into the batch + std::vector ids; + // batch indices of the output + std::vector out_ids; + std::vector seq; + const llama_batch * batch = nullptr; + + // buffers for the ubatch + std::vector ubatch_token; + std::vector ubatch_embd; + std::vector ubatch_pos; + std::vector ubatch_n_seq_id; + std::vector ubatch_seq_id; + std::vector ubatch_output; + + llama_ubatch reserve_ubatch(size_t n_ubatch, bool has_embd = false) { + // clear empty sequences + // the previous ubatch is assumed to be gone, + // so nothing should refer to values in these sequences anymore. + for (size_t i = seq.size(); i-- > 0;) { + if (seq[i].length == 0) { + seq.pop_back(); + } else { + break; + } + } + ubatch_token.resize(!has_embd ? n_ubatch : 0); + ubatch_embd.resize(has_embd ? n_embd * n_ubatch : 0); + ubatch_pos.resize(n_ubatch); + ubatch_n_seq_id.resize(n_ubatch); + ubatch_seq_id.resize(n_ubatch); + ubatch_output.resize(n_ubatch); + llama_ubatch ubatch = { + /*equal_seqs =*/ true, + /*n_tokens =*/ 0, + /*n_seq_tokens =*/ 0, + /*n_seqs =*/ 0, + /*token =*/ !has_embd ? ubatch_token.data() : nullptr, + /*embd =*/ has_embd ? ubatch_embd.data() : nullptr, + /*pos =*/ ubatch_pos.data(), + /*n_seq_id =*/ ubatch_n_seq_id.data(), + /*seq_id =*/ ubatch_seq_id.data(), + /*output =*/ ubatch_output.data(), + }; + return ubatch; + } + + void add_seq_to_ubatch(llama_ubatch & ubatch, llama_sbatch_seq & seq, size_t length) { + GGML_ASSERT(batch != nullptr); + GGML_ASSERT(length <= seq.length); + // Can only add sequences of equal lengths to a batch, + // otherwise it isn't clear to which sequence a token belongs + GGML_ASSERT(seq.n_seq_id == 0 || ubatch.n_seqs == 0 || length == (size_t) ubatch.n_tokens / ubatch.n_seqs); + GGML_ASSERT((seq.n_seq_id != 0) == ubatch.equal_seqs); + // NOTE: loops are separated for cache-friendliness + if (batch->token) { + if (ubatch.equal_seqs) { + for (size_t i = 0; i < length; ++i) { + ubatch.token[ubatch.n_tokens + i] = batch->token[ids[seq.offset + i]]; + } + } else { + // simple split + ubatch.token = batch->token + seq.offset; + } + } else { + ubatch.token = nullptr; + } + if (batch->embd) { + if (ubatch.equal_seqs) { + for (size_t i = 0; i < length; ++i) { + memcpy( + ubatch.embd + n_embd * (ubatch.n_tokens + i), + batch->embd + n_embd * ids[seq.offset + i], + n_embd * sizeof(float) + ); + } + } else { + // simple split + ubatch.embd = batch->embd + (n_embd * seq.offset); + } + } else { + ubatch.embd = nullptr; + } + // from here on, the else branches are deprecated; + // they are helpers for smoother batch API transition + if (batch->pos) { + if (ubatch.equal_seqs) { + for (size_t i = 0; i < length; ++i) { + ubatch.pos[ubatch.n_tokens + i] = batch->pos[ids[seq.offset + i]]; + } + } else { + // simple split + ubatch.pos = batch->pos + seq.offset; + } + } else { + for (size_t i = 0; i < length; ++i) { + llama_pos bi = ids[seq.offset + i]; + ubatch.pos[ubatch.n_tokens + i] = batch->all_pos_0 + (bi * batch->all_pos_1); + } + } + if (ubatch.equal_seqs) { + ubatch.n_seq_id[ubatch.n_seqs] = seq.n_seq_id; + if (seq.seq_id) { + ubatch.seq_id[ubatch.n_seqs] = seq.seq_id; + } else { + GGML_ASSERT(seq.n_seq_id == 1); + ubatch.seq_id[ubatch.n_seqs] = &seq.all_seq_id; + } + } else { + // simple split + if (batch->n_seq_id) { + for (size_t i = 0; i < length; ++i) { + ubatch.n_seq_id = batch->n_seq_id + seq.offset; + } + } else { + for (size_t i = 0; i < length; ++i) { + ubatch.n_seq_id[ubatch.n_seqs + i] = 1; + } + } + if (batch->seq_id) { + for (size_t i = 0; i < length; ++i) { + ubatch.seq_id = batch->seq_id + seq.offset; + } + } else { + for (size_t i = 0; i < length; ++i) { + ubatch.seq_id[ubatch.n_seqs + i] = &seq.all_seq_id; + } + } + } + if (logits_all) { + for (size_t i = 0; i < length; ++i) { + ubatch.output[ubatch.n_tokens + i] = 1; + out_ids.push_back(ids[seq.offset + i]); + } + } else if (batch->logits) { + if (ubatch.equal_seqs) { + for (size_t i = 0; i < length; ++i) { + size_t id = ids[seq.offset + i]; + int8_t is_output = batch->logits[id]; + ubatch.output[ubatch.n_tokens + i] = is_output; + if (is_output) { out_ids.push_back(id); } + } + } else { + // simple split + ubatch.output = batch->logits + seq.offset; + for (size_t i = 0; i < length; ++i) { + if (ubatch.output[i] != 0) { out_ids.push_back(seq.offset + i); } + } + } + } else { + // only get last output + for (size_t i = 0; i < length; ++i) { + size_t id = ids[seq.offset + i]; + int8_t is_last = id == ids.size() - 1; + ubatch.output[ubatch.n_tokens + i] = is_last; + if (is_last) { out_ids.push_back(id); } + } + } + if (ubatch.n_tokens == 0 && ubatch.n_seqs == 0) { + ubatch.n_seq_tokens = ubatch.equal_seqs ? length : 1; + } + ubatch.n_tokens += length; + ubatch.n_seqs += ubatch.equal_seqs ? 1 : length; // virtual sequences for simple splits + seq.offset += length; + seq.length -= length; + n_tokens -= length; + GGML_ASSERT(ubatch.n_tokens == ubatch.n_seq_tokens * ubatch.n_seqs); + } + + // simple split, unknown number of sequences of unequal lengths + llama_ubatch split_simple(size_t n_ubatch) { + n_ubatch = n_tokens < n_ubatch ? n_tokens : n_ubatch; + llama_ubatch ubatch = reserve_ubatch(n_ubatch, /* has_embd */ batch->embd != nullptr); + ubatch.equal_seqs = false; + if (!seq.empty()) { + llama_sbatch_seq & s = seq[0]; + size_t length = s.length < n_ubatch ? s.length : n_ubatch; + GGML_ASSERT(seq.size() == 1 && s.n_seq_id == 0); // don't mix with other splits + add_seq_to_ubatch(ubatch, s, length); + } + return ubatch; + } + + // make batches of equal-length sequences + llama_ubatch split_equal(size_t n_ubatch) { + n_ubatch = n_tokens < n_ubatch ? n_tokens : n_ubatch; + llama_ubatch ubatch = reserve_ubatch(n_ubatch, /* has_embd */ batch->embd != nullptr); + if (!seq.empty()) { + size_t length = 0; + size_t n_tokens_in_ubatch = 0; + GGML_ASSERT(seq[0].n_seq_id > 0); // should not be mixed with simple splits + // smallest first, because it's easier to split this way; + // starting from the end to pop in constant time. + for (size_t i = seq.size(); i-- > 0;) { + llama_sbatch_seq & s = seq[i]; + GGML_ASSERT(s.length > 0); + if (length == 0) { + length = s.length < n_ubatch ? s.length : n_ubatch; + } + add_seq_to_ubatch(ubatch, s, length); + n_tokens_in_ubatch += length; + // shared prompts can't be mixed with any of their sequences, + // so it's safer to compute them in their own ubatch + if (s.n_seq_id > 1) { break; } + // stop when there isn't enough space for another sequence + if (length + n_tokens_in_ubatch > n_ubatch) { break; } + } + } + return ubatch; + } + + // sequence-wise split + llama_ubatch split_seq(size_t n_ubatch) { + n_ubatch = n_tokens < n_ubatch ? n_tokens : n_ubatch; + llama_ubatch ubatch = reserve_ubatch(n_ubatch, /* has_embd */ batch->embd != nullptr); + if (!seq.empty()) { + llama_sbatch_seq & s = seq[seq.size() - 1]; + size_t length = s.length < n_ubatch ? s.length : n_ubatch; + GGML_ASSERT(s.n_seq_id > 0); // should not be mixed with simple splits + add_seq_to_ubatch(ubatch, s, length); + } + return ubatch; + } + + void from_batch(const llama_batch & batch, const size_t n_embd, const bool simple_split = false, const bool logits_all = false) { + GGML_ASSERT(batch.n_tokens >= 0); + this->batch = &batch; + this->n_embd = n_embd; + this->logits_all = logits_all; + + n_tokens = batch.n_tokens; + ids.resize(n_tokens); + out_ids.clear(); + // TODO: reserve out_ids and seq + + for (size_t i = 0; i < n_tokens; ++i) { + ids[i] = i; + } + if (simple_split) { + seq.resize(1); + llama_sbatch_seq & s = seq[0]; + s.n_seq_id = 0; + s.seq_id = nullptr; + s.offset = 0; + s.length = n_tokens; + s.all_seq_id = batch.all_seq_id; + return; + } + std::sort(ids.begin(), ids.end(), + [&batch](size_t a, size_t b) { + int32_t n_seq_a = batch.n_seq_id ? batch.n_seq_id[a] : 1; + int32_t n_seq_b = batch.n_seq_id ? batch.n_seq_id[b] : 1; + // sort by seq_id, then by pos + if (n_seq_a == n_seq_b) { + if (batch.seq_id) { + for (int32_t i = 0; i < n_seq_a; ++i) { + llama_seq_id seq_id_a = batch.seq_id[a][i]; + llama_seq_id seq_id_b = batch.seq_id[b][i]; + // smaller seq_ids go first + if (seq_id_a != seq_id_b) { + return seq_id_a < seq_id_b; + } + } + } + // when all else is equal, sort by pos + if (batch.pos) { + return batch.pos[a] < batch.pos[b]; + } + // no pos, sort by id (assuming batch.all_pos_1 is positive) + return a < b; + } + // shared prompts go first + return n_seq_a > n_seq_b; + } + ); + // init seq + llama_sbatch_seq * last_seq = nullptr; + + if (batch.n_seq_id != nullptr && batch.seq_id != nullptr) { + for (size_t i = 0; i < n_tokens; ++i) { + const size_t bi = ids[i]; + const int32_t n_seqs = batch.n_seq_id[bi]; + llama_seq_id * seq_ids = batch.seq_id[bi]; + if (last_seq != nullptr) { + bool same = n_seqs == last_seq->n_seq_id; + for (int32_t j = 0; same && j < n_seqs; ++j) { + if (seq_ids[j] != last_seq->seq_id[j]) { + same = false; + } + } + if (same) { + last_seq->length += 1; + continue; + } + } + llama_sbatch_seq new_seq = {n_seqs, seq_ids, i, 1, batch.all_seq_id}; + seq.push_back(new_seq); + last_seq = &seq.back(); + } + } else { + llama_sbatch_seq new_seq = {1, nullptr, 0, n_tokens, batch.all_seq_id}; + seq.push_back(new_seq); + } + // keep shared prompts first at the end, then sort by length descending. + std::sort(seq.begin(), seq.end(), + [](llama_sbatch_seq & a, llama_sbatch_seq & b) { + if (a.n_seq_id == b.n_seq_id) { + return a.length > b.length; + } + return a.n_seq_id < b.n_seq_id; + } + ); + } +}; + struct llama_context { llama_context(const llama_model & model) : model(model) @@ -2724,6 +3076,7 @@ struct llama_context { struct llama_cparams cparams; struct llama_sampling sampling; + struct llama_sbatch sbatch; struct llama_kv_cache kv_self; struct llama_control_vector cvec; @@ -2984,8 +3337,7 @@ static bool llama_kv_cache_init( cache.has_shift = false; - // TODO: find a nicer way to add other recurrent model architectures - cache.recurrent = model.arch == LLM_ARCH_MAMBA; + cache.recurrent = llama_model_is_recurrent(&model); cache.v_trans = !cache.recurrent && !cparams.flash_attn; cache.head = 0; @@ -2998,13 +3350,6 @@ static bool llama_kv_cache_init( cache.cells.clear(); cache.cells.resize(kv_size); - if (cache.recurrent) { - // init state copy sources - for (uint32_t i = 0; i < cache.size; ++i) { - cache.cells[i].src = i; - } - } - // count used buffer types std::map buft_layer_count; if (offload) { @@ -3072,46 +3417,162 @@ static bool llama_kv_cache_init( // to the first cell of the slot. static bool llama_kv_cache_find_slot( struct llama_kv_cache & cache, - const struct llama_batch & batch) { + const struct llama_ubatch & batch) { const uint32_t n_tokens = batch.n_tokens; + const uint32_t n_seqs = batch.n_seqs; + const uint32_t n_seq_tokens = batch.n_seq_tokens; if (cache.recurrent) { // For recurrent state architectures (like Mamba), - // each KV cache cell can store the state for a whole sequence. - - llama_seq_id min = cache.size - 1; - llama_seq_id max = 0; - - for (uint32_t i = 0; i < n_tokens; ++i) { - for (int32_t j = 0; j < batch.n_seq_id[i]; ++j) { - llama_seq_id seq_id = batch.seq_id[i][j]; - // make sure it's a valid seq_id - if ((uint32_t) seq_id < cache.size) { - if (seq_id > max) { - max = seq_id; - } - if (seq_id < min) { - min = seq_id; + // each cache cell can store the state for a whole sequence. + // A slot should be always be contiguous. + + // can only process batches with an equal number of new tokens in each sequence + GGML_ASSERT(batch.equal_seqs); + + int32_t min = cache.size - 1; + int32_t max = 0; + + // everything should fit if all seq_ids are smaller than the max + for (uint32_t s = 0; s < n_seqs; ++s) { + const uint32_t n_seq_id = batch.n_seq_id[s]; + for (uint32_t j = 0; j < n_seq_id; ++j) { + const llama_seq_id seq_id = batch.seq_id[s][j]; + + if (seq_id < 0 || (uint32_t) seq_id >= cache.size) { + // too big seq_id + // TODO: would it be possible to resize the cache instead? + LLAMA_LOG_ERROR("%s: seq_id=%d >= n_seq_max=%d Try using a bigger --parallel value\n", __func__, seq_id, cache.size); + return false; + } + if (j > 0) { + llama_kv_cell & seq = cache.cells[seq_id]; + if (seq.tail >= 0) { + llama_kv_cell & cell = cache.cells[seq.tail]; + // clear cells from seq_ids that become shared + // (should not normally happen, but let's handle it anyway) + cell.seq_id.erase(seq_id); + seq.tail = -1; + if (cell.seq_id.empty()) { + cell.pos = -1; + cell.src = -1; + cache.used -= 1; + } } - // Assuming the tokens are in-order - if (batch.pos[i] != cache.cells[seq_id].pos + 1) { - // What should happen when the pos backtracks or skips a value? - // Clearing the state mid-batch would require special-casing which isn't done. - LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d\n", - __func__, batch.pos[i], cache.cells[seq_id].pos, seq_id); + } + } + } + +#ifndef NDEBUG + { + std::vector tails_verif; + tails_verif.assign(cache.size, -1); + for (uint32_t i = 0; i < cache.size; ++i) { + llama_kv_cell & cell = cache.cells[i]; + for (llama_seq_id seq_id : cell.seq_id) { + if (tails_verif[seq_id] != -1) { + LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tails_verif[seq_id]); } - if (cache.cells[seq_id].pos < 0 && 0 <= batch.pos[i]) { - cache.used += 1; + tails_verif[seq_id] = i; + } + } + for (uint32_t i = 0; i < cache.size; ++i) { + if (tails_verif[i] != cache.cells[i].tail) { + LLAMA_LOG_ERROR("%s: wrong tail for seq_id %d, (%d instead of %d)\n", __func__, i, cache.cells[i].tail, tails_verif[i]); + } + } + } +#endif + + // find next empty cell + uint32_t next_empty_cell = cache.head; + + for (uint32_t i = 0; i < cache.size; ++i) { + if (next_empty_cell >= cache.size) { next_empty_cell -= cache.size; } + llama_kv_cell & cell = cache.cells[next_empty_cell]; + if (cell.is_empty()) { break; } + next_empty_cell += 1; + } + + // find usable cell range + for (uint32_t s = 0; s < n_seqs; ++s) { + const llama_seq_id seq_id = batch.seq_id[s][0]; + llama_kv_cell & seq_meta = cache.cells[seq_id]; + bool has_cell = false; + if (seq_meta.tail >= 0) { + llama_kv_cell & cell = cache.cells[seq_meta.tail]; + GGML_ASSERT(cell.has_seq_id(seq_id)); + // does this seq_id "own" the cell? + if (cell.seq_id.size() == 1) { has_cell = true; } + } + if (!has_cell) { + llama_kv_cell & empty_cell = cache.cells[next_empty_cell]; + GGML_ASSERT(empty_cell.is_empty()); + // copy old tail into the empty cell + if (seq_meta.tail >= 0) { + llama_kv_cell & orig_cell = cache.cells[seq_meta.tail]; + empty_cell.pos = orig_cell.pos; + empty_cell.src = orig_cell.src; + orig_cell.seq_id.erase(seq_id); + empty_cell.seq_id.insert(seq_id); // will be overwritten + } + seq_meta.tail = next_empty_cell; + // find next empty cell + if (s + 1 < n_seqs) { + next_empty_cell += 1; + for (uint32_t i = 0; i < cache.size; ++i) { + if (next_empty_cell >= cache.size) { next_empty_cell -= cache.size; } + llama_kv_cell & cell = cache.cells[next_empty_cell]; + if (cell.is_empty()) { break; } + next_empty_cell += 1; } - cache.cells[seq_id].pos = batch.pos[i]; - // NOTE: seq_ids are not inserted here; they are handled when the input tensors are set - } else { - // too big seq_id - // TODO: would it be possible to resize the KV cache size instead? - LLAMA_LOG_ERROR("%s: seq_id=%d >= kv_size=%d Try using a bigger --parallel value\n", __func__, seq_id, cache.size); - return false; } } + if (min > seq_meta.tail) { min = seq_meta.tail; } + if (max < seq_meta.tail) { max = seq_meta.tail; } + } + + // gather and re-order + for (uint32_t s = 0; s < n_seqs; ++s) { + int32_t dst_id = s + min; + int32_t src_id = cache.cells[batch.seq_id[s][0]].tail; + if (dst_id != src_id) { + llama_kv_cell & dst_cell = cache.cells[dst_id]; + llama_kv_cell & src_cell = cache.cells[src_id]; + + std::swap(dst_cell.pos, src_cell.pos); + std::swap(dst_cell.src, src_cell.src); + std::swap(dst_cell.seq_id, src_cell.seq_id); + + // swap tails (assuming they NEVER overlap) + for (const llama_seq_id seq_id : src_cell.seq_id) { + cache.cells[seq_id].tail = src_id; + } + for (const llama_seq_id seq_id : dst_cell.seq_id) { + cache.cells[seq_id].tail = dst_id; + } + } + } + + // update the pos of the used seqs + for (uint32_t s = 0; s < n_seqs; ++s) { + const llama_pos last_pos = batch.pos[n_seq_tokens * s + n_seq_tokens - 1]; + int32_t cell_id = s + min; + llama_kv_cell & cell = cache.cells[cell_id]; + + if (cell.pos >= 0 && last_pos != cell.pos + (llama_pos) n_seq_tokens) { + // What should happen when the pos backtracks or skips a value? + // Clearing the state mid-batch would require special-casing which isn't done. + LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d with %u new tokens\n", + __func__, last_pos, cell.pos, batch.seq_id[s][0], n_seq_tokens); + } + cell.pos = last_pos; + cell.seq_id.clear(); + for (int32_t j = 0; j < batch.n_seq_id[s]; ++j) { + const llama_seq_id seq_id = batch.seq_id[s][j]; + cell.seq_id.insert(seq_id); + cache.cells[seq_id].tail = cell_id; + } } // allow getting the range of used cells, from head to head + n @@ -3119,7 +3580,7 @@ static bool llama_kv_cache_find_slot( cache.n = max - min + 1; // sanity check - return max >= min; + return cache.n >= n_seqs; } // otherwise, one cell per token. @@ -3157,11 +3618,14 @@ static bool llama_kv_cache_find_slot( } } - for (uint32_t i = 0; i < n_tokens; i++) { - cache.cells[cache.head + i].pos = batch.pos[i]; + for (uint32_t s = 0; s < n_seqs; s++) { + for (uint32_t i = 0; i < n_seq_tokens; ++i) { + uint32_t k = s*n_seq_tokens + i; + cache.cells[cache.head + k].pos = batch.pos[k]; - for (int32_t j = 0; j < batch.n_seq_id[i]; j++) { - cache.cells[cache.head + i].seq_id.insert(batch.seq_id[i][j]); + for (int32_t j = 0; j < batch.n_seq_id[s]; j++) { + cache.cells[cache.head + k].seq_id.insert(batch.seq_id[s][j]); + } } } @@ -3187,6 +3651,8 @@ static void llama_kv_cache_clear(struct llama_kv_cache & cache) { for (int32_t i = 0; i < (int32_t) cache.size; ++i) { cache.cells[i].pos = -1; cache.cells[i].seq_id.clear(); + cache.cells[i].src = -1; + cache.cells[i].tail = -1; } cache.head = 0; cache.used = 0; @@ -3213,9 +3679,16 @@ static bool llama_kv_cache_seq_rm( return false; } if (0 <= seq_id) { - // partial intersection is invalid - if ((0 < p0 && p0 <= cache.cells[seq_id].pos) || (0 < p1 && p1 <= cache.cells[seq_id].pos)) { - return false; + int32_t & tail_id = cache.cells[seq_id].tail; + if (tail_id >= 0) { + const llama_kv_cell & cell = cache.cells[tail_id]; + // partial intersection is invalid + if ((0 < p0 && p0 <= cell.pos) || (0 < p1 && p1 <= cell.pos)) { + return false; + } + if (p0 <= cell.pos && p1 < cell.pos) { + tail_id = -1; + } } } else { // seq_id is negative, then the range should include everything or nothing @@ -3239,6 +3712,7 @@ static bool llama_kv_cache_seq_rm( if (cache.cells[i].pos >= 0) cache.used--; cache.cells[i].pos = -1; + cache.cells[i].src = -1; if (new_head == cache.size) new_head = i; } } @@ -3261,23 +3735,29 @@ static void llama_kv_cache_seq_cp( if (cache.recurrent) { if ((uint32_t) seq_id_dst < cache.size && (uint32_t) seq_id_src < cache.size) { - seq_id_src = cache.cells[seq_id_src].src; - GGML_ASSERT((uint32_t) seq_id_src < cache.size); - // intent to "copy from" - // supports copy chains thanks to taking the source of the source - cache.cells[seq_id_dst].src = seq_id_src; - - // preserve the "keep or clear" status of the copied sequence - if (cache.cells[seq_id_src].has_seq_id(seq_id_src)) { - cache.cells[seq_id_dst].seq_id.insert(seq_id_dst); - } else { - cache.cells[seq_id_dst].seq_id.erase(seq_id_dst); + llama_kv_cell & tail_src = cache.cells[seq_id_src]; + llama_kv_cell & tail_dst = cache.cells[seq_id_dst]; + if (tail_dst.tail >= 0) { + // clear destination seq_id if it wasn't empty + llama_kv_cell & cell_dst = cache.cells[tail_dst.tail]; + + cell_dst.seq_id.erase(seq_id_dst); + tail_dst.tail = -1; + if (cell_dst.seq_id.empty()) { + cell_dst.pos = -1; + cell_dst.delta = -1; + cell_dst.src = -1; + cache.used -= 1; + } } + if (tail_src.tail >= 0) { + llama_kv_cell & cell_src = cache.cells[tail_src.tail]; - cache.do_copy = true; - - cache.cells[seq_id_dst].pos = cache.cells[seq_id_src].pos; + cell_src.seq_id.insert(seq_id_dst); + tail_dst.tail = tail_src.tail; + } } + return; } // otherwise, this is the KV cache of a Transformer-like model @@ -3295,9 +3775,13 @@ static void llama_kv_cache_seq_keep(struct llama_kv_cache & cache, llama_seq_id uint32_t new_head = cache.size; for (uint32_t i = 0; i < cache.size; ++i) { + if (cache.recurrent && (llama_seq_id) i != seq_id) { + cache.cells[i].tail = -1; + } if (!cache.cells[i].has_seq_id(seq_id)) { if (cache.cells[i].pos >= 0) cache.used--; cache.cells[i].pos = -1; + cache.cells[i].src = -1; cache.cells[i].seq_id.clear(); if (new_head == cache.size) new_head = i; } else { @@ -3326,9 +3810,12 @@ static void llama_kv_cache_seq_add( if (cache.recurrent) { // for Mamba-like models, only the pos needs to be shifted if (0 <= seq_id && seq_id < (int64_t) cache.size) { - llama_kv_cell & cell = cache.cells[seq_id]; - if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) { - cell.pos += delta; + const int32_t tail_id = cache.cells[seq_id].tail; + if (tail_id >= 0) { + llama_kv_cell & cell = cache.cells[tail_id]; + if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) { + cell.pos += delta; + } } } return; @@ -3372,9 +3859,12 @@ static void llama_kv_cache_seq_div( if (cache.recurrent) { // for Mamba-like models, only the pos needs to be changed if (0 <= seq_id && seq_id < (int64_t) cache.size) { - llama_kv_cell & cell = cache.cells[seq_id]; - if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) { - cell.pos /= d; + const int32_t tail_id = cache.cells[seq_id].tail; + if (tail_id >= 0) { + llama_kv_cell & cell = cache.cells[tail_id]; + if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) { + cell.pos /= d; + } } } return; @@ -3406,7 +3896,9 @@ static llama_pos llama_kv_cache_seq_pos_max(struct llama_kv_cache & cache, llama } static void llama_kv_cache_defrag(struct llama_kv_cache & cache) { - cache.do_defrag = true; + if (!cache.recurrent) { + cache.do_defrag = true; + } } static uint32_t llama_kv_cache_get_padding(const struct llama_cparams & cparams) { @@ -7948,7 +8440,7 @@ static struct ggml_tensor * llm_build_inp_embd( struct ggml_context * ctx, struct llama_context & lctx, const llama_hparams & hparams, - const llama_batch & batch, + const llama_ubatch & batch, struct ggml_tensor * tok_embd, const llm_build_cb & cb) { const int64_t n_embd = hparams.n_embd; @@ -8497,12 +8989,180 @@ static struct ggml_tensor * llm_build_kv( return cur; } +static struct ggml_tensor * llm_build_copy_mask_state( + struct ggml_context * ctx, + struct ggml_cgraph * graph, + struct ggml_tensor * s, + struct ggml_tensor * state_copy, + struct ggml_tensor * state_mask, + int32_t n_state, + int32_t kv_size, + int32_t kv_head, + int32_t n_kv, + int32_t n_seqs) { + struct ggml_tensor * states = ggml_reshape_2d(ctx, s, n_state, kv_size); + + // copy states + // NOTE: assuming the copy destinations are ALL contained between kv_head and kv_head + n_kv + // this shrinks the tensors's ne[1] to n_kv + states = ggml_get_rows(ctx, states, state_copy); + + // clear states of sequences which are starting at the beginning of this batch + // FIXME: zero-out NANs? + states = ggml_mul(ctx, states, state_mask); + + // copy states which won't be changed further (between n_seqs and n_rs) + ggml_build_forward_expand(graph, + ggml_cpy(ctx, + ggml_view_1d(ctx, states, n_state*(n_kv - n_seqs), n_seqs*n_state*ggml_element_size(states)), + ggml_view_1d(ctx, s, n_state*(n_kv - n_seqs), (kv_head + n_seqs)*n_state*ggml_element_size(s)))); + + // the part of the states that will be used and modified + return ggml_view_2d(ctx, states, n_state, n_seqs, states->nb[1], 0); +} + +// TODO: split +static struct ggml_tensor * llm_build_mamba( + struct ggml_context * ctx, + struct llama_context & lctx, + const llama_ubatch & batch, + struct ggml_cgraph * graph, + struct ggml_tensor * cur, + struct ggml_tensor * state_copy, + struct ggml_tensor * state_mask, + int32_t kv_head, + int32_t n_kv, + const llm_build_cb & cb, + int il) { + const llama_model & model = lctx.model; + const llama_hparams & hparams = model.hparams; + const llama_kv_cache & kv = lctx.kv_self; + const int64_t d_conv = hparams.ssm_d_conv; + const int64_t d_inner = hparams.ssm_d_inner; + const int64_t d_state = hparams.ssm_d_state; + const int64_t dt_rank = hparams.ssm_dt_rank; + const int64_t n_seqs = batch.n_seqs; + // Some variants of Mamba arch (e.g. FalconMamba do apply layer norm on B and Dt layers) + const bool ssm_dt_b_c_rms = hparams.ssm_dt_b_c_rms; + // Use the same RMS norm as the final layer norm + const float norm_rms_eps = hparams.f_norm_rms_eps; + + const int64_t n_seq_tokens = batch.n_seq_tokens; + + GGML_ASSERT(n_seqs != 0); + GGML_ASSERT(batch.equal_seqs); + GGML_ASSERT(batch.n_tokens == n_seq_tokens * n_seqs); + + struct ggml_tensor * conv_states_all = kv.k_l[il]; + struct ggml_tensor * ssm_states_all = kv.v_l[il]; + + // (ab)using the KV cache to store the states + struct ggml_tensor * conv = llm_build_copy_mask_state(ctx, + graph, conv_states_all, state_copy, state_mask, + hparams.n_embd_k_s(), kv.size, kv_head, n_kv, n_seqs); + conv = ggml_reshape_3d(ctx, conv, d_conv - 1, d_inner, n_seqs); + struct ggml_tensor * ssm = llm_build_copy_mask_state(ctx, + graph, ssm_states_all, state_copy, state_mask, + hparams.n_embd_v_s(), kv.size, kv_head, n_kv, n_seqs); + ssm = ggml_reshape_3d(ctx, ssm, d_state, d_inner, n_seqs); + + // {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs} + cur = ggml_reshape_3d(ctx, cur, cur->ne[0], n_seq_tokens, n_seqs); + + // {n_embd, 2*d_inner} @ {n_embd, n_seq_tokens, n_seqs} => {2*d_inner, n_seq_tokens, n_seqs} + struct ggml_tensor * xz = llm_build_lora_mm(lctx, ctx, model.layers[il].ssm_in, cur); + // split the above in two + // => {d_inner, n_seq_tokens, n_seqs} + struct ggml_tensor * x = ggml_view_3d(ctx, xz, d_inner, xz->ne[1], xz->ne[2], xz->nb[1], xz->nb[2], 0); + struct ggml_tensor * z = ggml_view_3d(ctx, xz, d_inner, xz->ne[1], xz->ne[2], xz->nb[1], xz->nb[2], d_inner*ggml_element_size(xz)); + + // conv + { + // => {d_conv - 1 + n_seq_tokens, d_inner, n_seqs} + struct ggml_tensor * conv_x = ggml_concat(ctx, conv, ggml_transpose(ctx, x), 0); + + // copy last (d_conv - 1) columns back into the state cache + struct ggml_tensor * last_conv = ggml_view_3d(ctx, conv_x, d_conv - 1, d_inner, n_seqs, conv_x->nb[1], conv_x->nb[2], n_seq_tokens*(conv_x->nb[0])); + + ggml_build_forward_expand(graph, + ggml_cpy(ctx, last_conv, + ggml_view_1d(ctx, conv_states_all, + (d_conv - 1)*(d_inner)*(n_seqs), + kv_head*(d_conv - 1)*(d_inner)*ggml_element_size(conv_states_all)))); + + // 1D convolution + // The equivalent is to make a self-overlapping view of conv_x + // over d_conv columns at each stride in the 3rd dimension, + // then element-wise multiply that with the conv1d weight, + // then sum the elements of each row, + // (the last two steps are a dot product over rows (also doable with mul_mat)) + // then permute away the ne[0] dimension, + // and then you're left with the resulting x tensor. + // For simultaneous sequences, all sequences need to have the same length. + x = ggml_ssm_conv(ctx, conv_x, model.layers[il].ssm_conv1d); + + // bias + x = ggml_add(ctx, x, model.layers[il].ssm_conv1d_b); + + x = ggml_silu(ctx, x); + } + + // ssm + { + // {d_inner, dt_rank + 2*d_state} @ {d_inner, n_seq_tokens, n_seqs} => {dt_rank + 2*d_state, n_seq_tokens, n_seqs} + struct ggml_tensor * x_db = llm_build_lora_mm(lctx, ctx, model.layers[il].ssm_x, x); + // split + struct ggml_tensor * dt = ggml_view_3d(ctx, x_db, dt_rank, n_seq_tokens, n_seqs, x_db->nb[1], x_db->nb[2], 0); + struct ggml_tensor * B = ggml_view_3d(ctx, x_db, d_state, n_seq_tokens, n_seqs, x_db->nb[1], x_db->nb[2], ggml_element_size(x_db)*dt_rank); + struct ggml_tensor * C = ggml_view_3d(ctx, x_db, d_state, n_seq_tokens, n_seqs, x_db->nb[1], x_db->nb[2], ggml_element_size(x_db)*(dt_rank+d_state)); + + // Some Mamba variants (e.g. FalconMamba) apply RMS norm in B, C & Dt layers + if (ssm_dt_b_c_rms) { + dt = ggml_rms_norm(ctx, dt, norm_rms_eps); + B = ggml_rms_norm(ctx, B, norm_rms_eps); + C = ggml_rms_norm(ctx, C, norm_rms_eps); + } + + // {dt_rank, d_inner} @ {dt_rank, n_seq_tokens, n_seqs} => {d_inner, n_seq_tokens, n_seqs} + dt = llm_build_lora_mm(lctx, ctx, model.layers[il].ssm_dt, dt); + dt = ggml_add(ctx, dt, model.layers[il].ssm_dt_b); + + // Custom operator to optimize the parallel associative scan + // as described in the Annex D of the Mamba paper. + // => {d_inner, n_seq_tokens, n_seqs} and {d_state, d_inner, n_seqs} + struct ggml_tensor * y_ssm = ggml_ssm_scan(ctx, ssm, x, dt, model.layers[il].ssm_a, B, C); + + // store last states + ggml_build_forward_expand(graph, + ggml_cpy(ctx, + ggml_view_1d(ctx, y_ssm, d_state*d_inner*n_seqs, x->nb[3]), + ggml_view_1d(ctx, ssm_states_all, d_state*d_inner*n_seqs, kv_head*d_state*d_inner*ggml_element_size(ssm_states_all)))); + + struct ggml_tensor * y = ggml_view_3d(ctx, y_ssm, d_inner, n_seq_tokens, n_seqs, x->nb[1], x->nb[2], 0); + + // TODO: skip computing output earlier for unused tokens + + // {d_inner, n_seq_tokens, n_seqs} * {d_inner} => {d_inner, n_seq_tokens, n_seqs} + y = ggml_add(ctx, y, ggml_mul(ctx, x, model.layers[il].ssm_d)); + y = ggml_mul(ctx, y, ggml_silu(ctx, ggml_cont(ctx, z))); + + // {d_inner, n_embd} @ {d_inner, n_seq_tokens, n_seqs} => {n_embd, n_seq_tokens, n_seqs} + cur = llm_build_lora_mm(lctx, ctx, model.layers[il].ssm_out, y); + } + + // {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens} + cur = ggml_reshape_2d(ctx, cur, cur->ne[0], n_seq_tokens * n_seqs); + cb(cur, "mamba_out", il); + + return cur; +} + struct llm_build_context { const llama_model & model; llama_context & lctx; const llama_hparams & hparams; const llama_cparams & cparams; - const llama_batch & batch; + const llama_ubatch & batch; const llama_kv_cache & kv_self; const int64_t n_embd; @@ -8548,7 +9208,7 @@ struct llm_build_context { // TODO: consider making the entire interface noexcept llm_build_context( llama_context & lctx, - const llama_batch & batch, + const llama_ubatch & batch, const llm_build_cb & cb, bool worst_case) : model (lctx.model), @@ -8655,29 +9315,6 @@ struct llm_build_context { return gf; } - struct ggml_cgraph * build_s_copy() { - struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false); - - GGML_ASSERT(kv_self.recurrent); - - struct ggml_tensor * state_copy = build_inp_s_copy(); - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * conv_states = ggml_reshape_2d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s(), kv_self.size); - struct ggml_tensor * ssm_states = ggml_reshape_2d(ctx0, kv_self.v_l[il], hparams.n_embd_v_s(), kv_self.size); - - conv_states = ggml_get_rows(ctx0, conv_states, state_copy); - ssm_states = ggml_get_rows(ctx0, ssm_states, state_copy); - - // TODO: name the intermediate tensors with cb() - - ggml_build_forward_expand(gf, ggml_cpy(ctx0, conv_states, kv_self.k_l[il])); - ggml_build_forward_expand(gf, ggml_cpy(ctx0, ssm_states, kv_self.v_l[il])); - } - - return gf; - } - struct ggml_cgraph * build_defrag(const std::vector & ids) { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false); @@ -8812,7 +9449,7 @@ struct llm_build_context { } struct ggml_tensor * build_inp_s_copy() { - lctx.inp_s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, kv_self.size); + lctx.inp_s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_kv); cb(lctx.inp_s_copy, "inp_s_copy", -1); ggml_set_input(lctx.inp_s_copy); return lctx.inp_s_copy; @@ -8825,13 +9462,6 @@ struct llm_build_context { return lctx.inp_s_mask; } - struct ggml_tensor * build_inp_s_seq() { - lctx.inp_s_seq = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_kv, n_tokens); - cb(lctx.inp_s_seq, "inp_s_seq", -1); - ggml_set_input(lctx.inp_s_seq); - return lctx.inp_s_seq; - } - struct ggml_cgraph * append_pooling(struct ggml_cgraph * gf) { // find result_norm tensor for input struct ggml_tensor * inp = nullptr; @@ -12161,136 +12791,31 @@ struct llm_build_context { struct ggml_cgraph * build_mamba() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false); - const int64_t d_model = n_embd; - const int64_t d_conv = hparams.ssm_d_conv; - const int64_t d_inner = hparams.ssm_d_inner; - GGML_ASSERT(2 * d_model == d_inner); - const int64_t d_state = hparams.ssm_d_state; - const int64_t dt_rank = hparams.ssm_dt_rank; - // Some variants of Mamba arch (e.g. FalconMamba do apply layer norm on B and Dt layers) - const bool ssm_dt_b_c_rms = hparams.ssm_dt_b_c_rms; - // Use the same RMS norm as the final layer norm - const float norm_rms_eps = hparams.f_norm_rms_eps; - struct ggml_tensor * cur; struct ggml_tensor * inpL; // {n_embd, n_tokens} inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + struct ggml_tensor * state_copy = build_inp_s_copy(); struct ggml_tensor * state_mask = build_inp_s_mask(); - struct ggml_tensor * state_seq = build_inp_s_seq(); for (int il = 0; il < n_layer; ++il) { - // (ab)using the KV cache to store the states - struct ggml_tensor * conv_states = ggml_reshape_2d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s(), kv_self.size); - struct ggml_tensor * ssm_states = ggml_reshape_2d(ctx0, kv_self.v_l[il], hparams.n_embd_v_s(), kv_self.size); - - // clear states of sequences which are starting at the beginning of this batch - { - conv_states = ggml_mul(ctx0, - ggml_view_2d(ctx0, conv_states, conv_states->ne[0], n_kv, conv_states->nb[1], kv_head*conv_states->nb[1]), - state_mask); - ssm_states = ggml_mul(ctx0, - ggml_view_2d(ctx0, ssm_states, ssm_states->ne[0], n_kv, ssm_states->nb[1], kv_head*ssm_states->nb[1]), - state_mask); - } - - conv_states = ggml_reshape_3d(ctx0, conv_states, d_conv - 1, d_inner, n_kv); - ssm_states = ggml_reshape_3d(ctx0, ssm_states, d_state, d_inner, n_kv); - // norm cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, cb, il); cb(cur, "attn_norm", il); - // {n_embd, 2*d_inner} * {n_embd, n_tokens} => {2*d_inner, n_tokens} - struct ggml_tensor * xz = llm_build_lora_mm(lctx, ctx0, model.layers[il].ssm_in, cur); - // split the above in two - // => {d_inner, n_tokens} - struct ggml_tensor * x = ggml_view_2d(ctx0, xz, d_inner, xz->ne[1], xz->nb[1], 0); - struct ggml_tensor * z = ggml_view_2d(ctx0, xz, d_inner, xz->ne[1], xz->nb[1], ggml_element_size(xz)*d_inner); - - // conv - { - // Custom operator which is needed only to ease simultaneous sequence processing. - // For a single sequence, the equivalent is to concatenate the columns of conv_states and x, - // then make a self-overlapping view of that over d_conv columns at each stride in the 3rd dimension, - // then element-wise multiply that with the conv1d weigth, - // then sum the elements of each row, - // (the last two steps are a dot product over rows (also doable with mul_mat)) - // then permute away the ne[0] dimension, - // and then you're left with the resulting x tensor. - // The new conv_states is the last (d_conv - 1) columns - // of the last 3rd dimensional "layer" of the self-overlapping view. - // For simultaneous sequences, it's more complicated. - struct ggml_tensor * x_conv = ggml_ssm_conv(ctx0, conv_states, x, model.layers[il].ssm_conv1d, state_seq); - - // store last (d_conv - 1) columns of the conv_state part of x_conv back into the KV cache - ggml_build_forward_expand(gf, - ggml_cpy(ctx0, - ggml_view_2d(ctx0, x_conv, d_conv - 1, d_inner*n_kv, d_conv*ggml_element_size(x_conv), (1+d_inner*n_tokens)*ggml_element_size(x_conv)), - ggml_view_1d(ctx0, kv_self.k_l[il], (d_conv - 1)*(d_inner)*(n_kv), kv_head*(d_conv - 1)*(d_inner)*ggml_element_size(x_conv)))); - - // extract x from x_conv - x = ggml_view_2d(ctx0, x_conv, d_inner, n_tokens, d_inner*ggml_element_size(x_conv), 0); - - // bias - x = ggml_add(ctx0, x, model.layers[il].ssm_conv1d_b); - - x = ggml_silu(ctx0, x); - } - - // ssm - { - // {d_inner, dt_rank + 2*d_state} * {d_inner, n_tokens} => {dt_rank + 2*d_state, n_tokens} - struct ggml_tensor * x_db = llm_build_lora_mm(lctx, ctx0, model.layers[il].ssm_x, x); - // split - struct ggml_tensor * dt = ggml_view_2d(ctx0, x_db, dt_rank, n_tokens, x_db->nb[1], 0); - struct ggml_tensor * B = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*dt_rank); - struct ggml_tensor * C = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*(dt_rank+d_state)); - - // Some Mamba variants (e.g. FalconMamba) apply RMS norm in B, C & Dt layers - if (ssm_dt_b_c_rms) { - dt = ggml_rms_norm(ctx0, dt, norm_rms_eps); - B = ggml_rms_norm(ctx0, B, norm_rms_eps); - C = ggml_rms_norm(ctx0, C, norm_rms_eps); - } - - // {dt_rank, d_inner} * {dt_rank, n_tokens} => {d_inner, n_tokens} - dt = llm_build_lora_mm(lctx, ctx0, model.layers[il].ssm_dt, dt); - dt = ggml_add(ctx0, dt, model.layers[il].ssm_dt_b); - - // Custom operator to optimize the parallel associative scan - // as described in the Annex D of the Mamba paper. - // => {d_inner, n_tokens} and {d_state, d_inner, n_kv} combined, - // because only a single tensor can be returned. - struct ggml_tensor * y_ssm_states = ggml_ssm_scan(ctx0, ssm_states, x, dt, model.layers[il].ssm_a, B, C, state_seq); - - // store last states (the second part of y_ssm_states) - ggml_build_forward_expand(gf, - ggml_cpy(ctx0, - ggml_view_1d(ctx0, y_ssm_states, d_state*d_inner*n_kv, d_inner*n_tokens*ggml_element_size(y_ssm_states)), - ggml_view_1d(ctx0, kv_self.v_l[il], d_state*d_inner*n_kv, kv_head*d_state*d_inner*ggml_element_size(ssm_states)))); - - struct ggml_tensor * y = ggml_view_2d(ctx0, y_ssm_states, d_inner, n_tokens, d_inner*ggml_element_size(y_ssm_states), 0); - - if (il == n_layer - 1) { - // skip computing output for unused tokens - struct ggml_tensor * inp_out_ids = build_inp_out_ids(); - x = ggml_get_rows(ctx0, x, inp_out_ids); - y = ggml_get_rows(ctx0, y, inp_out_ids); - z = ggml_get_rows(ctx0, z, inp_out_ids); - inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); - } - - // {d_inner, n_tokens} * {d_inner} => {d_inner, n_tokens} - y = ggml_add(ctx0, y, ggml_mul(ctx0, x, model.layers[il].ssm_d)); - y = ggml_mul(ctx0, y, ggml_silu(ctx0, z)); + cur = llm_build_mamba(ctx0, lctx, batch, gf, cur, + state_copy, state_mask, + kv_head, n_kv, cb, il); - // {d_inner, n_embd} * {d_inner, n_tokens} => {n_embd, n_tokens} - cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].ssm_out, y); + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); } // residual @@ -14156,8 +14681,8 @@ struct llm_build_context { }; static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector & ids) { - llama_batch dummy; - dummy.n_tokens = 0; + llama_ubatch dummy = {}; + dummy.equal_seqs = true; llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { }; @@ -14173,8 +14698,8 @@ static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const } static struct ggml_cgraph * llama_build_graph_k_shift(llama_context & lctx) { - llama_batch dummy; - dummy.n_tokens = 0; + llama_ubatch dummy = {}; + dummy.equal_seqs = true; llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { }; @@ -14189,26 +14714,9 @@ static struct ggml_cgraph * llama_build_graph_k_shift(llama_context & lctx) { return result; } -static struct ggml_cgraph * llama_build_graph_s_copy(llama_context & lctx) { - llama_batch dummy; - dummy.n_tokens = 0; - - llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { }; - - struct llm_build_context llm(lctx, dummy, cb, false); - - llm.init(); - - struct ggml_cgraph * result = llm.build_s_copy(); - - llm.free(); - - return result; -} - static struct ggml_cgraph * llama_build_graph( llama_context & lctx, - const llama_batch & batch, + const llama_ubatch & batch, bool worst_case) { const auto & model = lctx.model; @@ -14478,7 +14986,7 @@ static int32_t llama_relative_position_bucket(llama_pos x, llama_pos y, uint64_t return relative_bucket; } -static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { +static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) { // // set input data // @@ -14517,10 +15025,10 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { for (int i = 0; i < n_tokens; ++i) { data[i] = i; } - } else if (batch.logits) { + } else if (batch.output) { int32_t n_outputs = 0; for (int i = 0; i < n_tokens; ++i) { - if (batch.logits[i]) { + if (batch.output[i]) { data[n_outputs++] = i; } } @@ -14544,8 +15052,10 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { if (lctx.inp_KQ_mask || lctx.inp_KQ_mask_swa) { // NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache. if (cparams.causal_attn && !lctx.is_encoding) { - const int64_t n_kv = kv_self.n; - const int64_t n_tokens = batch.n_tokens; + const int64_t n_kv = kv_self.n; + const int64_t n_tokens = batch.n_tokens; + const int64_t n_seq_tokens = batch.n_seq_tokens; + const int64_t n_seqs = batch.n_seqs; float * data = nullptr; @@ -14565,32 +15075,35 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { // of the correct sequence for each token of the batch. // It's assumed that if a token in the batch has multiple sequences, they are equivalent. for (int h = 0; h < 1; ++h) { - for (int j = 0; j < n_tokens; ++j) { - const llama_pos pos = batch.pos[j]; - const llama_seq_id seq_id = batch.seq_id[j][0]; + for (int s = 0; s < n_seqs; ++s) { + const llama_seq_id seq_id = batch.seq_id[s][0]; - for (int i = 0; i < n_kv; ++i) { - float f; - if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) { - f = -INFINITY; - } else { - if (hparams.use_alibi) { - f = -std::abs(lctx.kv_self.cells[i].pos - pos); + for (int j = 0; j < n_seq_tokens; ++j) { + const llama_pos pos = batch.pos[s*n_seq_tokens + j]; + + for (int i = 0; i < n_kv; ++i) { + float f; + if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) { + f = -INFINITY; } else { - f = 0.0f; + if (hparams.use_alibi) { + f = -std::abs(kv_self.cells[i].pos - pos); + } else { + f = 0.0f; + } } - } - if (data) { - data[h*(n_kv*n_tokens) + j*n_kv + i] = f; - } + if (data) { + data[h*(n_kv*n_tokens) + s*(n_kv*n_seq_tokens) + j*n_kv + i] = f; + } - // may need to cut off old tokens for sliding window - if (data_swa) { - if (pos - lctx.kv_self.cells[i].pos >= (int32_t)hparams.n_swa) { - f = -INFINITY; + // may need to cut off old tokens for sliding window + if (data_swa) { + if (pos - kv_self.cells[i].pos >= (int32_t)hparams.n_swa) { + f = -INFINITY; + } + data_swa[h*(n_kv*n_tokens) + s*(n_kv*n_seq_tokens) + j*n_kv + i] = f; } - data_swa[h*(n_kv*n_tokens) + j*n_kv + i] = f; } } } @@ -14612,8 +15125,10 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } } else { + const int64_t n_tokens = batch.n_tokens; + const int64_t n_seq_tokens = batch.n_seq_tokens; + const int64_t n_seqs = batch.n_seqs; // when using kv cache, the mask needs to match the kv cache size - const int64_t n_tokens = batch.n_tokens; const int64_t n_stride = hparams.causal_attn && !lctx.is_encoding ? kv_self.n : n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer)); @@ -14621,27 +15136,35 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { float * data = (float *) lctx.inp_KQ_mask->data; for (int h = 0; h < 1; ++h) { - for (int j = 0; j < n_tokens; ++j) { - const llama_seq_id seq_id = batch.seq_id[j][0]; - - for (int i = 0; i < n_tokens; ++i) { - float f = -INFINITY; - for (int s = 0; s < batch.n_seq_id[i]; ++s) { - if (batch.seq_id[i][s] == seq_id) { - if (hparams.use_alibi) { - f = -std::abs(batch.pos[i] - batch.pos[j]); - } else { - f = 0.0f; + for (int s1 = 0; s1 < n_seqs; ++s1) { + const llama_seq_id seq_id = batch.seq_id[s1][0]; + + for (int j = 0; j < n_seq_tokens; ++j) { + const int32_t tj = s1*n_seq_tokens + j; + + for (int s0 = 0; s0 < n_seqs; ++s0) { + for (int i = 0; i < n_seq_tokens; ++i) { + const int32_t ti = s0*n_seq_tokens + i; + float f = -INFINITY; + + for (int s = 0; s < batch.n_seq_id[s0]; ++s) { + if (batch.seq_id[s0][s] == seq_id) { + if (hparams.use_alibi) { + f = -std::abs(batch.pos[ti] - batch.pos[tj]); + } else { + f = 0.0f; + } + break; + } } - break; + + data[h*(n_tokens*n_tokens) + tj*n_stride + ti] = f; } } - data[h*(n_tokens*n_tokens) + j*n_stride + i] = f; - } - - for (int i = n_tokens; i < n_stride; ++i) { - data[h*(n_tokens*n_tokens) + j*n_stride + i] = -INFINITY; + for (int i = n_tokens; i < n_stride; ++i) { + data[h*(n_tokens*n_tokens) + tj*n_stride + i] = -INFINITY; + } } } } @@ -14649,7 +15172,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) { - const int64_t n_tokens = batch.n_tokens; + const int64_t n_tokens = batch.n_tokens; + const int64_t n_seq_tokens = batch.n_seq_tokens; + const int64_t n_seqs = batch.n_seqs; GGML_ASSERT(lctx.inp_mean); GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer)); @@ -14658,12 +15183,14 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { memset(lctx.inp_mean->data, 0, n_tokens * n_tokens * ggml_element_size(lctx.inp_mean)); std::vector sum(n_tokens, 0); - for (int i = 0; i < n_tokens; ++i) { - const llama_seq_id seq_id = batch.seq_id[i][0]; + for (int s = 0; s < n_seqs; ++s) { + const llama_seq_id seq_id = batch.seq_id[s][0]; + + // TODO: adapt limits to n_seqs when batch.equal_seqs is true GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN"); - sum[seq_id] += 1; + sum[seq_id] += batch.n_seq_tokens; } std::vector div(n_tokens, 0.0f); @@ -14674,14 +15201,19 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } - for (int i = 0; i < n_tokens; ++i) { - const llama_seq_id seq_id = batch.seq_id[i][0]; - data[seq_id*n_tokens + i] = div[seq_id]; + for (int s = 0; s < n_seqs; ++s) { + const llama_seq_id seq_id = batch.seq_id[s][0]; + + for (int i = 0; i < n_seq_tokens; ++i) { + data[seq_id*n_tokens + s*n_seq_tokens + i] = div[seq_id]; + } } } if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_CLS) { - const int64_t n_tokens = batch.n_tokens; + const int64_t n_tokens = batch.n_tokens; + const int64_t n_seq_tokens = batch.n_seq_tokens; + const int64_t n_seqs = batch.n_seqs; GGML_ASSERT(lctx.inp_cls); GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer)); @@ -14689,20 +15221,26 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { uint32_t * data = (uint32_t *) lctx.inp_cls->data; memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls)); - for (int i = 0; i < n_tokens; ++i) { - const llama_seq_id seq_id = batch.seq_id[i][0]; - const llama_pos pos = batch.pos[i]; + for (int s = 0; s < n_seqs; ++s) { + const llama_seq_id seq_id = batch.seq_id[s][0]; + // TODO: adapt limits to n_seqs when batch.equal_seqs is true GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS"); - if (pos == 0) { - data[seq_id] = i; + for (int i = 0; i < n_seq_tokens; ++i) { + const llama_pos pos = batch.pos[s*n_seq_tokens + i]; + + if (pos == 0) { + data[seq_id] = s*n_seq_tokens + i; + } } } } if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_LAST) { - const int64_t n_tokens = batch.n_tokens; + const int64_t n_tokens = batch.n_tokens; + const int64_t n_seq_tokens = batch.n_seq_tokens; + const int64_t n_seqs = batch.n_seqs; GGML_ASSERT(lctx.inp_cls); GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer)); @@ -14713,15 +15251,19 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { std::vector last_pos(n_tokens, -1); std::vector last_row(n_tokens, -1); - for (int i = 0; i < n_tokens; ++i) { - const llama_seq_id seq_id = batch.seq_id[i][0]; - const llama_pos pos = batch.pos[i]; + for (int s = 0; s < n_seqs; ++s) { + const llama_seq_id seq_id = batch.seq_id[s][0]; + // TODO: adapt limits to n_seqs when batch.equal_seqs is true GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == LAST"); - if (pos >= last_pos[seq_id]) { - last_pos[seq_id] = pos; - last_row[seq_id] = i; + for (int i = 0; i < n_seq_tokens; ++i) { + const llama_pos pos = batch.pos[s*n_seq_tokens + i]; + + if (pos >= last_pos[seq_id]) { + last_pos[seq_id] = pos; + last_row[seq_id] = s*n_seq_tokens + i; + } } } @@ -14739,41 +15281,39 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_mask->buffer)); float * data = (float *) lctx.inp_s_mask->data; - // states which are not affected by the current batch are left untouched + // clear unused states for (int i = 0; i < n_kv; ++i) { - llama_seq_id seq_id = i + lctx.kv_self.head; - llama_kv_cell & kv_cell = lctx.kv_self.cells[seq_id]; - bool has_self_seq = kv_cell.has_seq_id(seq_id); + uint32_t cell_id = i + kv_self.head; + llama_kv_cell & kv_cell = lctx.kv_self.cells[cell_id]; - data[i] = (float) has_self_seq; + data[i] = (float) (kv_cell.src >= 0); - // ensure current sequences will be kept - if (!has_self_seq && kv_cell.pos >= 0) { - kv_cell.seq_id.insert(seq_id); + // only clear once + if (kv_cell.src < 0) { + kv_cell.src = cell_id; } } } - // For Mamba (and other recurrent architectures), - // update the correct state(s)/sequence(s) for each token of the batch. - // Like with the KQ_mask, if a token in the batch has multiple sequences, - // they are assumed to be equivalent (not here, but in ggml_ssm_scan and ggml_ssm_conv). - if (lctx.inp_s_seq) { - const int64_t n_tokens = batch.n_tokens; - GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_seq->buffer)); - int32_t * data = (int32_t *) lctx.inp_s_seq->data; + if (lctx.inp_s_copy) { + GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_copy->buffer)); + int32_t * data = (int32_t *) lctx.inp_s_copy->data; - for (int j = 0; j < n_tokens; ++j) { - const int32_t n_seq = batch.n_seq_id[j]; - GGML_ASSERT(0 < n_seq); // a token should be part of at least 1 sequence + // assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n + for (uint32_t i = 0; i < n_kv; ++i) { + const uint32_t cell_id = i + kv_self.head; + llama_kv_cell & kv_cell = lctx.kv_self.cells[cell_id]; - for (int i = 0; i < n_kv; ++i) { - if (i < n_seq) { - // for this type of model, the head is the minimum seq_id of the batch - data[j*n_kv + i] = batch.seq_id[j][i] - kv_self.head; - } else { - data[j*n_kv + i] = -1; - } + // prevent out-of-bound sources + if (kv_cell.src < 0 || (uint32_t) kv_cell.src >= kv_self.size) { + kv_cell.src = cell_id; + } + + data[i] = kv_cell.src; + + // ensure copy only happens once + if (kv_cell.src != (int32_t) cell_id) { + kv_cell.src = cell_id; } } } @@ -14783,6 +15323,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { const int64_t n_tokens = batch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_pos_bucket->buffer)); + GGML_ASSERT(!batch.equal_seqs); // TODO: use batch.n_seqs instead of failing int32_t * data = (int32_t *) lctx.inp_pos_bucket->data; @@ -14818,6 +15359,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { const int64_t n_tokens = batch.n_tokens; GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask_cross->buffer)); + GGML_ASSERT(!batch.equal_seqs); // TODO: use batch.n_seqs instead of failing float * data = (float *) lctx.inp_KQ_mask_cross->data; @@ -14911,6 +15453,43 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) { return n_outputs_max; } +// make the outputs have the same order they had in the user-provided batch +static void llama_output_reorder(struct llama_context * ctx) { + std::vector & out_ids = ctx->sbatch.out_ids; + if (!out_ids.empty()) { + uint32_t n_vocab = ctx->model.hparams.n_vocab; + uint32_t n_embd = ctx->model.hparams.n_embd; + int32_t n_outputs = ctx->n_outputs; + GGML_ASSERT((size_t) n_outputs == out_ids.size()); + // TODO: is there something more efficient which also minimizes swaps? + // selection sort, to minimize swaps (from https://en.wikipedia.org/wiki/Selection_sort) + for (int32_t i = 0; i < n_outputs - 1; ++i) { + int32_t j_min = i; + for (int32_t j = i + 1; j < n_outputs; ++j) { + if (out_ids[j] < out_ids[j_min]) { + j_min = j; + } + } + if (j_min == i) { continue; } + std::swap(out_ids[i], out_ids[j_min]); + if (ctx->logits_size > 0) { + for (uint32_t k = 0; k < n_vocab; k++) { + std::swap(ctx->logits[i*n_vocab + k], ctx->logits[j_min*n_vocab + k]); + } + } + if (ctx->embd_size > 0) { + for (uint32_t k = 0; k < n_embd; k++) { + std::swap(ctx->embd[i*n_embd + k], ctx->embd[j_min*n_embd + k]); + } + } + } + std::fill(ctx->output_ids.begin(), ctx->output_ids.end(), -1); + for (int32_t i = 0; i < n_outputs; ++i) { + ctx->output_ids[out_ids[i]] = i; + } + out_ids.clear(); + } +} static void llama_graph_compute( llama_context & lctx, @@ -14983,15 +15562,11 @@ static int llama_decode_internal( const auto n_ubatch = cparams.n_ubatch; - // TODO: simplify or deprecate - std::vector pos; - std::vector n_seq_id; - std::vector seq_id_arr; - std::vector> seq_id; - // this indicates we are doing pooled embedding, so we ignore batch.logits and output all tokens const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE; + lctx.embd_seq.clear(); + // count outputs if (batch_all.logits && !embd_pooled) { for (uint32_t i = 0; i < n_tokens_all; ++i) { @@ -15004,55 +15579,42 @@ static int llama_decode_internal( n_outputs = 1; } + lctx.sbatch.from_batch(batch_all, n_embd, + /* simple_split */ !kv_self.recurrent, + /* logits_all */ n_outputs == n_tokens_all); + // reserve output buffer if (llama_output_reserve(lctx, n_outputs) < n_outputs) { LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_outputs); return -2; }; - // set output mappings - if (batch_all.logits) { - int32_t i_logits = 0; - for (uint32_t i = 0; i < n_tokens_all; ++i) { - if (batch_all.logits[i]) { - lctx.output_ids[i] = i_logits++; + while (lctx.sbatch.n_tokens > 0) { + llama_ubatch ubatch; + if (kv_self.recurrent) { + if (embd_pooled) { + // Pooled embeddings cannot be split across ubatches (yet) + ubatch = lctx.sbatch.split_seq(n_ubatch); + } else { + // recurrent model architectures are easier to implement + // with equal-length sequences + ubatch = lctx.sbatch.split_equal(n_ubatch); } + } else { + ubatch = lctx.sbatch.split_simple(n_ubatch); } - } else { - for (uint32_t i = 0; i < n_outputs; ++i) { - lctx.output_ids[i] = i; - } - } - - for (uint32_t cur_token = 0; cur_token < n_tokens_all; cur_token += n_ubatch) { - const uint32_t n_tokens = std::min(n_ubatch, n_tokens_all - cur_token); - llama_batch u_batch = { - /* .n_tokens = */ (int32_t) n_tokens, - /* .token = */ batch_all.token ? batch_all.token + cur_token : nullptr, - /* .embd = */ batch_all.embd ? batch_all.embd + cur_token*n_embd : nullptr, - /* .pos = */ batch_all.pos ? batch_all.pos + cur_token : nullptr, - /* .n_seq_id = */ batch_all.n_seq_id ? batch_all.n_seq_id + cur_token : nullptr, - /* .seq_id = */ batch_all.seq_id ? batch_all.seq_id + cur_token : nullptr, - /* .logits = */ batch_all.logits ? batch_all.logits + cur_token : nullptr, - /* .all_pos_0 = */ batch_all.all_pos_0 + (llama_pos) cur_token*batch_all.all_pos_1, - /* .all_pos_1 = */ batch_all.all_pos_1, - /* .all_seq_id = */ batch_all.all_seq_id, - }; + const uint32_t n_tokens = ubatch.n_tokens; // count the outputs in this u_batch { int32_t n_outputs_new = 0; - if (u_batch.logits && !embd_pooled) { - for (uint32_t i = 0; i < n_tokens; i++) { - n_outputs_new += u_batch.logits[i] != 0; - } - } else if (n_outputs == n_tokens_all) { + if (n_outputs == n_tokens_all) { n_outputs_new = n_tokens; } else { - // keep last output only - if (cur_token + n_tokens >= n_tokens_all) { - n_outputs_new = 1; + GGML_ASSERT(ubatch.output); + for (uint32_t i = 0; i < n_tokens; i++) { + n_outputs_new += (int32_t) (ubatch.output[i] != 0); } } @@ -15063,32 +15625,6 @@ static int llama_decode_internal( int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch; GGML_ASSERT(n_threads > 0); - // helpers for smoother batch API transition - // after deprecating the llama_eval calls, these will be removed - if (u_batch.pos == nullptr) { - pos.resize(n_tokens); - for (uint32_t i = 0; i < n_tokens; i++) { - pos[i] = u_batch.all_pos_0 + i*u_batch.all_pos_1; - } - - u_batch.pos = pos.data(); - } - - if (u_batch.seq_id == nullptr) { - n_seq_id.resize(n_tokens); - seq_id.resize(n_tokens); - seq_id_arr.resize(n_tokens); - for (uint32_t i = 0; i < n_tokens; i++) { - n_seq_id[i] = 1; - seq_id[i].resize(1); - seq_id[i][0] = u_batch.all_seq_id; - seq_id_arr[i] = seq_id[i].data(); - } - - u_batch.n_seq_id = n_seq_id.data(); - u_batch.seq_id = seq_id_arr.data(); - } - // non-causal masks do not use the KV cache if (hparams.causal_attn) { llama_kv_cache_update(&lctx); @@ -15099,7 +15635,7 @@ static int llama_decode_internal( kv_self.head = 0; } - if (!llama_kv_cache_find_slot(kv_self, u_batch)) { + if (!llama_kv_cache_find_slot(kv_self, ubatch)) { return 1; } @@ -15118,7 +15654,7 @@ static int llama_decode_internal( ggml_backend_sched_reset(lctx.sched); ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data); - ggml_cgraph * gf = llama_build_graph(lctx, u_batch, false); + ggml_cgraph * gf = llama_build_graph(lctx, ubatch, false); // the output is always the last tensor in the graph struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1]; @@ -15146,7 +15682,7 @@ static int llama_decode_internal( ggml_backend_sched_alloc_graph(lctx.sched, gf); - llama_set_inputs(lctx, u_batch); + llama_set_inputs(lctx, ubatch); llama_graph_compute(lctx, gf, n_threads); @@ -15204,12 +15740,11 @@ static int llama_decode_internal( case LLAMA_POOLING_TYPE_CLS: case LLAMA_POOLING_TYPE_LAST: { - // extract sequence embeddings + // extract sequence embeddings (cleared before processing each batch) auto & embd_seq_out = lctx.embd_seq; - embd_seq_out.clear(); - for (uint32_t i = 0; i < n_tokens; i++) { - const llama_seq_id seq_id = u_batch.seq_id[i][0]; + for (uint32_t s = 0; s < ubatch.n_seqs; ++s) { + const llama_seq_id seq_id = ubatch.seq_id[s][0]; if (embd_seq_out.find(seq_id) != embd_seq_out.end()) { continue; } @@ -15226,6 +15761,25 @@ static int llama_decode_internal( n_outputs_prev += lctx.n_outputs; } + // set output mappings + { + bool sorted_output = true; + + GGML_ASSERT(lctx.sbatch.out_ids.size() == n_outputs); + + for (size_t i = 0; i < n_outputs; ++i) { + size_t out_id = lctx.sbatch.out_ids[i]; + lctx.output_ids[out_id] = i; + if (out_id != i) { + sorted_output = false; + } + } + + if (sorted_output) { + lctx.sbatch.out_ids.clear(); + } + } + // set to total number of outputs in the batch, for use in llama_get_logits_ith lctx.n_outputs = n_outputs; @@ -15290,11 +15844,9 @@ static int llama_encode_internal( const int64_t n_embd = hparams.n_embd; - // TODO: simplify or deprecate - std::vector pos; - std::vector n_seq_id; - std::vector seq_id_arr; - std::vector> seq_id; + lctx.sbatch.from_batch(batch, n_embd, /* simple_split */ true, /* logits_all */ true); + + const llama_ubatch ubatch = lctx.sbatch.split_simple(n_tokens); // reserve output buffer if (llama_output_reserve(lctx, n_tokens) < n_tokens) { @@ -15312,36 +15864,10 @@ static int llama_encode_internal( const int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch; GGML_ASSERT(n_threads > 0); - // helpers for smoother batch API transition - // after deprecating the llama_eval calls, these will be removed - if (batch.pos == nullptr) { - pos.resize(n_tokens); - for (uint32_t i = 0; i < n_tokens; i++) { - pos[i] = batch.all_pos_0 + i*batch.all_pos_1; - } - - batch.pos = pos.data(); - } - - if (batch.seq_id == nullptr) { - n_seq_id.resize(n_tokens); - seq_id.resize(n_tokens); - seq_id_arr.resize(n_tokens); - for (uint32_t i = 0; i < n_tokens; i++) { - n_seq_id[i] = 1; - seq_id[i].resize(1); - seq_id[i][0] = batch.all_seq_id; - seq_id_arr[i] = seq_id[i].data(); - } - - batch.n_seq_id = n_seq_id.data(); - batch.seq_id = seq_id_arr.data(); - } - ggml_backend_sched_reset(lctx.sched); ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data); - ggml_cgraph * gf = llama_build_graph(lctx, batch, false); + ggml_cgraph * gf = llama_build_graph(lctx, ubatch, false); // the output embeddings after the final encoder normalization struct ggml_tensor * embd = nullptr; @@ -15365,7 +15891,7 @@ static int llama_encode_internal( ggml_backend_sched_alloc_graph(lctx.sched, gf); - llama_set_inputs(lctx, batch); + llama_set_inputs(lctx, ubatch); llama_graph_compute(lctx, gf, n_threads); @@ -15379,12 +15905,13 @@ static int llama_encode_internal( float * embd_out = lctx.embd_enc.data(); ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_tokens*n_embd*sizeof(float)); + GGML_ASSERT(!ubatch.equal_seqs); // TODO: handle equal splits // remember the sequence ids used during the encoding - needed for cross attention later lctx.seq_ids_enc.resize(n_tokens); for (uint32_t i = 0; i < n_tokens; i++) { - for (int s = 0; s < batch.n_seq_id[i]; s++) { - llama_seq_id seq_id = batch.seq_id[i][s]; + for (int s = 0; s < ubatch.n_seq_id[i]; s++) { + llama_seq_id seq_id = ubatch.seq_id[i][s]; lctx.seq_ids_enc[i].insert(seq_id); } } @@ -15409,8 +15936,10 @@ static int llama_encode_internal( auto & embd_seq_out = lctx.embd_seq; embd_seq_out.clear(); + GGML_ASSERT(!ubatch.equal_seqs); // TODO: handle equal splits + for (uint32_t i = 0; i < n_tokens; i++) { - const llama_seq_id seq_id = batch.seq_id[i][0]; + const llama_seq_id seq_id = ubatch.seq_id[i][0]; if (embd_seq_out.find(seq_id) != embd_seq_out.end()) { continue; } @@ -15688,32 +16217,6 @@ static void llama_kv_cache_update_internal(struct llama_context & lctx) { } } - if (lctx.kv_self.recurrent && lctx.kv_self.do_copy) { - { - ggml_backend_sched_reset(lctx.sched); - - ggml_cgraph * gf = llama_build_graph_s_copy(lctx); - - ggml_backend_sched_alloc_graph(lctx.sched, gf); - - llama_set_s_copy(lctx); - - llama_graph_compute(lctx, gf, lctx.cparams.n_threads); - - need_reserve = true; - } - - { - auto & kv_self = lctx.kv_self; - - kv_self.do_copy = false; - - for (uint32_t i = 0; i < kv_self.size; ++i) { - kv_self.cells[i].src = i; - } - } - } - // defragment the KV cache if needed if (lctx.kv_self.do_defrag) { llama_kv_cache_defrag_internal(lctx); @@ -15727,10 +16230,11 @@ static void llama_kv_cache_update_internal(struct llama_context & lctx) { if (need_reserve) { // TODO: extract to a function // build worst-case graph - int n_tokens = (int)std::min(lctx.cparams.n_ctx, lctx.cparams.n_ubatch); - int n_past = lctx.cparams.n_ctx - n_tokens; + uint32_t n_seqs = 1; // TODO: worst-case number of sequences + uint32_t n_tokens = std::min(lctx.cparams.n_ctx, lctx.cparams.n_ubatch); llama_token token = llama_token_bos(&lctx.model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph - ggml_cgraph * gf = llama_build_graph(lctx, llama_batch_get_one(&token, n_tokens, n_past, 0), true); + llama_ubatch ubatch = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr}; + ggml_cgraph * gf = llama_build_graph(lctx, ubatch, true); // initialize scheduler with the worst-case graph ggml_backend_sched_reset(lctx.sched); @@ -16326,12 +16830,15 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)model.hparams.n_layer; // sanity checks - // - // - qs.n_attention_wv == 0 for Mamba models - // - qs.n_attention_wv == model.hparams.n_layer for Transformer models - // - qs.n_attention_wv == 3 * model.hparams.n_layer for Encoder-Decoder models - // - GGML_ASSERT((qs.n_attention_wv == 0 || qs.n_attention_wv == (int)model.hparams.n_layer || qs.n_attention_wv == 3 * (int)model.hparams.n_layer) && "n_attention_wv is unexpected"); + { + const auto & n_head_kv_iter = model.hparams.n_head_kv_arr.begin(); + // attention layers have a non-zero number of kv heads + int32_t n_attn_layer = model.hparams.n_layer - std::count(n_head_kv_iter, n_head_kv_iter + model.hparams.n_layer, 0); + if (llama_model_has_encoder(&model)) { + n_attn_layer *= 3; + } + GGML_ASSERT((qs.n_attention_wv == n_attn_layer) && "n_attention_wv is unexpected"); + } size_t total_size_org = 0; size_t total_size_new = 0; @@ -17140,7 +17647,7 @@ struct llama_context * llama_new_context_with_model( ggml_type type_v = params.type_v; // Mamba only needs a constant number of KV cache cells per sequence - if (model->arch == LLM_ARCH_MAMBA) { + if (llama_model_is_recurrent(model)) { // Mamba needs at least as many KV cells as there are sequences kept at any time kv_size = std::max((uint32_t) 1, params.n_seq_max); // it's probably best to keep as much precision as possible for the states @@ -17372,10 +17879,11 @@ struct llama_context * llama_new_context_with_model( } // build worst-case graph - int n_tokens = (int)std::min(cparams.n_ctx, cparams.n_ubatch); - int n_past = cparams.n_ctx - n_tokens; + uint32_t n_seqs = 1; // TODO: worst-case number of sequences + uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch); llama_token token = llama_token_bos(&ctx->model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph - ggml_cgraph * gf = llama_build_graph(*ctx, llama_batch_get_one(&token, n_tokens, n_past, 0), true); + llama_ubatch ubatch = { true, n_tokens, n_tokens / n_seqs, n_seqs, &token, nullptr, nullptr, nullptr, nullptr, nullptr}; + ggml_cgraph * gf = llama_build_graph(*ctx, ubatch, true); // initialize scheduler with the worst-case graph if (!ggml_backend_sched_reserve(ctx->sched, gf)) { @@ -17615,6 +18123,13 @@ llama_token llama_model_decoder_start_token(const struct llama_model * model) { return model->hparams.dec_start_token_id; } +bool llama_model_is_recurrent(const struct llama_model * model) { + switch (model->arch) { + case LLM_ARCH_MAMBA: return true; + default: return false; + } +} + uint32_t llama_model_quantize( const char * fname_inp, const char * fname_out, @@ -17936,7 +18451,9 @@ struct llama_data_write { write_string(rng_str); } - void write_output_ids(const struct llama_context * ctx) { + void write_output_ids(struct llama_context * ctx) { + llama_output_reorder(ctx); + const uint32_t n_outputs = ctx->n_outputs; std::vector output_pos; @@ -18224,8 +18741,11 @@ struct llama_data_read { llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1); - llama_batch batch = llama_batch_init(cell_count, 0, 1); + llama_ubatch batch = ctx->sbatch.reserve_ubatch(cell_count, /* has_embd */ false); batch.n_tokens = cell_count; + batch.n_seq_tokens = cell_count; + batch.n_seqs = 1; + for (uint32_t i = 0; i < cell_count; ++i) { llama_pos pos; uint32_t n_seq_id; @@ -18239,11 +18759,10 @@ struct llama_data_read { } batch.pos[i] = pos; - batch.n_seq_id[i] = 1; - batch.seq_id[i][0] = dest_seq_id; } + batch.n_seq_id[0] = 1; + batch.seq_id[0] = &dest_seq_id; if (!llama_kv_cache_find_slot(kv_self, batch)) { - llama_batch_free(batch); LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__); return false; } @@ -18255,9 +18774,6 @@ struct llama_data_read { GGML_ASSERT(kv_self.cells[kv_self.head + cell_count - 1].pos == batch.pos[cell_count - 1]); GGML_ASSERT(kv_self.cells[kv_self.head].has_seq_id(dest_seq_id)); GGML_ASSERT(kv_self.cells[kv_self.head + cell_count - 1].has_seq_id(dest_seq_id)); - - // Cleanup - llama_batch_free(batch); } else { // whole KV cache restore @@ -18289,6 +18805,15 @@ struct llama_data_read { } cell.seq_id.insert(seq_id); + + if (kv_self.recurrent) { + int32_t & tail = kv_self.cells[seq_id].tail; + if (tail != -1) { + LLAMA_LOG_ERROR("%s: duplicate tail for seq_id %d in cell %d and %d\n", __func__, seq_id, i, tail); + return false; + } + tail = i; + } } } @@ -18296,6 +18821,14 @@ struct llama_data_read { kv_self.used = cell_count; } + if (kv_self.recurrent) { + for (uint32_t i = 0; i < cell_count; ++i) { + uint32_t cell_id = kv_self.head + i; + // make sure the recurrent states will keep their restored state + kv_self.cells[cell_id].src = cell_id; + } + } + return true; } @@ -18883,7 +19416,18 @@ struct llama_batch llama_batch_get_one( } struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_t n_seq_max) { - llama_batch batch = { 0, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, 0, 0, 0, }; + llama_batch batch = { + /*n_tokens =*/ 0, + /*tokens =*/ nullptr, + /*embd =*/ nullptr, + /*pos =*/ nullptr, + /*n_seq_id =*/ nullptr, + /*seq_id =*/ nullptr, + /*logits =*/ nullptr, + /*all_pos_0 =*/ 0, + /*all_pos_1 =*/ 0, + /*all_seq_id =*/ 0, + }; if (embd) { batch.embd = (float *) malloc(sizeof(float) * n_tokens_alloc * embd); @@ -18969,6 +19513,10 @@ void llama_synchronize(struct llama_context * ctx) { float * llama_get_logits(struct llama_context * ctx) { llama_synchronize(ctx); + // reorder logits for backward compatibility + // TODO: maybe deprecate this + llama_output_reorder(ctx); + return ctx->logits; } @@ -19013,6 +19561,10 @@ float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) { float * llama_get_embeddings(struct llama_context * ctx) { llama_synchronize(ctx); + // reorder embeddings for backward compatibility + // TODO: maybe deprecate this + llama_output_reorder(ctx); + return ctx->embd; }