diff --git a/examples/baby-llama/baby-llama.cpp b/examples/baby-llama/baby-llama.cpp index a99ece9a6..b5916f230 100644 --- a/examples/baby-llama/baby-llama.cpp +++ b/examples/baby-llama/baby-llama.cpp @@ -556,6 +556,14 @@ struct ggml_tensor * forward( struct ggml_tensor * kc = kv_self.k; struct ggml_tensor * vc = kv_self.v; + struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); + { + int * data = (int *) KQ_pos->data; + for (int i = 0; i < N; ++i) { + data[i] = n_past + i; + } + } + // inpL shape [n_embd,N,1,1] struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens); for (int il = 0; il < n_layer; ++il) { @@ -583,8 +591,8 @@ struct ggml_tensor * forward( // wk shape [n_embd, n_embd, 1, 1] // Qcur shape [n_embd/n_head, n_head, N, 1] // Kcur shape [n_embd/n_head, n_head, N, 1] - struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0); - struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0); + struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0, 0); + struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0, 0); // store key and value to memory { @@ -810,9 +818,18 @@ struct ggml_tensor * forward_batch( struct ggml_tensor * kc = kv_self.k; struct ggml_tensor * vc = kv_self.v; + struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); + { + int * data = (int *) KQ_pos->data; + for (int i = 0; i < N; ++i) { + data[i] = n_past + i; + } + } + // inpL shape [n_embd,N*n_batch,1] struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens); assert_shape_2d(inpL, n_embd, N*n_batch); + for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -840,8 +857,8 @@ struct ggml_tensor * forward_batch( // wk shape [n_embd, n_embd, 1, 1] // Qcur shape [n_embd/n_head, n_head, N, n_batch] // Kcur shape [n_embd/n_head, n_head, N, n_batch] - struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0); - struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0); + struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0, 0); + struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0, 0); assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch); assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch); @@ -1100,6 +1117,14 @@ struct ggml_tensor * forward_lora( struct ggml_tensor * kc = kv_self.k; struct ggml_tensor * vc = kv_self.v; + struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); + { + int * data = (int *) KQ_pos->data; + for (int i = 0; i < N; ++i) { + data[i] = n_past + i; + } + } + // inpL shape [n_embd,N,1,1] struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens); for (int il = 0; il < n_layer; ++il) { @@ -1133,7 +1158,7 @@ struct ggml_tensor * forward_lora( model->layers[il].wqb, cur)), n_embd/n_head, n_head, N), - n_past, n_rot, 0, 0); + KQ_pos, n_rot, 0, 0); struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, @@ -1142,7 +1167,7 @@ struct ggml_tensor * forward_lora( model->layers[il].wkb, cur)), n_embd/n_head, n_head, N), - n_past, n_rot, 0, 0); + KQ_pos, n_rot, 0, 0); // store key and value to memory { diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index 947aa7ed3..d91566586 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -679,15 +679,23 @@ struct ggml_tensor * llama_build_train_graphs( } }; + // KQ_pos - contains the positions + struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, N); + { + int * data = (int *) KQ_pos->data; + for (int i = 0; i < N; ++i) { + data[i] = n_past + i; + } + } + // rope has so much parameters that we make a custom function for it - auto rope = [ctx, n_rot, n_ctx, rope_freq_base, rope_freq_scale] + auto rope = [ctx, KQ_pos, n_rot, n_ctx, rope_freq_base, rope_freq_scale] (struct ggml_tensor * t) -> struct ggml_tensor * { // not capturing these, to silcence warnings - const int n_past = 0; const int rope_mode = 0; return ggml_rope_custom(ctx, - t, n_past, n_rot, rope_mode, n_ctx, + t, KQ_pos, n_rot, rope_mode, n_ctx, rope_freq_base, rope_freq_scale); }; diff --git a/ggml-metal.m b/ggml-metal.m index d793083d9..d4027d350 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1210,7 +1210,9 @@ void ggml_metal_graph_compute( } break; case GGML_OP_ROPE: { - const int n_past = ((int32_t *) dst->op_params)[0]; + GGML_ASSERT(ne10 == ne02); + + //const int n_past = ((int32_t *) dst->op_params)[0]; const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; @@ -1221,28 +1223,29 @@ void ggml_metal_graph_compute( [encoder setComputePipelineState:ctx->pipeline_rope]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&n_past length:sizeof( int) atIndex:18]; - [encoder setBytes:&n_dims length:sizeof( int) atIndex:19]; - [encoder setBytes:&mode length:sizeof( int) atIndex:20]; - [encoder setBytes:&freq_base length:sizeof(float) atIndex:21]; - [encoder setBytes:&freq_scale length:sizeof(float) atIndex:22]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:5]; + [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:6]; + [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:7]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:8]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:10]; + [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:11]; + [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:12]; + [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:13]; + [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:14]; + [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:15]; + [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:16]; + [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:17]; + [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:18]; + //[encoder setBytes:&n_past length:sizeof( int) atIndex:19]; + [encoder setBytes:&n_dims length:sizeof( int) atIndex:20]; + [encoder setBytes:&mode length:sizeof( int) atIndex:21]; + [encoder setBytes:&freq_base length:sizeof(float) atIndex:22]; + [encoder setBytes:&freq_scale length:sizeof(float) atIndex:23]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, 1, 1)]; } break; diff --git a/ggml-metal.metal b/ggml-metal.metal index c913fe1d9..16937be5e 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -854,29 +854,30 @@ kernel void kernel_alibi_f32( } kernel void kernel_rope( - device const void * src0, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - constant int & n_past, - constant int & n_dims, - constant int & mode, - constant float & freq_base, - constant float & freq_scale, + device const void * src0, + device const int32_t * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant int64_t & ne03, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant uint64_t & nb03, + constant int64_t & ne0, + constant int64_t & ne1, + constant int64_t & ne2, + constant int64_t & ne3, + constant uint64_t & nb0, + constant uint64_t & nb1, + constant uint64_t & nb2, + constant uint64_t & nb3, + constant int & n_past, + constant int & n_dims, + constant int & mode, + constant float & freq_base, + constant float & freq_scale, uint tiitg[[thread_index_in_threadgroup]], uint3 tptg[[threads_per_threadgroup]], uint3 tgpig[[threadgroup_position_in_grid]]) { @@ -886,7 +887,9 @@ kernel void kernel_rope( const bool is_neox = mode & 2; - const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2); + device const int32_t * pos = src1; + + const int64_t p = pos[i2]; const float theta_0 = freq_scale * (float)p; const float inv_ndims = -1.f/n_dims; @@ -1320,8 +1323,8 @@ kernel void kernel_mul_mat_q3_K_f32( float yl[32]; - const uint16_t kmask1 = 0x3030; - const uint16_t kmask2 = 0x0f0f; + //const uint16_t kmask1 = 0x3030; + //const uint16_t kmask2 = 0x0f0f; const int tid = tiisg/4; const int ix = tiisg%4; diff --git a/ggml.c b/ggml.c index 37124f1e5..e4faafee6 100644 --- a/ggml.c +++ b/ggml.c @@ -6968,7 +6968,7 @@ struct ggml_tensor * ggml_soft_max_back_inplace( static struct ggml_tensor * ggml_rope_impl( struct ggml_context * ctx, struct ggml_tensor * a, - int n_past, + struct ggml_tensor * b, int n_dims, int mode, int n_ctx, @@ -6977,6 +6977,10 @@ static struct ggml_tensor * ggml_rope_impl( float xpos_base, bool xpos_down, bool inplace) { + GGML_ASSERT(ggml_is_vector(b)); + GGML_ASSERT(b->type == GGML_TYPE_I32); + GGML_ASSERT(a->ne[2] == b->ne[0]); + bool is_node = false; if (a->grad) { @@ -6985,7 +6989,7 @@ static struct ggml_tensor * ggml_rope_impl( struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - int32_t params[8] = { n_past, n_dims, mode, n_ctx }; + int32_t params[8] = { /*n_past*/ 0, n_dims, mode, n_ctx }; memcpy(params + 4, &freq_base, sizeof(float)); memcpy(params + 5, &freq_scale, sizeof(float)); memcpy(params + 6, &xpos_base, sizeof(float)); @@ -6995,6 +6999,7 @@ static struct ggml_tensor * ggml_rope_impl( result->op = GGML_OP_ROPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; + result->src[1] = b; return result; } @@ -7002,55 +7007,55 @@ static struct ggml_tensor * ggml_rope_impl( struct ggml_tensor * ggml_rope( struct ggml_context * ctx, struct ggml_tensor * a, - int n_past, + struct ggml_tensor * b, int n_dims, int mode, int n_ctx) { - return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, false); + return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, false); } struct ggml_tensor * ggml_rope_inplace( struct ggml_context * ctx, struct ggml_tensor * a, - int n_past, + struct ggml_tensor * b, int n_dims, int mode, int n_ctx) { - return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, true); + return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, 10000.0f, 1.0f, 0.0f, false, true); } struct ggml_tensor * ggml_rope_custom( struct ggml_context * ctx, struct ggml_tensor * a, - int n_past, + struct ggml_tensor * b, int n_dims, int mode, int n_ctx, float freq_base, float freq_scale) { - return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, false); + return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, false); } struct ggml_tensor * ggml_rope_custom_inplace( struct ggml_context * ctx, struct ggml_tensor * a, - int n_past, + struct ggml_tensor * b, int n_dims, int mode, int n_ctx, float freq_base, float freq_scale) { - return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, true); + return ggml_rope_impl(ctx, a, b, n_dims, mode, n_ctx, freq_base, freq_scale, 0.0f, false, true); } struct ggml_tensor * ggml_rope_xpos_inplace( struct ggml_context * ctx, struct ggml_tensor * a, - int n_past, + struct ggml_tensor * b, int n_dims, float base, bool down) { - return ggml_rope_impl(ctx, a, n_past, n_dims, 0, 0, 10000.0f, 1.0f, base, down, true); + return ggml_rope_impl(ctx, a, b, n_dims, 0, 0, 10000.0f, 1.0f, base, down, true); } // ggml_rope_back @@ -7058,7 +7063,7 @@ struct ggml_tensor * ggml_rope_xpos_inplace( struct ggml_tensor * ggml_rope_back( struct ggml_context * ctx, struct ggml_tensor * a, - int n_past, + struct ggml_tensor * b, int n_dims, int mode, int n_ctx, @@ -7066,7 +7071,10 @@ struct ggml_tensor * ggml_rope_back( float freq_scale, float xpos_base, bool xpos_down) { - GGML_ASSERT(n_past >= 0); + GGML_ASSERT(ggml_is_vector(b)); + GGML_ASSERT(b->type == GGML_TYPE_I32); + GGML_ASSERT(a->ne[2] == b->ne[0]); + GGML_ASSERT((mode & 4) == 0 && "ggml_rope_back() for ChatGLM not implemented yet"); bool is_node = false; @@ -7077,7 +7085,7 @@ struct ggml_tensor * ggml_rope_back( struct ggml_tensor * result = ggml_dup_tensor(ctx, a); - int32_t params[8] = { n_past, n_dims, mode, n_ctx }; + int32_t params[8] = { /*n_past*/ 0, n_dims, mode, n_ctx }; memcpy(params + 4, &freq_base, sizeof(float)); memcpy(params + 5, &freq_scale, sizeof(float)); memcpy(params + 6, &xpos_base, sizeof(float)); @@ -7087,6 +7095,7 @@ struct ggml_tensor * ggml_rope_back( result->op = GGML_OP_ROPE_BACK; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; + result->src[1] = b; return result; } @@ -12620,8 +12629,8 @@ static void ggml_compute_forward_clamp( static void ggml_compute_forward_rope_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, + const struct ggml_tensor * src1, struct ggml_tensor * dst) { - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } @@ -12631,9 +12640,9 @@ static void ggml_compute_forward_rope_f32( // these two only relevant for xPos RoPE: float xpos_base; - bool xpos_down; + bool xpos_down; - const int n_past = ((int32_t *) dst->op_params)[0]; + //const int n_past = ((int32_t *) dst->op_params)[0]; const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; const int n_ctx = ((int32_t *) dst->op_params)[3]; @@ -12669,14 +12678,14 @@ static void ggml_compute_forward_rope_f32( const float theta_scale = powf(freq_base, -2.0f/n_dims); - const bool is_skip = mode & 1; const bool is_neox = mode & 2; const bool is_glm = mode & 4; - const bool is_diff = mode & 8; // TODO: temporary + + const int32_t * pos = (const int32_t *) src1->data; for (int64_t i3 = 0; i3 < ne3; i3++) { - for (int64_t i2 = (is_skip ? n_past : 0); i2 < ne2; i2++) { - const int64_t p = is_diff ? n_past : is_skip ? i2 : n_past + i2; + for (int64_t i2 = 0; i2 < ne2; i2++) { + const int64_t p = pos[i2]; for (int64_t i1 = 0; i1 < ne1; i1++) { if (ir++ < ir0) continue; if (ir > ir1) break; @@ -12713,7 +12722,7 @@ static void ggml_compute_forward_rope_f32( const float cos_theta = cosf(theta); const float sin_theta = sinf(theta); // zeta scaling for xPos only: - float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), (n_past + i2) / xpos_base) : 1.0f; + float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f; if (xpos_down) zeta = 1.0f / zeta; theta *= theta_scale; @@ -12758,8 +12767,8 @@ static void ggml_compute_forward_rope_f32( static void ggml_compute_forward_rope_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, + const struct ggml_tensor * src1, struct ggml_tensor * dst) { - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } @@ -12767,15 +12776,13 @@ static void ggml_compute_forward_rope_f16( float freq_base; float freq_scale; - const int n_past = ((int32_t *) dst->op_params)[0]; + //const int n_past = ((int32_t *) dst->op_params)[0]; const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; const int n_ctx = ((int32_t *) dst->op_params)[3]; memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float)); memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float)); - assert(n_past >= 0); - GGML_TENSOR_UNARY_OP_LOCALS; //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); @@ -12806,9 +12813,11 @@ static void ggml_compute_forward_rope_f16( const bool is_neox = mode & 2; const bool is_glm = mode & 4; + const int32_t * pos = (const int32_t *) src1->data; + for (int64_t i3 = 0; i3 < ne3; i3++) { - for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) { - const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2); + for (int64_t i2 = 0; i2 < ne2; i2++) { + const int64_t p = pos[i2]; for (int64_t i1 = 0; i1 < ne1; i1++) { if (ir++ < ir0) continue; if (ir > ir1) break; @@ -12887,15 +12896,16 @@ static void ggml_compute_forward_rope_f16( static void ggml_compute_forward_rope( const struct ggml_compute_params * params, const struct ggml_tensor * src0, + const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F16: { - ggml_compute_forward_rope_f16(params, src0, dst); + ggml_compute_forward_rope_f16(params, src0, src1, dst); } break; case GGML_TYPE_F32: { - ggml_compute_forward_rope_f32(params, src0, dst); + ggml_compute_forward_rope_f32(params, src0, src1, dst); } break; default: { @@ -12909,6 +12919,7 @@ static void ggml_compute_forward_rope( static void ggml_compute_forward_rope_back_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, + const struct ggml_tensor * src1, struct ggml_tensor * dst) { if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -12926,7 +12937,7 @@ static void ggml_compute_forward_rope_back_f32( float xpos_base; bool xpos_down; - const int n_past = ((int32_t *) dst->op_params)[0]; + //const int n_past = ((int32_t *) dst->op_params)[0]; const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; const int n_ctx = ((int32_t *) dst->op_params)[3]; UNUSED(n_ctx); @@ -12935,8 +12946,6 @@ static void ggml_compute_forward_rope_back_f32( memcpy(&xpos_base, (int32_t *) dst->op_params + 6, sizeof(float)); memcpy(&xpos_down, (int32_t *) dst->op_params + 7, sizeof(bool)); - assert(n_past >= 0); - GGML_TENSOR_UNARY_OP_LOCALS; //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); @@ -12963,9 +12972,11 @@ static void ggml_compute_forward_rope_back_f32( const bool is_neox = mode & 2; + const int32_t * pos = (const int32_t *) src1->data; + for (int64_t i3 = 0; i3 < ne3; i3++) { - for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) { - const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2); + for (int64_t i2 = 0; i2 < ne2; i2++) { + const int64_t p = pos[i2]; for (int64_t i1 = 0; i1 < ne1; i1++) { if (ir++ < ir0) continue; if (ir > ir1) break; @@ -12977,7 +12988,7 @@ static void ggml_compute_forward_rope_back_f32( const float cos_theta = cosf(theta); const float sin_theta = sinf(theta); // zeta scaling for xPos only: - float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), (n_past + i2) / xpos_base) : 1.0f; + float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f; if (xpos_down) zeta = 1.0f / zeta; theta *= theta_scale; @@ -13020,6 +13031,7 @@ static void ggml_compute_forward_rope_back_f32( static void ggml_compute_forward_rope_back_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, + const struct ggml_tensor * src1, struct ggml_tensor * dst) { if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { @@ -13030,12 +13042,10 @@ static void ggml_compute_forward_rope_back_f16( // dx = rope_back(dy, src1) // src0 is dy, src1 contains options - const int n_past = ((int32_t *) dst->op_params)[0]; + //const int n_past = ((int32_t *) dst->op_params)[0]; const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; - assert(n_past >= 0); - GGML_TENSOR_UNARY_OP_LOCALS; //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); @@ -13062,9 +13072,11 @@ static void ggml_compute_forward_rope_back_f16( const bool is_neox = mode & 2; + const int32_t * pos = (const int32_t *) src1->data; + for (int64_t i3 = 0; i3 < ne3; i3++) { - for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) { - const int64_t p = ((mode & 1) == 0 ? n_past + i2 : i2); + for (int64_t i2 = 0; i2 < ne2; i2++) { + const int64_t p = pos[i2]; for (int64_t i1 = 0; i1 < ne1; i1++) { if (ir++ < ir0) continue; if (ir > ir1) break; @@ -13116,15 +13128,16 @@ static void ggml_compute_forward_rope_back_f16( static void ggml_compute_forward_rope_back( const struct ggml_compute_params * params, const struct ggml_tensor * src0, + const struct ggml_tensor * src1, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F16: { - ggml_compute_forward_rope_back_f16(params, src0, dst); + ggml_compute_forward_rope_back_f16(params, src0, src1, dst); } break; case GGML_TYPE_F32: { - ggml_compute_forward_rope_back_f32(params, src0, dst); + ggml_compute_forward_rope_back_f32(params, src0, src1, dst); } break; default: { @@ -15861,11 +15874,11 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm } break; case GGML_OP_ROPE: { - ggml_compute_forward_rope(params, tensor->src[0], tensor); + ggml_compute_forward_rope(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_ROPE_BACK: { - ggml_compute_forward_rope_back(params, tensor->src[0], tensor); + ggml_compute_forward_rope_back(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_ALIBI: { @@ -16503,7 +16516,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { // necessary for llama if (src0->grad) { - const int n_past = ((int32_t *) tensor->op_params)[0]; + //const int n_past = ((int32_t *) tensor->op_params)[0]; const int n_dims = ((int32_t *) tensor->op_params)[1]; const int mode = ((int32_t *) tensor->op_params)[2]; const int n_ctx = ((int32_t *) tensor->op_params)[3]; @@ -16520,7 +16533,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor src0->grad, ggml_rope_back(ctx, tensor->grad, - n_past, + src1, n_dims, mode, n_ctx, @@ -16534,7 +16547,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor case GGML_OP_ROPE_BACK: { if (src0->grad) { - const int n_past = ((int32_t *) tensor->op_params)[0]; + //const int n_past = ((int32_t *) tensor->op_params)[0]; const int n_dims = ((int32_t *) tensor->op_params)[1]; const int mode = ((int32_t *) tensor->op_params)[2]; const int n_ctx = ((int32_t *) tensor->op_params)[3]; @@ -16551,7 +16564,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor src0->grad, ggml_rope_impl(ctx, tensor->grad, - n_past, + src1, n_dims, mode, n_ctx, diff --git a/ggml.h b/ggml.h index f45456876..e2bfa1ae4 100644 --- a/ggml.h +++ b/ggml.h @@ -1219,14 +1219,15 @@ extern "C" { struct ggml_tensor * b); // rotary position embedding - // if mode & 1 == 1, skip n_past elements + // if mode & 1 == 1, skip n_past elements (DEPRECATED) // if mode & 2 == 1, GPT-NeoX style // if mode & 4 == 1, ChatGLM style - // TODO: avoid creating a new tensor every time + // + // b is an int32 vector with size a->ne[2], it contains the positions GGML_API struct ggml_tensor * ggml_rope( struct ggml_context * ctx, struct ggml_tensor * a, - int n_past, + struct ggml_tensor * b, int n_dims, int mode, int n_ctx); @@ -1235,7 +1236,7 @@ extern "C" { GGML_API struct ggml_tensor * ggml_rope_inplace( struct ggml_context * ctx, struct ggml_tensor * a, - int n_past, + struct ggml_tensor * b, int n_dims, int mode, int n_ctx); @@ -1244,7 +1245,7 @@ extern "C" { GGML_API struct ggml_tensor * ggml_rope_custom( struct ggml_context * ctx, struct ggml_tensor * a, - int n_past, + struct ggml_tensor * b, int n_dims, int mode, int n_ctx, @@ -1255,7 +1256,7 @@ extern "C" { GGML_API struct ggml_tensor * ggml_rope_custom_inplace( struct ggml_context * ctx, struct ggml_tensor * a, - int n_past, + struct ggml_tensor * b, int n_dims, int mode, int n_ctx, @@ -1266,7 +1267,7 @@ extern "C" { GGML_API struct ggml_tensor * ggml_rope_xpos_inplace( struct ggml_context * ctx, struct ggml_tensor * a, - int n_past, + struct ggml_tensor * b, int n_dims, float base, bool down); @@ -1276,7 +1277,7 @@ extern "C" { GGML_API struct ggml_tensor * ggml_rope_back( struct ggml_context * ctx, struct ggml_tensor * a, - int n_past, + struct ggml_tensor * b, int n_dims, int mode, int n_ctx, diff --git a/llama.cpp b/llama.cpp index f129c59f3..9d41689f7 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2428,6 +2428,16 @@ static struct ggml_cgraph * llm_build_llama( } } + // KQ_pos - contains the positions + struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); + ggml_allocr_alloc(lctx.alloc, KQ_pos); + if (!ggml_allocr_is_measure(lctx.alloc)) { + int * data = (int *) KQ_pos->data; + for (int i = 0; i < N; ++i) { + data[i] = n_past + i; + } + } + for (int il = 0; il < n_layer; ++il) { ggml_format_name(inpL, "layer_inp_%d", il); @@ -2464,11 +2474,11 @@ static struct ggml_cgraph * llm_build_llama( offload_func_kq(tmpq); ggml_set_name(tmpq, "tmpq"); - struct ggml_tensor * Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, N), n_past, n_embd_head, 0, 0, freq_base, freq_scale); + struct ggml_tensor * Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, N), KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale); offload_func_kq(Kcur); ggml_set_name(Kcur, "Kcur"); - struct ggml_tensor * Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, N), n_past, n_embd_head, 0, 0, freq_base, freq_scale); + struct ggml_tensor * Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, N), KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale); offload_func_kq(Qcur); ggml_set_name(Qcur, "Qcur"); @@ -2754,6 +2764,7 @@ static struct ggml_cgraph * llm_build_baichaun( } #endif // GGML_USE_CUBLAS + // KQ_scale struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); ggml_allocr_alloc(lctx.alloc, KQ_scale); if (!ggml_allocr_is_measure(lctx.alloc)) { @@ -2761,6 +2772,32 @@ static struct ggml_cgraph * llm_build_baichaun( } ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)"); + // KQ_mask + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_past + N, N, 1); + ggml_allocr_alloc(lctx.alloc, KQ_mask); + if (!ggml_allocr_is_measure(lctx.alloc)) { + float * data = (float *) KQ_mask->data; + memset(data, 0, ggml_nbytes(KQ_mask)); + + for (int h = 0; h < 1; ++h) { + for (int j = 0; j < N; ++j) { + for (int i = n_past + j + 1; i < n_past + N; ++i) { + data[h*(n_past + N)*N + j*(n_past + N) + i] = -INFINITY; + } + } + } + } + + // KQ_pos - contains the positions + struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); + ggml_allocr_alloc(lctx.alloc, KQ_pos); + if (!ggml_allocr_is_measure(lctx.alloc)) { + int * data = (int *) KQ_pos->data; + for (int i = 0; i < N; ++i) { + data[i] = n_past + i; + } + } + for (int il = 0; il < n_layer; ++il) { ggml_format_name(inpL, "layer_inp_%d", il); @@ -2801,11 +2838,11 @@ static struct ggml_cgraph * llm_build_baichaun( struct ggml_tensor * Qcur; switch (model.type) { case MODEL_7B: - Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, N), n_past, n_embd_head, 0, 0, freq_base, freq_scale); - Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, N), n_past, n_embd_head, 0, 0, freq_base, freq_scale); + Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, N), KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale); + Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, N), KQ_pos, n_embd_head, 0, 0, freq_base, freq_scale); break; case MODEL_13B: - Kcur = ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N); + Kcur = ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N); Qcur = ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, N); break; default: @@ -2874,12 +2911,14 @@ static struct ggml_cgraph * llm_build_baichaun( switch (model.type) { case MODEL_7B: - KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past); + KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask); + //KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past); break; case MODEL_13B: KQ_scaled_alibi =ggml_alibi(ctx0, KQ_scaled, n_past, n_head, 8); ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi"); - KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled_alibi, n_past); + KQ_masked = ggml_add(ctx0, KQ_scaled_alibi, KQ_mask); + //KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled_alibi, n_past); break; default: GGML_ASSERT(false); @@ -3114,6 +3153,7 @@ static struct ggml_cgraph * llm_build_falcon( } #endif // GGML_USE_CUBLAS + // KQ_scale struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); ggml_allocr_alloc(lctx.alloc, KQ_scale); if (!ggml_allocr_is_measure(lctx.alloc)) { @@ -3121,6 +3161,32 @@ static struct ggml_cgraph * llm_build_falcon( } ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)"); + // KQ_mask + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_past + N, N, 1); + ggml_allocr_alloc(lctx.alloc, KQ_mask); + if (!ggml_allocr_is_measure(lctx.alloc)) { + float * data = (float *) KQ_mask->data; + memset(data, 0, ggml_nbytes(KQ_mask)); + + for (int h = 0; h < 1; ++h) { + for (int j = 0; j < N; ++j) { + for (int i = n_past + j + 1; i < n_past + N; ++i) { + data[h*(n_past + N)*N + j*(n_past + N) + i] = -INFINITY; + } + } + } + } + + // KQ_pos - contains the positions + struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); + ggml_allocr_alloc(lctx.alloc, KQ_pos); + if (!ggml_allocr_is_measure(lctx.alloc)) { + int * data = (int *) KQ_pos->data; + for (int i = 0; i < N; ++i) { + data[i] = n_past + i; + } + } + for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * attn_norm; @@ -3197,9 +3263,9 @@ static struct ggml_cgraph * llm_build_falcon( offload_func_v(tmpv); // using mode = 2 for neox mode - struct ggml_tensor * Qcur = ggml_rope_custom(ctx0, tmpq, n_past, n_embd_head, 2, 0, freq_base, freq_scale); + struct ggml_tensor * Qcur = ggml_rope_custom(ctx0, tmpq, KQ_pos, n_embd_head, 2, 0, freq_base, freq_scale); offload_func_kq(Qcur); - struct ggml_tensor * Kcur = ggml_rope_custom(ctx0, tmpk, n_past, n_embd_head, 2, 0, freq_base, freq_scale); + struct ggml_tensor * Kcur = ggml_rope_custom(ctx0, tmpk, KQ_pos, n_embd_head, 2, 0, freq_base, freq_scale); offload_func_kq(Kcur); { diff --git a/tests/test-grad0.cpp b/tests/test-grad0.cpp index 468cde66a..7b0c0fcdb 100644 --- a/tests/test-grad0.cpp +++ b/tests/test-grad0.cpp @@ -1404,6 +1404,11 @@ int main(int argc, const char ** argv) { for (int n_past = 1; n_past < ne2[2]; ++n_past) { x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f); + struct ggml_tensor * p = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne2[2]); + for (int i = 0; i < ne2[2]; ++i) { + ((int32_t *) p->data)[i] = n_past + i; + } + ggml_set_param(ctx0, x[0]); const bool skip_past = (mode & 1); @@ -1415,7 +1420,7 @@ int main(int argc, const char ** argv) { continue; } - struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], n_past, n_rot, mode, 0)); + struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], p, n_rot, mode, 0)); GGML_PRINT_DEBUG("rope f32: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode); check_gradient("rope f32", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY); @@ -1438,6 +1443,11 @@ int main(int argc, const char ** argv) { for (int n_past = 1; n_past < ne2[2]; ++n_past) { x[0] = get_random_tensor_f16(ctx0, ndims, ne2, -1.0f, 1.0f); + struct ggml_tensor * p = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne2[2]); + for (int i = 0; i < ne2[2]; ++i) { + ((int32_t *) p->data)[i] = n_past + i; + } + ggml_set_param(ctx0, x[0]); const bool skip_past = (mode & 1); @@ -1449,7 +1459,7 @@ int main(int argc, const char ** argv) { continue; } - struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], n_past, n_rot, mode, 0)); + struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], p, n_rot, mode, 0)); GGML_PRINT_DEBUG("rope f16: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode); check_gradient("rope f16", ctx0, x, f, ndims, nargs, 1e-1f, 1e-1f, INFINITY); diff --git a/tests/test-rope.cpp b/tests/test-rope.cpp index a35bbd353..26c1f42dc 100644 --- a/tests/test-rope.cpp +++ b/tests/test-rope.cpp @@ -144,7 +144,17 @@ int main(int /*argc*/, const char ** /*argv*/) { const int64_t ne[4] = { 2*n_rot, 32, 73, 1 }; const int n_past_0 = 100; - const int n_past_1 = 33; + const int n_past_2 = 33; + + struct ggml_tensor * p0 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2]); + struct ggml_tensor * p1 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2]); + struct ggml_tensor * p2 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne[2]); + + for (int i = 0; i < ne[2]; ++i) { + ((int32_t *) p0->data)[i] = n_past_0 + i; + ((int32_t *) p1->data)[i] = n_past_2 - n_past_0; + ((int32_t *) p2->data)[i] = n_past_2 + i; + } // test mode 0, 2, 4 (standard, GPT-NeoX, GLM) const int mode = m == 0 ? 0 : m == 1 ? 2 : 4; @@ -152,12 +162,12 @@ int main(int /*argc*/, const char ** /*argv*/) { x = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f); // 100, 101, 102, ..., 172 - struct ggml_tensor * r0 = ggml_rope(ctx0, x, n_past_0, n_rot, mode, 1024); + struct ggml_tensor * r0 = ggml_rope(ctx0, x, p0, n_rot, mode, 1024); // -67, -67, -67, ..., -67 - struct ggml_tensor * r1 = ggml_rope(ctx0, r0, n_past_1 - n_past_0, n_rot, mode + 8, 1024); // diff mode + struct ggml_tensor * r1 = ggml_rope(ctx0, r0, p1, n_rot, mode, 1024); // "context swap", i.e. forget n_past_0 - n_past_2 tokens // 33, 34, 35, ..., 105 - struct ggml_tensor * r2 = ggml_rope(ctx0, x, n_past_1, n_rot, mode, 1024); + struct ggml_tensor * r2 = ggml_rope(ctx0, x, p2, n_rot, mode, 1024); ggml_cgraph * gf = ggml_new_graph(ctx0);