From 938f6087421889a3af7d0786c64406ced2be81b8 Mon Sep 17 00:00:00 2001 From: Chenguang Li <87689256+noemotiovon@users.noreply.github.com> Date: Fri, 29 Nov 2024 14:46:55 +0800 Subject: [PATCH] CANN: RoPE operator optimization (#10563) * [cann] RoPE operator optimization * [CANN]Code Formatting --------- Co-authored-by: noemotiovon --- ggml/src/ggml-cann/aclnn_ops.cpp | 241 ++++++++++++++++++++++++++++--- ggml/src/ggml-cann/ggml-cann.cpp | 13 +- 2 files changed, 222 insertions(+), 32 deletions(-) diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp index d707efc5d..b2d857e1e 100644 --- a/ggml/src/ggml-cann/aclnn_ops.cpp +++ b/ggml/src/ggml-cann/aclnn_ops.cpp @@ -2965,7 +2965,7 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, aclTensor* acl_cos_repeat_tensor, aclTensor* acl_sin_repeat_tensor, float theta_scale, float freq_scale, - bool is_neox) { + float attn_factor, bool is_neox) { // int sin/cos cache, cache has different repeat method depond on // @param.is_neox @@ -3017,6 +3017,7 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, ggml_type_size(src2->type), arange_ne, arange_nb, GGML_MAX_DIMS); aclnn_div_tensor(ctx, acl_theta_scale_tensor, acl_freq_factors_tensor, nullptr, true); + ACL_CHECK(aclDestroyTensor(acl_freq_factors_tensor)); } // position @@ -3047,16 +3048,6 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, aclnn_mul(ctx, acl_position_tensor, acl_theta_scale_tensor, acl_theta_tensor); - // // power[] * position[] * freq_scale / freq_factors[] - // ggml_cann_pool_alloc theta_final_allocator(ctx.pool(), - // theta_length * - // sizeof(float_t)); - // aclTensor* acl_theat_final_tensor = aclnn_zero( - // ctx, theta_final_allocator.get(), sizeof(float_t) * theta_length, - // theta_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t)); - // aclnn_inplace_addcdiv(ctx, acl_theat_final_tensor, acl_theta_tensor, - // acl_freq_factors_tensor, freq_scale); - // permute: [0,1,2,3]->[0,2,1,3] int64_t permute_ne[] = {arange_length, 1, position_length, 1}; size_t permute_nb[GGML_MAX_DIMS]; @@ -3092,6 +3083,12 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst, GGML_MAX_DIMS, ACL_FORMAT_ND); aclnn_cos(ctx, acl_permute_tensor, acl_cos_tensor); + // attn_factor + if (attn_factor != 1) { + aclnn_muls(ctx, acl_sin_tensor, attn_factor, nullptr, true); + aclnn_muls(ctx, acl_cos_tensor, attn_factor, nullptr, true); + } + // repeat if (is_neox) { int64_t repeatsArray[] = {1, 1, 1, 2}; @@ -3155,15 +3152,11 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { memcpy(&beta_fast, (int32_t*)dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t*)dst->op_params + 10, sizeof(float)); - // TODO: attn_factor != 1 - GGML_ASSERT(attn_factor == 1); // TODO: n_dims <= ne0 GGML_ASSERT(n_dims == ne0); GGML_ASSERT(n_dims % 2 == 0); // TODO: ext_factor != 0 GGML_ASSERT(ext_factor == 0); - // TODO: type == GGML_TYPE_F16 - GGML_ASSERT(src0->type == GGML_TYPE_F32); const float theta_scale = powf(freq_base, -2.0f / n_dims); @@ -3194,7 +3187,217 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ggml_cann_create_tensor(cos_buffer, ACL_FLOAT, sizeof(float_t), sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS); aclnn_cache_init(ctx, dst, acl_cos_reshape_tensor, acl_sin_reshape_tensor, - theta_scale, freq_scale, is_neox); + theta_scale, freq_scale, attn_factor, is_neox); + + aclTensor* acl_src = ggml_cann_create_tensor(src0); + aclTensor* acl_dst = ggml_cann_create_tensor(dst); + +#ifdef ASCEND_310P + // Special ROPE operation for 310P + + // roll input + void* input_roll_buffer; + aclTensor* acl_minus_one_tensor; + void* minus_one_scale_buffer = nullptr; + ggml_cann_pool_alloc roll_allocator(ctx.pool(), ggml_nbytes(src0)); + ggml_cann_pool_alloc minus_one_scale_allocator( + ctx.pool(), sizeof(float_t) * src0->ne[0]); + if (!is_neox) { + // roll input: [q0,q1,q2,q3,...] -> [q1,q0,q3,q2,...] + input_roll_buffer = roll_allocator.get(); + int64_t input_roll_ne[4] = {2, src0->ne[1] * (src0->ne[0] / 2), + src0->ne[2], src0->ne[3]}; + size_t input_roll_nb[GGML_MAX_DIMS]; + input_roll_nb[0] = ggml_type_size(src0->type); + for (int i = 1; i < GGML_MAX_DIMS; i++) { + input_roll_nb[i] = input_roll_nb[i - 1] * input_roll_ne[i - 1]; + } + aclTensor* acl_input_roll_tensor = ggml_cann_create_tensor( + input_roll_buffer, ggml_cann_type_mapping(src0->type), + ggml_type_size(src0->type), input_roll_ne, input_roll_nb, + GGML_MAX_DIMS); + aclTensor* acl_input_tensor = ggml_cann_create_tensor( + src0->data, ggml_cann_type_mapping(src0->type), + ggml_type_size(src0->type), input_roll_ne, input_roll_nb, + GGML_MAX_DIMS); + + int64_t shifts[] = {1}; + int64_t dims[] = {3}; + aclnn_roll(ctx, acl_input_tensor, acl_input_roll_tensor, shifts, dims); + ACL_CHECK(aclDestroyTensor(acl_input_roll_tensor)); + ACL_CHECK(aclDestroyTensor(acl_input_tensor)); + + // init [-1, 1, -1, 1, ...] + minus_one_scale_buffer = minus_one_scale_allocator.get(); + + int64_t minus_one_ne[4] = {src0->ne[0], 1, 1, 1}; + size_t minus_one_nb[GGML_MAX_DIMS]; + minus_one_nb[0] = sizeof(float_t); + for (int i = 1; i < GGML_MAX_DIMS; i++) { + minus_one_nb[i] = minus_one_nb[i - 1] * minus_one_ne[i - 1]; + } + acl_minus_one_tensor = aclnn_values( + ctx, minus_one_scale_buffer, sizeof(float_t) * src0->ne[0], + minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t), 1); + int64_t dim = 3; + int64_t* index = new int64_t[src0->ne[0]]; + for (int i = 0; i < src0->ne[0]; i++) { + index[i] = i / 2 * 2; + } + int64_t index_num = src0->ne[0]; + float value = -1; + aclnn_index_fill_tensor(ctx, acl_minus_one_tensor, dim, index, + index_num, value); + } else { + // roll input: [q0,q1,q2,...] -> + // [q_half,q_half+1,...,q_end,q0,q1,...q_half-1] + input_roll_buffer = roll_allocator.get(); + aclTensor* acl_input_roll_tensor = ggml_cann_create_tensor( + input_roll_buffer, ggml_cann_type_mapping(src0->type), + ggml_type_size(src0->type), src0->ne, src0->nb, GGML_MAX_DIMS); + aclTensor* acl_input_tensor = ggml_cann_create_tensor(src0); + + int64_t shifts[] = {src0->ne[0] / 2}; + int64_t dims[] = {3}; + aclnn_roll(ctx, acl_input_tensor, acl_input_roll_tensor, shifts, dims); + + ACL_CHECK(aclDestroyTensor(acl_input_roll_tensor)); + ACL_CHECK(aclDestroyTensor(acl_input_tensor)); + // init [-1, -1, -1, 1, 1，1，...] + minus_one_scale_buffer = minus_one_scale_allocator.get(); + int64_t minus_one_ne[4] = {src0->ne[0], 1, 1, 1}; + size_t minus_one_nb[GGML_MAX_DIMS]; + minus_one_nb[0] = sizeof(float_t); + for (int i = 1; i < GGML_MAX_DIMS; i++) { + minus_one_nb[i] = minus_one_nb[i - 1] * minus_one_ne[i - 1]; + } + acl_minus_one_tensor = aclnn_values( + ctx, minus_one_scale_buffer, sizeof(float_t) * src0->ne[0], + minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t), 1); + // -1 * first half + int64_t first_half_ne[4] = {src0->ne[0] / 2, 1, 1, 1}; + size_t first_half_nb[GGML_MAX_DIMS]; + first_half_nb[0] = sizeof(float_t); + for (int i = 1; i < GGML_MAX_DIMS; i++) { + first_half_nb[i] = first_half_nb[i - 1] * first_half_ne[i - 1]; + } + aclTensor* acl_first_half_tensor = ggml_cann_create_tensor( + minus_one_scale_buffer, ACL_FLOAT, sizeof(float_t), first_half_ne, + first_half_nb, GGML_MAX_DIMS); + bool inplace = true; + float scale = -1; + aclnn_muls(ctx, acl_first_half_tensor, scale, nullptr, inplace); + ACL_CHECK(aclDestroyTensor(acl_first_half_tensor)); + } + + // TODO: n_dims < ne0 + GGML_ASSERT(n_dims == src0->ne[0]); + + // input * scale + ggml_cann_pool_alloc roll_mul_scale_allocator(ctx.pool(), + ggml_nbytes(src0)); + void* input_roll_mul_scale_buffer = roll_mul_scale_allocator.get(); + size_t input_nb[GGML_MAX_DIMS]; + input_nb[0] = ggml_type_size(src0->type); + for (int i = 1; i < GGML_MAX_DIMS; i++) { + input_nb[i] = input_nb[i - 1] * src0->ne[i - 1]; + } + aclTensor* acl_input_roll_mul_scale_tensor = ggml_cann_create_tensor( + input_roll_mul_scale_buffer, ggml_cann_type_mapping(src0->type), + ggml_type_size(src0->type), src0->ne, input_nb, GGML_MAX_DIMS); + aclTensor* acl_input_roll_reshape_tensor = ggml_cann_create_tensor( + input_roll_buffer, ggml_cann_type_mapping(src0->type), + ggml_type_size(src0->type), src0->ne, input_nb, GGML_MAX_DIMS); + + aclnn_mul(ctx, acl_input_roll_reshape_tensor, acl_minus_one_tensor, + acl_input_roll_mul_scale_tensor); + + // output + void* output_fp32_buffer; + if (src0->type == GGML_TYPE_F32) { + aclnn_inplace_mul(ctx, acl_src, acl_cos_reshape_tensor); + aclnn_inplace_mul(ctx, acl_input_roll_mul_scale_tensor, + acl_sin_reshape_tensor); + aclnn_add(ctx, acl_src, acl_input_roll_mul_scale_tensor, acl_dst); + // TODO: ne0 != n_dims in mode2 + } else if (src0->type == GGML_TYPE_F16) { + size_t input_fp32_nb[GGML_MAX_DIMS]; + input_fp32_nb[0] = sizeof(float_t); + for (int i = 1; i < GGML_MAX_DIMS; i++) { + input_fp32_nb[i] = input_fp32_nb[i - 1] * dst->ne[i - 1]; + } + ggml_cann_pool_alloc fp32_allocator1( + ctx.pool(), ggml_nelements(dst) * sizeof(float_t)); + void* input_fp32_buffer1 = fp32_allocator1.get(); + aclTensor* input_fp32_tensor1 = ggml_cann_create_tensor( + input_fp32_buffer1, ACL_FLOAT, sizeof(float_t), dst->ne, + input_fp32_nb, GGML_MAX_DIMS); + ggml_cann_pool_alloc fp32_allocator2( + ctx.pool(), ggml_nelements(dst) * sizeof(float_t)); + void* input_fp32_buffer2 = fp32_allocator2.get(); + aclTensor* input_fp32_tensor2 = ggml_cann_create_tensor( + input_fp32_buffer2, ACL_FLOAT, sizeof(float_t), dst->ne, + input_fp32_nb, GGML_MAX_DIMS); + + ggml_cann_pool_alloc fp32_allocator( + ctx.pool(), ggml_nelements(dst) * sizeof(float_t)); + output_fp32_buffer = fp32_allocator.get(); + aclTensor* output_fp32_tensor = ggml_cann_create_tensor( + output_fp32_buffer, ACL_FLOAT, sizeof(float_t), dst->ne, + input_fp32_nb, GGML_MAX_DIMS); + aclnn_mul(ctx, acl_src, acl_cos_reshape_tensor, input_fp32_tensor1); + aclnn_mul(ctx, acl_input_roll_mul_scale_tensor, acl_sin_reshape_tensor, + input_fp32_tensor2); + aclnn_add(ctx, input_fp32_tensor1, input_fp32_tensor2, + output_fp32_tensor); + aclnn_cast(ctx, output_fp32_tensor, acl_dst, ACL_FLOAT16); + + ACL_CHECK(aclDestroyTensor(input_fp32_tensor1)); + ACL_CHECK(aclDestroyTensor(input_fp32_tensor2)); + ACL_CHECK(aclDestroyTensor(output_fp32_tensor)); + ACL_CHECK(aclDestroyTensor(acl_sin_reshape_tensor)); + ACL_CHECK(aclDestroyTensor(acl_minus_one_tensor)); + ACL_CHECK(aclDestroyTensor(acl_input_roll_mul_scale_tensor)); + ACL_CHECK(aclDestroyTensor(acl_input_roll_reshape_tensor)); + ACL_CHECK(aclDestroyTensor(acl_src)); + } + return; +#endif + + // src0 == GGML_TYPE_F16 + // TODO: optimization this `if` code + if (src0->type == GGML_TYPE_F16) { + ggml_cann_pool_alloc sin_final_allocator( + ctx.pool(), src0->ne[0] * src0->ne[2] * ggml_type_size(src0->type)); + ggml_cann_pool_alloc cos_final_allocator( + ctx.pool(), src0->ne[0] * src0->ne[2] * ggml_type_size(src0->type)); + void* sin_final_buffer = sin_final_allocator.get(); + void* cos_final_buffer = cos_final_allocator.get(); + + int64_t sin_final_ne[4] = {src0->ne[0], 1, src0->ne[2], 1}; + size_t sin_final_nb[GGML_MAX_DIMS]; + sin_final_nb[0] = ggml_type_size(src0->type); + for (int i = 1; i < GGML_MAX_DIMS; i++) { + sin_final_nb[i] = sin_final_nb[i - 1] * sin_final_ne[i - 1]; + } + aclTensor* acl_sin_final_tensor = ggml_cann_create_tensor( + sin_final_buffer, ggml_cann_type_mapping(src0->type), + ggml_type_size(src0->type), sin_final_ne, sin_final_nb, + GGML_MAX_DIMS); + aclTensor* acl_cos_final_tensor = ggml_cann_create_tensor( + cos_final_buffer, ggml_cann_type_mapping(src0->type), + ggml_type_size(src0->type), sin_final_ne, sin_final_nb, + GGML_MAX_DIMS); + + aclnn_cast(ctx, acl_sin_reshape_tensor, acl_sin_final_tensor, + ggml_cann_type_mapping(src0->type)); + aclnn_cast(ctx, acl_cos_reshape_tensor, acl_cos_final_tensor, + ggml_cann_type_mapping(src0->type)); + ACL_CHECK(aclDestroyTensor(acl_cos_reshape_tensor)); + ACL_CHECK(aclDestroyTensor(acl_sin_reshape_tensor)); + acl_sin_reshape_tensor = acl_sin_final_tensor; + acl_cos_reshape_tensor = acl_cos_final_tensor; + } uint64_t workspaceSize = 0; aclOpExecutor* executor; @@ -3206,10 +3409,8 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { acl_mode = 1; } - aclTensor* acl_x = ggml_cann_create_tensor(src0); - aclTensor* acl_dst = ggml_cann_create_tensor(dst); ACL_CHECK(aclnnRotaryPositionEmbeddingGetWorkspaceSize( - acl_x, acl_cos_reshape_tensor, acl_sin_reshape_tensor, acl_mode, + acl_src, acl_cos_reshape_tensor, acl_sin_reshape_tensor, acl_mode, acl_dst, &workspaceSize, &executor)); if (workspaceSize > 0) { ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); @@ -3219,7 +3420,7 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) { ACL_CHECK(aclnnRotaryPositionEmbedding(workspaceAddr, workspaceSize, executor, ctx.stream())); - ACL_CHECK(aclDestroyTensor(acl_x)); + ACL_CHECK(aclDestroyTensor(acl_src)); ACL_CHECK(aclDestroyTensor(acl_cos_reshape_tensor)); ACL_CHECK(aclDestroyTensor(acl_sin_reshape_tensor)); ACL_CHECK(aclDestroyTensor(acl_dst)); diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp index bcb54e444..04e25b8ab 100644 --- a/ggml/src/ggml-cann/ggml-cann.cpp +++ b/ggml/src/ggml-cann/ggml-cann.cpp @@ -1739,7 +1739,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, case GGML_OP_ROPE: { // TODO: with ops-test v == 1 float * ext_factor = (float*)((int32_t*)op->op_params + 7); - float * attn_factor = (float*)((int32_t*)op->op_params + 8); // TODO: n_dims <= ne0 if (op->src[0]->ne[0] != op->op_params[1]) { return false; @@ -1748,17 +1747,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, if (*ext_factor != 0) { return false; } - // TODO: attn_factor != 1 - if (*attn_factor != 1) { - return false; - } - //TODO: type == GGML_TYPE_F16 - switch (op->src[0]->type) { - case GGML_TYPE_F32: - return true; - default: - return false; - } + return true; } case GGML_OP_UPSCALE: { // aclnnUpsampleNearest2dGetWorkspaceSize not support