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zdnn: refactor codebase + add docs (#16178)

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* zdnn: initial matmul refactor

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: rm static from funcs

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: update ggml-zdnn.h

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: change header files to hpp

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: switch to common.hpp

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: move mulmat forward around

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: rm inline from utils

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* ggml-zdnn: code cleanup

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

* docs: add zDNN docs

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>

---------

Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
This commit is contained in:
Aaron Teo
2025-09-23 14:53:05 +08:00
committed by GitHub
parent 0bc7cc7154
commit 264f1b5187
11 changed files with 337 additions and 269 deletions
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193
ggml/src/ggml-zdnn/ggml-zdnn.cpp
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@@ -1,187 +1,38 @@
#include "zdnn.h"
#include "ggml-zdnn.h"
#include "ggml-zdnn-impl.h"
#include "ggml-impl.h"
#include "ggml-backend-impl.h"
#include "ggml-zdnn/common.hpp"
#include "ggml-zdnn/mmf.hpp"
#include "ggml-zdnn/utils.hpp"
#include "ggml.h"
#include <vector>
#include <memory>
#include <csignal>
#include <csignal> // raise(SIGTRAP)
#include <unistd.h>
inline zdnn_data_types ggml_zdnn_type_mapping(ggml_type type) {
switch (type) {
case GGML_TYPE_F32:
return FP32;
case GGML_TYPE_F16:
return FP16;
case GGML_TYPE_BF16:
return BFLOAT;
case GGML_TYPE_I8:
return INT8;
case GGML_TYPE_I32:
return INT32;
case GGML_TYPE_Q8_0:
return INT8;
default:
GGML_ABORT("%s: fatal: unable to determine zTensor data type",
__func__);
break;
}
static void ggml_zdnn_compute_forward_mul_mat(
const ggml_backend_zdnn_context * ctx,
ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0]; // weights
const ggml_tensor * src1 = dst->src[1]; // inputs
// TODO: implement support for quantized types
// we currently only support f32, f16, and bf16
ggml_zdnn_mul_mat_f(ctx, src0, src1, dst);
}
inline void ggml_zdnn_create_tensor(zdnn_tensor_desc & pre_tfm_desc,
zdnn_tensor_desc & tfm_desc,
zdnn_ztensor & ztensor,
const ggml_tensor * src,
const int64_t * ne,
const zdnn_data_layouts layout) {
zdnn_init_pre_transformed_desc(
layout,
ggml_zdnn_type_mapping(src->type),
&pre_tfm_desc,
ne[3], ne[2], ne[1], ne[0]
);
static bool ggml_zdnn_compute_forward(
ggml_backend_zdnn_context * ctx,
ggml_tensor * dst) {
ZDNN_CHECK(zdnn_generate_transformed_desc(&pre_tfm_desc, &tfm_desc));
ZDNN_CHECK(zdnn_init_ztensor_with_malloc(&pre_tfm_desc, &tfm_desc, &ztensor));
}
inline void ggml_zdnn_load_tensor(zdnn_ztensor & ztensor,
void * buffer) {
ZDNN_CHECK(zdnn_transform_ztensor(&ztensor, buffer));
}
inline void ggml_zdnn_init_tensor(ggml_backend_zdnn_buffer * buffer, const ggml_tensor * tensor) {
switch (tensor->op) {
case GGML_OP_MUL_MAT:
{
zdnn_init_pre_transformed_desc(
ZDNN_2D,
ggml_zdnn_type_mapping(tensor->type),
&buffer->pre_tfm_desc,
tensor->ne[1], tensor->ne[0]
);
} break;
default:
{
// For 4D tensors, GGML uses NCHW layout. However, because zDNN
// automatically transforms everything to NHWC, we will use it
// directly to avoid the performance penalty changing the
// layout and reshaping the tensor.
zdnn_init_pre_transformed_desc(
ZDNN_NHWC,
ggml_zdnn_type_mapping(tensor->type),
&buffer->pre_tfm_desc,
tensor->ne[3], tensor->ne[2], tensor->ne[1], tensor->ne[0]
);
// TODO: Consider adding a ggml check.
// TODO: If tensor = 4D, use ZDNN_NCHW by default.
// TODO: If tensor = 2D, use ZDNN_NHWC by default.
} break;
}
ZDNN_CHECK(zdnn_generate_transformed_desc(&buffer->pre_tfm_desc, &buffer->tfm_desc));
ZDNN_CHECK(zdnn_init_ztensor_with_malloc(&buffer->pre_tfm_desc, &buffer->tfm_desc, &buffer->ztensor));
}
static void ggml_zdnn_mul_mat_op(ggml_backend_zdnn_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_TENSOR_BINARY_OP_LOCALS;
const enum ggml_type type = src0->type;
GGML_ASSERT(ne0 == ne01);
GGML_ASSERT(ne1 == ne11);
GGML_ASSERT(ne2 == ne12);
GGML_ASSERT(ne3 == ne13);
// we don't support permuted src0 or src1
GGML_ASSERT(nb00 == ggml_type_size(type));
GGML_ASSERT(nb10 == ggml_type_size(src1->type));
// dst cannot be transposed or permuted
GGML_ASSERT(nb0 == sizeof(float));
GGML_ASSERT(nb0 <= nb1);
GGML_ASSERT(nb1 <= nb2);
GGML_ASSERT(nb2 <= nb3);
const ggml_tensor * weights = src0;
const ggml_tensor * inputs = src1;
ggml_tensor * output = dst;
ggml_backend_zdnn_buffer * weights_extra = (ggml_backend_zdnn_buffer *)weights->extra;
ggml_backend_zdnn_buffer * inputs_extra = (ggml_backend_zdnn_buffer *)inputs->extra;
ggml_backend_zdnn_buffer * output_extra = (ggml_backend_zdnn_buffer *)output->extra;
ggml_backend_zdnn_buffer * bias_extra = (ggml_backend_zdnn_buffer *)output_extra->extra;
const int64_t weights_rows = ne01;
const int64_t weights_cols = ne00;
const int64_t inputs_rows = ne11;
const int64_t inputs_cols = ne10;
assert(inputs_cols == weights_cols);
const int64_t output_rows = ne1;
const int64_t output_cols = ne0;
// GGML_LOG_INFO("%s: tensor '%s' tensor dimensions: [%ld, %ld, %ld, %ld] pre_tfm_desc dimensions: [%ld, %ld, %ld, %ld]\n",
// __func__, weights_extra->name,
// weights->ne[3], weights->ne[2], weights->ne[1], weights->ne[0],
// weights_extra->pre_tfm_desc.dim1,
// weights_extra->pre_tfm_desc.dim2,
// weights_extra->pre_tfm_desc.dim3,
// weights_extra->pre_tfm_desc.dim4);
// GGML_LOG_INFO("%s: tensor '%s' tensor dimensions: [%ld, %ld, %ld, %ld] pre_tfm_desc dimensions: [%ld, %ld, %ld, %ld]\n",
// __func__, inputs_extra->name,
// inputs->ne[3], inputs->ne[2], inputs->ne[1], inputs->ne[0],
// inputs_extra->pre_tfm_desc.dim1,
// inputs_extra->pre_tfm_desc.dim2,
// inputs_extra->pre_tfm_desc.dim3,
// inputs_extra->pre_tfm_desc.dim4);
GGML_ASSERT(weights_extra->pre_tfm_desc.dim1 == weights->ne[0] && "weights_extra->pre_tfm_desc.dim1 must match weights->ne[0]");
GGML_ASSERT(weights_extra->pre_tfm_desc.dim2 == weights->ne[1] && "weights_extra->pre_tfm_desc.dim2 must match weights->ne[1]");
GGML_ASSERT(inputs_extra->pre_tfm_desc.dim1 == inputs->ne[0] && "inputs_extra->pre_tfm_desc.dim1 must match inputs->ne[0]");
GGML_ASSERT(inputs_extra->pre_tfm_desc.dim2 == inputs->ne[1] && "inputs_extra->pre_tfm_desc.dim2 must match inputs->ne[1]");
ZDNN_CHECK(zdnn_matmul_transpose_op(&inputs_extra->ztensor, &weights_extra->ztensor, &bias_extra->ztensor,
false, true, MATMUL_OP_ADDITION, &output_extra->ztensor));
// TODO: Remove in the future as we are currently DLF16 -> FP32 then in the next op, FP32 -> DLF16 again. Inefficient.
ZDNN_CHECK(zdnn_transform_origtensor(&output_extra->ztensor, output->data));
GGML_UNUSED(ctx);
GGML_UNUSED(weights_rows);
GGML_UNUSED(weights_cols);
GGML_UNUSED(inputs_rows);
GGML_UNUSED(inputs_cols);
GGML_UNUSED(output_rows);
GGML_UNUSED(output_cols);
}
static void ggml_zdnn_mul_mat_dispatch(ggml_backend_zdnn_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
// debug helpers
// GGML_LOG_INFO("%s: use_mul_mat_vec = %d\n", __func__, use_mul_mat_vec);
// GGML_LOG_INFO("%s: use_mul_mat_vec_q = %d\n", __func__, use_mul_mat_vec_q);
// GGML_LOG_INFO("%s: use_mul_mat_q = %d\n", __func__, use_mul_mat_q);
// GGML_LOG_INFO("%s: src0: %8d %8d %8d %8d\n", __func__, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]);
// GGML_LOG_INFO("%s: %8d %8d %8d %8d\n", __func__, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]);
// GGML_LOG_INFO("%s: src1: %8d %8d %8d %8d\n", __func__, src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3]);
// GGML_LOG_INFO("%s: %8d %8d %8d %8d\n", __func__, src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3]);
// GGML_LOG_INFO("%s: src0 is contiguous %d, transposed %d, type = %s, name = %s\n", __func__, ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
// GGML_LOG_INFO("%s: src1 is contiguous %d, transposed %d, type = %s, name = %s\n", __func__, ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
ggml_zdnn_mul_mat_op(ctx, src0, src1, dst);
}
static bool ggml_zdnn_compute_forward(ggml_backend_zdnn_context * ctx, ggml_tensor * dst) {
switch (dst->op) {
case GGML_OP_MUL_MAT:
ggml_zdnn_mul_mat_dispatch(ctx, dst->src[0], dst->src[1], dst);
break;
{
ggml_zdnn_compute_forward_mul_mat(ctx, dst);
} break;
default:
return false;