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Merge branch 'upstream' into concedo_experimental

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# Conflicts:
#	common/CMakeLists.txt
#	ggml/src/ggml-cann/ggml-cann.cpp
#	ggml/src/ggml-opencl/ggml-opencl.cpp
#	ggml/src/ggml-opencl/kernels/scale.cl
#	ggml/src/ggml-sycl/backend.hpp
#	ggml/src/ggml-sycl/ggml-sycl.cpp
#	tests/test-backend-ops.cpp
This commit is contained in:
Concedo
2025-07-10 17:43:08 +08:00
parent c45b8dc56f 704bb7a71c
commit c52cbdce52
22 changed files with 774 additions and 496 deletions
Download Patch File Download Diff File Expand all files Collapse all files
ggml/src/ggml-cpu/ops.cpp
+20 -8
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@@ -4643,9 +4643,11 @@ static void ggml_compute_forward_scale_f32(
GGML_ASSERT(ggml_is_contiguous(dst));
GGML_ASSERT(ggml_are_same_shape(src0, dst));
// scale factor
float v;
memcpy(&v, dst->op_params, sizeof(float));
float s; // scale factor
float b; // bias
memcpy(&s, (float *) dst->op_params + 0, sizeof(float));
memcpy(&b, (float *) dst->op_params + 1, sizeof(float));
const int ith = params->ith;
const int nth = params->nth;
@@ -4664,12 +4666,22 @@ static void ggml_compute_forward_scale_f32(
const size_t nb1 = dst->nb[1];
for (int i1 = ir0; i1 < ir1; i1++) {
if (dst->data != src0->data) {
// src0 is same shape as dst => same indices
memcpy((char *)dst->data + i1*nb1, (char *)src0->data + i1*nb01, nc * sizeof(float));
if (b == 0.0f) {
for (int i1 = ir0; i1 < ir1; i1++) {
if (dst->data != src0->data) {
// src0 is same shape as dst => same indices
// TODO: add x parameter to ggml_vec_scale_f32 and remove this memcpy
memcpy((char *)dst->data + i1*nb1, (char *)src0->data + i1*nb01, nc * sizeof(float));
}
ggml_vec_scale_f32(nc, (float *) ((char *) dst->data + i1*nb1), s);
}
} else {
for (int i1 = ir0; i1 < ir1; i1++) {
ggml_vec_mad1_f32(nc,
(float *) ((char *) dst->data + i1*nb1),
(float *) ((char *) src0->data + i1*nb1),
s, b);
}
ggml_vec_scale_f32(nc, (float *) ((char *) dst->data + i1*nb1), v);
}
}
ggml/src/ggml-cpu/vec.h
+39
View File
@@ -351,6 +351,45 @@ inline static void ggml_vec_mad_f32_unroll(const int n, const int xs, const int
#endif
}
inline static void ggml_vec_mad1_f32(const int n, float * y, const float * x, const float s, const float b) {
#if defined(GGML_USE_ACCELERATE)
vDSP_vsmsa(x, 1, &s, &b, y, 1, n);
#elif defined(GGML_SIMD)
#if defined(__ARM_FEATURE_SVE)
// scalar ; TODO: Write SVE code
for (int i = 0; i < n; ++i) {
y[i] = x[i]*s + b;
}
#else
const int np = (n & ~(GGML_F32_STEP - 1));
GGML_F32_VEC vs = GGML_F32_VEC_SET1(s);
GGML_F32_VEC vb = GGML_F32_VEC_SET1(b);
GGML_F32_VEC ay[GGML_F32_ARR];
for (int i = 0; i < np; i += GGML_F32_STEP) {
for (int j = 0; j < GGML_F32_ARR; j++) {
ay[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR);
ay[j] = GGML_F32_VEC_FMA(ay[j], vs, vb);
GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ay[j]);
}
}
// leftovers
for (int i = np; i < n; ++i) {
y[i] = x[i]*s + b;
}
#endif
#else
// scalar
for (int i = 0; i < n; ++i) {
y[i] = x[i]*s + b;
}
#endif
}
//inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { for (int i = 0; i < n; ++i) y[i] *= v; }
inline static void ggml_vec_scale_f32(const int n, float * y, const float v) {
#if defined(GGML_USE_ACCELERATE)
ggml/src/ggml-cuda/ggml-cuda.cu
+2 -2
View File
@@ -3340,8 +3340,8 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
case GGML_OP_SSM_SCAN: {
if (op->src[3]->ne[0] == 1) {
// Mamba2
// (kernel only supports d_state == 128 && d_head % 16 == 0)
return op->src[0]->ne[0] == 128 && op->src[0]->ne[1] % 16 == 0;
// (kernel only supports (d_state == 128 || d_state == 256) && d_head % 16 == 0)
return (op->src[0]->ne[0] == 128 || op->src[0]->ne[0] == 256) && op->src[0]->ne[1] % 16 == 0;
} else {
// Mamba
// (kernel only supports d_state == 16, d_head == 1, n_head % 128 == 0, n_group == 1)
ggml/src/ggml-cuda/scale.cu
+8 -6
View File
@@ -1,18 +1,18 @@
#include "scale.cuh"
static __global__ void scale_f32(const float * x, float * dst, const float scale, const int k) {
static __global__ void scale_f32(const float * x, float * dst, const float scale, const float bias, const int k) {
const int i = blockDim.x*blockIdx.x + threadIdx.x;
if (i >= k) {
return;
}
dst[i] = scale * x[i];
dst[i] = scale * x[i] + bias;
}
static void scale_f32_cuda(const float * x, float * dst, const float scale, const int k, cudaStream_t stream) {
static void scale_f32_cuda(const float * x, float * dst, const float scale, const float bias, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE;
scale_f32<<<num_blocks, CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, k);
scale_f32<<<num_blocks, CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, bias, k);
}
void ggml_cuda_op_scale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
@@ -25,7 +25,9 @@ void ggml_cuda_op_scale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
GGML_ASSERT( dst->type == GGML_TYPE_F32);
float scale;
memcpy(&scale, dst->op_params, sizeof(float));
float bias;
memcpy(&scale, (float *) dst->op_params + 0, sizeof(float));
memcpy(&bias, (float *) dst->op_params + 1, sizeof(float));
scale_f32_cuda(src0_d, dst_d, scale, ggml_nelements(src0), stream);
scale_f32_cuda(src0_d, dst_d, scale, bias, ggml_nelements(src0), stream);
}
ggml/src/ggml-cuda/ssm-scan.cu
+13 -2
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@@ -201,11 +201,11 @@ static void ssm_scan_f32_cuda(const float * src0, const float * src1, const floa
const int src5_nb3, const int64_t s_off, const int64_t d_state, const int64_t head_dim,
const int64_t n_head, const int64_t n_group, const int64_t n_tok, const int64_t n_seq,
cudaStream_t stream) {
const int threads = 128;
// NOTE: if you change conditions here, be sure to update the corresponding supports_op condition!
if (src3_nb1 == sizeof(float)) {
// Mamba-2
if (d_state == 128) {
const int threads = 128;
GGML_ASSERT(d_state % threads == 0);
// NOTE: can be any power of two between 4 and 64
const int splitH = 16;
@@ -215,10 +215,21 @@ static void ssm_scan_f32_cuda(const float * src0, const float * src1, const floa
src0, src1, src2, src3, src4, src5, src6, dst,
src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2, src3_nb1,
src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, head_dim, n_group, n_tok);
} else if (d_state == 256) { // Falcon-H1
const int threads = 256;
// NOTE: can be any power of two between 8 and 64
const int splitH = 16;
GGML_ASSERT(head_dim % splitH == 0);
const dim3 blocks((n_head * head_dim + (splitH - 1)) / splitH, n_seq, 1);
ssm_scan_f32_group<16, 256><<<blocks, threads, 0, stream>>>(
src0, src1, src2, src3, src4, src5, src6, dst,
src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2, src3_nb1,
src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, head_dim, n_group, n_tok);
} else {
GGML_ABORT("doesn't support d_state!=128.");
GGML_ABORT("doesn't support d_state!=(128 or 256).");
}
} else {
const int threads = 128;
// Mamba-1
GGML_ASSERT(n_head % threads == 0);
GGML_ASSERT(head_dim == 1);
ggml/src/ggml-metal/ggml-metal.m
+4 -1
View File
@@ -2256,7 +2256,9 @@ static bool ggml_metal_encode_node(
GGML_ASSERT(ggml_is_contiguous(src0));
float scale;
memcpy(&scale, dst->op_params, sizeof(scale));
float bias;
memcpy(&scale, ((const int32_t *) dst->op_params) + 0, sizeof(float));
memcpy(&bias, ((const int32_t *) dst->op_params) + 1, sizeof(float));
int64_t n = ggml_nelements(dst);
@@ -2273,6 +2275,7 @@ static bool ggml_metal_encode_node(
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&scale length:sizeof(scale) atIndex:2];
[encoder setBytes:&bias length:sizeof(bias) atIndex:3];
[encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
} break;
ggml/src/ggml-metal/ggml-metal.metal
+4 -2
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@@ -1014,16 +1014,18 @@ kernel void kernel_scale(
device const float * src0,
device float * dst,
constant float & scale,
constant float & bias,
uint tpig[[thread_position_in_grid]]) {
dst[tpig] = src0[tpig] * scale;
dst[tpig] = src0[tpig] * scale + bias;
}
kernel void kernel_scale_4(
device const float4 * src0,
device float4 * dst,
constant float & scale,
constant float & bias,
uint tpig[[thread_position_in_grid]]) {
dst[tpig] = src0[tpig] * scale;
dst[tpig] = src0[tpig] * scale + bias;
}
kernel void kernel_clamp(
ggml/src/ggml-vulkan/ggml-vulkan.cpp
+1 -1
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@@ -7532,7 +7532,7 @@ static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context& subctx, con
(uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
(uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size,
0,
op_params[0], 0.0f,
op_params[0], op_params[1],
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
}, dryrun);
}
ggml/src/ggml-vulkan/vulkan-shaders/scale.comp
+1 -1
View File
@@ -18,7 +18,7 @@ void main() {
continue;
}
data_d[get_doffset() + idx] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + idx]) * FLOAT_TYPE(p.param1));
data_d[get_doffset() + idx] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + idx]) * FLOAT_TYPE(p.param1) + FLOAT_TYPE(p.param2));
idx += num_threads;
}
}
ggml/src/ggml.c
+23 -5
View File
@@ -3085,12 +3085,14 @@ static struct ggml_tensor * ggml_scale_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
float s,
float b,
bool inplace) {
GGML_ASSERT(ggml_is_padded_1d(a));
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
ggml_set_op_params(result, &s, sizeof(s));
float params[2] = { s, b };
ggml_set_op_params(result, &params, sizeof(params));
result->op = GGML_OP_SCALE;
result->src[0] = a;
@@ -3102,14 +3104,30 @@ struct ggml_tensor * ggml_scale(
struct ggml_context * ctx,
struct ggml_tensor * a,
float s) {
return ggml_scale_impl(ctx, a, s, false);
return ggml_scale_impl(ctx, a, s, 0.0, false);
}
struct ggml_tensor * ggml_scale_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
float s) {
return ggml_scale_impl(ctx, a, s, true);
return ggml_scale_impl(ctx, a, s, 0.0, true);
}
struct ggml_tensor * ggml_scale_bias(
struct ggml_context * ctx,
struct ggml_tensor * a,
float s,
float b) {
return ggml_scale_impl(ctx, a, s, b, false);
}
struct ggml_tensor * ggml_scale_bias_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
float s,
float b) {
return ggml_scale_impl(ctx, a, s, b, true);
}
// ggml_set
@@ -5793,7 +5811,7 @@ static void ggml_compute_backward(
} break;
case GGML_OP_MEAN: {
if (src0_needs_grads) {
ggml_add1_or_set(ctx, cgraph, isrc0, ggml_scale_impl(ctx, grad, 1.0f/src0->ne[0], false));
ggml_add1_or_set(ctx, cgraph, isrc0, ggml_scale_impl(ctx, grad, 1.0f/src0->ne[0], 0.0, false));
}
} break;
case GGML_OP_REPEAT: {
@@ -5870,7 +5888,7 @@ static void ggml_compute_backward(
if (src0_needs_grads) {
float s;
memcpy(&s, tensor->op_params, sizeof(float));
ggml_add_or_set(ctx, cgraph, isrc0, ggml_scale_impl(ctx, grad, s, false));
ggml_add_or_set(ctx, cgraph, isrc0, ggml_scale_impl(ctx, grad, s, 0.0, false));
}
} break;
case GGML_OP_SET: {
ggml/src/gguf.cpp
+8 -1
View File
@@ -675,7 +675,14 @@ struct gguf_context * gguf_init_from_file_impl(FILE * file, struct gguf_init_par
gguf_free(ctx);
return nullptr;
}
ctx->size += GGML_PAD(ggml_nbytes(&ti.t), ctx->alignment);
size_t padded_size = GGML_PAD(ggml_nbytes(&ti.t), ctx->alignment);
if (SIZE_MAX - ctx->size < padded_size) {
GGML_LOG_ERROR("%s: tensor '%s' size overflow, cannot accumulate size %zu + %zu\n",
__func__, ti.t.name, ctx->size, padded_size);
gguf_free(ctx);
return nullptr;
}
ctx->size += padded_size;
}
}