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ggml : fix YARN + add tests + add asserts (llama/7617)

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* tests : add rope tests

ggml-ci

* ggml : fixes (hopefully)

ggml-ci

* tests : add non-cont tests

ggml-ci

* cuda : add asserts for rope/norm + fix DS2

ggml-ci

* ggml : assert contiguousness

* tests : reduce RoPE tests

ggml-ci
This commit is contained in:
Georgi Gerganov 2024-05-29 20:17:31 +03:00
parent 4b19cc3ed4
commit 1f35ce61c1
9 changed files with 75 additions and 63 deletions
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4
ggml-cuda.cu
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@ -1870,7 +1870,7 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
}
}
#else
if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) {
if (r2 == 1 && r3 == 1 && ggml_is_contiguous_2(src0) && ggml_is_contiguous_2(src1)) {
// there is no broadcast and src0, src1 are contiguous across dims 2, 3
// use cublasGemmStridedBatchedEx
CUBLAS_CHECK(
@ -2886,7 +2886,9 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
case GGML_OP_CONT:
case GGML_OP_DIAG_MASK_INF:
case GGML_OP_SOFT_MAX:
return true;
case GGML_OP_ROPE:
return ggml_is_contiguous(op->src[0]);
case GGML_OP_IM2COL:
case GGML_OP_POOL_2D:
case GGML_OP_SUM_ROWS:

6
ggml-cuda/norm.cu
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@ -170,6 +170,8 @@ void ggml_cuda_op_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
@ -188,6 +190,8 @@ void ggml_cuda_op_group_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
@ -202,6 +206,8 @@ void ggml_cuda_op_rms_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);

18
ggml-cuda/rope.cu
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@ -61,7 +61,7 @@ static __global__ void rope(
template<typename T, bool has_pos, bool has_freq_facs>
static __global__ void rope_neox(
const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims, const float * freq_factors
float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors
) {
const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
@ -85,15 +85,13 @@ static __global__ void rope_neox(
const int i = row*ncols + ib*n_dims + ic/2;
const int i2 = row/p_delta_rows;
float cur_rot = inv_ndims * ic - ib;
const int p = has_pos ? pos[i2] : 0;
const float freq_factor = has_freq_facs ? freq_factors[ic/2] : 1.0f;
const float theta_base = p*freq_scale*powf(theta_scale, col/2.0f)/freq_factor;
const float theta_base = p*powf(theta_scale, col/2.0f)/freq_factor;
float cos_theta, sin_theta;
rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta);
rope_yarn(theta_base, freq_scale, corr_dims, ic, ext_factor, attn_factor, &cos_theta, &sin_theta);
const float x0 = x[i + 0];
const float x1 = x[i + n_dims/2];
@ -174,30 +172,29 @@ static void rope_neox_cuda(
const dim3 block_nums(nrows, num_blocks_x, 1);
const float theta_scale = powf(freq_base, -2.0f/n_dims);
const float inv_ndims = -1.0f / n_dims;
if (pos == nullptr) {
if (freq_factors == nullptr) {
rope_neox<T, false, false><<<block_nums, block_dims, 0, stream>>>(
x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
theta_scale, inv_ndims, freq_factors
theta_scale, freq_factors
);
} else {
rope_neox<T, false, true><<<block_nums, block_dims, 0, stream>>>(
x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
theta_scale, inv_ndims, freq_factors
theta_scale, freq_factors
);
}
} else {
if (freq_factors == nullptr) {
rope_neox<T, true, false><<<block_nums, block_dims, 0, stream>>>(
x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
theta_scale, inv_ndims, freq_factors
theta_scale, freq_factors
);
} else {
rope_neox<T, true, true><<<block_nums, block_dims, 0, stream>>>(
x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims,
theta_scale, inv_ndims, freq_factors
theta_scale, freq_factors
);
}
}
@ -254,6 +251,7 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16);
GGML_ASSERT(src0->type == dst->type);

4
ggml-kompute.cpp
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@ -1597,7 +1597,9 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml
{
GGML_ASSERT(ne00 == ne10);
// TODO: assert that dim2 and dim3 are contiguous
ggml_is_contiguous_2(src0);
ggml_is_contiguous_2(src1);
GGML_ASSERT(ne12 % ne02 == 0);
GGML_ASSERT(ne13 % ne03 == 0);

8
ggml-metal.m
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@ -1519,7 +1519,9 @@ static enum ggml_status ggml_metal_graph_compute(
{
GGML_ASSERT(ne00 == ne10);
// TODO: assert that dim2 and dim3 are contiguous
ggml_is_contiguous_2(src0);
ggml_is_contiguous_2(src1);
GGML_ASSERT(ne12 % ne02 == 0);
GGML_ASSERT(ne13 % ne03 == 0);
@ -2187,6 +2189,7 @@ static enum ggml_status ggml_metal_graph_compute(
case GGML_OP_RMS_NORM:
{
GGML_ASSERT(ne00 % 4 == 0);
GGML_ASSERT(ggml_is_contiguous_1(src0));
float eps;
memcpy(&eps, dst->op_params, sizeof(float));
@ -2214,6 +2217,7 @@ static enum ggml_status ggml_metal_graph_compute(
case GGML_OP_GROUP_NORM:
{
GGML_ASSERT(ne00 % 4 == 0);
GGML_ASSERT(ggml_is_contiguous(src0));
//float eps;
//memcpy(&eps, dst->op_params, sizeof(float));
@ -2247,6 +2251,8 @@ static enum ggml_status ggml_metal_graph_compute(
} break;
case GGML_OP_NORM:
{
GGML_ASSERT(ggml_is_contiguous_1(src0));
float eps;
memcpy(&eps, dst->op_params, sizeof(float));

16
ggml-metal.metal
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@ -1767,13 +1767,13 @@ kernel void kernel_rope(
const int64_t p = pos[i2];
const float theta_0 = (float)p;
const float theta_base = (float)p;
const float inv_ndims = -1.f/n_dims;
if (!is_neox) {
for (int64_t i0 = 2*tiitg; i0 < ne0; i0 += 2*tptg.x) {
const float theta = theta_base * pow(freq_base, inv_ndims*i0);
const float theta = theta_0 * pow(freq_base, inv_ndims*i0);
float cos_theta, sin_theta;
rope_yarn(theta, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
@ -1789,18 +1789,14 @@ kernel void kernel_rope(
} else {
for (int64_t ic = 2*tiitg; ic < ne0; ic += 2*tptg.x) {
if (ic < n_dims) {
const int64_t ib = 0;
const int64_t i0 = ic/2;
// simplified from `(ib * n_dims + ic) * inv_ndims`
const float cur_rot = inv_ndims*ic - ib;
const float freq_factor = src2 != src0 ? src2[ic/2] : 1.0f;
const float freq_factor = src2 != src0 ? src2[i0] : 1.0f;
const float theta = theta_0 * pow(freq_base, cur_rot) / freq_factor;
const float theta = theta_base * pow(freq_base, inv_ndims*ic);
float cos_theta, sin_theta;
rope_yarn(theta, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta);
const int64_t i0 = ib*n_dims + ic/2;
rope_yarn(theta/freq_factor, freq_scale, corr_dims, ic, ext_factor, attn_factor, &cos_theta, &sin_theta);
device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);

2
ggml-sycl.cpp
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@ -15183,7 +15183,7 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0,
const int64_t r2 = ne12/ne02;
const int64_t r3 = ne13/ne03;
if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) {
if (r2 == 1 && r3 == 1 && ggml_is_contiguous_2(src0) && ggml_is_contiguous_2(src1)) {
// there is no broadcast and src0, src1 are contiguous across dims 2, 3
SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch(
*g_sycl_handles[g_main_device], oneapi::mkl::transpose::trans,

70
ggml.c
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@ -3221,7 +3221,11 @@ GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor) {
tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
}
static inline bool ggml_is_contiguous_except_dim_1(const struct ggml_tensor * tensor) {
GGML_CALL bool ggml_is_contiguous_0(const struct ggml_tensor * tensor) {
return ggml_is_contiguous(tensor);
}
GGML_CALL bool ggml_is_contiguous_1(const struct ggml_tensor * tensor) {
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
return
@ -3230,6 +3234,14 @@ static inline bool ggml_is_contiguous_except_dim_1(const struct ggml_tensor * te
tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
}
GGML_CALL bool ggml_is_contiguous_2(const struct ggml_tensor * tensor) {
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
return
tensor->nb[0] == ggml_type_size(tensor->type) &&
tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
}
GGML_CALL bool ggml_is_permuted(const struct ggml_tensor * tensor) {
static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
@ -11420,8 +11432,8 @@ static void ggml_compute_forward_gelu_f32(
const struct ggml_tensor * src0 = dst->src[0];
GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
GGML_ASSERT(ggml_is_contiguous_1(src0));
GGML_ASSERT(ggml_is_contiguous_1(dst));
GGML_ASSERT(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@ -11483,8 +11495,8 @@ static void ggml_compute_forward_gelu_quick_f32(
const struct ggml_tensor * src0 = dst->src[0];
GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
GGML_ASSERT(ggml_is_contiguous_1(src0));
GGML_ASSERT(ggml_is_contiguous_1(dst));
GGML_ASSERT(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@ -11546,8 +11558,8 @@ static void ggml_compute_forward_silu_f32(
const struct ggml_tensor * src0 = dst->src[0];
GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
GGML_ASSERT(ggml_is_contiguous_1(src0));
GGML_ASSERT(ggml_is_contiguous_1(dst));
GGML_ASSERT(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@ -11658,9 +11670,9 @@ static void ggml_compute_forward_silu_back_f32(
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * grad = dst->src[1];
GGML_ASSERT(ggml_is_contiguous_except_dim_1(grad));
GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0));
GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst));
GGML_ASSERT(ggml_is_contiguous_1(grad));
GGML_ASSERT(ggml_is_contiguous_1(src0));
GGML_ASSERT(ggml_is_contiguous_1(dst));
GGML_ASSERT(ggml_are_same_shape(src0, dst));
GGML_ASSERT(ggml_are_same_shape(src0, grad));
@ -14358,7 +14370,7 @@ static void ggml_compute_forward_rope_f32(
int ir = 0;
const float theta_scale = powf(freq_base, -2.0f/n_dims);
const float inv_ndims = -1.f/n_dims;
float corr_dims[2];
ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims);
@ -14442,29 +14454,22 @@ static void ggml_compute_forward_rope_f32(
dst_data[1] = x0*sin_theta*zeta + x1*cos_theta*zeta;
}
} else {
// TODO: this might be wrong for ne0 != n_dims - need double check
// it seems we have to rope just the first n_dims elements and do nothing with the rest
// ref: https://github.com/ml-explore/mlx/blob/dc2edc762c797e3b8de50b1dad4dc0a131691033/benchmarks/python/llama_jax_bench.py#L11-L26
theta_base *= freq_scale;
// ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py
for (int64_t ic = 0; ic < ne0; ic += 2) {
if (ic < n_dims) {
const int64_t ib = 0;
const int64_t i0 = ic/2;
// simplified from `(ib * n_dims + ic) * inv_ndims`
float cur_rot = inv_ndims * ic - ib;
float freq_factor = freq_factors ? freq_factors[ic/2] : 1.0f;
const float freq_factor = freq_factors ? freq_factors[i0] : 1.0f;
float cos_theta, sin_theta;
rope_yarn(
theta_base/freq_factor, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor,
theta_base/freq_factor, freq_scale, corr_dims, ic, ext_factor, attn_factor,
&cos_theta, &sin_theta
);
sin_theta *= sin_sign;
theta_base *= theta_scale;
const int64_t i0 = ib*n_dims + ic/2;
const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
@ -14543,7 +14548,7 @@ static void ggml_compute_forward_rope_f16(
int ir = 0;
const float theta_scale = powf(freq_base, -2.0f/n_dims);
const float inv_ndims = -1.f/n_dims;
float corr_dims[2];
ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims);
@ -14623,29 +14628,22 @@ static void ggml_compute_forward_rope_f16(
dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta);
}
} else {
// TODO: this might be wrong for ne0 != n_dims - need double check
// it seems we have to rope just the first n_dims elements and do nothing with the rest
// ref: https://github.com/ml-explore/mlx/blob/dc2edc762c797e3b8de50b1dad4dc0a131691033/benchmarks/python/llama_jax_bench.py#L11-L26
theta_base *= freq_scale;
// ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py
for (int64_t ic = 0; ic < ne0; ic += 2) {
if (ic < n_dims) {
const int64_t ib = 0;
const int64_t i0 = ic/2;
// simplified from `(ib * n_dims + ic) * inv_ndims`
float cur_rot = inv_ndims * ic - ib;
float freq_factor = freq_factors ? freq_factors[ic/2] : 1.0f;
const float freq_factor = freq_factors ? freq_factors[i0] : 1.0f;
float cos_theta, sin_theta;
rope_yarn(
theta_base/freq_factor, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor,
theta_base/freq_factor, freq_scale, corr_dims, ic, ext_factor, attn_factor,
&cos_theta, &sin_theta
);
sin_theta *= sin_sign;
theta_base *= theta_scale;
const int64_t i0 = ib*n_dims + ic/2;
const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);

6
ggml.h
View File

@ -756,7 +756,6 @@ extern "C" {
GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype);
GGML_API GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor);
GGML_API GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor);
GGML_API GGML_CALL bool ggml_is_permuted (const struct ggml_tensor * tensor);
GGML_API GGML_CALL bool ggml_is_empty (const struct ggml_tensor * tensor);
GGML_API bool ggml_is_scalar (const struct ggml_tensor * tensor);
@ -765,6 +764,11 @@ extern "C" {
GGML_API bool ggml_is_3d (const struct ggml_tensor * tensor);
GGML_API int ggml_n_dims (const struct ggml_tensor * tensor); // returns 1 for scalars
GGML_API GGML_CALL bool ggml_is_contiguous (const struct ggml_tensor * tensor);
GGML_API GGML_CALL bool ggml_is_contiguous_0(const struct ggml_tensor * tensor); // same as ggml_is_contiguous()
GGML_API GGML_CALL bool ggml_is_contiguous_1(const struct ggml_tensor * tensor); // contiguous for dims >= 1
GGML_API GGML_CALL bool ggml_is_contiguous_2(const struct ggml_tensor * tensor); // contiguous for dims >= 2
GGML_API bool ggml_are_same_shape (const struct ggml_tensor * t0, const struct ggml_tensor * t1);
GGML_API bool ggml_are_same_stride(const struct ggml_tensor * t0, const struct ggml_tensor * t1);