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Merge branch 'ggerganov:master' into master
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@sealad886
sealad886 authored May 27, 2024
2 parents 9dc742e + 10b1e45 commit 7fa43b0
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Showing 3 changed files with 68 additions and 43 deletions.
3 changes: 3 additions & 0 deletions Makefile
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Original file line number Diff line number Diff line change
Expand Up @@ -441,6 +441,9 @@ endif # JETSON_EOL_MODULE_DETECT
ifdef LLAMA_DEBUG
MK_NVCCFLAGS += -lineinfo
endif # LLAMA_DEBUG
ifdef LLAMA_CUDA_DEBUG
MK_NVCCFLAGS += --device-debug
endif # LLAMA_CUDA_DEBUG
ifdef LLAMA_CUDA_NVCC
NVCC = $(CCACHE) $(LLAMA_CUDA_NVCC)
else
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13 changes: 7 additions & 6 deletions ggml-cuda.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -2510,9 +2510,9 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t

bool use_cuda_graph = true;
bool cuda_graph_update_required = false;
// pointer to CUDA cpy kernel, which is required to identify
// vector of pointers to CUDA cpy kernels, which are required to identify
// kernel parameters which need updated in the graph for each token
void * ggml_cuda_cpy_fn_ptr = nullptr;
std::vector<void *> ggml_cuda_cpy_fn_ptrs;

if (cuda_ctx->cuda_graph->graph == nullptr) {
if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) {
Expand Down Expand Up @@ -2588,9 +2588,10 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
if (node->op == GGML_OP_CPY) {
// store the copy op parameter which changes with each token.
cuda_ctx->cuda_graph->updated_kernel_arg.push_back((char **) &(node->src[1]->data));
if (ggml_cuda_cpy_fn_ptr == nullptr) {
// store a pointer to the copy op CUDA kernel to identify it later
ggml_cuda_cpy_fn_ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]);
// store a pointer to each copy op CUDA kernel to identify it later
void * ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]);
if (std::find(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), ptr) == ggml_cuda_cpy_fn_ptrs.end()) {
ggml_cuda_cpy_fn_ptrs.push_back(ptr);
}
}

Expand Down Expand Up @@ -2720,7 +2721,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t
if (!cuda_graph_update_required) { // on update steps, the live parameters will already be captured
int k = 0;
for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) {
if (cuda_ctx->cuda_graph->params[i].func == ggml_cuda_cpy_fn_ptr) {
if(count(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), cuda_ctx->cuda_graph->params[i].func) > 0) {
char ** updated_kernel_arg_ptr = cuda_ctx->cuda_graph->updated_kernel_arg.at(k++);
cuda_ctx->cuda_graph->params[i].kernelParams[1] = updated_kernel_arg_ptr;
CUDA_CHECK(cudaGraphKernelNodeSetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]));
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95 changes: 58 additions & 37 deletions ggml-sycl.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -8830,12 +8830,11 @@ static void rope(
dst[i + 1] = x0*sin_theta + x1*cos_theta;
}

template<typename T, bool has_pos>
template<typename T, bool has_pos, bool has_freq_facs>
static 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 sycl::nd_item<3> &item_ct1) {
float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims,
const float * freq_factors, const sycl::nd_item<3> &item_ct1) {
const int col = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
item_ct1.get_local_id(1));

Expand Down Expand Up @@ -8863,8 +8862,10 @@ static void rope_neox(
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 * dpct::pow(theta_scale, col / 2.0f);
p * freq_scale * dpct::pow(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);
Expand Down Expand Up @@ -12413,7 +12414,7 @@ static void rope_neox_sycl(const T *x, T *dst, int ncols, int n_dims, int nrows,
const int32_t *pos, float freq_scale,
int p_delta_rows, float freq_base, float ext_factor,
float attn_factor, rope_corr_dims corr_dims,
dpct::queue_ptr stream) {
const float * freq_factors, dpct::queue_ptr stream) {
GGML_ASSERT(ncols % 2 == 0);
const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1);
const int num_blocks_x = (ncols + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE);
Expand All @@ -12423,38 +12424,48 @@ static void rope_neox_sycl(const T *x, T *dst, int ncols, int n_dims, int nrows,
const float inv_ndims = -1.0f / n_dims;

if (pos == nullptr) {
/*
DPCT1049:42: The work-group size passed to the SYCL kernel may exceed
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});

stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope_neox<T, false>(x, dst, ncols, n_dims, pos, freq_scale,
p_delta_rows, ext_factor, attn_factor,
corr_dims, theta_scale, inv_ndims,
item_ct1);
});
if (freq_factors == nullptr) {
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope_neox<T, false, false>(x, dst, ncols, n_dims, pos, freq_scale,
p_delta_rows, ext_factor, attn_factor,
corr_dims, theta_scale, inv_ndims, freq_factors,
item_ct1);
});
} else {
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope_neox<T, false, true>(x, dst, ncols, n_dims, pos, freq_scale,
p_delta_rows, ext_factor, attn_factor,
corr_dims, theta_scale, inv_ndims, freq_factors,
item_ct1);
});
}
} else {
/*
DPCT1049:43: The work-group size passed to the SYCL kernel may exceed
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});

stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope_neox<T, true>(x, dst, ncols, n_dims, pos, freq_scale,
p_delta_rows, ext_factor, attn_factor,
corr_dims, theta_scale, inv_ndims, item_ct1);
});
if (freq_factors == nullptr) {
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope_neox<T, true, false>(x, dst, ncols, n_dims, pos, freq_scale,
p_delta_rows, ext_factor, attn_factor,
corr_dims, theta_scale, inv_ndims, freq_factors, item_ct1);
});
} else {
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope_neox<T, true, true>(x, dst, ncols, n_dims, pos, freq_scale,
p_delta_rows, ext_factor, attn_factor,
corr_dims, theta_scale, inv_ndims, freq_factors, item_ct1);
});
}
}
}

Expand Down Expand Up @@ -13986,9 +13997,7 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1,
ggml_tensor *dst, const float *src0_dd,
const float *src1_dd, float *dst_dd,
const dpct::queue_ptr &main_stream) {
#pragma message("TODO: implement phi3 frequency factors support")
#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7225")
GGML_ASSERT(dst->src[2] == nullptr && "phi3 frequency factors not implemented yet");
const ggml_tensor * src2 = dst->src[2];

GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16);
Expand All @@ -14014,6 +14023,7 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1,
memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float));
memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float));

const float * freq_factors = nullptr;
const int32_t * pos = nullptr;
if ((mode & 1) == 0) {
GGML_ASSERT(src1->type == GGML_TYPE_I32);
Expand All @@ -14024,6 +14034,16 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1,
const bool is_neox = mode & 2;
const bool is_glm = mode & 4;

if (is_neox) {
pos = (const int32_t *) src1_dd;

if (src2 != nullptr) {
freq_factors = (const float *) src2->data;
}
} else {
GGML_ASSERT(src2 == nullptr && "TODO: freq_factors not implemented for !is_neox");
}

rope_corr_dims corr_dims;
ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v);

Expand All @@ -14035,13 +14055,13 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1,
if (src0->type == GGML_TYPE_F32) {
rope_neox_sycl(
(const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
attn_factor, corr_dims, main_stream
attn_factor, corr_dims, freq_factors, main_stream
);
} else if (src0->type == GGML_TYPE_F16) {
rope_neox_sycl((const sycl::half *)src0_dd, (sycl::half *)dst_dd,
ne00, n_dims, nrows, pos, freq_scale, ne01,
freq_base, ext_factor, attn_factor, corr_dims,
main_stream);
freq_factors, main_stream);
} else {
GGML_ASSERT(false);
}
Expand Down Expand Up @@ -15243,6 +15263,7 @@ static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1
}
} else {
bool use_mul_mat_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type);
use_mul_mat_q = use_mul_mat_q && (src0->type != GGML_TYPE_IQ2_XXS);

if (use_xmx && min_compute_capability >= VER_GEN9 && src1->ne[1] > XMX_MAX_BATCH_SIZE) {
use_mul_mat_q = false;
Expand Down

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