root/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-12-24 10:24:35 +00:00
Code Issues Actions 5 Packages Projects Releases Wiki Activity

ggml/examples: add backend support for numerical optimization (ggml/949)

Browse Source 
* CUDA eval works

* stochastic gradient descent op

* Adam except decay

* CUDA CROSS_ENTROPY_LOSS_BACK

* CUDA mnist-fc training works

* backend CLI arg

* refactor gguf load

* remove sched from opt_step_adam

* implement l1 regularization (weight decay)

* extra call to add optimizer

* initialize gradients with ggml_graph_reset

* gradient accumulation

* increment iter per eval instead of epoch

* adjust backend interfaces

* fix ggml_graph_reset without backend

* fix ggml graph export/import

* fixup

* rename

* revert ggml_opt changes

* more general CUDA repeat_back

* update documentation, fix CNN

* validation split

* add clarifying comment

* optimize PyTorch training

* adjust buffer size, thread count

* fix 0.0f validation split

* Update examples/mnist/mnist-common.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* fix gradient accumulation

* tensor flag for accumulators -> tensor hash set

* Update include/ggml.h

Co-authored-by: slaren <slarengh@gmail.com>

* Update tests/test-backend-ops.cpp

Co-authored-by: slaren <slarengh@gmail.com>

* Update tests/test-backend-ops.cpp

Co-authored-by: slaren <slarengh@gmail.com>

* fix test prints

* Update src/ggml-backend.c

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* better CUDA support for noncontiguous out_prod

* add comment

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: slaren <slarengh@gmail.com>
This commit is contained in:
Johannes Gäßler 2024-09-20 19:04:44 +03:00 committed by Georgi Gerganov
parent a6809c6a2e
commit 424c5d00a9
24 changed files with 883 additions and 129 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
ggml/include/ggml-backend.h
View File

@ -66,6 +66,7 @@ extern "C" {
// "offset" refers to the offset of the tensor data for setting/getting data
GGML_API GGML_CALL void ggml_backend_tensor_set( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
GGML_API GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
GGML_API GGML_CALL void ggml_backend_tensor_memset( struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size);
GGML_API void ggml_backend_synchronize(ggml_backend_t backend);
@ -122,7 +123,7 @@ extern "C" {
// The backend registry is a registry of all the available backends, and allows initializing backends in a generic way
GGML_API size_t ggml_backend_reg_get_count(void);
GGML_API size_t ggml_backend_reg_find_by_name(const char * name);
GGML_API size_t ggml_backend_reg_find_by_name(const char * name); // returns index of backend with name, or SIZE_MAX if not found
GGML_API ggml_backend_t ggml_backend_reg_init_backend_from_str(const char * backend_str); // str is backend_name:params (params is optional)
GGML_API const char * ggml_backend_reg_get_name(size_t i);
GGML_API ggml_backend_t ggml_backend_reg_init_backend(size_t i, const char * params); // params is backend-specific

40
ggml/include/ggml.h
View File

@ -534,6 +534,7 @@ extern "C" {
GGML_OP_CROSS_ENTROPY_LOSS,
GGML_OP_CROSS_ENTROPY_LOSS_BACK,
GGML_OP_OPT_STEP_ADAMW,
GGML_OP_COUNT,
};
@ -571,10 +572,12 @@ extern "C" {
GGML_LOG_LEVEL_DEBUG = 4,
};
// this tensor...
enum ggml_tensor_flag {
GGML_TENSOR_FLAG_INPUT = 1,
GGML_TENSOR_FLAG_OUTPUT = 2,
GGML_TENSOR_FLAG_PARAM = 4,
GGML_TENSOR_FLAG_INPUT = 1, // ...is an input for the GGML compute graph
GGML_TENSOR_FLAG_OUTPUT = 2, // ...is an output for the GGML compute graph
GGML_TENSOR_FLAG_PARAM = 4, // ...contains trainable parameters
GGML_TENSOR_FLAG_LOSS = 8, // ...defines loss for numerical optimization (multiple loss tensors add up)
};
// n-dimensional tensor
@ -2037,23 +2040,44 @@ extern "C" {
struct ggml_tensor * b,
struct ggml_tensor * c);
// AdamW optimizer step
// Paper: https://arxiv.org/pdf/1711.05101v3.pdf
// PyTorch: https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html
GGML_API struct ggml_tensor * ggml_opt_step_adamw(
struct ggml_context * ctx,
struct ggml_tensor * a,
float alpha,
float beta1,
float beta2,
float eps,
float wd); // weight decay
//
// automatic differentiation
//
GGML_API void ggml_set_param(
struct ggml_context * ctx,
struct ggml_tensor * tensor);
GGML_API void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor);
GGML_API void ggml_set_loss(struct ggml_tensor * tensor);
GGML_API void ggml_build_forward_expand (struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep);
GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool accumulate, bool keep);
GGML_API void ggml_build_opt_adamw(
struct ggml_context * ctx,
struct ggml_cgraph * gf,
struct ggml_cgraph * gb,
float alpha,
float beta1,
float beta2,
float eps,
float wd); // weight decay
// graph allocation in a context
GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx); // size = GGML_DEFAULT_GRAPH_SIZE, grads = false
GGML_API struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t size, bool grads);
GGML_API struct ggml_cgraph * ggml_graph_dup (struct ggml_context * ctx, struct ggml_cgraph * cgraph);
GGML_API void ggml_graph_cpy (struct ggml_cgraph * src, struct ggml_cgraph * dst);
GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph); // zero grads
GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph); // set regular grads + optimizer momenta to 0, set loss grad to 1
GGML_API void ggml_graph_clear (struct ggml_cgraph * cgraph);
GGML_API int ggml_graph_size (struct ggml_cgraph * cgraph);

19
ggml/src/ggml-backend-impl.h
View File

@ -38,15 +38,16 @@ extern "C" {
typedef void * ggml_backend_buffer_context_t;
struct ggml_backend_buffer_i {
const char * (*GGML_CALL get_name) (ggml_backend_buffer_t buffer);
void (*GGML_CALL free_buffer)(ggml_backend_buffer_t buffer);
void * (*GGML_CALL get_base) (ggml_backend_buffer_t buffer);
void (*GGML_CALL init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
void (*GGML_CALL set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
void (*GGML_CALL get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
bool (*GGML_CALL cpy_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst); // dst is in the buffer, src may be in any buffer
void (*GGML_CALL clear) (ggml_backend_buffer_t buffer, uint8_t value);
void (*GGML_CALL reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras
const char * (*GGML_CALL get_name) (ggml_backend_buffer_t buffer);
void (*GGML_CALL free_buffer) (ggml_backend_buffer_t buffer);
void * (*GGML_CALL get_base) (ggml_backend_buffer_t buffer);
void (*GGML_CALL init_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
void (*GGML_CALL memset_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size);
void (*GGML_CALL set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
void (*GGML_CALL get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
bool (*GGML_CALL cpy_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst); // dst is in the buffer, src may be in any buffer
void (*GGML_CALL clear) (ggml_backend_buffer_t buffer, uint8_t value);
void (*GGML_CALL reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras
};
struct ggml_backend_buffer {

25
ggml/src/ggml-backend.c
View File

@ -246,6 +246,22 @@ GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void *
buf->iface.get_tensor(buf, tensor, data, offset, size);
}
GGML_API GGML_CALL void ggml_backend_tensor_memset(struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
GGML_ASSERT(buf != NULL && "tensor buffer not set");
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
if (!size) {
return;
}
GGML_ASSERT(buf->iface.memset_tensor != NULL && "memset not supported by backend buffer");
buf->iface.memset_tensor(buf, tensor, value, offset, size);
}
void ggml_backend_synchronize(ggml_backend_t backend) {
if (backend->iface.synchronize == NULL) {
return;
@ -569,6 +585,12 @@ GGML_CALL static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t
free(buffer->context);
}
GGML_CALL static void ggml_backend_cpu_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
memset((char *)tensor->data + offset, value, size);
GGML_UNUSED(buffer);
}
GGML_CALL static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
memcpy((char *)tensor->data + offset, data, size);
@ -600,6 +622,7 @@ static struct ggml_backend_buffer_i cpu_backend_buffer_i = {
/* .free_buffer = */ ggml_backend_cpu_buffer_free_buffer,
/* .get_base = */ ggml_backend_cpu_buffer_get_base,
/* .init_tensor = */ NULL, // no initialization required
/* .memset_tensor = */ ggml_backend_cpu_buffer_memset_tensor,
/* .set_tensor = */ ggml_backend_cpu_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_cpu_buffer_get_tensor,
/* .cpy_tensor = */ ggml_backend_cpu_buffer_cpy_tensor,
@ -613,6 +636,7 @@ static struct ggml_backend_buffer_i cpu_backend_buffer_i_from_ptr = {
/* .free_buffer = */ NULL, // ptr is not owned by the buffer, so it does not need to be freed
/* .get_base = */ ggml_backend_cpu_buffer_get_base,
/* .init_tensor = */ NULL, // no initialization required
/* .memset_tensor = */ ggml_backend_cpu_buffer_memset_tensor,
/* .set_tensor = */ ggml_backend_cpu_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_cpu_buffer_get_tensor,
/* .cpy_tensor = */ ggml_backend_cpu_buffer_cpy_tensor,
@ -980,6 +1004,7 @@ static struct ggml_backend_buffer_i ggml_backend_multi_buffer_context_interface(
/* .free_buffer = */ ggml_backend_multi_buffer_free_buffer,
/* .get_base = */ NULL,
/* .init_tensor = */ NULL,
/* .memset_tensor = */ NULL,
/* .set_tensor = */ NULL,
/* .get_tensor = */ NULL,
/* .cpy_tensor = */ NULL,

1
ggml/src/ggml-cann.cpp
View File

@ -1037,6 +1037,7 @@ static ggml_backend_buffer_i ggml_backend_cann_buffer_interface = {
/* .free_buffer = */ ggml_backend_cann_buffer_free_buffer,
/* .get_base = */ ggml_backend_cann_buffer_get_base,
/* .init_tensor = */ ggml_backend_cann_buffer_init_tensor,
/* .memset_tensor = */ NULL,
/* .set_tensor = */ ggml_backend_cann_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_cann_buffer_get_tensor,
/* .cpy_tensor = */ ggml_backend_cann_buffer_cpy_tensor,

42
ggml/src/ggml-cuda.cu
View File

@ -21,6 +21,8 @@
#include "ggml-cuda/mmq.cuh"
#include "ggml-cuda/mmvq.cuh"
#include "ggml-cuda/norm.cuh"
#include "ggml-cuda/opt-step-adamw.cuh"
#include "ggml-cuda/out-prod.cuh"
#include "ggml-cuda/pad.cuh"
#include "ggml-cuda/pool2d.cuh"
#include "ggml-cuda/quantize.cuh"
@ -493,6 +495,14 @@ GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t
}
}
GGML_CALL static void ggml_backend_cuda_buffer_memset_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
ggml_cuda_set_device(ctx->device);
CUDA_CHECK(cudaMemsetAsync((char *)tensor->data + offset, value, size, cudaStreamPerThread));
CUDA_CHECK(cudaStreamSynchronize(cudaStreamPerThread));
}
GGML_CALL static void ggml_backend_cuda_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
ggml_backend_cuda_buffer_context * ctx = (ggml_backend_cuda_buffer_context *)buffer->context;
@ -544,6 +554,7 @@ static ggml_backend_buffer_i ggml_backend_cuda_buffer_interface = {
/* .free_buffer = */ ggml_backend_cuda_buffer_free_buffer,
/* .get_base = */ ggml_backend_cuda_buffer_get_base,
/* .init_tensor = */ ggml_backend_cuda_buffer_init_tensor,
/* .memset_tensor = */ ggml_backend_cuda_buffer_memset_tensor,
/* .set_tensor = */ ggml_backend_cuda_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_cuda_buffer_get_tensor,
/* .cpy_tensor = */ ggml_backend_cuda_buffer_cpy_tensor,
@ -860,6 +871,7 @@ static struct ggml_backend_buffer_i ggml_backend_cuda_split_buffer_interface = {
/* .free_buffer = */ ggml_backend_cuda_split_buffer_free_buffer,
/* .get_base = */ ggml_backend_cuda_split_buffer_get_base,
/* .init_tensor = */ ggml_backend_cuda_split_buffer_init_tensor,
/* .memset_tensor = */ NULL,
/* .set_tensor = */ ggml_backend_cuda_split_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_cuda_split_buffer_get_tensor,
/* .cpy_tensor = */ NULL,
@ -2168,6 +2180,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
case GGML_OP_REPEAT:
ggml_cuda_op_repeat(ctx, dst);
break;
case GGML_OP_REPEAT_BACK:
ggml_cuda_op_repeat_back(ctx, dst);
break;
case GGML_OP_GET_ROWS:
ggml_cuda_op_get_rows(ctx, dst);
break;
@ -2201,6 +2216,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
case GGML_UNARY_OP_NEG:
ggml_cuda_op_neg(ctx, dst);
break;
case GGML_UNARY_OP_STEP:
ggml_cuda_op_step(ctx, dst);
break;
case GGML_UNARY_OP_GELU:
ggml_cuda_op_gelu(ctx, dst);
break;
@ -2267,6 +2285,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
case GGML_OP_MUL_MAT_ID:
ggml_cuda_mul_mat_id(ctx, dst);
break;
case GGML_OP_OUT_PROD:
ggml_cuda_out_prod(ctx, dst);
break;
case GGML_OP_SCALE:
ggml_cuda_op_scale(ctx, dst);
break;
@ -2324,6 +2345,12 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
case GGML_OP_CROSS_ENTROPY_LOSS:
ggml_cuda_cross_entropy_loss(ctx, dst);
break;
case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
ggml_cuda_cross_entropy_loss_back(ctx, dst);
break;
case GGML_OP_OPT_STEP_ADAMW:
ggml_cuda_opt_step_adamw(ctx, dst);
break;
default:
return false;
}
@ -2761,6 +2788,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
case GGML_OP_UNARY:
switch (ggml_get_unary_op(op)) {
case GGML_UNARY_OP_NEG:
case GGML_UNARY_OP_STEP:
case GGML_UNARY_OP_GELU:
case GGML_UNARY_OP_SILU:
case GGML_UNARY_OP_RELU:
@ -2813,6 +2841,8 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
return false;
}
} break;
case GGML_OP_OUT_PROD:
return op->type == GGML_TYPE_F32 && op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && op->ne[2] == 1 && op->ne[3] == 1;
case GGML_OP_GET_ROWS:
{
switch (op->src[0]->type) {
@ -2869,6 +2899,12 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
} break;
case GGML_OP_DUP:
case GGML_OP_REPEAT:
{
ggml_type src0_type = op->src[0]->type;
return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
} break;
case GGML_OP_REPEAT_BACK:
return op->type == GGML_TYPE_F32 && op->src[0]->ne[3] == 1;
case GGML_OP_CONCAT:
{
ggml_type src0_type = op->src[0]->type;
@ -2935,9 +2971,11 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
}
return ggml_cuda_info().devices[cuda_ctx->device].cc >= CC_VOLTA &&
op->src[1]->type == GGML_TYPE_F16 && op->src[2]->type == GGML_TYPE_F16;
case GGML_OP_CROSS_ENTROPY_LOSS:
return true;
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
case GGML_OP_CROSS_ENTROPY_LOSS:
case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
case GGML_OP_OPT_STEP_ADAMW:
return true;
default:
return false;
}

67
ggml/src/ggml-cuda/binbcast.cu
View File

@ -1,4 +1,5 @@
#include "binbcast.cuh"
#include <cstdint>
static __device__ __forceinline__ float op_repeat(const float a, const float b) {
return b;
@ -90,6 +91,30 @@ static __global__ void k_bin_bcast_unravel(const src0_t * src0, const src1_t * s
dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
}
template <typename T>
static __global__ void k_repeat_back(
const T * __restrict__ src, T * __restrict__ dst, const int64_t ne00, const int64_t ne01, const int64_t ne02,
const int64_t ne0, const int64_t ne1, const int64_t ne2) {
const int64_t tid0 = (int64_t) blockIdx.x*blockDim.x + threadIdx.x;
const int64_t tid1 = (int64_t) blockIdx.y*blockDim.y + threadIdx.y;
const int64_t tid2 = (int64_t) blockIdx.z*blockDim.z + threadIdx.z;
if (tid0 >= ne0) {
return;
}
T sum = 0;
for (int64_t i2 = tid2; i2 < ne02; i2 += ne2) {
for (int64_t i1 = tid1; i1 < ne01; i1 += ne1) {
for (int64_t i0 = tid0; i0 < ne00; i0 += ne0) {
sum += src[i2*ne01*ne00 + i1*ne00 + i0];
}
}
}
dst[tid2*ne1*ne0 + tid1*ne0 + tid0] = sum;
}
template<float (*bin_op)(const float, const float)>
struct bin_bcast_cuda {
template<typename src0_t, typename src1_t, typename dst_t>
@ -247,6 +272,16 @@ struct bin_bcast_cuda {
}
};
template <typename T>
static void repeat_back_cuda(
const T * src, T * dst, const int64_t ne00, const int64_t ne01, const int64_t ne02,
const int64_t ne0, const int64_t ne1, const int64_t ne2, cudaStream_t stream) {
const dim3 block_dims(WARP_SIZE, 1, 1);
const dim3 block_nums((ne0 + WARP_SIZE - 1) / WARP_SIZE, ne1, ne2);
k_repeat_back<T><<<block_nums, block_dims, 0, stream>>>(src, dst, ne00, ne01, ne02, ne0, ne1, ne2);
}
template<class op>
static void ggml_cuda_op_bin_bcast(
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
@ -286,3 +321,35 @@ void ggml_cuda_op_mul(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
void ggml_cuda_op_div(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_div>>(dst->src[0], dst->src[1], dst, dst->src[0]->data, dst->src[1]->data, dst->data, ctx.stream());
}
void ggml_cuda_op_repeat_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
GGML_ASSERT(src0->type == dst->type);
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(dst));
GGML_ASSERT(ggml_can_repeat(dst, src0));
cudaStream_t stream = ctx.stream();
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
GGML_ASSERT(src0->ne[3] == 1);
const int64_t ne0 = dst->ne[0];
const int64_t ne1 = dst->ne[1];
const int64_t ne2 = dst->ne[2];
GGML_ASSERT(dst->ne[3] == 1);
switch (dst->type) {
case GGML_TYPE_F32: {
const float * src0_d = (const float *) src0->data;
float * dst_d = (float *) dst->data;
repeat_back_cuda<float>(src0_d, dst_d, ne00, ne01, ne02, ne0, ne1, ne2, stream);
} break;
default: {
GGML_ASSERT(false);
} break;
}
}

2
ggml/src/ggml-cuda/binbcast.cuh
View File

@ -5,3 +5,5 @@ void ggml_cuda_op_add(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_sub(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_mul(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_div(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_repeat_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

60
ggml/src/ggml-cuda/cross-entropy-loss.cu
View File

@ -71,6 +71,32 @@ static __global__ void cross_entropy_loss_f32(const float * logits, const float
dst[blockIdx.x] = loss;
}
static __global__ void cross_entropy_loss_back_f32(const float * logits, const float * labels, const float * loss, float * dst, const int nclasses) {
extern __shared__ float tmp[];
float maxval = -INFINITY;
for (int i = threadIdx.x; i < nclasses; i += WARP_SIZE) {
const float val = logits[blockIdx.x*nclasses + i];
maxval = fmaxf(maxval, val);
tmp[i] = val;
}
maxval = warp_reduce_max(maxval);
float sum = 0.0f;
for (int i = threadIdx.x; i < nclasses; i += WARP_SIZE) {
const float val = expf(tmp[i] - maxval);
sum += val;
tmp[i] = val;
}
sum = warp_reduce_sum(sum);
const float sm_scale = 1.0f/sum;
const float d_by_nrows = *loss/gridDim.x;
for (int i = threadIdx.x; i < nclasses; i += WARP_SIZE) {
dst[blockIdx.x*nclasses + i] = (tmp[i]*sm_scale - labels[blockIdx.x*nclasses + i])*d_by_nrows;
}
}
void ggml_cuda_cross_entropy_loss(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
@ -104,3 +130,37 @@ void ggml_cuda_cross_entropy_loss(ggml_backend_cuda_context & ctx, ggml_tensor *
// Combine results from individual blocks:
sum_f32_cuda(pool, dst_tmp.ptr, dst_d, blocks_num.x, stream);
}
void ggml_cuda_cross_entropy_loss_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
const ggml_tensor * opt0 = dst->src[2];
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT(opt0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(src1));
GGML_ASSERT(ggml_is_contiguous(opt0));
GGML_ASSERT(ggml_is_contiguous(dst));
GGML_ASSERT(ggml_are_same_shape(src0, src1));
GGML_ASSERT(ggml_are_same_shape(src0, dst));
const int64_t ne00 = src0->ne[0];
const int64_t nrows = ggml_nrows(src0);
const float * src0_d = (const float *) src0->data;
const float * src1_d = (const float *) src1->data;
const float * opt0_d = (const float *) opt0->data;
float * dst_d = (float *) dst->data;
cudaStream_t stream = ctx.stream();
const dim3 blocks_dim(WARP_SIZE, 1, 1);
const dim3 blocks_num(nrows, 1, 1);
const int shmem = ne00*sizeof(float);
cross_entropy_loss_back_f32<<<blocks_num, blocks_dim, shmem, stream>>>(src0_d, src1_d, opt0_d, dst_d, ne00);
}

2
ggml/src/ggml-cuda/cross-entropy-loss.cuh
View File

@ -3,3 +3,5 @@
#define CUDA_CROSS_ENTROPY_LOSS_BLOCK_SIZE 256
void ggml_cuda_cross_entropy_loss(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_cross_entropy_loss_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

80
ggml/src/ggml-cuda/opt-step-adamw.cu Normal file
View File

@ -0,0 +1,80 @@
#include "opt-step-adamw.cuh"
#include <cstdint>
static __global__ void opt_step_adamw_f32(
float * __restrict__ x, const float * __restrict__ g, float * __restrict__ g_m, float * __restrict__ g_v, const int64_t k,
const float alpha, const float beta1, const float beta2, const float eps, const float wd,
const float beta1h, const float beta2h) {
const int64_t i = (int64_t) blockIdx.x*blockDim.x + threadIdx.x;
if (i >= k) {
return;
}
const float gi = g[i];
const float gmi = g_m[i]*beta1 + gi*(1.0f - beta1);
const float gvi = g_v[i]*beta2 + gi*gi*(1.0f - beta2);
g_m[i] = gmi;
g_v[i] = gvi;
const float mh = gmi*beta1h;
const float vh = sqrtf(gvi*beta2h) + eps;
x[i] = x[i]*(1.0f - alpha*wd) - mh/vh;
}
static void opt_step_adamw_f32_cuda(
float * x, const float * g, float * g_m, float * g_v, const int64_t k,
const float alpha, const float beta1, const float beta2, const float eps, const float wd,
const float beta1h, const float beta2h, cudaStream_t stream) {
const dim3 block_dims(CUDA_OPT_STEP_ADAMW_BLOCK_SIZE, 1, 1);
const dim3 block_nums((k + CUDA_OPT_STEP_ADAMW_BLOCK_SIZE - 1) / CUDA_OPT_STEP_ADAMW_BLOCK_SIZE, 1, 1);
opt_step_adamw_f32<<<block_nums, block_dims, 0, stream>>>(x, g, g_m, g_v, k, alpha, beta1, beta2, eps, wd, beta1h, beta2h);
}
void ggml_cuda_opt_step_adamw(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src0_grad = dst->src[1];
const ggml_tensor * src0_grad_m = dst->src[2];
const ggml_tensor * src0_grad_v = dst->src[3];
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(src0_grad->type == GGML_TYPE_F32);
GGML_ASSERT(src0_grad_m->type == GGML_TYPE_F32);
GGML_ASSERT(src0_grad_v->type == GGML_TYPE_F32);
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(src0_grad));
GGML_ASSERT(ggml_is_contiguous(src0_grad_m));
GGML_ASSERT(ggml_is_contiguous(src0_grad_v));
GGML_ASSERT(ggml_are_same_shape(src0, src0_grad));
GGML_ASSERT(ggml_are_same_shape(src0, src0_grad_m));
GGML_ASSERT(ggml_are_same_shape(src0, src0_grad_v));
float * src0_d = (float *) src0->data;
const float * src0_grad_d = (const float *) src0_grad->data;
float * src0_grad_m_d = (float *) src0_grad_m->data;
float * src0_grad_v_d = (float *) src0_grad_v->data;
cudaStream_t stream = ctx.stream();
const int64_t ne = ggml_nelements(src0);
int64_t iter; memcpy(&iter, &dst->op_params[0], sizeof(int64_t));
float alpha; memcpy(&alpha, &dst->op_params[2], sizeof(float));
float beta1; memcpy(&beta1, &dst->op_params[3], sizeof(float));
float beta2; memcpy(&beta2, &dst->op_params[4], sizeof(float));
float eps; memcpy(&eps, &dst->op_params[5], sizeof(float));
float wd; memcpy(&wd, &dst->op_params[6], sizeof(float));
const float beta1h = alpha/(1.0f - powf(beta1, iter));
const float beta2h = 1.0f/(1.0f - powf(beta2, iter));
opt_step_adamw_f32_cuda(src0_d, src0_grad_d, src0_grad_m_d, src0_grad_v_d, ne, alpha, beta1, beta2, eps, wd, beta1h, beta2h, stream);
iter++;
memcpy(&dst->op_params[0], &iter, sizeof(int64_t));
}

5
ggml/src/ggml-cuda/opt-step-adamw.cuh Normal file
View File

@ -0,0 +1,5 @@
#include "common.cuh"
#define CUDA_OPT_STEP_ADAMW_BLOCK_SIZE 256
void ggml_cuda_opt_step_adamw(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

52
ggml/src/ggml-cuda/out-prod.cu Normal file
View File

@ -0,0 +1,52 @@
#include "out-prod.cuh"
#include "vendors/cuda.h"
#include <cstdint>
void ggml_cuda_out_prod(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
GGML_TENSOR_BINARY_OP_LOCALS
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(dst));
GGML_ASSERT(ne01 == ne11);
GGML_ASSERT(ne0 == ne00);
GGML_ASSERT(ne1 == ne10);
GGML_ASSERT(ne2 == src0->ne[2]);
GGML_ASSERT(ne2 == src1->ne[2]);
GGML_ASSERT(ne3 == src0->ne[3]);
GGML_ASSERT(ne3 == src1->ne[3]);
const float * src0_d = (const float *) src0->data;
const float * src1_d = (const float *) src1->data;
float * dst_d = (float *) dst->data;
cudaStream_t stream = ctx.stream();
cublasHandle_t handle = ctx.cublas_handle();
const float alpha = 1.0f;
const float beta = 0.0f;
GGML_ASSERT(ne2 == 1);
GGML_ASSERT(ne3 == 1);
CUBLAS_CHECK(cublasSetStream(handle, stream));
const bool src1_T = ggml_is_transposed(src1);
const cublasOperation_t src1_cublas_op = src1_T ? CUBLAS_OP_N : CUBLAS_OP_T;
const int64_t ldb = (src1_T ? nb10 : nb11) / sizeof(float);
GGML_ASSERT( (src1_T ? nb11 : nb10) == sizeof(float));
CUBLAS_CHECK(
cublasSgemm(handle, CUBLAS_OP_N, src1_cublas_op,
ne0, ne1, ne01,
&alpha, src0_d, ne00,
src1_d, ldb,
&beta, dst_d, ne0));
}

3
ggml/src/ggml-cuda/out-prod.cuh Normal file
View File

@ -0,0 +1,3 @@
#include "common.cuh"
void ggml_cuda_out_prod(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

29
ggml/src/ggml-cuda/unary.cu
View File

@ -10,6 +10,16 @@ static __global__ void neg_f32(const float * x, float * dst, const int k) {
dst[i] = -x[i];
}
static __global__ void step_f32(const float * x, float * dst, const int k) {
const int i = blockDim.x*blockIdx.x + threadIdx.x;
if (i >= k) {
return;
}
dst[i] = x[i] > 0.0f;
}
static __global__ void gelu_f32(const float * x, float * dst, const int k) {
const float GELU_COEF_A = 0.044715f;
const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
@ -134,6 +144,11 @@ static void neg_f32_cuda(const float * x, float * dst, const int k, cudaStream_t
neg_f32<<<num_blocks, CUDA_NEG_BLOCK_SIZE, 0, stream>>>(x, dst, k);
}
static void step_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_STEP_BLOCK_SIZE - 1) / CUDA_STEP_BLOCK_SIZE;
step_f32<<<num_blocks, CUDA_STEP_BLOCK_SIZE, 0, stream>>>(x, dst, k);
}
static void gelu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_GELU_BLOCK_SIZE - 1) / CUDA_GELU_BLOCK_SIZE;
gelu_f32<<<num_blocks, CUDA_GELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
@ -213,6 +228,20 @@ void ggml_cuda_op_neg(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
neg_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
}
void ggml_cuda_op_step(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const float * src0_d = (const float *)src0->data;
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);
step_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream);
}
void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const float * src0_d = (const float *)src0->data;

3
ggml/src/ggml-cuda/unary.cuh
View File

@ -1,6 +1,7 @@
#include "common.cuh"
#define CUDA_NEG_BLOCK_SIZE 256
#define CUDA_STEP_BLOCK_SIZE 256
#define CUDA_GELU_BLOCK_SIZE 256
#define CUDA_SILU_BLOCK_SIZE 256
#define CUDA_TANH_BLOCK_SIZE 256
@ -15,6 +16,8 @@
void ggml_cuda_op_neg(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_step(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_silu(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

1
ggml/src/ggml-kompute.cpp
View File

@ -1872,6 +1872,7 @@ static ggml_backend_buffer_i ggml_backend_kompute_buffer_i = {
/* .free_buffer = */ ggml_backend_kompute_buffer_free_buffer,
/* .get_base = */ ggml_backend_kompute_buffer_get_base,
/* .init_tensor = */ NULL,
/* .memset_tensor = */ NULL,
/* .set_tensor = */ ggml_backend_kompute_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_kompute_buffer_get_tensor,
/* .cpy_tensor = */ NULL,

1
ggml/src/ggml-metal.m
View File

@ -3167,6 +3167,7 @@ static struct ggml_backend_buffer_i ggml_backend_metal_buffer_i = {
/* .free_buffer = */ ggml_backend_metal_buffer_free_buffer,
/* .get_base = */ ggml_backend_metal_buffer_get_base,
/* .init_tensor = */ NULL,
/* .memset_tensor = */ NULL,
/* .set_tensor = */ ggml_backend_metal_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_metal_buffer_get_tensor,
/* .cpy_tensor = */ ggml_backend_metal_buffer_cpy_tensor,

1
ggml/src/ggml-rpc.cpp
View File

@ -469,6 +469,7 @@ static ggml_backend_buffer_i ggml_backend_rpc_buffer_interface = {
/* .free_buffer = */ ggml_backend_rpc_buffer_free_buffer,
/* .get_base = */ ggml_backend_rpc_buffer_get_base,
/* .init_tensor = */ ggml_backend_rpc_buffer_init_tensor,
/* .memset_tensor = */ NULL,
/* .set_tensor = */ ggml_backend_rpc_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_rpc_buffer_get_tensor,
/* .cpy_tensor = */ ggml_backend_rpc_buffer_cpy_tensor,

1
ggml/src/ggml-sycl.cpp
View File

@ -4323,6 +4323,7 @@ static struct ggml_backend_buffer_i ggml_backend_sycl_buffer_interface = {
/* .free_buffer = */ ggml_backend_sycl_buffer_free_buffer,
/* .get_base = */ ggml_backend_sycl_buffer_get_base,
/* .init_tensor = */ ggml_backend_sycl_buffer_init_tensor,
/* .memset_tensor = */ NULL,
/* .set_tensor = */ ggml_backend_sycl_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_sycl_buffer_get_tensor,
/* .cpy_tensor = */ ggml_backend_sycl_buffer_cpy_tensor,

1
ggml/src/ggml-vulkan.cpp
View File

@ -6246,6 +6246,7 @@ static ggml_backend_buffer_i ggml_backend_vk_buffer_interface = {
/* .free_buffer = */ ggml_backend_vk_buffer_free_buffer,
/* .get_base = */ ggml_backend_vk_buffer_get_base,
/* .init_tensor = */ ggml_backend_vk_buffer_init_tensor,
/* .memset_tensor = */ NULL,
/* .set_tensor = */ ggml_backend_vk_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_vk_buffer_get_tensor,
/* .cpy_tensor = */ ggml_backend_vk_buffer_cpy_tensor,

424
ggml/src/ggml.c
View File

@ -1,6 +1,7 @@
#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnings on Windows
#define _USE_MATH_DEFINES // For M_PI on MSVC
#include "ggml-backend.h"
#include "ggml-impl.h"
#include "ggml-cpu-impl.h"
#include "ggml-quants.h"
@ -2997,9 +2998,10 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"CROSS_ENTROPY_LOSS",
"CROSS_ENTROPY_LOSS_BACK",
"OPT_STEP_ADAMW",
};
static_assert(GGML_OP_COUNT == 79, "GGML_OP_COUNT != 79");
static_assert(GGML_OP_COUNT == 80, "GGML_OP_COUNT != 80");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@ -3090,9 +3092,10 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"cross_entropy_loss(x,y)",
"cross_entropy_loss_back(x,y)",
"adamw(x)",
};
static_assert(GGML_OP_COUNT == 79, "GGML_OP_COUNT != 79");
static_assert(GGML_OP_COUNT == 80, "GGML_OP_COUNT != 80");
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
@ -4094,7 +4097,11 @@ static void ggml_set_op_params_f32(struct ggml_tensor * tensor, uint32_t i, floa
}
struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) {
memset(tensor->data, 0, ggml_nbytes(tensor));
if (tensor->buffer) {
ggml_backend_tensor_memset(tensor, 0, 0, ggml_nbytes(tensor));
} else {
memset(tensor->data, 0, ggml_nbytes(tensor));
}
return tensor;
}
@ -8320,11 +8327,46 @@ struct ggml_tensor * ggml_cross_entropy_loss_back(
return result;
}
// opt_step_adamw
struct ggml_tensor * ggml_opt_step_adamw(
struct ggml_context * ctx,
struct ggml_tensor * a,
float alpha,
float beta1,
float beta2,
float eps,
float wd) {
GGML_ASSERT(a->grad);
GGML_ASSERT(alpha > 0.0f);
GGML_ASSERT(beta1 >= 0.0f && beta1 <= 1.0f);
GGML_ASSERT(beta2 >= 0.0f && beta2 <= 1.0f);
GGML_ASSERT(eps >= 0.0f);
GGML_ASSERT(wd >= 0.0f && wd <= 1.0f);
struct ggml_tensor * result = ggml_view_tensor(ctx, a);
result->op = GGML_OP_OPT_STEP_ADAMW;
result->grad = NULL;
result->src[0] = a;
result->src[1] = a->grad;
result->src[2] = ggml_dup_tensor(ctx, a->grad);
result->src[3] = ggml_dup_tensor(ctx, a->grad);
const int64_t iter = 1;
memcpy(&result->op_params[0], &iter, sizeof(int64_t));
ggml_set_op_params_f32(result, 2, alpha);
ggml_set_op_params_f32(result, 3, beta1);
ggml_set_op_params_f32(result, 4, beta2);
ggml_set_op_params_f32(result, 5, eps);
ggml_set_op_params_f32(result, 6, wd);
return result;
}
////////////////////////////////////////////////////////////////////////////////
void ggml_set_param(
struct ggml_context * ctx,
struct ggml_tensor * tensor) {
void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor) {
tensor->flags |= GGML_TENSOR_FLAG_PARAM;
GGML_ASSERT(tensor->grad == NULL);
@ -8332,6 +8374,13 @@ void ggml_set_param(
ggml_format_name(tensor->grad, "%s (grad)", tensor->name);
}
void ggml_set_loss(struct ggml_tensor * tensor) {
GGML_ASSERT(ggml_is_scalar(tensor));
GGML_ASSERT(tensor->type == GGML_TYPE_F32);
GGML_ASSERT(tensor->grad);
tensor->flags |= GGML_TENSOR_FLAG_LOSS;
}
// ggml_compute_forward_dup
static void ggml_compute_forward_dup_same_cont(
@ -17406,7 +17455,7 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32(
const int64_t ir0 = dr*ith;
const int64_t ir1 = MIN(ir0 + dr, nr);
float * d = (float *) opt0->data;
const float d_by_nr = ((const float *) opt0->data)[0] / (float) nr;
for (int64_t i1 = ir0; i1 < ir1; i1++) {
float * ds0 = (float *)((char *) dst->data + i1*dst->nb[1]);
@ -17430,7 +17479,7 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32(
// grad(src0) = (softmax(src0) - src1) * grad(cross_entropy_loss(src0, src1)) / nr
ggml_vec_sub_f32(nc, ds0, ds0, s1);
ggml_vec_scale_f32(nc, ds0, d[0] / (float) nr);
ggml_vec_scale_f32(nc, ds0, d_by_nr);
#ifndef NDEBUG
for (int i = 0; i < nc; ++i) {
@ -17459,6 +17508,94 @@ static void ggml_compute_forward_cross_entropy_loss_back(
}
}
static void ggml_compute_forward_opt_step_adamw_f32(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src0_grad = dst->src[1];
const struct ggml_tensor * src0_grad_m = dst->src[2];
const struct ggml_tensor * src0_grad_v = dst->src[3];
GGML_ASSERT(ggml_are_same_shape(src0, src0_grad));
const int ith = params->ith;
const int nth = params->nth;
const int nr = ggml_nrows(src0);
GGML_TENSOR_UNARY_OP_LOCALS
GGML_ASSERT(nb00 == sizeof(float));
// rows per thread
const int dr = (nr + nth - 1)/nth;
// row range for this thread
const int ir0 = dr*ith;
const int ir1 = MIN(ir0 + dr, nr);
/* const float gnorm = 1.0f; */
int64_t iter; memcpy(&iter, &dst->op_params[0], sizeof(int64_t));
const float alpha = ggml_get_op_params_f32(dst, 2);
const float beta1 = ggml_get_op_params_f32(dst, 3);
const float beta2 = ggml_get_op_params_f32(dst, 4);
const float eps = ggml_get_op_params_f32(dst, 5);
const float wd = ggml_get_op_params_f32(dst, 6);
const float beta1h = alpha/(1.0f - powf(beta1, iter));
const float beta2h = 1.0f/(1.0f - powf(beta2, iter));
for (int ir = ir0; ir < ir1; ++ir) {
const int64_t i03 = ir/(ne02*ne01);
const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
const size_t offset = i03*nb03 + i02*nb02 + i01*nb01;
float * w = (float *) ((char *) src0->data + offset); // weight
const float * g = (const float *) ((const char *) src0_grad->data + offset); // grad
float * m = (float *) ((char *) src0_grad_m->data + offset);
float * v = (float *) ((char *) src0_grad_v->data + offset);
for (int i00 = 0; i00 < ne00; ++i00) {
m[i00] = m[i00]*beta1 + g[i00]*(1.0f - beta1);
v[i00] = v[i00]*beta2 + g[i00]*g[i00]*(1.0f - beta2);
const float mh = m[i00]*beta1h;
const float vh = sqrtf(v[i00]*beta2h) + eps;
// The weight decay is applied independently of the Adam momenta m and v.
// This is NOT equivalent to l2 regularization that adds w[i00]*w[i00] to the loss.
// See: https://arxiv.org/pdf/1711.05101v3.pdf
w[i00] = w[i00]*(1.0f - alpha*wd) - mh/vh;
}
}
ggml_barrier(params->threadpool);
if (ith != 0) {
return;
}
iter++;
memcpy(&dst->op_params[0], &iter, sizeof(int64_t));
}
static void ggml_compute_forward_opt_step_adamw(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
switch (src0->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_opt_step_adamw_f32(params, dst);
} break;
default:
{
GGML_ABORT("fatal error");
}
}
}
/////////////////////////////////
static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
@ -17804,6 +17941,11 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
ggml_compute_forward_cross_entropy_loss_back(params, tensor);
}
break;
case GGML_OP_OPT_STEP_ADAMW:
{
ggml_compute_forward_opt_step_adamw(params, tensor);
}
break;
case GGML_OP_NONE:
{
// nop
@ -17958,7 +18100,7 @@ void ggml_build_backward_gradient_checkpointing(
struct ggml_tensor * * checkpoints,
int n_checkpoints) {
ggml_graph_cpy(gf, gb_tmp);
ggml_build_backward_expand(ctx, gf, gb_tmp, true);
ggml_build_backward_expand(ctx, gf, gb_tmp, false, true);
if (n_checkpoints <= 0) {
ggml_graph_cpy(gb_tmp, gb);
@ -17996,42 +18138,93 @@ void ggml_build_backward_gradient_checkpointing(
ggml_hash_map_free(replacements);
}
// functions to change gradients considering the case that input a might be initial gradient with zero value
// utility functions to change gradients
// if a is in acc_table, modify gradients in-place and mark result as gradient accumulator
// else if a is in zero_table, replace a
// else, just add/subtract/etc. the gradients
static struct ggml_tensor * ggml_add_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_hash_set * zero_table) {
static struct ggml_tensor * ggml_add_or_set(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_hash_set * zero_table,
struct ggml_hash_set * acc_table) {
if (ggml_hash_contains(acc_table, a)) {
struct ggml_tensor * ret = ggml_add_impl(ctx, a, b, true);
const size_t insert_result = ggml_hash_insert(acc_table, ret);
GGML_ASSERT(insert_result != GGML_HASHSET_FULL);
GGML_ASSERT(insert_result != GGML_HASHSET_ALREADY_EXISTS);
return ret;
}
if (ggml_hash_contains(zero_table, a)) {
return b;
} else {
return ggml_add_impl(ctx, a, b, false);
}
return ggml_add_impl(ctx, a, b, false);
}
static struct ggml_tensor * ggml_acc_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, size_t nb1, size_t nb2, size_t nb3, size_t offset, struct ggml_hash_set * zero_table) {
static struct ggml_tensor * ggml_acc_or_set(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
const size_t nb1,
const size_t nb2,
const size_t nb3,
const size_t offset,
struct ggml_hash_set * zero_table,
struct ggml_hash_set * acc_table) {
if (ggml_hash_contains(acc_table, a)) {
struct ggml_tensor * ret = ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, true);
const size_t insert_result = ggml_hash_insert(acc_table, ret);
GGML_ASSERT(insert_result != GGML_HASHSET_FULL);
GGML_ASSERT(insert_result != GGML_HASHSET_ALREADY_EXISTS);
return ret;
}
if (ggml_hash_contains(zero_table, a)) {
struct ggml_tensor * a_zero = ggml_scale(ctx, a, 0.0f);
struct ggml_tensor * a_zero = ggml_scale(ctx, a, 0.0f); // FIXME this is going to produce NaN if a contains inf/NaN
return ggml_acc_impl(ctx, a_zero, b, nb1, nb2, nb3, offset, false);
} else {
return ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, false);
}
return ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, false);
}
static struct ggml_tensor * ggml_add1_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_hash_set * zero_table) {
static struct ggml_tensor * ggml_add1_or_set(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_hash_set * zero_table,
struct ggml_hash_set * acc_table) {
if (ggml_hash_contains(acc_table, a)) {
struct ggml_tensor * ret = ggml_add1_impl(ctx, a, b, true);
const size_t insert_result = ggml_hash_insert(acc_table, ret);
GGML_ASSERT(insert_result != GGML_HASHSET_FULL);
GGML_ASSERT(insert_result != GGML_HASHSET_ALREADY_EXISTS);
return ret;
}
if (ggml_hash_contains(zero_table, a)) {
return ggml_repeat(ctx, b, a);
} else {
return ggml_add1_impl(ctx, a, b, false);
}
return ggml_add1_impl(ctx, a, b, false);
}
static struct ggml_tensor * ggml_sub_or_set(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, struct ggml_hash_set * zero_table) {
static struct ggml_tensor * ggml_sub_or_set(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_hash_set * zero_table,
struct ggml_hash_set * acc_table) {
if (ggml_hash_contains(acc_table, a)) {
struct ggml_tensor * ret = ggml_sub_impl(ctx, a, b, true);
const size_t insert_result = ggml_hash_insert(acc_table, ret);
GGML_ASSERT(insert_result != GGML_HASHSET_FULL);
GGML_ASSERT(insert_result != GGML_HASHSET_ALREADY_EXISTS);
return ret;
}
if (ggml_hash_contains(zero_table, a)) {
return ggml_neg(ctx, b);
} else {
return ggml_sub_impl(ctx, a, b, false);
}
return ggml_sub_impl(ctx, a, b, false);
}
static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor * tensor, struct ggml_hash_set * zero_table) {
static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor * tensor, struct ggml_hash_set * zero_table, struct ggml_hash_set * acc_table) {
struct ggml_tensor * src0 = tensor->src[0];
struct ggml_tensor * src1 = tensor->src[1];
struct ggml_tensor * src2 = tensor->src[2];
@ -18040,38 +18233,38 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
case GGML_OP_DUP:
{
if (src0->grad) {
src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table);
src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table);
}
} break;
case GGML_OP_ADD:
{
if (src0->grad) {
src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table);
src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table);
}
if (src1->grad) {
if (ggml_are_same_shape(src0, src1)) {
src1->grad = ggml_add_or_set(ctx, src1->grad, tensor->grad, zero_table);
src1->grad = ggml_add_or_set(ctx, src1->grad, tensor->grad, zero_table, acc_table);
} else {
src1->grad = ggml_add_or_set(ctx, src1->grad, ggml_repeat_back(ctx, tensor->grad, src1), zero_table);
src1->grad = ggml_add_or_set(ctx, src1->grad, ggml_repeat_back(ctx, tensor->grad, src1), zero_table, acc_table);
}
}
} break;
case GGML_OP_ADD1:
{
if (src0->grad) {
src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table);
src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table);
}
if (src1->grad) {
src1->grad = ggml_add_or_set(ctx,
src1->grad,
ggml_mean(ctx, tensor->grad), // TODO: should probably be sum instead of mean
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_ACC:
{
if (src0->grad) {
src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table);
src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table);
}
if (src1->grad) {
const size_t nb1 = ((int32_t *) tensor->op_params)[0];
@ -18093,16 +18286,16 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
ggml_reshape(ctx,
ggml_cont(ctx, tensor_grad_view),
src1->grad),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_SUB:
{
if (src0->grad) {
src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table);
src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table);
}
if (src1->grad) {
src1->grad = ggml_sub_or_set(ctx, src1->grad, tensor->grad, zero_table);
src1->grad = ggml_sub_or_set(ctx, src1->grad, tensor->grad, zero_table, acc_table);
}
} break;
case GGML_OP_MUL:
@ -18112,14 +18305,14 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
ggml_add_or_set(ctx,
src0->grad,
ggml_mul(ctx, src1, tensor->grad),
zero_table);
zero_table, acc_table);
}
if (src1->grad) {
src1->grad =
ggml_add_or_set(ctx,
src1->grad,
ggml_mul(ctx, src0, tensor->grad),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_DIV:
@ -18129,7 +18322,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
ggml_add_or_set(ctx,
src0->grad,
ggml_div(ctx, tensor->grad, src1),
zero_table);
zero_table, acc_table);
}
if (src1->grad) {
src1->grad =
@ -18138,7 +18331,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
ggml_mul(ctx,
tensor->grad,
ggml_div(ctx, tensor, src1)),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_SQR:
@ -18150,7 +18343,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
ggml_scale(ctx,
ggml_mul(ctx, src0, tensor->grad),
2.0f),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_SQRT:
@ -18164,7 +18357,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
tensor->grad,
tensor),
0.5f),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_LOG:
@ -18176,7 +18369,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
ggml_div(ctx,
tensor->grad,
src0),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_SIN:
@ -18188,7 +18381,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
ggml_mul(ctx,
tensor->grad,
ggml_cos(ctx, src0)),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_COS:
@ -18200,7 +18393,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
ggml_mul(ctx,
tensor->grad,
ggml_sin(ctx, src0)),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_SUM:
@ -18210,7 +18403,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
ggml_add1_or_set(ctx,
src0->grad,
tensor->grad,
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_SUM_ROWS:
@ -18222,7 +18415,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
ggml_repeat(ctx,
tensor->grad,
src0->grad),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_MEAN:
@ -18237,7 +18430,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
src0->grad = ggml_add_or_set(ctx,
src0->grad,
ggml_repeat_back(ctx, tensor->grad, src0->grad),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_REPEAT_BACK:
@ -18247,7 +18440,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
src0->grad = ggml_add_or_set(ctx,
src0->grad,
ggml_repeat(ctx, tensor->grad, src0->grad),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_CONCAT:
@ -18272,7 +18465,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
src0->grad = ggml_add_or_set(ctx,
src0->grad,
ggml_rms_norm_back(ctx, src0, tensor->grad, eps),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_RMS_NORM_BACK:
@ -18320,7 +18513,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
ggml_add_or_set(ctx,
src0->grad, // [n,m,q1,r1]
s1_tg, // [n,m,q1,r1]
zero_table);
zero_table, acc_table);
}
if (src1->grad) {
src1->grad =
@ -18338,7 +18531,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
src0, // [n,m,q1,r1]
ggml_transpose(ctx, // [p,m,qq,rr]
tensor->grad)), // [m,p,qq,rr]
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_MUL_MAT_ID:
@ -18360,7 +18553,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
ggml_add_or_set(ctx,
src0->grad,
ggml_scale_impl(ctx, tensor->grad, s, false),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_SET:
@ -18389,7 +18582,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
tensor->grad,
ggml_neg(ctx, tensor_grad_view),
nb1, nb2, nb3, offset, false),
zero_table);
zero_table, acc_table);
}
if (src1->grad) {
@ -18399,7 +18592,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
ggml_reshape(ctx,
ggml_cont(ctx, tensor_grad_view),
src1->grad),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_CPY:
@ -18410,7 +18603,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
// tensor = src0 * 1 + src1 * 0
if (src0->grad) {
// dsrc0 = dtensor * 1
src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table);
src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table);
}
if (src1->grad) {
// dsrc1 = dtensor * 0 -> noop
@ -18422,7 +18615,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
if (src0->grad) {
GGML_ASSERT(ggml_is_contiguous(src0->grad));
GGML_ASSERT(ggml_is_contiguous(tensor->grad));
src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table);
src0->grad = ggml_add_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table);
}
} break;
case GGML_OP_RESHAPE:
@ -18436,7 +18629,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
? tensor->grad
: ggml_cont(ctx, tensor->grad),
src0->grad),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_VIEW:
@ -18465,7 +18658,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
nb3 = (nb3 / n0) * ng;
}
src0->grad = ggml_acc_or_set(ctx, src0->grad, tensor->grad, nb1, nb2, nb3, offset, zero_table);
src0->grad = ggml_acc_or_set(ctx, src0->grad, tensor->grad, nb1, nb2, nb3, offset, zero_table, acc_table);
}
} break;
case GGML_OP_PERMUTE:
@ -18490,7 +18683,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
axes_backward[1],
axes_backward[2],
axes_backward[3]),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_TRANSPOSE:
@ -18500,7 +18693,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
src0->grad =
ggml_add_or_set(ctx, src0->grad,
ggml_transpose(ctx, tensor->grad),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_GET_ROWS:
@ -18512,7 +18705,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
// last ggml_get_rows_back argument src0->grad is only
// necessary to setup correct output shape
ggml_get_rows_back(ctx, tensor->grad, src1, src0->grad),
zero_table);
zero_table, acc_table);
}
if (src1->grad) {
// noop
@ -18536,7 +18729,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
/* ggml_diag_mask_inf_impl() shouldn't be here */
/* ref: https://github.com/ggerganov/llama.cpp/pull/4203#discussion_r1412377992 */
ggml_diag_mask_zero_impl(ctx, tensor->grad, n_past, false),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_DIAG_MASK_ZERO:
@ -18547,7 +18740,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
src0->grad =
ggml_add_or_set(ctx, src0->grad,
ggml_diag_mask_zero_impl(ctx, tensor->grad, n_past, false),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_SOFT_MAX:
@ -18557,7 +18750,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
src0->grad =
ggml_add_or_set(ctx, src0->grad,
ggml_soft_max_back(ctx, tensor->grad, tensor),
zero_table);
zero_table, acc_table);
}
} break;
@ -18598,7 +18791,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
attn_factor,
beta_fast,
beta_slow),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_ROPE_BACK:
@ -18634,7 +18827,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
beta_fast,
beta_slow,
false),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_CLAMP:
@ -18659,7 +18852,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
src1->grad = ggml_add_or_set(ctx,
src1->grad,
ggml_im2col_back(ctx, src0, tensor->grad, src1->ne, s0, s1, p0, p1, d0, d1, is_2D),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_IM2COL_BACK:
@ -18688,7 +18881,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
src0->grad = ggml_add_or_set(ctx,
src0->grad,
ggml_pool_2d_back(ctx, tensor->grad, src0, op, k0, k1, s0, s1, p0, p1),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_POOL_2D_BACK:
@ -18753,7 +18946,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
src0->grad = ggml_add_or_set(ctx,
src0->grad,
grad_q,
zero_table);
zero_table, acc_table);
}
if (src1->grad) {
struct ggml_tensor * view_k = ggml_view_1d(ctx, flash_grad, elem_k, offs_k);
@ -18761,7 +18954,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
src1->grad = ggml_add_or_set(ctx,
src1->grad,
grad_k,
zero_table);
zero_table, acc_table);
}
if (src2->grad) {
struct ggml_tensor * view_v = ggml_view_1d(ctx, flash_grad, elem_v, offs_v);
@ -18769,7 +18962,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
src2->grad = ggml_add_or_set(ctx,
src2->grad,
grad_v,
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_FLASH_ATTN_BACK:
@ -18795,7 +18988,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
ggml_mul(ctx,
ggml_sgn(ctx, src0),
tensor->grad),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_UNARY_OP_SGN:
@ -18807,7 +19000,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
case GGML_UNARY_OP_NEG:
{
if (src0->grad) {
src0->grad = ggml_sub_or_set(ctx, src0->grad, tensor->grad, zero_table);
src0->grad = ggml_sub_or_set(ctx, src0->grad, tensor->grad, zero_table, acc_table);
}
} break;
case GGML_UNARY_OP_STEP:
@ -18832,7 +19025,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
ggml_mul(ctx,
ggml_step(ctx, src0),
tensor->grad),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_UNARY_OP_SIGMOID:
@ -18854,7 +19047,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
src0->grad = ggml_add_or_set(ctx,
src0->grad,
ggml_silu_back(ctx, src0, tensor->grad),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_UNARY_OP_EXP:
@ -18863,7 +19056,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
src0->grad = ggml_add_or_set(ctx,
src0->grad,
ggml_mul(ctx, tensor, tensor->grad),
zero_table);
zero_table, acc_table);
}
} break;
default:
@ -18893,13 +19086,17 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
src0,
src1,
tensor->grad),
zero_table);
zero_table, acc_table);
}
} break;
case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
{
GGML_ABORT("fatal error"); // not supported
}
case GGML_OP_OPT_STEP_ADAMW:
{
GGML_ABORT("fatal error"); // not supported
}
case GGML_OP_NONE:
{
// nop
@ -18989,7 +19186,7 @@ void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor *
ggml_build_forward_impl(cgraph, tensor, true);
}
void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep) {
void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool accumulate, bool keep) {
GGML_ASSERT(gf->n_nodes > 0);
GGML_ASSERT(gf->grads);
@ -19005,21 +19202,35 @@ void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph *
}
}
// remember original gradients which start with zero values
// keep tables of original gradients for replacement/accumulation logic
struct ggml_hash_set zero_table = ggml_hash_set_new(gf->size);
struct ggml_hash_set acc_table = ggml_hash_set_new(gf->size);
for (int i = 0; i < gf->n_nodes; i++) {
if (gf->grads[i]) {
ggml_hash_insert(&zero_table, gf->grads[i]);
struct ggml_tensor * node = gf->nodes[i];
if (node->grad) {
{
const size_t insert_result = ggml_hash_insert(&zero_table, node->grad);
GGML_ASSERT(insert_result != GGML_HASHSET_FULL);
GGML_ASSERT(insert_result != GGML_HASHSET_ALREADY_EXISTS);
}
// only gradients of trainable parameters should be accumulated
if (accumulate && (node->flags & GGML_TENSOR_FLAG_PARAM)) {
const size_t insert_result = ggml_hash_insert(&acc_table, node->grad);
GGML_ASSERT(insert_result != GGML_HASHSET_FULL);
GGML_ASSERT(insert_result != GGML_HASHSET_ALREADY_EXISTS);
}
}
}
for (int i = gf->n_nodes - 1; i >= 0; i--) {
struct ggml_tensor * node = gf->nodes[i];
// inplace operations to add gradients are not created by ggml_compute_backward
// inplace operations to add gradients are not created by ggml_compute_backward except for gradient accumulation
// use allocator to automatically make inplace operations
if (node->grad) {
ggml_compute_backward(ctx, node, &zero_table);
ggml_compute_backward(ctx, node, &zero_table, &acc_table);
}
}
@ -19033,8 +19244,30 @@ void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph *
}
ggml_hash_set_free(&zero_table);
ggml_hash_set_free(&acc_table);
}
void ggml_build_opt_adamw(
struct ggml_context * ctx,
struct ggml_cgraph * gf,
struct ggml_cgraph * gb,
float alpha,
float beta1,
float beta2,
float eps,
float wd) {
for (int i = 0; i < gf->n_nodes; i++) {
struct ggml_tensor * node = gf->nodes[i];
if (node->flags & GGML_TENSOR_FLAG_PARAM) {
GGML_PRINT_DEBUG("%s: found root node %p\n", __func__, (void *) node);
struct ggml_tensor * opt_step = ggml_opt_step_adamw(ctx, node, alpha, beta1, beta2, eps, wd);
ggml_build_forward_expand(gb, opt_step);
}
}
}
static void * incr_ptr_aligned(void ** p, size_t size, size_t align) {
void * ptr = *p;
ptr = (void *) GGML_PAD((uintptr_t) ptr, align);
@ -19162,10 +19395,28 @@ void ggml_graph_reset(struct ggml_cgraph * cgraph) {
GGML_ASSERT(cgraph->grads != NULL);
for (int i = 0; i < cgraph->n_nodes; i++) {
struct ggml_tensor * grad = cgraph->grads[i];
struct ggml_tensor * node = cgraph->nodes[i];
if (grad) {
ggml_set_zero(grad);
// initial gradients of loss should be 1, 0 otherwise
if (node->grad) {
if (node->flags & GGML_TENSOR_FLAG_LOSS) {
GGML_ASSERT(node->grad->buffer);
GGML_ASSERT(node->type == GGML_TYPE_F32);
GGML_ASSERT(ggml_is_scalar(node));
const float onef = 1.0f;
ggml_backend_tensor_set(node->grad, &onef, 0, ggml_nbytes(node->grad));
} else {
ggml_set_zero(node->grad);
}
}
GGML_ASSERT(node);
if (node->op == GGML_OP_OPT_STEP_ADAMW) {
// set iteration to 1 and clear momenta
ggml_set_op_params_i32(node, 0, 1);
ggml_set_zero(node->src[2]);
ggml_set_zero(node->src[3]);
}
}
}
@ -19458,6 +19709,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
} break;
case GGML_OP_CROSS_ENTROPY_LOSS:
case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
case GGML_OP_OPT_STEP_ADAMW:
{
n_tasks = n_threads;
} break;
@ -21851,7 +22103,7 @@ enum ggml_opt_result ggml_opt_resume(
ggml_build_forward_expand(gf, f);
struct ggml_cgraph * gb = ggml_graph_dup(ctx, gf);
ggml_build_backward_expand(ctx, gf, gb, true);
ggml_build_backward_expand(ctx, gf, gb, false, true);
return ggml_opt_resume_g(ctx, opt, f, gf, gb, NULL, NULL);
}

148
tests/test-backend-ops.cpp
View File

@ -799,10 +799,11 @@ struct test_case {
out = ggml_sum(ctx, out);
ggml_set_name(out, "sum_of_out");
}
ggml_set_loss(out);
ggml_build_forward_expand(gf, out);
ggml_graph_cpy(gf, gb);
ggml_build_backward_expand(ctx, gf, gb, false);
ggml_build_backward_expand(ctx, gf, gb, false, false);
if (expect.size() != 1 || expect[0] != 0.0f) {
GGML_ASSERT(ggml_graph_n_nodes(gb) > ggml_graph_n_nodes(gf));
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
@ -837,22 +838,11 @@ struct test_case {
return false;
}
// randomize tensors
initialize_tensors(ctx);
for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
if (!t->grad) {
continue;
}
initialize_tensors(ctx); // Randomizes all tensors (including gradients).
ggml_graph_reset(gb); // Sets gradients to 1 if loss, 0 otherwise.
std::vector<float> tmp(ggml_nelements(t->grad));
ggml_backend_tensor_set(t->grad, tmp.data(), 0, ggml_nbytes(t->grad));
}
// build graphs
const float onef = 1.0f;
ggml_backend_graph_compute(backend, gf);
ggml_backend_tensor_set(out->grad, &onef, 0, ggml_nbytes(out->grad));
ggml_backend_graph_compute(backend, gb);
bool ok = true;
@ -1681,6 +1671,50 @@ struct test_mul_mat_id : public test_case {
}
};
// GGML_OP_OUT_PROD
struct test_out_prod : public test_case {
const ggml_type type_a;
const ggml_type type_b;
const int64_t m;
const int64_t n;
const int64_t k;
const std::array<int64_t, 2> bs; // dims 3 and 4
const bool trans_b;
std::string vars() override {
return VARS_TO_STR7(type_a, type_b, m, n, k, bs, trans_b);
}
double max_nmse_err() override {
return 5e-4;
}
test_out_prod(ggml_type type_a = GGML_TYPE_F32, ggml_type type_b = GGML_TYPE_F32,
int64_t m = 32, int64_t n = 32, int64_t k = 32,
std::array<int64_t, 2> bs = {10, 10},
bool trans_b = false)
: type_a(type_a), type_b(type_b), m(m), n(n), k(k), bs(bs), trans_b(trans_b) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor_4d(ctx, type_a, m, k, bs[0], bs[1]);
ggml_set_name(a, "a");
ggml_tensor * b;
if (trans_b) {
b = ggml_new_tensor_4d(ctx, type_b, k, n, bs[0], bs[1]);
b = ggml_transpose(ctx, b);
} else {
b = ggml_new_tensor_4d(ctx, type_b, n, k, bs[0], bs[1]);
}
ggml_set_name(b, "b");
ggml_tensor * out = ggml_out_prod(ctx, a, b);
ggml_set_name(out, "out");
return out;
}
};
// GGML_OP_SQR
struct test_sqr : public test_case {
const ggml_type type;
@ -2666,6 +2700,51 @@ struct test_cross_entropy_loss : public test_case {
}
};
// GGML_OP_OPT_STEP_ADAMW
struct test_opt_step_adamw : public test_case {
const ggml_type type;
const std::array<int64_t, 4> ne;
const float alpha;
const float beta1;
const float beta2;
const float eps;
const float wd;
std::string vars() override {
return VARS_TO_STR7(type, ne, alpha, beta1, beta2, eps, wd);
}
test_opt_step_adamw(ggml_type type = GGML_TYPE_F32,
std::array<int64_t, 4> ne = {10, 5, 4, 3},
float alpha = 1e-3f,
float beta1 = 0.9f,
float beta2 = 0.999f,
float eps = 1e-8f,
float wd = 0.0f)
: type(type), ne(ne), alpha(alpha), beta1(beta1), beta2(beta2), eps(eps), wd(wd) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * a = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]);
ggml_set_param(ctx, a); // Despite tensor a having gradients the output tensor will not.
ggml_set_name(a, "a");
ggml_tensor * out = ggml_opt_step_adamw(ctx, a, alpha, beta1, beta2, eps, wd);
ggml_set_name(out, "out");
return out;
}
void initialize_tensors(ggml_context * ctx) override {
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
init_tensor_uniform(t, 0.0f, 1.0f); // grad_v needs non-negative values.
}
}
bool grad_precise() override {
return true;
}
};
enum llm_norm_type {
LLM_NORM,
LLM_NORM_RMS,
@ -3159,14 +3238,15 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
test_cases.emplace_back(new test_conv_transpose_1d({3,2,1,1}, {3,1,2,1}, 1, 0, 1));
test_cases.emplace_back(new test_conv_transpose_1d({2,1,1,1}, {3,1,1,1}, 1, 0, 1));
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 5, 4, 3}, {1, 1, 1, 1}));
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 5, 4, 3}, {2, 1, 1, 1}));
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 5, 4, 3}, {1, 2, 1, 1}));
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 5, 4, 3}, {1, 1, 2, 1}));
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 5, 4, 3}, {1, 1, 1, 2}));
test_cases.emplace_back(new test_repeat(GGML_TYPE_I32, {10, 5, 4, 3}, {2, 1, 1, 1}));
test_cases.emplace_back(new test_repeat(GGML_TYPE_I16, {10, 5, 4, 3}, {1, 1, 1, 2}));
for (int ne3 : {1, 3}) { // CUDA backwards pass only supports ne3 == 1
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 5, 4, ne3}, {1, 1, 1, 1}));
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 5, 4, ne3}, {2, 1, 1, 1}));
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 5, 4, ne3}, {1, 2, 1, 1}));
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 5, 4, ne3}, {1, 1, 2, 1}));
test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 5, 4, ne3}, {1, 1, 1, 2}));
test_cases.emplace_back(new test_repeat(GGML_TYPE_I32, {10, 5, 4, ne3}, {2, 1, 1, 1}));
test_cases.emplace_back(new test_repeat(GGML_TYPE_I16, {10, 5, 4, ne3}, {1, 1, 1, 2}));
}
test_cases.emplace_back(new test_dup(GGML_TYPE_F32));
test_cases.emplace_back(new test_dup(GGML_TYPE_F16));
@ -3350,6 +3430,27 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
}
}
for (ggml_type type_a : base_types) {
for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) {
test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 1, 16, { 1, 1}));
test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 1, 16, {10, 1}));
test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 1, 16, {10, 1}));
test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 1, 16, {10, 10}));
test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 1, 16, {10, 10}));
test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 1, 16, {10, 10}));
test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 1, 16, {10, 10}));
test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 16, 16, { 1, 1}));
test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 16, 16, { 1, 1}, true));
test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 16, 16, {10, 1}));
test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 16, 16, {10, 1}));
test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 16, 16, {10, 10}));
test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 16, 16, {10, 10}));
test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 16, 16, {10, 10}));
test_cases.emplace_back(new test_out_prod(type_a, type_b, 256, 16, 16, {10, 10}));
}
}
test_cases.emplace_back(new test_sqr());
test_cases.emplace_back(new test_sqrt());
test_cases.emplace_back(new test_log());
@ -3476,6 +3577,9 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
}
test_cases.emplace_back(new test_cross_entropy_loss());
for (float wd : {0.0f, 1e-2f}) {
test_cases.emplace_back(new test_opt_step_adamw(GGML_TYPE_F32, {10, 5, 4, 3}, 1.0f, 1e-3f, 0.9f, 0.999f, wd));
}
// these tests are disabled to save execution time, but they can be handy for debugging
#if 0

2
tests/test-grad0.cpp
View File

@ -240,7 +240,7 @@ static bool check_gradient(
struct ggml_cgraph * gb = ggml_new_graph_custom(ctx0, GGML_DEFAULT_GRAPH_SIZE, true);
ggml_build_forward_expand(gf, f);
ggml_graph_cpy(gf, gb);
ggml_build_backward_expand(ctx0, gf, gb, false);
ggml_build_backward_expand(ctx0, gf, gb, false, false);
ggml_graph_compute_with_ctx(ctx0, gf, n_threads);