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

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* 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
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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
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@ -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
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@ -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
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@ -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
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@ -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
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@ -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
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@ -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
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@ -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
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@ -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);