Merge branch 'master' into compilade/mamba2
This commit is contained in:
Francis Couture-Harpin
commit
7d6cb36895
105 changed files with 8055 additions and 5231 deletions
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@ -229,14 +229,16 @@
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#define GGML_MAX_PARAMS 2048
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#define GGML_MAX_CONTEXTS 64
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#define GGML_MAX_SRC 10
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#ifndef GGML_MAX_NAME
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#define GGML_MAX_NAME 64
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#define GGML_MAX_N_THREADS 512
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#endif
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#define GGML_MAX_OP_PARAMS 64
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#ifndef GGML_MAX_NAME
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# define GGML_MAX_NAME 64
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#endif
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#define GGML_DEFAULT_N_THREADS 4
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#define GGML_DEFAULT_GRAPH_SIZE 2048
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#if UINTPTR_MAX == 0xFFFFFFFF
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#define GGML_MEM_ALIGN 4
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#else
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@ -259,21 +261,21 @@
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#define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1))
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#ifndef NDEBUG
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#define GGML_UNREACHABLE() do { fprintf(stderr, "statement should be unreachable\n"); abort(); } while(0)
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# define GGML_UNREACHABLE() do { fprintf(stderr, "statement should be unreachable\n"); abort(); } while(0)
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#elif defined(__GNUC__)
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#define GGML_UNREACHABLE() __builtin_unreachable()
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# define GGML_UNREACHABLE() __builtin_unreachable()
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#elif defined(_MSC_VER)
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#define GGML_UNREACHABLE() __assume(0)
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# define GGML_UNREACHABLE() __assume(0)
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#else
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#define GGML_UNREACHABLE() ((void) 0)
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# define GGML_UNREACHABLE() ((void) 0)
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#endif
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#ifdef __cplusplus
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#define GGML_NORETURN [[noreturn]]
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# define GGML_NORETURN [[noreturn]]
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#elif defined(_MSC_VER)
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#define GGML_NORETURN __declspec(noreturn)
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# define GGML_NORETURN __declspec(noreturn)
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#else
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#define GGML_NORETURN _Noreturn
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# define GGML_NORETURN _Noreturn
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#endif
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#define GGML_ABORT(...) ggml_abort(__FILE__, __LINE__, __VA_ARGS__)
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@ -534,6 +536,7 @@ extern "C" {
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GGML_OP_CROSS_ENTROPY_LOSS,
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GGML_OP_CROSS_ENTROPY_LOSS_BACK,
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GGML_OP_OPT_STEP_ADAMW,
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GGML_OP_COUNT,
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};
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@ -569,12 +572,15 @@ extern "C" {
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GGML_LOG_LEVEL_WARN = 2,
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GGML_LOG_LEVEL_ERROR = 3,
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GGML_LOG_LEVEL_DEBUG = 4,
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GGML_LOG_LEVEL_CONT = 5, // continue previous log
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};
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// this tensor...
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enum ggml_tensor_flag {
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GGML_TENSOR_FLAG_INPUT = 1,
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GGML_TENSOR_FLAG_OUTPUT = 2,
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GGML_TENSOR_FLAG_PARAM = 4,
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GGML_TENSOR_FLAG_INPUT = 1, // ...is an input for the GGML compute graph
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GGML_TENSOR_FLAG_OUTPUT = 2, // ...is an output for the GGML compute graph
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GGML_TENSOR_FLAG_PARAM = 4, // ...contains trainable parameters
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GGML_TENSOR_FLAG_LOSS = 8, // ...defines loss for numerical optimization (multiple loss tensors add up)
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};
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// n-dimensional tensor
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@ -1404,14 +1410,14 @@ extern "C" {
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// supports 3D: a->ne[2] == b->ne[1]
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GGML_API struct ggml_tensor * ggml_get_rows(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b);
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struct ggml_tensor * a, // data
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struct ggml_tensor * b); // row indices
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GGML_API struct ggml_tensor * ggml_get_rows_back(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b,
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struct ggml_tensor * c);
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struct ggml_tensor * a, // gradients of ggml_get_rows result
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struct ggml_tensor * b, // row indices
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struct ggml_tensor * c); // data for ggml_get_rows, only used for its shape
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GGML_API struct ggml_tensor * ggml_diag(
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struct ggml_context * ctx,
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@ -1562,9 +1568,9 @@ extern "C" {
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// a - dy
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GGML_API struct ggml_tensor * ggml_rope_back(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b,
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struct ggml_tensor * c,
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struct ggml_tensor * a, // gradients of ggml_rope result
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struct ggml_tensor * b, // positions
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struct ggml_tensor * c, // freq factors
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int n_dims,
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int mode,
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int n_ctx_orig,
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@ -1978,6 +1984,9 @@ extern "C" {
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typedef void (*ggml_custom2_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, int ith, int nth, void * userdata);
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typedef void (*ggml_custom3_op_t)(struct ggml_tensor * dst , const struct ggml_tensor * a, const struct ggml_tensor * b, const struct ggml_tensor * c, int ith, int nth, void * userdata);
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#define GGML_N_TASKS_MAX (-1)
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// n_tasks == GGML_N_TASKS_MAX means to use max number of tasks
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GGML_API struct ggml_tensor * ggml_map_custom1(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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@ -2029,33 +2038,55 @@ extern "C" {
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// loss function
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GGML_API struct ggml_tensor * ggml_cross_entropy_loss(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b);
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struct ggml_context * ctx,
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struct ggml_tensor * a, // logits
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struct ggml_tensor * b); // labels
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GGML_API struct ggml_tensor * ggml_cross_entropy_loss_back(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b,
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struct ggml_tensor * c);
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struct ggml_context * ctx,
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struct ggml_tensor * a, // logits
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struct ggml_tensor * b, // labels
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struct ggml_tensor * c); // gradients of cross_entropy_loss result
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// AdamW optimizer step
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// Paper: https://arxiv.org/pdf/1711.05101v3.pdf
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// PyTorch: https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html
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GGML_API struct ggml_tensor * ggml_opt_step_adamw(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * grad,
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float alpha,
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float beta1,
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float beta2,
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float eps,
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float wd); // weight decay
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//
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// automatic differentiation
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//
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GGML_API void ggml_set_param(
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struct ggml_context * ctx,
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struct ggml_tensor * tensor);
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GGML_API void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor);
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GGML_API void ggml_set_loss(struct ggml_tensor * tensor);
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GGML_API void ggml_build_forward_expand (struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
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GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep);
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GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool accumulate);
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GGML_API void ggml_build_opt_adamw(
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struct ggml_context * ctx,
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struct ggml_cgraph * gf,
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struct ggml_cgraph * gb,
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float alpha,
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float beta1,
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float beta2,
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float eps,
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float wd); // weight decay
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// graph allocation in a context
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GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx); // size = GGML_DEFAULT_GRAPH_SIZE, grads = false
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GGML_API struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t size, bool grads);
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GGML_API struct ggml_cgraph * ggml_graph_dup (struct ggml_context * ctx, struct ggml_cgraph * cgraph);
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GGML_API void ggml_graph_cpy (struct ggml_cgraph * src, struct ggml_cgraph * dst);
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GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph); // zero grads
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GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph); // set regular grads + optimizer momenta to 0, set loss grad to 1
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GGML_API void ggml_graph_clear (struct ggml_cgraph * cgraph);
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GGML_API int ggml_graph_size (struct ggml_cgraph * cgraph);
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@ -2481,6 +2512,9 @@ extern "C" {
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GGML_API int ggml_cpu_has_cann (void);
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GGML_API int ggml_cpu_has_llamafile (void);
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// get the sve vector length in bytes
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GGML_API int ggml_cpu_get_sve_cnt(void);
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//
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// Internal types and functions exposed for tests and benchmarks
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//
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