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fix compile errors, rwkv not working

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Concedo 2023-07-10 18:23:25 +08:00
parent 15576bc865
commit 2827920044
7 changed files with 13 additions and 19 deletions
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6
gpttype_adapter.cpp
View file

@ -563,7 +563,7 @@ ModelLoadResult gpttype_load_model(const load_model_inputs inputs, FileFormat in
rwkv_ctx_v3->logits_out = (float *)malloc(logitbufsiz);
rwkv_ctx_v3->state_in = nullptr;
bool testeval = rwkv_eval(rwkv_ctx_v3, 0, rwkv_ctx_v3->state_in, rwkv_ctx_v3->state_out, rwkv_ctx_v3->logits_out);
bool testeval = rwkv_eval(rwkv_ctx_v3, params.n_threads, 0, rwkv_ctx_v3->state_in, rwkv_ctx_v3->state_out, rwkv_ctx_v3->logits_out);
if (!testeval)
{
printf("\nError: RWKV Init Eval Failed!\n");
@ -1162,12 +1162,12 @@ generation_outputs gpttype_generate(const generation_inputs inputs, generation_o
{
if(embd.size()>1)
{
evalres = rwkv_eval_sequence(rwkv_ctx_v3, (uint32_t*)embd.data(), embd.size(), rwkv_ctx_v3->state_in, rwkv_ctx_v3->state_out, rwkv_ctx_v3->logits_out);
evalres = rwkv_eval_sequence(rwkv_ctx_v3, params.n_threads, (uint32_t*)embd.data(), embd.size(), rwkv_ctx_v3->state_in, rwkv_ctx_v3->state_out, rwkv_ctx_v3->logits_out);
}
else
{
bool ignoreLogits = (!startedsampling && ((int)embd_inp.size() > input_consumed + 2));
evalres = rwkv_eval(rwkv_ctx_v3, embd[0], rwkv_ctx_v3->state_in, rwkv_ctx_v3->state_out, ignoreLogits?nullptr:rwkv_ctx_v3->logits_out);
evalres = rwkv_eval(rwkv_ctx_v3, params.n_threads, embd[0], rwkv_ctx_v3->state_in, rwkv_ctx_v3->state_out, ignoreLogits?nullptr:rwkv_ctx_v3->logits_out);
}
memcpy(logits.data(), rwkv_ctx_v3->logits_out, sizeof(float) * rwkv_vocab.size());

3
otherarch/gpt2_v3.cpp
View file

@ -447,7 +447,6 @@ bool gpt2_eval(
struct ggml_context * ctx0 = ggml_init(params);
struct ggml_cgraph gf = {};
gf.n_threads = n_threads;
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
@ -708,7 +707,7 @@ bool gpt2_eval(
// run the computation
ggml_build_forward_expand(&gf, inpL);
ggml_graph_compute (ctx0, &gf);
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
//if (n_past%100 == 0) {
// ggml_graph_print (&gf);

3
otherarch/gptj_v3.cpp
View file

@ -445,7 +445,6 @@ bool gptj_eval(
struct ggml_context * ctx0 = ggml_init(params);
struct ggml_cgraph gf = {};
gf.n_threads = n_threads;
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
@ -620,7 +619,7 @@ bool gptj_eval(
// run the computation
ggml_build_forward_expand(&gf, inpL);
ggml_graph_compute (ctx0, &gf);
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
//if (n_past%100 == 0) {
// ggml_graph_print (&gf);

3
otherarch/mpt_v3.cpp
View file

@ -383,7 +383,6 @@ bool mpt_eval(const mpt_model & model, const int n_threads, const int n_past,
struct ggml_context * ctx0 = ggml_init(params);
struct ggml_cgraph gf = {};
gf.n_threads = n_threads;
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
memcpy(embd->data, embd_inp.data(), N * ggml_element_size(embd));
@ -543,7 +542,7 @@ bool mpt_eval(const mpt_model & model, const int n_threads, const int n_past,
// run the computation
ggml_build_forward_expand(&gf, inpL);
ggml_graph_compute(ctx0, &gf);
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
// std::cout << "Qcur" << std::endl;
// print_tensor(Qcur);

3
otherarch/neox_v3.cpp
View file

@ -461,7 +461,6 @@ bool gpt_neox_eval(
struct ggml_context * ctx0 = ggml_init(params);
struct ggml_cgraph gf = {};
gf.n_threads = n_threads;
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
@ -639,7 +638,7 @@ bool gpt_neox_eval(
// run the computation
ggml_build_forward_expand(&gf, inpL);
ggml_graph_compute (ctx0, &gf);
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
//if (n_past%100 == 0) {
// ggml_graph_print (&gf);

10
otherarch/rwkv_v3.cpp
View file

@ -1511,7 +1511,6 @@ struct rwkv_context * rwkv_new_context_impl(std::shared_ptr<struct rwkv_instance
serial_graph.tokens = ggml_new_i32(serial_graph.ctx.ctx, 0);
serial_graph.cgraph.reset(new(std::nothrow) struct ggml_cgraph());
RWKV_ASSERT_NULL_MSG(RWKV_ERROR_ALLOC, serial_graph.cgraph, "Failed to allocate serial graph");
serial_graph.cgraph->n_threads = n_threads;
RWKV_ASSERT_NULL(RWKV_ERROR_GRAPH, rwkv_build_serial_graph(
serial_graph.ctx.ctx, instance->model,
@ -1609,7 +1608,7 @@ void rwkv_get_outputs(const struct rwkv_context * ctx, float * state_out, float
}
}
bool rwkv_eval(struct rwkv_context * ctx, const uint32_t token, const float * state_in, float * state_out, float * logits_out) {
bool rwkv_eval(struct rwkv_context * ctx, const int n_threads, const uint32_t token, const float * state_in, float * state_out, float * logits_out) {
ctx->last_error = RWKV_ERROR_NONE;
const struct rwkv_file_header & header = ctx->instance->model.header;
@ -1628,13 +1627,13 @@ bool rwkv_eval(struct rwkv_context * ctx, const uint32_t token, const float * st
ctx->serial_graph.cgraph->n_leafs = ctx->serial_graph.post_logits_leafs;
}
ggml_graph_compute(ctx->serial_graph.ctx.ctx, ctx->serial_graph.cgraph.get());
ggml_graph_compute_with_ctx(ctx->serial_graph.ctx.ctx, ctx->serial_graph.cgraph.get(),n_threads);
rwkv_get_outputs(ctx, state_out, logits_out);
return true;
}
bool rwkv_eval_sequence(struct rwkv_context * ctx, const uint32_t * sequence, const size_t sequence_len, const float * state_in, float * state_out, float * logits_out) {
bool rwkv_eval_sequence(struct rwkv_context * ctx, const int n_threads, const uint32_t * sequence, const size_t sequence_len, const float * state_in, float * state_out, float * logits_out) {
ctx->last_error = RWKV_ERROR_NONE;
const struct rwkv_file_header & header = ctx->instance->model.header;
@ -1690,7 +1689,6 @@ bool rwkv_eval_sequence(struct rwkv_context * ctx, const uint32_t * sequence, co
sequence_graph.tokens = ggml_new_tensor_1d(sequence_graph.ctx.ctx, GGML_TYPE_I32, sequence_len);
sequence_graph.cgraph.reset(new(std::nothrow) struct ggml_cgraph());
RWKV_ASSERT_FALSE_MSG(RWKV_ERROR_ALLOC, sequence_graph.cgraph, "Failed to allocate sequence graph");
sequence_graph.cgraph->n_threads = 1;
RWKV_ASSERT_FALSE(RWKV_ERROR_GRAPH, rwkv_build_sequence_graph(
sequence_graph.ctx.ctx, ctx->instance->model,
@ -1717,7 +1715,7 @@ bool rwkv_eval_sequence(struct rwkv_context * ctx, const uint32_t * sequence, co
ctx->sequence_graph.cgraph->n_leafs = ctx->sequence_graph.post_logits_leafs;
}
ggml_graph_compute(ctx->sequence_graph.ctx.ctx, ctx->sequence_graph.cgraph.get());
ggml_graph_compute_with_ctx(ctx->sequence_graph.ctx.ctx, ctx->sequence_graph.cgraph.get(),n_threads);
rwkv_get_outputs(ctx, state_out, logits_out);
}

4
otherarch/rwkv_v3.h
View file

@ -111,7 +111,7 @@ extern "C" {
// - state_in: FP32 buffer of size rwkv_get_state_len(); or NULL, if this is a first pass.
// - state_out: FP32 buffer of size rwkv_get_state_len(). This buffer will be written to if non-NULL.
// - logits_out: FP32 buffer of size rwkv_get_logits_len(). This buffer will be written to if non-NULL.
RWKV_API bool rwkv_eval(struct rwkv_context * ctx, const uint32_t token, const float * state_in, float * state_out, float * logits_out);
RWKV_API bool rwkv_eval(struct rwkv_context *, const int n_threads, const uint32_t token, const float * state_in, float * state_out, float * logits_out);
// Evaluates the model for a sequence of tokens.
// Uses a faster algorithm than rwkv_eval if you do not need the state and logits for every token. Best used with batch sizes of 64 or so.
@ -125,7 +125,7 @@ extern "C" {
// - state_in: FP32 buffer of size rwkv_get_state_len(), or NULL if this is a first pass.
// - state_out: FP32 buffer of size rwkv_get_state_len(). This buffer will be written to if non-NULL.
// - logits_out: FP32 buffer of size rwkv_get_logits_len(). This buffer will be written to if non-NULL.
RWKV_API bool rwkv_eval_sequence(struct rwkv_context * ctx, const uint32_t * tokens, size_t sequence_len, const float * state_in, float * state_out, float * logits_out);
RWKV_API bool rwkv_eval_sequence(struct rwkv_context * ctx, const int n_threads, const uint32_t * tokens, size_t sequence_len, const float * state_in, float * state_out, float * logits_out);
// Returns the number of tokens in the given model's vocabulary.
// Useful for telling 20B_tokenizer models (n_vocab = 50277) apart from World models (n_vocab = 65536).