vbatts/llama.cpp
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Temporarily care for all layers

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niansa 2023-06-23 18:40:58 +02:00
parent 40621ea0ec
commit 4b267e88b6
1 changed files with 8 additions and 11 deletions
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19
ggml-vulkan.cpp
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@ -106,10 +106,6 @@ std::shared_ptr<kp::Tensor> ggml_vk_get_buffer(struct ggml_kompute_context * ctx
void ggml_vk_h2d_tensor(struct ggml_kompute_context * ctx, struct ggml_tensor * t) { void ggml_vk_h2d_tensor(struct ggml_kompute_context * ctx, struct ggml_tensor * t) {
printf("%s: Context: %p Tensor: %p\n", __func__, ctx, t); printf("%s: Context: %p Tensor: %p\n", __func__, ctx, t);
if (t->backend != GGML_BACKEND_GPU) {
return;
}
auto data = t->data; auto data = t->data;
auto size = ggml_nbytes(t); auto size = ggml_nbytes(t);
@ -121,6 +117,7 @@ void ggml_vk_h2d_tensor(struct ggml_kompute_context * ctx, struct ggml_tensor *
GGML_ASSERT(res->second->size() != size); GGML_ASSERT(res->second->size() != size);
res->second->setRawData(data); res->second->setRawData(data);
mgr.sequence()->eval<kp::OpTensorSyncDevice>({res->second}); mgr.sequence()->eval<kp::OpTensorSyncDevice>({res->second});
printf("%s: Updating Host->GPU tensor: %p\n", __func__, t);
} else { } else {
std::vector<byte> vec(size); std::vector<byte> vec(size);
memcpy(vec.data(), data, size); memcpy(vec.data(), data, size);
@ -130,16 +127,13 @@ void ggml_vk_h2d_tensor(struct ggml_kompute_context * ctx, struct ggml_tensor *
ctx->tensors_mutex.lock(); ctx->tensors_mutex.lock();
ctx->tensors.emplace(t, std::move(tensor)); ctx->tensors.emplace(t, std::move(tensor));
ctx->tensors_mutex.unlock(); ctx->tensors_mutex.unlock();
printf("%s: Creating Host->GPU tensor: %p\n", __func__, t);
} }
} }
void ggml_vk_d2h_tensor(struct ggml_kompute_context * ctx, struct ggml_tensor * t) { void ggml_vk_d2h_tensor(struct ggml_kompute_context * ctx, struct ggml_tensor * t) {
printf("%s: Context: %p Tensor: %p\n", __func__, ctx, t); printf("%s: Context: %p Tensor: %p\n", __func__, ctx, t);
if (t->backend != GGML_BACKEND_GPU) {
return;
}
auto data = t->data; auto data = t->data;
auto size = ggml_nbytes(t); auto size = ggml_nbytes(t);
@ -151,18 +145,21 @@ void ggml_vk_d2h_tensor(struct ggml_kompute_context * ctx, struct ggml_tensor *
auto tensor = res->second; auto tensor = res->second;
mgr.sequence()->eval<kp::OpTensorSyncLocal>({tensor}); mgr.sequence()->eval<kp::OpTensorSyncLocal>({tensor});
memcpy(data, tensor->data<void>(), size); memcpy(data, tensor->data<void>(), size);
printf("%s: Updating GPU->Host tensor: %p\n", __func__, t);
} }
static static
const std::shared_ptr<kp::Tensor> & ggml_vk_get_tensor(struct ggml_kompute_context * ctx, struct ggml_tensor * t) { const std::shared_ptr<kp::Tensor> & ggml_vk_get_tensor(struct ggml_kompute_context * ctx, struct ggml_tensor * t) {
printf("%s: Context: %p Tensor: %p\n", __func__, ctx, t); printf("%s: Context: %p Tensor: %p\n", __func__, ctx, t);
GGML_ASSERT(t->backend != GGML_BACKEND_GPU);
ctx->tensors_mutex.lock(); ctx->tensors_mutex.lock();
auto res = ctx->tensors.find(t); auto res = ctx->tensors.find(t);
ctx->tensors_mutex.unlock(); ctx->tensors_mutex.unlock();
GGML_ASSERT(res != ctx->tensors.end());
if (res == ctx->tensors.end()) {
ggml_vk_h2d_tensor(ctx, t);
return ggml_vk_get_tensor(ctx, t);
}
return res->second; return res->second;
} }