Allocate managed memory if device memory runs out #709
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karpathy
reviewed
Aug 16, 2024
| @@ -393,13 +393,13 @@ void gpt2_allocate_state(GPT2 *model, int B, int T) { | |||
| printf0("allocating %zu MiB for AdamW optimizer state v\n", (shard_num_parameters * sizeof(float)) >> 20); | |||
| assert(model->m_memory == nullptr); | |||
| assert(model->v_memory == nullptr); | |||
| cudaCheck(cudaMalloc((void**)&model->m_memory, shard_num_parameters * sizeof(float))); | |||
| cudaCheck(cudaMalloc((void**)&model->v_memory, shard_num_parameters * sizeof(float))); | |||
| cudaMallocConditionallyManaged((void**)&model->m_memory, shard_num_parameters * sizeof(float)); | |||
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imo we should try to update the import statements to show which file any function (e.g. the new Managed manaloc) comes from (here cuda_utils)
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Use cudaMallocManaged to allocate optimizer states if we run out of device memory, so we can still train (slowly) even if we cannot fit the optimizer state
This is based on #694 , which should be merged first