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https://github.com/ggerganov/whisper.cpp.git
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24f0aa460b
* DRAFT: Introduction of CUDA Graphs to LLama.cpp * FIx issues raised in comments * Tidied to now only use CUDA runtime (not mixed with driver calls) * disable for multi-gpu and batch size > 1 * Disable CUDA graphs for old GPU arch and with env var * added missing CUDA_CHECKs * Addressed comments * further addressed comments * limit to GGML_ALLOW_CUDA_GRAPHS defined in llama.cpp cmake * Added more comprehensive graph node checking * With mechanism to fall back if graph capture fails * Revert "With mechanism to fall back if graph capture fails" This reverts commit eb9f15fb6fcb81384f732c4601a5b25c016a5143. * Fall back if graph capture fails and address other comments * - renamed GGML_ALLOW_CUDA_GRAPHS to GGML_CUDA_USE_GRAPHS - rename env variable to disable CUDA graphs to GGML_CUDA_DISABLE_GRAPHS - updated Makefile build to enable CUDA graphs - removed graph capture failure checking in ggml_cuda_error using a global variable to track this is not thread safe, but I am also not safistied with checking an error by string if this is necessary to workaround some issues with graph capture with eg. cuBLAS, we can pass the ggml_backend_cuda_context to the error checking macro and store the result in the context - fixed several resource leaks - fixed issue with zero node graphs - changed fixed size arrays to vectors - removed the count of number of evaluations before start capturing, and instead changed the capture mode to relaxed - removed the check for multiple devices so that it is still possible to use a single device, instead checks for split buffers to disable cuda graphs with -sm row - changed the op for checking batch size to GGML_OP_ADD, should be more reliable than GGML_OP_SOFT_MAX - code style fixes - things to look into - VRAM usage of the cudaGraphExec_t, if it is significant we may need to make it optional - possibility of using cudaStreamBeginCaptureToGraph to keep track of which ggml graph nodes correspond to which cuda graph nodes * fix build without cuda graphs * remove outdated comment * replace minimum cc value with a constant --------- Co-authored-by: slaren <slarengh@gmail.com>
35 lines
1.1 KiB
Plaintext
35 lines
1.1 KiB
Plaintext
#include "clamp.cuh"
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static __global__ void clamp_f32(const float * x, float * dst, const float min, const float max, const int k) {
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const int i = blockDim.x*blockIdx.x + threadIdx.x;
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if (i >= k) {
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return;
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}
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dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]);
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}
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static void clamp_f32_cuda(const float * x, float * dst, const float min, const float max, const int k, cudaStream_t stream) {
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const int num_blocks = (k + CUDA_CLAMP_BLOCK_SIZE - 1) / CUDA_CLAMP_BLOCK_SIZE;
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clamp_f32<<<num_blocks, CUDA_CLAMP_BLOCK_SIZE, 0, stream>>>(x, dst, min, max, k);
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}
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void ggml_cuda_op_clamp(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
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const ggml_tensor * src0 = dst->src[0];
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const float * src0_d = (const float *)src0->data;
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float * dst_d = (float *)dst->data;
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cudaStream_t stream = ctx.stream();
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GGML_ASSERT(src0->type == GGML_TYPE_F32);
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GGML_ASSERT( dst->type == GGML_TYPE_F32);
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float min;
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float max;
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memcpy(&min, dst->op_params, sizeof(float));
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memcpy(&max, (float *) dst->op_params + 1, sizeof(float));
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clamp_f32_cuda(src0_d, dst_d, min, max, ggml_nelements(src0), stream);
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}
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