0b481bfcc2
just sets __AMDGCN_WAVEFRONT_SIZE forcefully to 32. Not correct (some GPU's don't support wave32), but works on the supported GPU's. Can disable with DISABLE_WARP_32 With this blockwise quantize works and with that nf4 is supported.
877 lines
41 KiB
Plaintext
877 lines
41 KiB
Plaintext
// Copyright (c) Facebook, Inc. and its affiliates.
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//
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// This source code is licensed under the MIT license found in the
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// LICENSE file in the root directory of this source tree.
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#include <ops.cuh>
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#include <kernels.cuh>
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#include <limits>
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#include <BinSearch.h>
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#include <cassert>
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#include <common.h>
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#ifdef BITS_AND_BYTES_USE_ROCM
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#include <hipcub/device/device_scan.hpp>
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#else
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#include <cub/device/device_scan.cuh>
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#endif
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using namespace BinSearch;
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using std::cout;
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using std::endl;
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void histogramScatterAdd2D(float* histogram, int *index1, int *index2, float *src, int maxidx1, int n)
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{
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int threads = 512;
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int num_blocks = n/threads;
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num_blocks = n % threads == 0 ? num_blocks : num_blocks + 1;
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kHistogramScatterAdd2D<<<num_blocks, 512>>>(histogram, index1, index2, src, maxidx1, n);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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}
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template <typename T> void estimateQuantiles(T *A, float *code, float offset, int n)
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{
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int num_blocks = n/4096;
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num_blocks = n % 4096 == 0 ? num_blocks : num_blocks + 1;
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CUDA_CHECK_RETURN(cudaMemset(code, 0, 256*sizeof(float)));
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kEstimateQuantiles<T><<<num_blocks, 512>>>(A, code, offset, std::numeric_limits<T>::max(), n);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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}
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void quantize(float *code, float *A, unsigned char *out, int n)
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{
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int num_blocks = n/1024;
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num_blocks = n % 1024 == 0 ? num_blocks : num_blocks + 1;
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kQuantize<<<num_blocks, 1024>>>(code, A, out, n);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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}
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void dequantize(float *code, unsigned char *A, float *out, int n)
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{
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int num_blocks = n/1024;
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num_blocks = n % 1024 == 0 ? num_blocks : num_blocks + 1;
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kDequantize<<<num_blocks, 1024>>>(code, A, out, n);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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}
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template <typename T, int STOCHASTIC, int DATA_TYPE> void quantizeBlockwise(float * code, T *A, float *absmax, unsigned char *out, float *rand, int rand_offset, int blocksize, const int n)
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{
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int num_blocks = n/blocksize;
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num_blocks = n % blocksize == 0 ? num_blocks : num_blocks + 1;
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if(blocksize == 4096)
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kQuantizeBlockwise<T, 4096, 4, STOCHASTIC, 0><<<num_blocks, 1024>>>(code, A, absmax, out, rand, rand_offset, n);
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else if(blocksize == 2048)
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kQuantizeBlockwise<T, 2048, 4, 0, DATA_TYPE><<<num_blocks, 512>>>(code, A, absmax, out, rand, rand_offset, n);
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else if(blocksize == 1024)
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kQuantizeBlockwise<T, 1024, 4, 0, DATA_TYPE><<<num_blocks, 256>>>(code, A, absmax, out, rand, rand_offset, n);
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else if(blocksize == 512)
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kQuantizeBlockwise<T, 512, 2, 0, DATA_TYPE><<<num_blocks, 256>>>(code, A, absmax, out, rand, rand_offset, n);
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else if(blocksize == 256)
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kQuantizeBlockwise<T, 256, 2, 0, DATA_TYPE><<<num_blocks, 128>>>(code, A, absmax, out, rand, rand_offset, n);
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else if(blocksize == 128)
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kQuantizeBlockwise<T, 128, 2, 0, DATA_TYPE><<<num_blocks, 64>>>(code, A, absmax, out, rand, rand_offset, n);
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else if(blocksize == 64)
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kQuantizeBlockwise<T, 64, 2, 0, DATA_TYPE><<<num_blocks, 32>>>(code, A, absmax, out, rand, rand_offset, n);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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}
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template<typename T, int DATA_TYPE> void dequantizeBlockwise(float *code, unsigned char *A, float *absmax, T *out, int blocksize, const int n)
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{
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int num_blocks = n/blocksize;
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num_blocks = n % blocksize == 0 ? num_blocks : num_blocks + 1;
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int tile_size = (DATA_TYPE > 0) ? 1024 : 512;
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if(DATA_TYPE > 0)
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kDequantizeBlockwise<T, 512, 64, 8, DATA_TYPE><<<(n+tile_size-1)/tile_size, 64>>>(code, A, absmax, out, blocksize/2, n);
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else
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kDequantizeBlockwise<T, 512, 64, 8, DATA_TYPE><<<(n+tile_size-1)/tile_size, 64>>>(code, A, absmax, out, blocksize, n);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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}
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//void matmul4bite(half *A, unsigned char *B, half*out, int lda, int ldb, int rowsA, int colsA, int colsB)
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//{
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// int num_blocks = (colsB+32-1)/32;
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// kMatmul_inference_4bit<NF4, half, half, half><<<num_blocks, 256>>>(A, B, out, lda, ldb, rowsA, colsA, colsB);
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// CUDA_CHECK_RETURN(cudaPeekAtLastError());
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//}
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template<typename T, int OPTIMIZER> void optimizer32bit(T* g, T* p,
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float* state1, float* state2, float *unorm, float max_unorm, float param_norm,
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const float beta1, const float beta2, const float eps, const float weight_decay,
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const int step, const float lr, const float gnorm_scale, bool skip_zeros, const int n)
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{
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int num_blocks = n/4096;
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num_blocks = n % 4096 == 0 ? num_blocks : num_blocks + 1;
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switch(OPTIMIZER)
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{
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case ADAM:
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if(max_unorm > 0.0f)
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{
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CUDA_CHECK_RETURN(cudaMemset(unorm, 0, 1*sizeof(float)));
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kPreconditionOptimizer32bit2State<T, OPTIMIZER, 4096, 8><<<num_blocks, 512>>>(g, p, state1, state2, unorm, beta1, beta2, eps, weight_decay, step, lr, gnorm_scale, n);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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}
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kOptimizer32bit2State<T, OPTIMIZER><<<num_blocks, 1024>>>(g, p, state1, state2, unorm, max_unorm, param_norm, beta1, beta2, eps, weight_decay, step, lr, gnorm_scale, skip_zeros, n);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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break;
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case MOMENTUM:
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case RMSPROP:
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case ADAGRAD:
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if(max_unorm > 0.0f)
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{
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CUDA_CHECK_RETURN(cudaMemset(unorm, 0, 1*sizeof(float)));
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kPreconditionOptimizer32bit1State<T, OPTIMIZER, 4096, 8><<<num_blocks, 512>>>(g, p, state1, unorm, beta1, beta2, eps, weight_decay, step, lr, gnorm_scale, n);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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}
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kOptimizer32bit1State<T, OPTIMIZER><<<num_blocks, 1024>>>(g, p, state1, unorm, max_unorm, param_norm, beta1, beta2, eps, weight_decay, step, lr, gnorm_scale, skip_zeros, n);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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break;
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case LION:
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// in lion, the momentum update after the parameter update
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kOptimizer32bit1State<T, OPTIMIZER><<<num_blocks, 1024>>>(g, p, state1, unorm, max_unorm, param_norm, beta1, beta2, eps, weight_decay, step, lr, gnorm_scale, skip_zeros, n);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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if(max_unorm > 0.0f)
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{
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CUDA_CHECK_RETURN(cudaMemset(unorm, 0, 1*sizeof(float)));
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kPreconditionOptimizer32bit1State<T, OPTIMIZER, 4096, 8><<<num_blocks, 512>>>(g, p, state1, unorm, beta1, beta2, eps, weight_decay, step, lr, gnorm_scale, n);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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}
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break;
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}
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}
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template<typename T, int OPTIMIZER> void optimizerStatic8bit(T* p, T* g,
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unsigned char* state1, unsigned char* state2,
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float *unorm, float max_unorm, float param_norm,
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float beta1, float beta2,
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float eps, int step, float lr,
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float* quantiles1, float* quantiles2,
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float* max1, float* max2, float* new_max1, float* new_max2,
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float weight_decay,
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const float gnorm_scale, int n)
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{
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int num_blocks = n/4096;
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num_blocks = n % 4096 == 0 ? num_blocks : num_blocks + 1;
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if(max_unorm > 0.0f){ CUDA_CHECK_RETURN(cudaMemset(unorm, 0, 1*sizeof(float))); }
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switch(OPTIMIZER)
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{
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case ADAM:
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CUDA_CHECK_RETURN(cudaMemset(new_max1, 0, 1*sizeof(float)));
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CUDA_CHECK_RETURN(cudaMemset(new_max2, 0, 1*sizeof(float)));
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kPreconditionOptimizerStatic8bit2State<T, OPTIMIZER><<<num_blocks, 256>>>(p, g, state1, state2, unorm, beta1, beta2, eps, step, quantiles1, quantiles2, max1, max2, new_max1, new_max2, gnorm_scale, n);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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kOptimizerStatic8bit2State<T, OPTIMIZER><<<num_blocks, 1024>>>(p, g, state1, state2, unorm, max_unorm, param_norm, beta1, beta2, eps, step, lr,
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quantiles1, quantiles2, max1, max2, new_max1, new_max2, weight_decay, gnorm_scale, n);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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break;
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case MOMENTUM:
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case RMSPROP:
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case ADAGRAD:
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CUDA_CHECK_RETURN(cudaMemset(new_max1, 0, 1*sizeof(float)));
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kPreconditionOptimizerStatic8bit1State<T, OPTIMIZER><<<num_blocks, 256>>>(p, g, state1, unorm, beta1, beta2, eps, step, quantiles1, max1, new_max1, weight_decay, gnorm_scale, n);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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kOptimizerStatic8bit1State<T, OPTIMIZER><<<num_blocks, 1024>>>(p, g, state1, unorm, max_unorm, param_norm, beta1, beta2, eps, step, lr,
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quantiles1, max1, new_max1, weight_decay, gnorm_scale, n);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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break;
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case LION:
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// in lion, the momentum update happens after the parameter update
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kOptimizerStatic8bit1State<T, OPTIMIZER><<<num_blocks, 1024>>>(p, g, state1, unorm, max_unorm, param_norm, beta1, beta2, eps, step, lr,
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quantiles1, max1, new_max1, weight_decay, gnorm_scale, n);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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CUDA_CHECK_RETURN(cudaMemset(new_max1, 0, 1*sizeof(float)));
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kPreconditionOptimizerStatic8bit1State<T, OPTIMIZER><<<num_blocks, 256>>>(p, g, state1, unorm, beta1, beta2, eps, step, quantiles1, max1, new_max1, weight_decay, gnorm_scale, n);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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break;
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default:
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break;
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}
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}
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#define BLOCKSIZE_2STATE 2048
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#define NUM_2STATE 8
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#define BLOCKSIZE_1STATE 2048
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#define NUM_1STATE 8
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template<typename T, int OPTIMIZER> void optimizerStatic8bitBlockwise(T* p, T* g,
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unsigned char* state1, unsigned char* state2, float beta1, float beta2, float eps, int step, float lr,
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float* quantiles1, float* quantiles2, float* absmax1, float* absmax2, float weight_decay, const float gnorm_scale, bool skip_zeros, int n)
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{
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int num_blocks = 0;
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switch(OPTIMIZER)
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{
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case ADAM:
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num_blocks = n/BLOCKSIZE_2STATE;
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num_blocks = n % BLOCKSIZE_2STATE == 0 ? num_blocks : num_blocks + 1;
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kOptimizerStatic8bit2StateBlockwise<T, OPTIMIZER, BLOCKSIZE_2STATE, NUM_2STATE><<<num_blocks, BLOCKSIZE_2STATE/NUM_2STATE>>>(p, g, state1, state2, beta1, beta2, eps, step, lr,
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quantiles1, quantiles2, absmax1, absmax2, weight_decay, gnorm_scale, skip_zeros, n);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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break;
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case MOMENTUM:
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case RMSPROP:
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case ADAGRAD:
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case LION:
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num_blocks = n/BLOCKSIZE_1STATE;
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num_blocks = n % BLOCKSIZE_1STATE == 0 ? num_blocks : num_blocks + 1;
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kOptimizerStatic8bit1StateBlockwise<T, OPTIMIZER, BLOCKSIZE_1STATE, NUM_1STATE><<<num_blocks, BLOCKSIZE_1STATE/NUM_1STATE>>>(p, g, state1, beta1, beta2, eps, step, lr,
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quantiles1, absmax1, weight_decay, gnorm_scale, skip_zeros, n);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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break;
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}
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}
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template<typename T> void percentileClipping(T * g, float *gnorm_vec, int step, const int n)
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{
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int num_blocks = n/2048;
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num_blocks = n % 2048 == 0 ? num_blocks : num_blocks + 1;
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CUDA_CHECK_RETURN(cudaMemset(&gnorm_vec[step % 100], 0, 1*sizeof(float)));
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kPercentileClipping<T, 2048, 4><<<num_blocks, 512>>>(g, gnorm_vec, step, n);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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}
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void gemmex(Context *context, bool transposeA, bool transposeB, int m, int n, int k, void *A, void *B, void *C, int lda, int ldb, int ldc)
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{
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const int falpha = 1;
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const int fbeta = 0;
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const void * alpha = &falpha;
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const void * beta = &fbeta;
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cublasStatus_t status;
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status = cublasGemmEx(context->m_handle,
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transposeA ? CUBLAS_OP_T : CUBLAS_OP_N,
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transposeB ? CUBLAS_OP_T : CUBLAS_OP_N,
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m, n, k,
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alpha, A, CUDA_R_8I, lda, B, CUDA_R_8I, ldb, beta,
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C, CUDA_R_32I, ldc,
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CUDA_R_32I, CUBLAS_GEMM_DEFAULT_TENSOR_OP);
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if (status != CUBLAS_STATUS_SUCCESS)
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{
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std::cout << "CUBLAS ERROR: Status " << status << std::endl;
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}
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}
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void strided_gemmex(Context *context, bool transposeA, bool transposeB, int m, int n, int k, void *A, void *B, void *C, int lda, int ldb, int ldc,
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long long int strideA, long long int strideB, long long int strideC, int batchCount)
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{
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const int falpha = 1;
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const int fbeta = 0;
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const void * alpha = &falpha;
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const void * beta = &fbeta;
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cublasStatus_t status;
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//cout << transposeA << transposeB << endl;
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//printf("%i %i %i\n", m,n,k);
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//printf("%i %i %i\n", lda,ldb,ldc);
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//printf("%i %i %i\n", strideA, strideB, strideC);
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//printf("%i\n", batchCount);
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status = cublasGemmStridedBatchedEx(context->m_handle,
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transposeA ? CUBLAS_OP_T : CUBLAS_OP_N,
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transposeB ? CUBLAS_OP_T : CUBLAS_OP_N,
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m, n, k,
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alpha, A, CUDA_R_8I, lda, (long long int)strideA, B, CUDA_R_8I, ldb, (long long int)strideB, beta,
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C, CUDA_R_32I, ldc, (long long int)strideC, batchCount,
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CUDA_R_32I, CUBLAS_GEMM_DEFAULT);
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if (status != CUBLAS_STATUS_SUCCESS)
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{
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std::cout << "CUBLAS ERROR: Status " << status << std::endl;
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}
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}
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int roundoff(int v, int d) {
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return (v + d - 1) / d * d;
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}
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#ifdef NO_CUBLASLT
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#else
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template<int ORDER> cublasLtOrder_t get_order()
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{
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switch(ORDER)
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{
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case ROW:
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return CUBLASLT_ORDER_ROW;
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break;
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case COL:
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return CUBLASLT_ORDER_COL;
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break;
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case COL32:
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return CUBLASLT_ORDER_COL32;
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break;
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case COL_TURING:
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return CUBLASLT_ORDER_COL4_4R2_8C;
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break;
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case COL_AMPERE:
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return CUBLASLT_ORDER_COL32_2R_4R4;
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break;
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default:
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break;
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}
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return CUBLASLT_ORDER_ROW;
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}
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template cublasLtOrder_t get_order<ROW>();
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template cublasLtOrder_t get_order<COL>();
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template cublasLtOrder_t get_order<COL32>();
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template cublasLtOrder_t get_order<COL_TURING>();
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template cublasLtOrder_t get_order<COL_AMPERE>();
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#endif
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template<int ORDER> int get_leading_dim(int dim1, int dim2)
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{
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switch(ORDER)
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{
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case ROW:
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return dim2;
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break;
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case COL:
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return dim1;
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break;
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case COL32:
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// 32*row tiles
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return dim1*32;
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break;
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case COL_TURING:
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return 32*roundoff(dim1, 8);
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break;
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case COL_AMPERE:
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// 32*32 tiles
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return 32*roundoff(dim1, 32);
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break;
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default:
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return 0;
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break;
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}
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}
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template int get_leading_dim<ROW>(int dim1, int dim2);
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template int get_leading_dim<COL>(int dim1, int dim2);
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template int get_leading_dim<COL32>(int dim1, int dim2);
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template <typename T, int SRC, int TARGET, bool transpose, int DTYPE> void transform(cublasLtHandle_t ltHandle, T *A, T *out, int dim1, int dim2)
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{
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#ifdef NO_CUBLASLT
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#else
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cublasLtOrder_t orderA = get_order<SRC>();
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cublasLtOrder_t orderOut = get_order<TARGET>();
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int ldA = get_leading_dim<SRC>(dim1, dim2);
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int ldOut = get_leading_dim<TARGET>(dim1, dim2);
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cublasLtMatrixLayout_t A_desc = NULL, out_desc = NULL;
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cublasLtMatrixTransformDesc_t A2Out_desc = NULL;
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cublasOperation_t opTranspose = CUBLAS_OP_T;
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float transformAlpha = 1.0f, transformBeta = 0.0f;
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if(DTYPE == 8)
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{
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checkCublasStatus(cublasLtMatrixLayoutCreate(&A_desc, CUDA_R_8I, dim1, dim2, ldA));
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checkCublasStatus(cublasLtMatrixLayoutCreate(&out_desc, CUDA_R_8I, dim1, dim2, ldOut));
|
|
}
|
|
else if(DTYPE == 32)
|
|
{
|
|
checkCublasStatus(cublasLtMatrixLayoutCreate(&A_desc, CUDA_R_32I, dim1, dim2, ldA));
|
|
checkCublasStatus(cublasLtMatrixLayoutCreate(&out_desc, CUDA_R_32I, dim1, dim2, ldOut));
|
|
}
|
|
else
|
|
{
|
|
printf("ERROR WRONG TYPE FOR TRANSFORM: %i\n", DTYPE);
|
|
}
|
|
|
|
checkCublasStatus(cublasLtMatrixLayoutSetAttribute(A_desc, CUBLASLT_MATRIX_LAYOUT_ORDER, &orderA, sizeof(orderA)));
|
|
checkCublasStatus(cublasLtMatrixLayoutSetAttribute(out_desc, CUBLASLT_MATRIX_LAYOUT_ORDER, &orderOut, sizeof(orderOut)));
|
|
|
|
checkCublasStatus(cublasLtMatrixTransformDescCreate(&A2Out_desc, CUDA_R_32F));
|
|
|
|
if(transpose){ checkCublasStatus(cublasLtMatrixTransformDescSetAttribute(A2Out_desc, CUBLASLT_MATRIX_TRANSFORM_DESC_TRANSA, &opTranspose, sizeof(opTranspose))); }
|
|
|
|
checkCublasStatus(cublasLtMatrixTransform(ltHandle, A2Out_desc, &transformAlpha, A, A_desc, &transformBeta, NULL, NULL, out, out_desc, 0));
|
|
|
|
if (A_desc) checkCublasStatus(cublasLtMatrixLayoutDestroy(A_desc));
|
|
if (out_desc) checkCublasStatus(cublasLtMatrixLayoutDestroy(out_desc));
|
|
if (A2Out_desc) checkCublasStatus(cublasLtMatrixTransformDescDestroy(A2Out_desc));
|
|
#endif
|
|
}
|
|
|
|
template void transform<int8_t, ROW, COL, false, 8>(cublasLtHandle_t ltHandle, int8_t *A, int8_t *out, int dim1, int dim2);
|
|
template void transform<int8_t, ROW, ROW, false, 8>(cublasLtHandle_t ltHandle, int8_t *A, int8_t *out, int dim1, int dim2);
|
|
template void transform<int8_t, ROW, COL32, false, 8>(cublasLtHandle_t ltHandle, int8_t *A, int8_t *out, int dim1, int dim2);
|
|
template void transform<int32_t, ROW, COL32, false, 32>(cublasLtHandle_t ltHandle, int32_t *A, int32_t *out, int dim1, int dim2);
|
|
template void transform<int8_t, ROW, COL_TURING, false, 8>(cublasLtHandle_t ltHandle, int8_t *A, int8_t *out, int dim1, int dim2);
|
|
template void transform<int8_t, ROW, COL_AMPERE, false, 8>(cublasLtHandle_t ltHandle, int8_t *A, int8_t *out, int dim1, int dim2);
|
|
template void transform<int8_t, COL32, ROW, false, 8>(cublasLtHandle_t ltHandle, int8_t *A, int8_t *out, int dim1, int dim2);
|
|
template void transform<int32_t, COL32, ROW, false, 32>(cublasLtHandle_t ltHandle, int32_t *A, int32_t *out, int dim1, int dim2);
|
|
|
|
template <int FORMATB, int DTYPE_OUT, int SCALE_ROWS> int igemmlt(cublasLtHandle_t ltHandle, int m, int n, int k, const int8_t *A, const int8_t *B, void *C, float *row_scale, int lda, int ldb, int ldc)
|
|
{
|
|
#ifdef NO_CUBLASLT
|
|
cout << "" << endl;
|
|
cout << "=============================================" << endl;
|
|
cout << "ERROR: Your GPU does not support Int8 Matmul!" << endl;
|
|
cout << "=============================================" << endl;
|
|
cout << "" << endl;
|
|
assert(false);
|
|
|
|
return 0;
|
|
#else
|
|
int has_error = 0;
|
|
cublasLtMatmulDesc_t matmulDesc = NULL;
|
|
cublasLtMatrixLayout_t Adesc = NULL, Bdesc = NULL, Cdesc = NULL;
|
|
cublasOperation_t opT = CUBLAS_OP_T;
|
|
cublasLtPointerMode_t alphaVec = CUBLASLT_POINTER_MODE_ALPHA_DEVICE_VECTOR_BETA_ZERO;
|
|
cublasLtOrder_t col32 = CUBLASLT_ORDER_COL32;
|
|
cublasLtOrder_t col_turing = CUBLASLT_ORDER_COL4_4R2_8C;
|
|
cublasLtOrder_t col_ampere = CUBLASLT_ORDER_COL32_2R_4R4;
|
|
|
|
has_error |= checkCublasStatus(cublasLtMatrixLayoutCreate(&Adesc, CUDA_R_8I, m, k, lda));
|
|
has_error |= checkCublasStatus(cublasLtMatrixLayoutCreate(&Bdesc, CUDA_R_8I, n, k, ldb));
|
|
|
|
has_error |= checkCublasStatus(cublasLtMatrixLayoutSetAttribute(Adesc, CUBLASLT_MATRIX_LAYOUT_ORDER, &col32, sizeof(col32)));
|
|
if(FORMATB == COL_TURING)
|
|
has_error |= checkCublasStatus(cublasLtMatrixLayoutSetAttribute(Bdesc, CUBLASLT_MATRIX_LAYOUT_ORDER, &col_turing, sizeof(col_turing)));
|
|
else
|
|
has_error |= checkCublasStatus(cublasLtMatrixLayoutSetAttribute(Bdesc, CUBLASLT_MATRIX_LAYOUT_ORDER, &col_ampere, sizeof(col_ampere)));
|
|
|
|
if(DTYPE_OUT == 32)
|
|
{
|
|
has_error |= checkCublasStatus(cublasLtMatmulDescCreate(&matmulDesc, CUBLAS_COMPUTE_32I, CUDA_R_32I));
|
|
has_error |= checkCublasStatus(cublasLtMatmulDescSetAttribute(matmulDesc, CUBLASLT_MATMUL_DESC_TRANSB, &opT, sizeof(opT)));
|
|
has_error |= checkCublasStatus(cublasLtMatrixLayoutCreate(&Cdesc, CUDA_R_32I, m, n, ldc));
|
|
has_error |= checkCublasStatus(cublasLtMatrixLayoutSetAttribute(Cdesc, CUBLASLT_MATRIX_LAYOUT_ORDER, &col32, sizeof(col32)));
|
|
int alpha = 1, beta = 0;
|
|
has_error |= checkCublasStatus(cublasLtMatmul(ltHandle, matmulDesc,&alpha, A, Adesc, B, Bdesc, &beta, (int32_t*)C, Cdesc, (int32_t*)C, Cdesc, NULL, NULL, 0, 0));
|
|
}
|
|
else
|
|
{
|
|
has_error |= checkCublasStatus(cublasLtMatmulDescCreate(&matmulDesc, CUBLAS_COMPUTE_32I, CUDA_R_32F));
|
|
has_error |= checkCublasStatus(cublasLtMatmulDescSetAttribute(matmulDesc, CUBLASLT_MATMUL_DESC_TRANSB, &opT, sizeof(opT)));
|
|
has_error |= checkCublasStatus(cublasLtMatrixLayoutCreate(&Cdesc, CUDA_R_8I, m, n, ldc));
|
|
has_error |= checkCublasStatus(cublasLtMatrixLayoutSetAttribute(Cdesc, CUBLASLT_MATRIX_LAYOUT_ORDER, &col32, sizeof(col32)));
|
|
if(!SCALE_ROWS)
|
|
{
|
|
float alpha = 1.0f, beta = 0.0f;
|
|
has_error |= checkCublasStatus(cublasLtMatmul(ltHandle, matmulDesc,&alpha, A, Adesc, B, Bdesc, &beta, (int8_t*)C, Cdesc, (int8_t*)C, Cdesc, NULL, NULL, 0, 0));
|
|
}
|
|
else
|
|
{
|
|
has_error |= checkCublasStatus(cublasLtMatmulDescSetAttribute(matmulDesc, CUBLASLT_MATMUL_DESC_POINTER_MODE, &alphaVec, sizeof(alphaVec)));
|
|
has_error |= checkCublasStatus(cublasLtMatmul(ltHandle, matmulDesc, row_scale, A, Adesc, B, Bdesc, NULL, (int8_t*)C, Cdesc, (int8_t*)C, Cdesc, NULL, NULL, 0, 0));
|
|
}
|
|
}
|
|
|
|
|
|
if (Cdesc) has_error |= checkCublasStatus(cublasLtMatrixLayoutDestroy(Cdesc));
|
|
if (Bdesc) has_error |= checkCublasStatus(cublasLtMatrixLayoutDestroy(Bdesc));
|
|
if (Adesc) has_error |= checkCublasStatus(cublasLtMatrixLayoutDestroy(Adesc));
|
|
if (matmulDesc) has_error |= checkCublasStatus(cublasLtMatmulDescDestroy(matmulDesc));
|
|
if(has_error == 1)
|
|
printf("error detected");
|
|
|
|
return has_error;
|
|
#endif
|
|
}
|
|
|
|
int fill_up_to_nearest_multiple(int value, int multiple)
|
|
{
|
|
return value + (value % multiple == 0 ? 0 : (multiple - (value % multiple)));
|
|
}
|
|
|
|
void dequant_mm_int32_fp16(int *A, float *rowStats, float *colStats, half *out, float* newRowStats, float* newcolStats, half *bias, int numRows, int numCols)
|
|
{
|
|
int threads = 512;
|
|
int tileCols = fill_up_to_nearest_multiple(numCols, 32);
|
|
int n = numRows*tileCols;
|
|
int subtile_rows = 128;
|
|
int tilesize = 32*subtile_rows;
|
|
int num_blocks = numRows/subtile_rows;
|
|
num_blocks += (numRows % subtile_rows == 0) ? 0 : 1;
|
|
num_blocks = num_blocks*(tileCols/32);
|
|
assert(threads <= tilesize);
|
|
|
|
kdequant_mm_int32_fp16<4, 128, 512><<<num_blocks, threads>>>(A, rowStats, colStats, out, newRowStats, newcolStats, bias, numRows, numCols, tileCols, n);
|
|
CUDA_CHECK_RETURN(cudaPeekAtLastError());
|
|
}
|
|
|
|
#define STATS_THREADS 64
|
|
#define STATS_ITEMS 4
|
|
#define STATS_ROWS 16
|
|
void getColRowStats(half * A, float *rowStats, float *colStats, int *nnz_count_row, float nnz_threshold, int rows, int cols)
|
|
{
|
|
int tile_cols = STATS_THREADS*STATS_ITEMS;
|
|
int tiledCols = fill_up_to_nearest_multiple(cols, tile_cols);
|
|
int tiledRows = fill_up_to_nearest_multiple(rows, STATS_ROWS);
|
|
int row_tiles = (tiledRows/STATS_ROWS);
|
|
int col_tiles = (tiledCols/tile_cols);
|
|
row_tiles = row_tiles > 0 ? row_tiles : 1;
|
|
col_tiles = col_tiles > 0 ? col_tiles : 1;
|
|
int num_blocks = row_tiles * col_tiles;
|
|
|
|
if(nnz_threshold == 0.0)
|
|
kgetColRowStats<half, STATS_THREADS, STATS_ITEMS, STATS_ROWS, STATS_THREADS*STATS_ITEMS, 0><<<num_blocks, STATS_THREADS>>>(A, rowStats, colStats, nnz_count_row, nnz_threshold, rows, cols, tiledRows, tiledCols);
|
|
else if(nnz_threshold != 0.0)
|
|
kgetColRowStats<half, STATS_THREADS, STATS_ITEMS, STATS_ROWS, STATS_THREADS*STATS_ITEMS, 1><<<num_blocks, STATS_THREADS>>>(A, rowStats, colStats, nnz_count_row, nnz_threshold, rows, cols, tiledRows, tiledCols);
|
|
CUDA_CHECK_RETURN(cudaPeekAtLastError());
|
|
|
|
}
|
|
|
|
void doubleRowColQuant(half * A, float *rowStats, float *colStats, char *out_col_normed, char *out_row_normed, int *rowidx, int *colidx, half *val, int *nnz_block_ptr, float threshold, int rows, int cols)
|
|
{
|
|
int threads = 64;
|
|
int items_per_thread = 4;
|
|
int tile_cols = threads*items_per_thread;
|
|
int tile_rows = 16;
|
|
int tiledCols = fill_up_to_nearest_multiple(cols, tile_cols);
|
|
int tiledRows = fill_up_to_nearest_multiple(rows, tile_rows);
|
|
int row_tiles = (tiledRows/tile_rows);
|
|
int col_tiles = (tiledCols/tile_cols);
|
|
row_tiles = row_tiles > 0 ? row_tiles : 1;
|
|
col_tiles = col_tiles > 0 ? col_tiles : 1;
|
|
int num_blocks = row_tiles * col_tiles;
|
|
|
|
|
|
if(threshold > 0.0f)
|
|
kDoubleRowColQuant<64, 4, 16, 64*4, 1><<<num_blocks, threads>>>(A, rowStats, colStats, out_col_normed, out_row_normed, rowidx, colidx, val, nnz_block_ptr, threshold, rows, cols, tiledCols);
|
|
else
|
|
kDoubleRowColQuant<64, 4, 16, 64*4, 0><<<num_blocks, threads>>>(A, rowStats, colStats, out_col_normed, out_row_normed, rowidx, colidx, val, nnz_block_ptr, threshold, rows, cols, tiledCols);
|
|
|
|
CUDA_CHECK_RETURN(cudaPeekAtLastError());
|
|
}
|
|
|
|
template <int FORMAT, int TRANSPOSE> void transformRowToFormat(char * A, char *out, int rows, int cols)
|
|
{
|
|
int threads = 256;
|
|
int items_per_thread = 8;
|
|
// we load 128 column values per warp
|
|
int tile_cols = 32*items_per_thread;
|
|
int tile_rows = 32;
|
|
int tiledCols = fill_up_to_nearest_multiple(cols, tile_cols);
|
|
int tiledRows = fill_up_to_nearest_multiple(rows, tile_rows);
|
|
int row_tiles = (tiledRows/tile_rows);
|
|
int col_tiles = (tiledCols/tile_cols);
|
|
row_tiles = row_tiles > 0 ? row_tiles : 1;
|
|
col_tiles = col_tiles > 0 ? col_tiles : 1;
|
|
int num_blocks = row_tiles * col_tiles;
|
|
|
|
int outCols = fill_up_to_nearest_multiple(cols, 32);
|
|
int outRows = fill_up_to_nearest_multiple(rows, 32);
|
|
if(FORMAT == COL_TURING)
|
|
{
|
|
if(TRANSPOSE)
|
|
outRows = fill_up_to_nearest_multiple(cols, 8);
|
|
else
|
|
outRows = fill_up_to_nearest_multiple(rows, 8);
|
|
}
|
|
else if(FORMAT == COL_AMPERE)
|
|
{
|
|
if(TRANSPOSE)
|
|
outRows = fill_up_to_nearest_multiple(cols, 32);
|
|
else
|
|
outRows = fill_up_to_nearest_multiple(rows, 32);
|
|
}
|
|
else
|
|
{
|
|
if(TRANSPOSE)
|
|
{
|
|
outCols = fill_up_to_nearest_multiple(rows, 32);
|
|
outRows = cols;
|
|
}
|
|
}
|
|
|
|
kTransformRowToFormat<256, 8, 32, 32*8, TRANSPOSE, FORMAT><<<num_blocks, threads>>>(A, out, rows, cols, tiledCols, outRows, outCols);
|
|
CUDA_CHECK_RETURN(cudaPeekAtLastError());
|
|
}
|
|
|
|
void spmm_coo(cusparseHandle_t handle, int *A_rowidx, int *A_colidx, half *A_vals, int A_nnz, int A_rows, int A_cols, int B_cols, int ldb, half *B, int ldc, half* C, bool transposed_B)
|
|
{
|
|
|
|
#ifdef NO_CUBLASLT
|
|
#else
|
|
|
|
cusparseSpMatDescr_t descA;
|
|
cusparseDnMatDescr_t descB, descC;
|
|
|
|
float alpha = 1.0f;
|
|
float beta = 0.0f;
|
|
void *dBuffer = NULL;
|
|
size_t bufferSize = 0;
|
|
|
|
CHECK_CUSPARSE( cusparseCreateCoo(&descA, A_rows, A_cols, A_nnz,
|
|
A_rowidx, A_colidx, A_vals,
|
|
CUSPARSE_INDEX_32I,
|
|
CUSPARSE_INDEX_BASE_ZERO, CUDA_R_16F) );
|
|
// Create dense matrix C
|
|
CHECK_CUSPARSE( cusparseCreateDnMat(&descC, A_rows, B_cols, ldc, C,
|
|
CUDA_R_16F, CUSPARSE_ORDER_ROW) );
|
|
// Create dense matrix B
|
|
if(transposed_B)
|
|
{
|
|
int tmp = A_cols;
|
|
A_cols = B_cols;
|
|
B_cols = tmp;
|
|
}
|
|
|
|
CHECK_CUSPARSE( cusparseCreateDnMat(&descB, A_cols, B_cols, ldb, B,
|
|
CUDA_R_16F, CUSPARSE_ORDER_ROW) );
|
|
// allocate an external buffer if needed
|
|
CHECK_CUSPARSE( cusparseSpMM_bufferSize(
|
|
handle,
|
|
CUSPARSE_OPERATION_NON_TRANSPOSE,
|
|
transposed_B ? CUSPARSE_OPERATION_TRANSPOSE : CUSPARSE_OPERATION_NON_TRANSPOSE,
|
|
&alpha, descA, descB, &beta, descC, CUDA_R_32F,
|
|
CUSPARSE_SPMM_ALG_DEFAULT, &bufferSize) );
|
|
CUDA_CHECK_RETURN( cudaMalloc(&dBuffer, bufferSize) );
|
|
|
|
// execute SpMM
|
|
CHECK_CUSPARSE( cusparseSpMM(handle,
|
|
CUSPARSE_OPERATION_NON_TRANSPOSE,
|
|
transposed_B ? CUSPARSE_OPERATION_TRANSPOSE : CUSPARSE_OPERATION_NON_TRANSPOSE,
|
|
&alpha, descA, descB, &beta, descC, CUDA_R_32F,
|
|
CUSPARSE_SPMM_ALG_DEFAULT, dBuffer));
|
|
|
|
// destroy matrix/vector descriptors
|
|
CHECK_CUSPARSE( cusparseDestroySpMat(descA) );
|
|
CHECK_CUSPARSE( cusparseDestroyDnMat(descB) );
|
|
CHECK_CUSPARSE( cusparseDestroyDnMat(descC) );
|
|
CUDA_CHECK_RETURN( cudaFree(dBuffer) );
|
|
#endif
|
|
}
|
|
|
|
template <typename T, int BITS> void spmm_coo_very_sparse_naive(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, T *B, half *out, float *dequant_stats, int nnz_rows, int nnz, int rowsA, int rowsB, int colsB)
|
|
{
|
|
|
|
kspmm_coo_very_sparse_naive<T, 8, BITS><<<nnz_rows, 256>>>(max_count, max_idx, offset_rowidx, rowidx, colidx, values, B, out, dequant_stats, nnz, rowsA, rowsB, colsB);
|
|
CUDA_CHECK_RETURN(cudaPeekAtLastError());
|
|
}
|
|
|
|
|
|
template <int FORMAT> void extractOutliers(char * A, int *idx, char *out, int idx_size, int rows, int cols)
|
|
{
|
|
int threads = 256;
|
|
// we load 128 column values per warp
|
|
int tiledCols = tiledCols = fill_up_to_nearest_multiple(cols, 32);
|
|
int tiledRows = 0;
|
|
|
|
int num_blocks = idx_size;
|
|
|
|
if(FORMAT == COL_TURING)
|
|
{
|
|
tiledRows = fill_up_to_nearest_multiple(rows, 8);
|
|
}
|
|
else if(FORMAT == COL_AMPERE)
|
|
{
|
|
tiledRows = fill_up_to_nearest_multiple(rows, 32);
|
|
}
|
|
|
|
kExtractOutliers<FORMAT><<<num_blocks, threads>>>(A, idx, out, idx_size, rows, cols, tiledRows, tiledCols);
|
|
CUDA_CHECK_RETURN(cudaPeekAtLastError());
|
|
}
|
|
|
|
|
|
|
|
|
|
template <typename T> void gemm_host(int m, int n, int k, T * A, T* B, T * out, int lda, int ldb, int ldc, int bits)
|
|
{
|
|
|
|
int num_blocks = (m+31)/32;
|
|
|
|
//cout << num_blocks << endl;
|
|
//cout << lda << endl;
|
|
//cout << ldb << endl;
|
|
//cout << ldc << endl;
|
|
|
|
//cout << m << endl;
|
|
//cout << n << endl;
|
|
//cout << k << endl;
|
|
//if(bits == 32)
|
|
//gemm_device<T, 32, 128><<< num_blocks, 128, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
|
|
//gemm_device<T, 32, 32><<< num_blocks, 32, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
|
|
if(bits == 16)
|
|
//gemm_device<T, 16, 256><<< num_blocks, 256, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
|
|
gemm_device<T, 16, 160><<< num_blocks, 160, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
|
|
//gemm_device<T, 16, 128><<< num_blocks, 128, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
|
|
//gemm_device<T, 16, 96><<< num_blocks, 96, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
|
|
//gemm_device<T, 16, 32><<< num_blocks, 32, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
|
|
//gemm_device<T, 16, 64><<< num_blocks, 64, 0, 0 >>>(m, n, k, A, B, out, lda, ldb, ldc);
|
|
}
|
|
|
|
template <typename T> void gemm_4bit_inference(int m, int n, int k, T * A, unsigned char* B, float *absmax, T * out, int lda, int ldb, int ldc, int blocksize)
|
|
{
|
|
|
|
int num_blocks = (m+31)/32;
|
|
|
|
//cout << num_blocks << endl;
|
|
//cout << lda << endl;
|
|
//cout << ldb << endl;
|
|
//cout << ldc << endl;
|
|
|
|
//cout << m << endl;
|
|
//cout << n << endl;
|
|
//cout << k << endl;
|
|
kgemm_4bit_inference<T, 96><<< num_blocks, 96, 0, 0 >>>(m, n, k, A, B, absmax, out, lda, ldb, ldc, blocksize);
|
|
//kgemm_4bit_inference<T, 256><<< num_blocks, 256, 0, 0 >>>(m, n, k, A, B, absmax, out, lda, ldb, ldc, blocksize);
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//kgemm_4bit_inference<T, 160><<< num_blocks, 160, 0, 0 >>>(m, n, k, A, B, absmax, out, lda, ldb, ldc, blocksize);
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//kgemm_4bit_inference<T, 32><<< num_blocks, 32, 0, 0 >>>(m, n, k, A, B, absmax, out, lda, ldb, ldc, blocksize);
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}
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template <typename T, int BITS> void gemm_4bit_inference_naive(int m, int n, int k, T * A, unsigned char* B, float *absmax, float *datatype, T * out, int lda, int ldb, int ldc, int blocksize)
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{
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|
|
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int num_blocks = (m+3)/4;
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kgemm_4bit_inference_naive<T, 128, BITS><<< num_blocks, 128, 0, 0 >>>(m, n, k, A, B, absmax, datatype, out, lda, ldb, ldc, blocksize);
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CUDA_CHECK_RETURN(cudaPeekAtLastError());
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|
}
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|
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template <typename T, int FUNC> void func(T *A, T *B, T value, long n)
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|
{
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int threads = 512;
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|
int blocks = n/threads;
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blocks = n % threads == 0 ? blocks : blocks + 1;
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blocks = blocks > 65535 ? 65535 : blocks;
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kfunc<T, FUNC><<<blocks, 512>>>(A, B, value, n);
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|
CUDA_CHECK_RETURN(cudaPeekAtLastError());
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|
}
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//==============================================================
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// TEMPLATE DEFINITIONS
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//==============================================================
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template void func<float, FILL>(float *A, float *B, float value, long n);
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template void func<unsigned char, FILL>(unsigned char *A, unsigned char *B, unsigned char value, long n);
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template void func<float, ARANGE>(float *A, float *B, float value, long n);
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template void func<float, _MUL>(float *A, float *B, float value, long n);
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template void gemm_4bit_inference<half>(int m, int n, int k, half * A, unsigned char* B, float *absmax, half * out, int lda, int ldb, int ldc, int blocksize);
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template void gemm_4bit_inference_naive<half, 16>(int m, int n, int k, half * A, unsigned char* B, float *absmax, float *datatype, half * out, int lda, int ldb, int ldc, int blocksize);
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|
template void gemm_4bit_inference_naive<__nv_bfloat16, 16>(int m, int n, int k, __nv_bfloat16 * A, unsigned char* B, float *absmax, float *datatype, __nv_bfloat16 * out, int lda, int ldb, int ldc, int blocksize);
|
|
template void gemm_4bit_inference_naive<float, 32>(int m, int n, int k, float * A, unsigned char* B, float *absmax, float *datatype, float * out, int lda, int ldb, int ldc, int blocksize);
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|
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//template void gemm_host<float>(int m, int n, int k, float * A, float* B, float * out, int lda, int ldb, int ldc, int bits);
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template void gemm_host<half>(int m, int n, int k, half * A, half* B, half * out, int lda, int ldb, int ldc, int bits);
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template void extractOutliers<COL_TURING>(char * A, int *idx, char *out, int idx_size, int rows, int cols);
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template void extractOutliers<COL_AMPERE>(char * A, int *idx, char *out, int idx_size, int rows, int cols);
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|
|
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template void spmm_coo_very_sparse_naive<half, 16>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, half *B, half *out, float *dequant_stats, int nnz_rows, int nnz, int rowsA, int rowsB, int colsB);
|
|
template void spmm_coo_very_sparse_naive<signed char, 8>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, signed char *B, half *out, float *dequant_stats, int nnz_rows, int nnz, int rowsA, int rowsB, int colsB);
|
|
|
|
template int igemmlt<COL_TURING, 32, 0>(cublasLtHandle_t ltHandle, int m, int n, int k, const int8_t *A, const int8_t *B, void *C, float *row_scale, int lda, int ldb, int ldc);
|
|
template int igemmlt<COL_TURING, 8, 0>(cublasLtHandle_t ltHandle, int m, int n, int k, const int8_t *A, const int8_t *B, void *C, float *row_scale, int lda, int ldb, int ldc);
|
|
template int igemmlt<COL_TURING, 8, 1>(cublasLtHandle_t ltHandle, int m, int n, int k, const int8_t *A, const int8_t *B, void *C, float *row_scale, int lda, int ldb, int ldc);
|
|
template int igemmlt<COL_AMPERE, 32, 0>(cublasLtHandle_t ltHandle, int m, int n, int k, const int8_t *A, const int8_t *B, void *C, float *row_scale, int lda, int ldb, int ldc);
|
|
template int igemmlt<COL_AMPERE, 8, 0>(cublasLtHandle_t ltHandle, int m, int n, int k, const int8_t *A, const int8_t *B, void *C, float *row_scale, int lda, int ldb, int ldc);
|
|
template int igemmlt<COL_AMPERE, 8, 1>(cublasLtHandle_t ltHandle, int m, int n, int k, const int8_t *A, const int8_t *B, void *C, float *row_scale, int lda, int ldb, int ldc);
|
|
|
|
template void transformRowToFormat<COL32, 0>(char * A, char *out, int rows, int cols);
|
|
template void transformRowToFormat<COL32, 1>(char * A, char *out, int rows, int cols);
|
|
template void transformRowToFormat<COL_TURING, 0>(char * A, char *out, int rows, int cols);
|
|
template void transformRowToFormat<COL_TURING, 1>(char * A, char *out, int rows, int cols);
|
|
template void transformRowToFormat<COL_AMPERE, 0>(char * A, char *out, int rows, int cols);
|
|
template void transformRowToFormat<COL_AMPERE, 1>(char * A, char *out, int rows, int cols);
|
|
|
|
template void estimateQuantiles(half *A, float *code, float offset, int n);
|
|
template void estimateQuantiles(float *A, float *code, float offset, int n);
|
|
|
|
template void quantizeBlockwise<half, 1, General8bit>(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
|
|
template void quantizeBlockwise<half, 0, General8bit>(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
|
|
template void quantizeBlockwise<half, 0, FP4>(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
|
|
template void quantizeBlockwise<half, 0, NF4>(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
|
|
template void quantizeBlockwise<float, 1, General8bit>(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
|
|
template void quantizeBlockwise<float, 0, General8bit>(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
|
|
template void quantizeBlockwise<float, 0, FP4>(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
|
|
template void quantizeBlockwise<float, 0, NF4>(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
|
|
template void quantizeBlockwise<__nv_bfloat16, 1, General8bit>(float * code, __nv_bfloat16 *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
|
|
template void quantizeBlockwise<__nv_bfloat16, 0, General8bit>(float * code, __nv_bfloat16 *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
|
|
template void quantizeBlockwise<__nv_bfloat16, 0, FP4>(float * code, __nv_bfloat16 *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
|
|
template void quantizeBlockwise<__nv_bfloat16, 0, NF4>(float * code, __nv_bfloat16 *A, float *absmax, unsigned char *out, float* rand, int rand_offset, int blocksize, const int n);
|
|
|
|
template void dequantizeBlockwise<float, General8bit>(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n);
|
|
template void dequantizeBlockwise<float, FP4>(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n);
|
|
template void dequantizeBlockwise<float, NF4>(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n);
|
|
template void dequantizeBlockwise<half, General8bit>(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n);
|
|
template void dequantizeBlockwise<half, FP4>(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n);
|
|
template void dequantizeBlockwise<half, NF4>(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n);
|
|
template void dequantizeBlockwise<__nv_bfloat16, General8bit>(float *code, unsigned char *A, float *absmax, __nv_bfloat16 *out, int blocksize, const int n);
|
|
template void dequantizeBlockwise<__nv_bfloat16, FP4>(float *code, unsigned char *A, float *absmax, __nv_bfloat16 *out, int blocksize, const int n);
|
|
template void dequantizeBlockwise<__nv_bfloat16, NF4>(float *code, unsigned char *A, float *absmax, __nv_bfloat16 *out, int blocksize, const int n);
|
|
|
|
#define MAKE_optimizer32bit(name, gtype) \
|
|
template void optimizer32bit<gtype, name>(gtype* g, gtype* p, \
|
|
float* state1, float* state2, float* unorm, float max_unorm, float param_norm, \
|
|
const float beta1, const float beta2, const float eps, const float weight_decay, \
|
|
const int step, const float lr, const float gnorm_scale, const bool skip_zeros, const int n);
|
|
|
|
MAKE_optimizer32bit(ADAM, half)
|
|
MAKE_optimizer32bit(ADAM, float)
|
|
MAKE_optimizer32bit(ADAM, __nv_bfloat16)
|
|
MAKE_optimizer32bit(MOMENTUM, half)
|
|
MAKE_optimizer32bit(MOMENTUM, float)
|
|
MAKE_optimizer32bit(RMSPROP, half)
|
|
MAKE_optimizer32bit(RMSPROP, float)
|
|
MAKE_optimizer32bit(LION, half)
|
|
MAKE_optimizer32bit(LION, float)
|
|
MAKE_optimizer32bit(LION, __nv_bfloat16)
|
|
MAKE_optimizer32bit(ADAGRAD, half)
|
|
MAKE_optimizer32bit(ADAGRAD, float)
|
|
|
|
#define MAKE_optimizerStatic8bit(name, gtype) \
|
|
template void optimizerStatic8bit<gtype, name>(gtype* p, gtype* g, unsigned char* state1, unsigned char* state2, \
|
|
float *unorm, float max_unorm, float param_norm, \
|
|
float beta1, float beta2, \
|
|
float eps, int step, float lr, \
|
|
float* quantiles1, float* quantiles2, \
|
|
float* max1, float* max2, float* new_max1, float* new_max2, \
|
|
float weight_decay, \
|
|
const float gnorm_scale, int n); \
|
|
|
|
MAKE_optimizerStatic8bit(ADAM, half)
|
|
MAKE_optimizerStatic8bit(ADAM, float)
|
|
MAKE_optimizerStatic8bit(MOMENTUM, half)
|
|
MAKE_optimizerStatic8bit(MOMENTUM, float)
|
|
MAKE_optimizerStatic8bit(RMSPROP, half)
|
|
MAKE_optimizerStatic8bit(RMSPROP, float)
|
|
MAKE_optimizerStatic8bit(LION, half)
|
|
MAKE_optimizerStatic8bit(LION, float)
|
|
|
|
#define MAKE_optimizerStatic8bitBlockwise(gtype, optim_name) \
|
|
template void optimizerStatic8bitBlockwise<gtype, optim_name>(gtype* p, gtype* g, \
|
|
unsigned char* state1, unsigned char* state2, float beta1, float beta2, float eps, int step, float lr, \
|
|
float* quantiles1, float* quantiles2, float* absmax1, float* absmax2, float weight_decay, const float gnorm_scale, bool skip_zeros, int n); \
|
|
|
|
MAKE_optimizerStatic8bitBlockwise(half, ADAM);
|
|
MAKE_optimizerStatic8bitBlockwise(float, ADAM);
|
|
MAKE_optimizerStatic8bitBlockwise(half, MOMENTUM);
|
|
MAKE_optimizerStatic8bitBlockwise(float, MOMENTUM);
|
|
MAKE_optimizerStatic8bitBlockwise(half, RMSPROP);
|
|
MAKE_optimizerStatic8bitBlockwise(float, RMSPROP);
|
|
MAKE_optimizerStatic8bitBlockwise(half, LION);
|
|
MAKE_optimizerStatic8bitBlockwise(float, LION);
|
|
MAKE_optimizerStatic8bitBlockwise(__nv_bfloat16, LION);
|
|
MAKE_optimizerStatic8bitBlockwise(half, ADAGRAD);
|
|
MAKE_optimizerStatic8bitBlockwise(float, ADAGRAD);
|
|
|
|
template void percentileClipping(float * g, float *gnorm_vec, int step, const int n);
|
|
template void percentileClipping(half * g, float *gnorm_vec, int step, const int n);
|
|
|
|
MAKE_optimizerStatic8bitBlockwise(__nv_bfloat16, ADAM);
|