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CUDA: Fixed 7b q3_K_S with mul_mat_vec_q (#2313)
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ggml-cuda.cu
10
ggml-cuda.cu
@ -220,7 +220,7 @@ typedef struct {
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static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_K block size/padding");
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#define WARP_SIZE 32
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#define MATRIX_ROW_PADDING 256 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses
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#define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses
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#define CUDA_ADD_BLOCK_SIZE 256
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#define CUDA_MUL_BLOCK_SIZE 256
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@ -2815,8 +2815,8 @@ inline void ggml_cuda_op_mul_mat_vec(
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#endif
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if (use_mul_mat_vec_q) {
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int64_t padded_row_size = ne00 + MATRIX_ROW_PADDING - 1;
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padded_row_size -= padded_row_size % MATRIX_ROW_PADDING;
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const int64_t padded_row_size = ne00 % MATRIX_ROW_PADDING == 0 ?
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ne00 : ne00 - ne00 % MATRIX_ROW_PADDING + MATRIX_ROW_PADDING;
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size_t as;
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void * src1_q8_1 = ggml_cuda_pool_malloc(padded_row_size*sizeof(block_q8_1)/QK8_1, &as);
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quantize_row_q8_1_cuda(src1_ddf_i, src1_q8_1, ne00, padded_row_size, cudaStream_main);
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@ -3642,7 +3642,7 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) {
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size_t size = ggml_nbytes_split(tensor, nrows_split);
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const size_t original_size = size;
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// pad last row to a multiple of 256 elements to avoid out-of-bounds memory accesses
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// pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses
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if (ne0 % MATRIX_ROW_PADDING != 0) {
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size += (MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING)
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* ggml_type_size(tensor->type)/ggml_blck_size(tensor->type);
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@ -3658,7 +3658,7 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) {
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}
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CUDA_CHECK(cudaMemcpy(buf, buf_host, size, cudaMemcpyHostToDevice));
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CUDA_CHECK(cudaMemcpy(buf, buf_host, original_size, cudaMemcpyHostToDevice));
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extra->data_device[id] = buf;
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