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ggml : force F32 precision for ggml_mul_mat

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Georgi Gerganov 2023-12-19 16:23:39 +02:00
parent a7aee47b98
commit a40f6110f0
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GPG Key ID: 449E073F9DC10735
2 changed files with 44 additions and 17 deletions
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55
ggml-cuda.cu
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@ -7389,7 +7389,7 @@ inline void ggml_cuda_op_mul_mat_cublas(
const int compute_capability = g_compute_capabilities[id];
if (compute_capability >= CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) {
if (compute_capability >= CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1]) {
// convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32
half * src0_as_f16 = nullptr;
size_t src0_as = 0;
@ -7412,26 +7412,47 @@ inline void ggml_cuda_op_mul_mat_cublas(
to_fp16_cuda(src1_ddf_i, src1_as_f16, ne, stream);
}
const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddf_i : src1_as_f16;
size_t dst_as = 0;
half * dst_f16 = (half *) ggml_cuda_pool_malloc(row_diff*src1_ncols * sizeof(half), &dst_as);
const half alpha_f16 = 1.0f;
const half beta_f16 = 0.0f;
switch (dst->op_params[0]) {
case GGML_PREC_DEFAULT:
{
size_t dst_as = 0;
half * dst_f16 = (half *) ggml_cuda_pool_malloc(row_diff*src1_ncols * sizeof(half), &dst_as);
CUBLAS_CHECK(cublasSetStream(g_cublas_handles[id], stream));
CUBLAS_CHECK(
cublasGemmEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
row_diff, src1_ncols, ne10,
&alpha_f16, src0_ptr, CUDA_R_16F, ne00,
src1_ptr, CUDA_R_16F, ne10,
&beta_f16, dst_f16, CUDA_R_16F, ldc,
CUBLAS_COMPUTE_16F,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
const half alpha_f16 = 1.0f;
const half beta_f16 = 0.0f;
const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
to_fp32_cuda(dst_f16, dst_dd_i, row_diff*src1_ncols, stream);
CUBLAS_CHECK(cublasSetStream(g_cublas_handles[id], stream));
CUBLAS_CHECK(
cublasGemmEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
row_diff, src1_ncols, ne10,
&alpha_f16, src0_ptr, CUDA_R_16F, ne00,
src1_ptr, CUDA_R_16F, ne10,
&beta_f16, dst_f16, CUDA_R_16F, ldc,
CUBLAS_COMPUTE_16F,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
ggml_cuda_pool_free(dst_f16, dst_as);
const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
to_fp32_cuda(dst_f16, dst_dd_i, row_diff*src1_ncols, stream);
ggml_cuda_pool_free(dst_f16, dst_as);
} break;
case GGML_PREC_F32:
{
const float alpha_f32 = 1.0f;
const float beta_f32 = 0.0f;
CUBLAS_CHECK(cublasSetStream(g_cublas_handles[id], stream));
CUBLAS_CHECK(
cublasGemmEx(g_cublas_handles[id], CUBLAS_OP_T, CUBLAS_OP_N,
row_diff, src1_ncols, ne10,
&alpha_f32, src0_ptr, CUDA_R_16F, ne00,
src1_ptr, CUDA_R_16F, ne10,
&beta_f32, dst_dd_i, CUDA_R_32F, ldc,
CUBLAS_COMPUTE_32F,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
} break;
}
if (src0_as != 0) {
ggml_cuda_pool_free(src0_as_f16, src0_as);

6
ggml.c
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@ -4090,6 +4090,12 @@ struct ggml_tensor * ggml_mul_mat(
const int64_t ne[4] = { a->ne[1], b->ne[1], b->ne[2], b->ne[3] };
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
// TMP: force f32 precision
{
const int32_t prec_i32 = GGML_PREC_F32;
ggml_set_op_params_i32(result, 0, prec_i32);
}
result->op = GGML_OP_MUL_MAT;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = a;