metal: TRI, FILL, EXPM1, SOFTPLUS (#16623)
* feat(wip): Port initial TRI impl from pervious work The kernel does not work and is not optimized, but the code compiles and runs, so this will be the starting point now that the core op has been merged. Branch: ggml-cumsum-tri Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove argument for constant val override This was added in the original draft, but later removed. With this, the kernel now passes tests. Branch: ggml-cumsum-tri Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Move the ttype conditional to templating to avoid conditional in kernel Branch: ggml-cumsum-tri Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Type fixes Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * feat: Add softplus for metal Branch: ggml-cumsum-tri Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add EXPM1 for metal Branch: ggml-cumsum-tri Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add FILL for metal Branch: ggml-cumsum-tri Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Branchless version of tri using _ggml_vec_tri_cmp as a mask Branch: ggml-cumsum-tri Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove unused arguments Branch: ggml-cumsum-tri Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use select instead of branch for softplus non-vec Branch: ggml-cumsum-tri Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Gabe Goodhart
GitHub
parent
9d0229967a
commit
bde188d60f
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@ -175,6 +175,7 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_unary(ggml_metal
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const char * op_str = "undefined";
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switch (op->op) {
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case GGML_OP_SCALE: op_str = "scale"; break;
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case GGML_OP_FILL: op_str = "fill"; break;
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case GGML_OP_CLAMP: op_str = "clamp"; break;
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case GGML_OP_SQR: op_str = "sqr"; break;
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case GGML_OP_SQRT: op_str = "sqrt"; break;
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@ -199,6 +200,8 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_unary(ggml_metal
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case GGML_UNARY_OP_HARDSWISH: op_str = "hardswish"; break;
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case GGML_UNARY_OP_HARDSIGMOID: op_str = "hardsigmoid"; break;
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case GGML_UNARY_OP_EXP: op_str = "exp"; break;
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case GGML_UNARY_OP_SOFTPLUS: op_str = "softplus"; break;
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case GGML_UNARY_OP_EXPM1: op_str = "expm1"; break;
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default: GGML_ABORT("fatal error");
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} break;
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default: GGML_ABORT("fatal error");
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@ -332,6 +335,28 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_cumsum_add(ggml_
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return res;
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}
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ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_tri(ggml_metal_library_t lib, const ggml_tensor * op) {
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GGML_ASSERT(op->op == GGML_OP_TRI);
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GGML_ASSERT(op->src[0]->nb[0] == ggml_type_size(op->src[0]->type));
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char base[256];
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char name[256];
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const char * op_str = "tri";
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const int ttype = op->op_params[0];
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snprintf(base, 256, "kernel_%s_%s_%d", op_str, ggml_type_name(op->src[0]->type), ttype);
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snprintf(name, 256, "%s", base);
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ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
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if (!res.pipeline) {
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res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
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}
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return res;
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}
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ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_soft_max(ggml_metal_library_t lib, const ggml_tensor * op) {
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GGML_ASSERT(!op->src[1] || op->src[1]->type == GGML_TYPE_F16 || op->src[1]->type == GGML_TYPE_F32);
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@ -114,6 +114,7 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_sum
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struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_sum_rows (ggml_metal_library_t lib, const struct ggml_tensor * op);
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struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_cumsum_blk (ggml_metal_library_t lib, const struct ggml_tensor * op);
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struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_cumsum_add (ggml_metal_library_t lib, const struct ggml_tensor * op);
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struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_tri (ggml_metal_library_t lib, const struct ggml_tensor * op);
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struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_soft_max (ggml_metal_library_t lib, const struct ggml_tensor * op);
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struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_conv (ggml_metal_library_t lib, const struct ggml_tensor * op);
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struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_scan (ggml_metal_library_t lib, const struct ggml_tensor * op);
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@ -818,6 +818,8 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
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case GGML_UNARY_OP_HARDSWISH:
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case GGML_UNARY_OP_HARDSIGMOID:
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case GGML_UNARY_OP_EXP:
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case GGML_UNARY_OP_SOFTPLUS:
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case GGML_UNARY_OP_EXPM1:
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return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
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default:
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return false;
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@ -850,6 +852,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
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case GGML_OP_ACC:
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case GGML_OP_REPEAT:
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case GGML_OP_SCALE:
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case GGML_OP_FILL:
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case GGML_OP_CONV_TRANSPOSE_1D:
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return true;
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case GGML_OP_CONV_TRANSPOSE_2D:
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@ -867,6 +870,8 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
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return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
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case GGML_OP_SUM:
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return has_simdgroup_reduction && ggml_is_contiguous(op->src[0]);
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case GGML_OP_TRI:
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return ggml_is_contiguous_rows(op->src[0]);
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case GGML_OP_SUM_ROWS:
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case GGML_OP_CUMSUM:
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case GGML_OP_MEAN:
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@ -182,6 +182,10 @@ typedef struct {
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float bias;
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} ggml_metal_kargs_scale;
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typedef struct {
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float val;
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} ggml_metal_kargs_fill;
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typedef struct {
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float min;
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float max;
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@ -831,6 +835,25 @@ typedef struct {
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float slope;
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} ggml_metal_kargs_leaky_relu;
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typedef struct {
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int32_t ne00;
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int32_t ne01;
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int32_t ne02;
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int32_t ne03;
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uint64_t nb00;
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uint64_t nb01;
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uint64_t nb02;
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uint64_t nb03;
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int32_t ne0;
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int32_t ne1;
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int32_t ne2;
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int32_t ne3;
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uint64_t nb0;
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uint64_t nb1;
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uint64_t nb2;
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uint64_t nb3;
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} ggml_metal_kargs_tri;
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typedef struct {
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int32_t ne00;
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int32_t ne01;
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@ -286,6 +286,10 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
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{
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n_fuse = ggml_metal_op_scale(ctx, idx);
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} break;
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case GGML_OP_FILL:
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{
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n_fuse = ggml_metal_op_fill(ctx, idx);
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} break;
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case GGML_OP_CLAMP:
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{
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n_fuse = ggml_metal_op_clamp(ctx, idx);
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@ -414,6 +418,10 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
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{
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n_fuse = ggml_metal_op_leaky_relu(ctx, idx);
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} break;
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case GGML_OP_TRI:
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{
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n_fuse = ggml_metal_op_tri(ctx, idx);
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} break;
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case GGML_OP_FLASH_ATTN_EXT:
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{
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n_fuse = ggml_metal_op_flash_attn_ext(ctx, idx);
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@ -733,6 +741,41 @@ int ggml_metal_op_scale(ggml_metal_op_t ctx, int idx) {
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return 1;
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}
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int ggml_metal_op_fill(ggml_metal_op_t ctx, int idx) {
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ggml_tensor * op = ctx->node(idx);
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ggml_metal_library_t lib = ctx->lib;
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ggml_metal_encoder_t enc = ctx->enc;
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GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
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GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
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GGML_TENSOR_LOCALS( int32_t, ne, op, ne);
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GGML_TENSOR_LOCALS(uint64_t, nb, op, nb);
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const float val = ggml_get_op_params_f32(op, 0);
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ggml_metal_kargs_fill args = {
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/*.val =*/ val
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};
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int64_t n = ggml_nelements(op);
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if (n % 4 == 0) {
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n /= 4;
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}
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auto pipeline = ggml_metal_library_get_pipeline_unary(lib, op);
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ggml_metal_encoder_set_pipeline(enc, pipeline);
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ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0);
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ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
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ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op), 2);
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ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, 1, 1, 1);
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return 1;
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}
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int ggml_metal_op_clamp(ggml_metal_op_t ctx, int idx) {
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ggml_tensor * op = ctx->node(idx);
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@ -3899,6 +3942,57 @@ int ggml_metal_op_leaky_relu(ggml_metal_op_t ctx, int idx) {
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return 1;
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}
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int ggml_metal_op_tri(ggml_metal_op_t ctx, int idx) {
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ggml_tensor * op = ctx->node(idx);
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ggml_metal_library_t lib = ctx->lib;
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ggml_metal_encoder_t enc = ctx->enc;
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GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
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GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
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GGML_TENSOR_LOCALS( int32_t, ne, op, ne);
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GGML_TENSOR_LOCALS(uint64_t, nb, op, nb);
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ggml_metal_kargs_tri args = {
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/*.ne00 =*/ ne00,
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/*.ne01 =*/ ne01,
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/*.ne02 =*/ ne02,
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/*.ne03 =*/ ne03,
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/*.nb00 =*/ nb00,
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/*.nb01 =*/ nb01,
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/*.nb02 =*/ nb02,
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/*.nb03 =*/ nb03,
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/*.ne0 =*/ ne0,
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/*.ne1 =*/ ne1,
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/*.ne2 =*/ ne2,
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/*.ne3 =*/ ne3,
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/*.nb0 =*/ nb0,
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/*.nb1 =*/ nb1,
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/*.nb2 =*/ nb2,
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/*.nb3 =*/ nb3,
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};
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auto pipeline = ggml_metal_library_get_pipeline_tri(lib, op);
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int nth = 32; // SIMD width
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while (nth < ne00 && nth < ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
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nth *= 2;
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}
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nth = std::min(nth, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
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nth = std::min(nth, ne00);
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ggml_metal_encoder_set_pipeline(enc, pipeline);
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ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0);
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ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
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ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op), 2);
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ggml_metal_encoder_dispatch_threadgroups(enc, ne01, ne02, ne03, nth, 1, 1);
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return 1;
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}
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int ggml_metal_op_opt_step_adamw(ggml_metal_op_t ctx, int idx) {
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ggml_tensor * op = ctx->node(idx);
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@ -47,6 +47,7 @@ int ggml_metal_op_concat (ggml_metal_op_t ctx, int idx);
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int ggml_metal_op_repeat (ggml_metal_op_t ctx, int idx);
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int ggml_metal_op_acc (ggml_metal_op_t ctx, int idx);
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int ggml_metal_op_scale (ggml_metal_op_t ctx, int idx);
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int ggml_metal_op_fill (ggml_metal_op_t ctx, int idx);
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int ggml_metal_op_clamp (ggml_metal_op_t ctx, int idx);
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int ggml_metal_op_unary (ggml_metal_op_t ctx, int idx);
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int ggml_metal_op_glu (ggml_metal_op_t ctx, int idx);
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@ -83,6 +84,7 @@ int ggml_metal_op_argmax (ggml_metal_op_t ctx, int idx);
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int ggml_metal_op_argsort (ggml_metal_op_t ctx, int idx);
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int ggml_metal_op_top_k (ggml_metal_op_t ctx, int idx);
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int ggml_metal_op_leaky_relu (ggml_metal_op_t ctx, int idx);
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int ggml_metal_op_tri (ggml_metal_op_t ctx, int idx);
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int ggml_metal_op_opt_step_adamw (ggml_metal_op_t ctx, int idx);
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int ggml_metal_op_opt_step_sgd (ggml_metal_op_t ctx, int idx);
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@ -1249,6 +1249,22 @@ kernel void kernel_scale_f32_4(
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dst[tpig] = src0[tpig] * args.scale + args.bias;
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}
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kernel void kernel_fill_f32(
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constant ggml_metal_kargs_fill & args,
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device const float * src0,
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device float * dst,
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uint tpig[[thread_position_in_grid]]) {
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dst[tpig] = args.val;
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}
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kernel void kernel_fill_f32_4(
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constant ggml_metal_kargs_fill & args,
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device const float4 * src0,
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device float4 * dst,
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uint tpig[[thread_position_in_grid]]) {
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dst[tpig] = args.val;
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}
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kernel void kernel_clamp_f32(
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constant ggml_metal_kargs_clamp & args,
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device const float * src0,
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@ -1595,6 +1611,36 @@ kernel void kernel_exp_f32_4(
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dst[tpig] = exp(src0[tpig]);
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}
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kernel void kernel_softplus_f32(
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device const float * src0,
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device float * dst,
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uint tpig[[thread_position_in_grid]]) {
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device const float & x = src0[tpig];
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dst[tpig] = select(log(1.0f + exp(x)), x, x > 20.0f);
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}
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kernel void kernel_softplus_f32_4(
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device const float4 * src0,
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device float4 * dst,
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uint tpig[[thread_position_in_grid]]) {
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device const float4 & x = src0[tpig];
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dst[tpig] = select(log(1.0f + exp(x)), x, x > 20.0f);
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}
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kernel void kernel_expm1_f32(
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device const float * src0,
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device float * dst,
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uint tpig[[thread_position_in_grid]]) {
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dst[tpig] = exp(src0[tpig]) - 1.0f;
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}
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kernel void kernel_expm1_f32_4(
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device const float4 * src0,
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device float4 * dst,
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uint tpig[[thread_position_in_grid]]) {
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dst[tpig] = exp(src0[tpig]) - 1.0f;
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}
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kernel void kernel_reglu_f32(
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constant ggml_metal_kargs_glu & args,
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device const char * src0,
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@ -1943,6 +1989,75 @@ typedef decltype(kernel_cumsum_add<float>) kernel_cumsum_add_t;
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template [[host_name("kernel_cumsum_add_f32")]] kernel kernel_cumsum_add_t kernel_cumsum_add<float>;
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template<uint32_t ttype>
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bool _ggml_vec_tri_cmp(const int i, const int r);
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template<>
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bool _ggml_vec_tri_cmp</* GGML_TRI_TYPE_LOWER */ 3>(const int i, const int r) {
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return i < r;
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}
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template<>
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bool _ggml_vec_tri_cmp</* GGML_TRI_TYPE_LOWER_DIAG */ 2>(const int i, const int r) {
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return i <= r;
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}
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template<>
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bool _ggml_vec_tri_cmp</* GGML_TRI_TYPE_UPPER */ 1>(const int i, const int r) {
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return i > r;
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}
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template<>
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bool _ggml_vec_tri_cmp</* GGML_TRI_TYPE_UPPER_DIAG */ 0>(const int i, const int r) {
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return i >= r;
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}
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|
||||
template<typename T, int ttype>
|
||||
kernel void kernel_tri(
|
||||
constant ggml_metal_kargs_tri & args,
|
||||
device const char * src0,
|
||||
device const char * dst,
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
const int i3 = tgpig.z;
|
||||
const int i2 = tgpig.y;
|
||||
const int i1 = tgpig.x;
|
||||
|
||||
if (i3 >= args.ne03 || i2 >= args.ne02 || i1 >= args.ne01) {
|
||||
return;
|
||||
}
|
||||
|
||||
device const T * src_row = (device const T *) ((device const char *) src0 + i1*args.nb01 + i2*args.nb02 + i3*args.nb03);
|
||||
device T * dst_row = (device T *) ((device char *) dst + i1*args.nb1 + i2*args.nb2 + i3*args.nb3);
|
||||
|
||||
// Each thread is a single element of the row if ne00 < max threads per
|
||||
// threadgroup, so this will loop once for each index that this thread is
|
||||
// responsible for
|
||||
for (int64_t i0 = tpitg.x; i0 < args.ne00; i0 += ntg.x) {
|
||||
// Use the comparison as a mask for branchless
|
||||
dst_row[i0] = static_cast<T>(_ggml_vec_tri_cmp<ttype>(i0, i1)) * src_row[i0];
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_tri<float, 0>) kernel_tri_t;
|
||||
|
||||
template [[host_name("kernel_tri_f32_0")]] kernel kernel_tri_t kernel_tri<float, 0>;
|
||||
template [[host_name("kernel_tri_f32_1")]] kernel kernel_tri_t kernel_tri<float, 1>;
|
||||
template [[host_name("kernel_tri_f32_2")]] kernel kernel_tri_t kernel_tri<float, 2>;
|
||||
template [[host_name("kernel_tri_f32_3")]] kernel kernel_tri_t kernel_tri<float, 3>;
|
||||
template [[host_name("kernel_tri_f16_0")]] kernel kernel_tri_t kernel_tri<half, 0>;
|
||||
template [[host_name("kernel_tri_f16_1")]] kernel kernel_tri_t kernel_tri<half, 1>;
|
||||
template [[host_name("kernel_tri_f16_2")]] kernel kernel_tri_t kernel_tri<half, 2>;
|
||||
template [[host_name("kernel_tri_f16_3")]] kernel kernel_tri_t kernel_tri<half, 3>;
|
||||
#if defined(GGML_METAL_HAS_BF16)
|
||||
template [[host_name("kernel_tri_bf16_0")]] kernel kernel_tri_t kernel_tri<bfloat, 0>;
|
||||
template [[host_name("kernel_tri_bf16_1")]] kernel kernel_tri_t kernel_tri<bfloat, 1>;
|
||||
template [[host_name("kernel_tri_bf16_2")]] kernel kernel_tri_t kernel_tri<bfloat, 2>;
|
||||
template [[host_name("kernel_tri_bf16_3")]] kernel kernel_tri_t kernel_tri<bfloat, 3>;
|
||||
#endif
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_soft_max(
|
||||
constant ggml_metal_kargs_soft_max & args,
|
||||
|
|
|
|||
Loading…
Reference in New Issue