mirror of
https://git.adityakumar.xyz/llama.cpp.git
synced 2024-11-09 23:29:44 +00:00
metal : add Q4_K implementation (#1733)
* Metal implementation for Q4_K Very slow for now: 42 ms / token, Q4_0 runs in 28 ms/token on my 30-core M2 Max GPU. * Optimizing Q4_K on metal The first token always takes longer, I guess because the metal kernel is being jit-compiled. So, using n = 128 to measure time. At this point Q4_K takes 29.5 ms / token compared to 27.2 ms / token for Q4_0. Quite a bit better than the initial attempt, but still not good enough. * Optimizing q4_K metal dot some more For n = 256 it is now 28.1 ms/token compared to 27 ms/token for q4_0. * Fix after merge with master --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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0035858273
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3 changed files with 184 additions and 19 deletions
18
.clang-tidy
18
.clang-tidy
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@ -1,18 +0,0 @@
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---
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Checks: >
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bugprone-*,
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-bugprone-easily-swappable-parameters,
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-bugprone-implicit-widening-of-multiplication-result,
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-bugprone-narrowing-conversions,
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readability-*,
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-readability-avoid-unconditional-preprocessor-if,
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-readability-function-cognitive-complexity,
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-readability-identifier-length,
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-readability-implicit-bool-conversion,
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-readability-magic-numbers,
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-readability-uppercase-literal-suffix,
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clang-analyzer-*,
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-clang-analyzer-security.insecureAPI.DeprecatedOrUnsafeBufferHandling,
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performance-*,
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portability-*,
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FormatStyle: none
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23
ggml-metal.m
23
ggml-metal.m
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@ -49,9 +49,11 @@ struct ggml_metal_context {
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GGML_METAL_DECL_KERNEL(diag_mask_inf);
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GGML_METAL_DECL_KERNEL(diag_mask_inf);
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GGML_METAL_DECL_KERNEL(get_rows_f16);
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GGML_METAL_DECL_KERNEL(get_rows_f16);
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GGML_METAL_DECL_KERNEL(get_rows_q4_0);
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GGML_METAL_DECL_KERNEL(get_rows_q4_0);
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GGML_METAL_DECL_KERNEL(get_rows_q4_k);
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GGML_METAL_DECL_KERNEL(rms_norm);
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GGML_METAL_DECL_KERNEL(rms_norm);
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GGML_METAL_DECL_KERNEL(mul_mat_f16_f32);
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GGML_METAL_DECL_KERNEL(mul_mat_f16_f32);
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GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32);
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GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32);
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GGML_METAL_DECL_KERNEL(mul_mat_q4_k_f32);
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GGML_METAL_DECL_KERNEL(rope);
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GGML_METAL_DECL_KERNEL(rope);
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GGML_METAL_DECL_KERNEL(cpy_f32_f16);
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GGML_METAL_DECL_KERNEL(cpy_f32_f16);
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GGML_METAL_DECL_KERNEL(cpy_f32_f32);
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GGML_METAL_DECL_KERNEL(cpy_f32_f32);
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@ -133,9 +135,11 @@ struct ggml_metal_context * ggml_metal_init(void) {
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GGML_METAL_ADD_KERNEL(diag_mask_inf);
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GGML_METAL_ADD_KERNEL(diag_mask_inf);
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GGML_METAL_ADD_KERNEL(get_rows_f16);
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GGML_METAL_ADD_KERNEL(get_rows_f16);
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GGML_METAL_ADD_KERNEL(get_rows_q4_0);
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GGML_METAL_ADD_KERNEL(get_rows_q4_0);
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GGML_METAL_ADD_KERNEL(get_rows_q4_k);
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GGML_METAL_ADD_KERNEL(rms_norm);
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GGML_METAL_ADD_KERNEL(rms_norm);
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GGML_METAL_ADD_KERNEL(mul_mat_f16_f32);
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GGML_METAL_ADD_KERNEL(mul_mat_f16_f32);
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GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32);
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GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32);
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GGML_METAL_ADD_KERNEL(mul_mat_q4_k_f32);
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GGML_METAL_ADD_KERNEL(rope);
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GGML_METAL_ADD_KERNEL(rope);
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GGML_METAL_ADD_KERNEL(cpy_f32_f16);
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GGML_METAL_ADD_KERNEL(cpy_f32_f16);
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GGML_METAL_ADD_KERNEL(cpy_f32_f32);
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GGML_METAL_ADD_KERNEL(cpy_f32_f32);
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@ -517,7 +521,20 @@ void ggml_metal_graph_compute(
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nth1 = 4;
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nth1 = 4;
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[encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_0_f32];
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[encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_0_f32];
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} break;
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} break;
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default: GGML_ASSERT(false && "not implemented");
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case GGML_TYPE_Q4_K:
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{
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GGML_ASSERT(ne02 == 1);
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GGML_ASSERT(ne12 == 1);
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nth0 = 4;
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nth1 = 16;
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[encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_k_f32];
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} break;
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default:
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{
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fprintf(stderr, "Asserting on type %d\n",(int)src0t);
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GGML_ASSERT(false && "not implemented");
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}
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};
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};
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@ -540,6 +557,9 @@ void ggml_metal_graph_compute(
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if (src0t == GGML_TYPE_Q4_0) {
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if (src0t == GGML_TYPE_Q4_0) {
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[encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
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[encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
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[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
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[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
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} else if (src0t == GGML_TYPE_Q4_K) {
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[encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
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[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
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} else {
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} else {
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[encoder setThreadgroupMemoryLength:nth0*sizeof(float) atIndex:0];
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[encoder setThreadgroupMemoryLength:nth0*sizeof(float) atIndex:0];
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[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
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[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
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@ -555,6 +575,7 @@ void ggml_metal_graph_compute(
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switch (src0->type) {
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switch (src0->type) {
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case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_get_rows_f16]; break;
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case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_get_rows_f16]; break;
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case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break;
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case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break;
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case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_k]; break;
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default: GGML_ASSERT(false && "not implemented");
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default: GGML_ASSERT(false && "not implemented");
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}
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}
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162
ggml-metal.metal
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ggml-metal.metal
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@ -503,3 +503,165 @@ kernel void kernel_cpy_f32_f32(
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dst_data[i00] = src[0];
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dst_data[i00] = src[0];
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}
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}
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}
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}
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//============================================ k-quants ======================================================
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#define QK_K 256
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typedef struct {
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half d; // super-block scale for quantized scales
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half dmin; // super-block scale for quantized mins
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uint8_t scales[3*QK_K/64]; // scales and mins, quantized with 6 bits
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uint8_t qs[QK_K/2]; // 4--bit quants
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} block_q4_k;
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static inline uchar4 get_scale_min_k4(int j, device const uint8_t * q) {
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uchar4 r;
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if (j < 4) {
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r[0] = q[j+0] & 63; r[1] = q[j+4] & 63;
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r[2] = q[j+1] & 63; r[3] = q[j+5] & 63;
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} else {
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r[0] = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
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r[1] = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4);
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r[2] = (q[j+5] & 0xF) | ((q[j-3] >> 6) << 4);
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r[3] = (q[j+5] >> 4) | ((q[j+1] >> 6) << 4);
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}
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return r;
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}
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static void dequantize_row_q4_k(device const block_q4_k * x, device float * y, int k) {
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assert(k % QK_K == 0);
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const int nb = k / QK_K;
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for (int i = 0; i < nb; i++) {
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const float d = x[i].d;
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const float min = x[i].dmin;
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device const uint8_t * q = x[i].qs;
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device const uint8_t * scales = x[i].scales;
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int is = 0;
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for (int j = 0; j < QK_K; j += 64) {
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const uchar4 sc = get_scale_min_k4(is, scales);
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const float d1 = d * sc[0]; const float m1 = min * sc[1];
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const float d2 = d * sc[2]; const float m2 = min * sc[3];
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for (int l = 0; l < 32; ++l) *y++ = d1 * (q[l] & 0xF) - m1;
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for (int l = 0; l < 32; ++l) *y++ = d2 * (q[l] >> 4) - m2;
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q += 32; is += 2;
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}
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}
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}
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kernel void kernel_get_rows_q4_k(
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device const void * src0,
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device const int * src1,
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device float * dst,
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constant int64_t & ne00,
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constant uint64_t & nb01,
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constant uint64_t & nb1,
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uint tpig[[thread_position_in_grid]]) {
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const int i = tpig;
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const int r = ((device int32_t *) src1)[i];
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dequantize_row_q4_k(
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(device const block_q4_k *) ((device char *) src0 + r*nb01),
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(device float *) ((device char *) dst + i*nb1), ne00);
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}
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kernel void kernel_mul_mat_q4_k_f32(
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device const void * src0,
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device const float * src1,
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device float * dst,
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constant int64_t & ne00,
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constant int64_t & ne01,
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constant uint64_t & nb00,
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constant uint64_t & nb01,
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constant uint64_t & nb02,
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constant int64_t & ne10,
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constant int64_t & ne11,
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constant uint64_t & nb10,
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constant uint64_t & nb11,
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constant uint64_t & nb12,
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constant int64_t & ne0,
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constant int64_t & ne1,
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threadgroup float * sum [[threadgroup(0)]],
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uint2 tgpig[[threadgroup_position_in_grid]],
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uint2 tpig[[thread_position_in_grid]], // we don't use this for now
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uint2 tpitg[[thread_position_in_threadgroup]],
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uint2 tptg[[threads_per_threadgroup]]) {
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const int nb = ne00/QK_K;
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const int64_t r0 = tgpig.x;
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const int64_t r1 = tgpig.y;
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device const block_q4_k * x = (device const block_q4_k *) src0 + r0*nb;
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device const float * yy = (device const float *) src1 + r1*ne10;
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const uint nth = tptg.x*tptg.y;
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const uint ith = tptg.y*tpitg.x + tpitg.y;
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const int tid = tpitg.y; // 0...16
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const int il = tid/4; // 0...3
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const int ir = tid%4; // 0...3
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const int n = 8;
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const int is = 2*il;
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sum[ith] = 0.0f;
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float sumf = 0;
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for (int i = tpitg.x; i < nb; i += tptg.x) {
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device const uint8_t * q = (x + i)->qs + 32*il + n*ir;
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device const float * y = yy + i*QK_K + 64*il + n*ir;
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device const uint8_t * scales = (x + i)->scales;
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const float dall = (float)((x + i)->d);
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const float dmin = (float)((x + i)->dmin);
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const uchar4 sc = get_scale_min_k4(is, scales);
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float4 s = {0.f, 0.f, 0.f, 0.f};
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for (int l = 0; l < n; ++l) {
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s[0] += y[l+ 0] * (q[l] & 0xF); s[1] += y[l+ 0];
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s[2] += y[l+32] * (q[l] >> 4); s[3] += y[l+32];
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}
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sumf += dall * (s[0] * sc[0] + s[2] * sc[2]) - dmin * (s[1] * sc[1] + s[3] * sc[3]);
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}
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sum[ith] = sumf;
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//
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// Accumulate the sum from all threads in the threadgroup
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// This version is slightly faster than the commented out one below,
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// which I copy-pasted from ggerganov's q4_0 dot product for metal.
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//
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threadgroup_barrier(mem_flags::mem_threadgroup);
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if (ith%4 == 0) {
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for (int i = 1; i < 4; ++i) sum[ith] += sum[ith + i];
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}
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threadgroup_barrier(mem_flags::mem_threadgroup);
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if (ith%16 == 0) {
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for (int i = 4; i < 16; i += 4) sum[ith] += sum[ith + i];
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}
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threadgroup_barrier(mem_flags::mem_threadgroup);
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if (ith == 0) {
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for (int i = 16; i < nth; i += 16) sum[0] += sum[i];
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dst[r1*ne0 + r0] = sum[0];
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}
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//// accumulate the sum from all threads in the threadgroup
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//threadgroup_barrier(mem_flags::mem_threadgroup);
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//for (uint i = nth/2; i > 0; i /= 2) {
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// if (ith < i) {
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// sum[ith] += sum[ith + i];
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// }
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// threadgroup_barrier(mem_flags::mem_threadgroup);
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//}
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//if (ith == 0) {
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// dst[r1*ne0 + r0] = sum[0];
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//}
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}
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