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-rw-r--r--llama.cpp/ggml/src/ggml-cuda/mmq.cu366
1 files changed, 366 insertions, 0 deletions
diff --git a/llama.cpp/ggml/src/ggml-cuda/mmq.cu b/llama.cpp/ggml/src/ggml-cuda/mmq.cu
new file mode 100644
index 0000000..9a69f41
--- /dev/null
+++ b/llama.cpp/ggml/src/ggml-cuda/mmq.cu
@@ -0,0 +1,366 @@
1#include "common.cuh"
2#include "mmq.cuh"
3#include "quantize.cuh"
4#include "mmid.cuh"
5
6static void ggml_cuda_mul_mat_q_switch_type(ggml_backend_cuda_context & ctx, const mmq_args & args, cudaStream_t stream) {
7 switch (args.type_x) {
8 case GGML_TYPE_Q4_0:
9 mul_mat_q_case<GGML_TYPE_Q4_0>(ctx, args, stream);
10 break;
11 case GGML_TYPE_Q4_1:
12 mul_mat_q_case<GGML_TYPE_Q4_1>(ctx, args, stream);
13 break;
14 case GGML_TYPE_Q5_0:
15 mul_mat_q_case<GGML_TYPE_Q5_0>(ctx, args, stream);
16 break;
17 case GGML_TYPE_Q5_1:
18 mul_mat_q_case<GGML_TYPE_Q5_1>(ctx, args, stream);
19 break;
20 case GGML_TYPE_Q8_0:
21 mul_mat_q_case<GGML_TYPE_Q8_0>(ctx, args, stream);
22 break;
23 case GGML_TYPE_MXFP4:
24 mul_mat_q_case<GGML_TYPE_MXFP4>(ctx, args, stream);
25 break;
26 case GGML_TYPE_Q2_K:
27 mul_mat_q_case<GGML_TYPE_Q2_K>(ctx, args, stream);
28 break;
29 case GGML_TYPE_Q3_K:
30 mul_mat_q_case<GGML_TYPE_Q3_K>(ctx, args, stream);
31 break;
32 case GGML_TYPE_Q4_K:
33 mul_mat_q_case<GGML_TYPE_Q4_K>(ctx, args, stream);
34 break;
35 case GGML_TYPE_Q5_K:
36 mul_mat_q_case<GGML_TYPE_Q5_K>(ctx, args, stream);
37 break;
38 case GGML_TYPE_Q6_K:
39 mul_mat_q_case<GGML_TYPE_Q6_K>(ctx, args, stream);
40 break;
41 case GGML_TYPE_IQ2_XXS:
42 mul_mat_q_case<GGML_TYPE_IQ2_XXS>(ctx, args, stream);
43 break;
44 case GGML_TYPE_IQ2_XS:
45 mul_mat_q_case<GGML_TYPE_IQ2_XS>(ctx, args, stream);
46 break;
47 case GGML_TYPE_IQ2_S:
48 mul_mat_q_case<GGML_TYPE_IQ2_S>(ctx, args, stream);
49 break;
50 case GGML_TYPE_IQ3_XXS:
51 mul_mat_q_case<GGML_TYPE_IQ3_XXS>(ctx, args, stream);
52 break;
53 case GGML_TYPE_IQ3_S:
54 mul_mat_q_case<GGML_TYPE_IQ3_S>(ctx, args, stream);
55 break;
56 case GGML_TYPE_IQ1_S:
57 mul_mat_q_case<GGML_TYPE_IQ1_S>(ctx, args, stream);
58 break;
59 case GGML_TYPE_IQ4_XS:
60 mul_mat_q_case<GGML_TYPE_IQ4_XS>(ctx, args, stream);
61 break;
62 case GGML_TYPE_IQ4_NL:
63 mul_mat_q_case<GGML_TYPE_IQ4_NL>(ctx, args, stream);
64 break;
65 default:
66 GGML_ABORT("fatal error");
67 break;
68 }
69}
70
71void ggml_cuda_mul_mat_q(
72 ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) {
73 GGML_ASSERT( src1->type == GGML_TYPE_F32);
74 GGML_ASSERT( dst->type == GGML_TYPE_F32);
75 GGML_ASSERT(!ids || ids->type == GGML_TYPE_I32); // Optional, used for batched GGML_MUL_MAT_ID.
76
77 GGML_TENSOR_BINARY_OP_LOCALS;
78
79 cudaStream_t stream = ctx.stream();
80 const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
81
82 const size_t ts_src0 = ggml_type_size(src0->type);
83 const size_t ts_src1 = ggml_type_size(src1->type);
84 const size_t ts_dst = ggml_type_size(dst->type);
85
86 GGML_ASSERT( nb00 == ts_src0);
87 GGML_ASSERT( nb10 == ts_src1);
88 GGML_ASSERT( nb0 == ts_dst);
89 GGML_ASSERT(!ids || ids->nb[0] == ggml_type_size(ids->type));
90
91 const char * src0_d = (const char *) src0->data;
92 const float * src1_d = (const float *) src1->data;
93 float * dst_d = (float *) dst->data;
94
95 // If src0 is a temporary compute buffer, clear any potential padding.
96 if (ggml_backend_buffer_get_usage(src0->buffer) == GGML_BACKEND_BUFFER_USAGE_COMPUTE) {
97 const size_t size_data = ggml_nbytes(src0);
98 const size_t size_alloc = ggml_backend_buffer_get_alloc_size(src0->buffer, src0);
99 if (size_alloc > size_data) {
100 GGML_ASSERT(ggml_is_contiguously_allocated(src0));
101 GGML_ASSERT(!src0->view_src);
102 CUDA_CHECK(cudaMemsetAsync((char *) src0->data + size_data, 0, size_alloc - size_data, stream));
103 }
104 }
105
106 const int64_t ne10_padded = GGML_PAD(ne10, MATRIX_ROW_PADDING);
107
108 const int64_t s01 = src0->nb[1] / ts_src0;
109 const int64_t s1 = dst->nb[1] / ts_dst;
110 const int64_t s02 = src0->nb[2] / ts_src0;
111 const int64_t s2 = dst->nb[2] / ts_dst;
112 const int64_t s03 = src0->nb[3] / ts_src0;
113 const int64_t s3 = dst->nb[3] / ts_dst;
114
115 const bool use_stream_k = (GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA)
116 || GGML_CUDA_CC_IS_CDNA(cc);
117
118 // TODO: tighter pool buffer size vs q8 path
119 const bool use_native_mxfp4 = blackwell_mma_available(cc) && src0->type == GGML_TYPE_MXFP4;
120
121 if (!ids) {
122 const size_t nbytes_src1_q8_1 = ne13*ne12 * ne11*ne10_padded * sizeof(block_q8_1)/QK8_1 +
123 get_mmq_x_max_host(cc)*sizeof(block_q8_1_mmq);
124 ggml_cuda_pool_alloc<char> src1_q8_1(ctx.pool(), nbytes_src1_q8_1);
125
126 {
127 const int64_t s11 = src1->nb[1] / ts_src1;
128 const int64_t s12 = src1->nb[2] / ts_src1;
129 const int64_t s13 = src1->nb[3] / ts_src1;
130 if (use_native_mxfp4) {
131 static_assert(sizeof(block_fp4_mmq) == 4 * sizeof(block_q8_1));
132 quantize_mmq_mxfp4_cuda(src1_d, nullptr, src1_q8_1.get(), src0->type, ne10, s11, s12, s13, ne10_padded,
133 ne11, ne12, ne13, stream);
134
135 } else {
136 quantize_mmq_q8_1_cuda(src1_d, nullptr, src1_q8_1.get(), src0->type, ne10, s11, s12, s13, ne10_padded,
137 ne11, ne12, ne13, stream);
138 }
139 CUDA_CHECK(cudaGetLastError());
140 }
141
142 // Stride depends on quantization format
143 const int64_t s12 = use_native_mxfp4 ?
144 ne11 * ne10_padded * sizeof(block_fp4_mmq) /
145 (8 * QK_MXFP4 * sizeof(int)) // block_fp4_mmq holds 256 values (8 blocks of 32)
146 :
147 ne11 * ne10_padded * sizeof(block_q8_1) / (QK8_1 * sizeof(int));
148 const int64_t s13 = ne12*s12;
149
150 const mmq_args args = {
151 src0_d, src0->type, (const int *) src1_q8_1.ptr, nullptr, nullptr, dst_d,
152 ne00, ne01, ne1, s01, ne11, s1,
153 ne02, ne12, s02, s12, s2,
154 ne03, ne13, s03, s13, s3,
155 use_stream_k, ne1};
156 ggml_cuda_mul_mat_q_switch_type(ctx, args, stream);
157 return;
158 }
159
160 GGML_ASSERT(ne13 == 1);
161 GGML_ASSERT(nb12 % nb11 == 0);
162 GGML_ASSERT(nb2 % nb1 == 0);
163
164 const int64_t n_expert_used = ids->ne[0];
165 const int64_t ne_get_rows = ne12 * n_expert_used;
166 GGML_ASSERT(ne1 == n_expert_used);
167
168 ggml_cuda_pool_alloc<int32_t> ids_src1(ctx.pool(), ne_get_rows);
169 ggml_cuda_pool_alloc<int32_t> ids_dst(ctx.pool(), ne_get_rows);
170 ggml_cuda_pool_alloc<int32_t> expert_bounds(ctx.pool(), ne02 + 1);
171
172 {
173 GGML_ASSERT(ids->nb[0] == ggml_element_size(ids));
174 const int si1 = ids->nb[1] / ggml_element_size(ids);
175 const int sis1 = nb12 / nb11;
176
177 ggml_cuda_launch_mm_ids_helper((const int32_t *) ids->data, ids_src1.get(), ids_dst.get(), expert_bounds.get(),
178 ne02, ne12, n_expert_used, ne11, si1, sis1, stream);
179 CUDA_CHECK(cudaGetLastError());
180 }
181
182 const size_t nbytes_src1_q8_1 = ne12*n_expert_used*ne10_padded * sizeof(block_q8_1)/QK8_1 +
183 get_mmq_x_max_host(cc)*sizeof(block_q8_1_mmq);
184 ggml_cuda_pool_alloc<char> src1_q8_1(ctx.pool(), nbytes_src1_q8_1);
185
186 const int64_t ne11_flat = ne12*n_expert_used;
187 const int64_t ne12_flat = 1;
188 const int64_t ne13_flat = 1;
189
190 {
191 const int64_t s11 = src1->nb[1] / ts_src1;
192 const int64_t s12 = src1->nb[2] / ts_src1;
193 const int64_t s13 = src1->nb[3] / ts_src1;
194
195 if (use_native_mxfp4) {
196 quantize_mmq_mxfp4_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, s11, s12, s13,
197 ne10_padded, ne11_flat, ne12_flat, ne13_flat, stream);
198 } else {
199 quantize_mmq_q8_1_cuda(src1_d, ids_src1.get(), src1_q8_1.get(), src0->type, ne10, s11, s12, s13,
200 ne10_padded, ne11_flat, ne12_flat, ne13_flat, stream);
201 }
202 CUDA_CHECK(cudaGetLastError());
203 }
204
205 const int64_t s12 = use_native_mxfp4 ? ne11 * ne10_padded * sizeof(block_fp4_mmq) / (8 * QK_MXFP4 * sizeof(int)) :
206 ne11 * ne10_padded * sizeof(block_q8_1) / (QK8_1 * sizeof(int));
207 const int64_t s13 = ne12*s12;
208
209 // Note that ne02 is used instead of ne12 because the number of y channels determines the z dimension of the CUDA grid.
210 const mmq_args args = {
211 src0_d, src0->type, (const int *) src1_q8_1.get(), ids_dst.get(), expert_bounds.get(), dst_d,
212 ne00, ne01, ne_get_rows, s01, ne_get_rows, s1,
213 ne02, ne02, s02, s12, s2,
214 ne03, ne13, s03, s13, s3,
215 use_stream_k, ne12};
216
217 ggml_cuda_mul_mat_q_switch_type(ctx, args, stream);
218}
219
220void ggml_cuda_op_mul_mat_q(
221 ggml_backend_cuda_context & ctx,
222 const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const char * src0_dd_i, const float * src1_ddf_i,
223 const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols,
224 const int64_t src1_padded_row_size, cudaStream_t stream) {
225
226 const int64_t ne00 = src0->ne[0];
227
228 const int64_t ne10 = src1->ne[0];
229 const int64_t ne11 = src1->ne[1];
230 GGML_ASSERT(ne10 % QK8_1 == 0);
231
232 const int64_t ne0 = dst->ne[0];
233
234 const int64_t row_diff = row_high - row_low;
235 const int64_t stride01 = ne00 / ggml_blck_size(src0->type);
236
237 const int id = ggml_cuda_get_device();
238 const int cc = ggml_cuda_info().devices[id].cc;
239
240 // the main device has a larger memory buffer to hold the results from all GPUs
241 // nrows_dst == nrows of the matrix that the kernel writes into
242 const int64_t nrows_dst = id == ctx.device ? ne0 : row_diff;
243
244 // The stream-k decomposition is only faster for recent NVIDIA GPUs.
245 // Also its fixup needs to allocate a temporary buffer in the memory pool.
246 // There are multiple parallel CUDA streams for src1_ncols != ne11 which would introduce a race condition for this buffer.
247 const bool use_stream_k = ((GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA)
248 || GGML_CUDA_CC_IS_CDNA(cc))
249 && src1_ncols == ne11;
250 const mmq_args args = {
251 src0_dd_i, src0->type, (const int *) src1_ddq_i, nullptr, nullptr, dst_dd_i,
252 ne00, row_diff, src1_ncols, stride01, ne11, nrows_dst,
253 1, 1, 0, 0, 0,
254 1, 1, 0, 0, 0,
255 use_stream_k, src1_ncols};
256
257 ggml_cuda_mul_mat_q_switch_type(ctx, args, stream);
258
259 GGML_UNUSED_VARS(src1, dst, src1_ddf_i, src1_padded_row_size);
260}
261
262bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11, int64_t n_experts) {
263#ifdef GGML_CUDA_FORCE_CUBLAS
264 return false;
265#endif // GGML_CUDA_FORCE_CUBLAS
266
267 bool mmq_supported;
268
269 switch (type) {
270 case GGML_TYPE_Q4_0:
271 case GGML_TYPE_Q4_1:
272 case GGML_TYPE_Q5_0:
273 case GGML_TYPE_Q5_1:
274 case GGML_TYPE_Q8_0:
275 case GGML_TYPE_MXFP4:
276 case GGML_TYPE_Q2_K:
277 case GGML_TYPE_Q3_K:
278 case GGML_TYPE_Q4_K:
279 case GGML_TYPE_Q5_K:
280 case GGML_TYPE_Q6_K:
281 case GGML_TYPE_IQ2_XXS:
282 case GGML_TYPE_IQ2_XS:
283 case GGML_TYPE_IQ2_S:
284 case GGML_TYPE_IQ3_XXS:
285 case GGML_TYPE_IQ3_S:
286 case GGML_TYPE_IQ1_S:
287 case GGML_TYPE_IQ4_XS:
288 case GGML_TYPE_IQ4_NL:
289 mmq_supported = true;
290 break;
291 default:
292 mmq_supported = false;
293 break;
294 }
295
296 if (!mmq_supported) {
297 return false;
298 }
299
300 if (turing_mma_available(cc)) {
301 return true;
302 }
303
304 if (ggml_cuda_highest_compiled_arch(cc) < GGML_CUDA_CC_DP4A) {
305 return false;
306 }
307
308#ifdef GGML_CUDA_FORCE_MMQ
309 return true;
310#endif //GGML_CUDA_FORCE_MMQ
311
312 if (GGML_CUDA_CC_IS_NVIDIA(cc)) {
313 return !fp16_mma_hardware_available(cc) || ne11 < MMQ_DP4A_MAX_BATCH_SIZE;
314 }
315
316 if (amd_mfma_available(cc)) {
317 // As of ROCM 7.0 rocblas/tensile performs very poorly on CDNA3 and hipblaslt (via ROCBLAS_USE_HIPBLASLT)
318 // performs better but is currently suffering from a crash on this architecture.
319 // TODO: Revisit when hipblaslt is fixed on CDNA3
320 if (GGML_CUDA_CC_IS_CDNA3(cc)) {
321 return true;
322 }
323 if (n_experts > 64 || ne11 <= 128) {
324 return true;
325 }
326 if (type == GGML_TYPE_Q4_0 || type == GGML_TYPE_Q4_1 || type == GGML_TYPE_Q5_0 || type == GGML_TYPE_Q5_1) {
327 return true;
328 }
329 if (ne11 <= 256 && (type == GGML_TYPE_Q4_K || type == GGML_TYPE_Q5_K)) {
330 return true;
331 }
332 return false;
333 }
334
335 if (amd_wmma_available(cc)) {
336 if (GGML_CUDA_CC_IS_RDNA3(cc)) {
337 // High expert counts are almost always better on MMQ due to
338 // the synchronization overhead in the cuBLAS/hipBLAS path:
339 // https://github.com/ggml-org/llama.cpp/pull/18202
340 if (n_experts >= 64) {
341 return true;
342 }
343
344 // For some quantization types MMQ can have lower peak TOPS than hipBLAS
345 // so it's only faster for sufficiently small batch sizes:
346 switch (type) {
347 case GGML_TYPE_Q2_K:
348 return ne11 <= 128;
349 case GGML_TYPE_Q6_K:
350 return ne11 <= (GGML_CUDA_CC_IS_RDNA3_0(cc) ? 128 : 256);
351 case GGML_TYPE_IQ2_XS:
352 case GGML_TYPE_IQ2_S:
353 return GGML_CUDA_CC_IS_RDNA3_5(cc) || ne11 <= 128;
354 default:
355 return true;
356 }
357 }
358
359 // For RDNA4 MMQ is consistently faster than dequantization + hipBLAS:
360 // https://github.com/ggml-org/llama.cpp/pull/18537#issuecomment-3706422301
361 return true;
362 }
363
364 return (!GGML_CUDA_CC_IS_CDNA(cc)) || ne11 < MMQ_DP4A_MAX_BATCH_SIZE;
365
366}
generated by cgit v1.2.3 (git 2.46.0) at 2026-04-30 02:00:33 +0000