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ggml : fix quant dot product with odd number of blocks (#8549)
* ggml : fix iq4_nl dot product with odd number of blocks * ggml : fix odd blocks for ARM_NEON (#8556) * ggml : fix iq4_nl dot product with odd number of blocks * ggml : fix q4_1 * ggml : fix q5_0 * ggml : fix q5_1 * ggml : fix iq4_nl metal ggml-ci * ggml : fix q4_0 * ggml : fix q8_0 ggml-ci * ggml : remove special Q4_0 code for first 2 blocks * ggml : fix sumf redefinition --------- Co-authored-by: slaren <slarengh@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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@ -1786,10 +1786,6 @@ static enum ggml_status ggml_metal_graph_compute(
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}
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};
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if (ggml_is_quantized(src0t)) {
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GGML_ASSERT(ne00 >= nth0*nth1);
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}
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[encoder setComputePipelineState:pipeline];
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[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
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[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
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@ -4757,7 +4757,7 @@ void kernel_mul_mv_iq4_nl_f32_impl(
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device const float4 * y4 = (device const float4 *)yb;
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yl[0] = y4[0]; yl[1] = y4[4]; yl[2] = y4[1]; yl[3] = y4[5];
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for (int row = 0; row < 2; ++row) {
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for (int row = 0; row < 2 && first_row + row < ne01; ++row) {
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device const block_iq4_nl & xb = x[row*nb + ib];
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device const uint16_t * q4 = (device const uint16_t *)(xb.qs + 8*it);
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@ -4789,7 +4789,7 @@ void kernel_mul_mv_iq4_nl_f32_impl(
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yb += 16 * QK4_NL;
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}
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for (int row = 0; row < 2; ++row) {
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for (int row = 0; row < 2 && first_row + row < ne01; ++row) {
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all_sum = simd_sum(sumf[row]);
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if (tiisg == 0) {
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dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum;
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File diff suppressed because it is too large
Load Diff
@ -79,8 +79,16 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m
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im = nullptr;
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}
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}
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ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], im);
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GGML_ASSERT(ggml_validate_row_data(tensor->type, dataq.data(), dataq.size()));
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// TODO: other cases
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//#pragma omp parallel for
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//for (int i = 0; i < tensor->ne[1]; i++) {
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// ggml_quantize_chunk(tensor->type, data.data(), dataq.data(),
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// i * tensor->ne[0], 1, tensor->ne[0], im);
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//}
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ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size());
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} else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) {
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// This is going to create some weird integers though.
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@ -2220,6 +2228,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
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test_cases.emplace_back(new test_rms_norm(GGML_TYPE_F32, {64, 10, 10, 10}, eps));
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}
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#if 1
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for (ggml_type type_a : base_types) {
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for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) {
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test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 1, 256, { 1, 1}, {1, 1}));
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@ -2239,6 +2248,24 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
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test_cases.emplace_back(new test_mul_mat(type_a, type_b, 16, 16, 256, {10, 10}, {2, 2}));
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}
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}
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#else
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// m = a rows
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// n = b rows
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// k = cols
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std::uniform_int_distribution<> dist_m(1, 128);
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std::uniform_int_distribution<> dist_n(16, 128);
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std::uniform_int_distribution<> dist_k(1, 16);
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for (int i = 0; i < 1000; i++) {
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for (ggml_type type_a : all_types) {
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for (ggml_type type_b : {GGML_TYPE_F32}) {
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int m = dist_m(rng);
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int n = dist_n(rng);
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int k = dist_k(rng) * ggml_blck_size(type_a);
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test_cases.emplace_back(new test_mul_mat(type_a, type_b, m, n, k, { 1, 1}, {1, 1}));
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}
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}
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}
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#endif
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for (ggml_type type_a : other_types) {
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for (ggml_type type_b : {GGML_TYPE_F32}) {
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