[SYCL] Fix Q8_0 reorder: garbage on 2nd prompt + crash on full VRAM (#21638)
* [SYCL] Fix Q8_0 reorder: add missing dequantize path for GEMM The Q8_0 reorder optimization (#21527) was missing a reorder-aware dequantizer for the GEMM code path used during prompt processing. After token generation reordered Q8_0 weights (via DMMV/MMVQ), the next prompt processing pass would read them with the standard dequantizer, producing garbage output. Add dequantize_block_q8_0_reorder() and wire it into both ggml_get_to_fp16_sycl() and ggml_get_to_fp32_sycl(), matching the pattern already used by Q4_0, Q4_K, and Q6_K. Fixes #21589 AI (Claude) was used to assist with root cause investigation and writing the kernel code. All code was human-reviewed and tested on real hardware. * SYCL: fix reorder crash when device memory is full The reorder optimization allocates a temporary buffer the full size of the weight tensor on the device. When VRAM is nearly full (large models on a single GPU), this allocation fails and the subsequent memcpy crashes on a NULL pointer. Fix: try device allocation first, fall back to host memory if device memory is full. The reorder kernel still works correctly reading from host memory over PCIe. This is slower for the one-time reorder (~21 t/s vs ~38 t/s on Intel Arc Pro B70), but the optimization is preserved for all subsequent inference. If both device and host allocation fail, skip the reorder and fall back to the unoptimized kernel path. Also fixes a bug where opt_for_reorder() marked tensors as reordered even when the reorder was skipped due to allocation failure. This caused DMMV/MMVQ kernels to read the original AoS data as if it were SoA, producing garbage output or NaN results. Tested on Intel Arc Pro B70 (32GB) with Q8_0, Q4_K_M models. Coding was AI-assisted (Claude), reviewed and tested on hardware by a human. Fixes #20478 * SYCL: add RAII temp buffer class + macro guard for host fallback Replace sycl_ext_malloc_with_fallback/sycl_ext_free_fallback free functions with sycl_reorder_temp_buffer RAII class. The host_fallback bool is now a private member, and cleanup happens automatically at scope exit. Add GGML_SYCL_HOST_MEM_FALLBACK cmake option (default ON) to guard the host memory fallback code path. Device access to host memory requires Linux kernel 6.8+ (Ubuntu 26.04+); users on older kernels can set -DGGML_SYCL_HOST_MEM_FALLBACK=OFF to disable it. Addresses arthw's review on PR #21638. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * SYCL: document GGML_SYCL_HOST_MEM_FALLBACK build option in SYCL.md Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * SYCL: add reorder-aware DMMV dequantizers for Q4_K and Q6_K Q4_K and Q6_K had reorder support for MMVQ and GEMM paths but not DMMV. When the DMMV path encountered reordered data it would abort. Add DMMV kernels that read from the SOA reorder layout for both types. Same math as the non-reorder versions, different memory access pattern. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Katostrofik
co-authored by
Claude Opus 4.6
Claude Opus 4.6
parent
408225bb1a
commit
b1be68e8ca
+318
-3
@@ -615,6 +615,162 @@ static void dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx,
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}
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}
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static void dequantize_mul_mat_vec_q4_k_reorder(const void *__restrict__ vx,
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const float *__restrict__ yy,
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float *__restrict__ dst,
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const int ncols, int nrows,
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const sycl::nd_item<3> &item_ct1) {
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const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
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item_ct1.get_local_id(1);
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if (row > nrows) return;
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const int num_blocks_per_row = ncols / QK_K;
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const int ib0 = row*num_blocks_per_row;
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// SOA base pointers for the reordered layout:
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// [qs: nb * QK_K/2] [scales: nb * K_SCALE_SIZE] [dm: nb * sizeof(half2)]
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const int nb = nrows * num_blocks_per_row;
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const uint8_t * qs_base = (const uint8_t *)vx;
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const uint8_t * scales_base = qs_base + (size_t)nb * (QK_K / 2);
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const sycl::half2 * dm_base = (const sycl::half2 *)(scales_base + (size_t)nb * K_SCALE_SIZE);
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#if QK_K == 256
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const uint16_t kmask1 = 0x3f3f;
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const uint16_t kmask2 = 0x0f0f;
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const uint16_t kmask3 = 0xc0c0;
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const int tid =
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item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...16
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const int ix =
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item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0,1
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const int step = 8/K_QUANTS_PER_ITERATION; // 8 or 4
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const int il = tid/step; // 0...3
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const int ir = tid - step*il; // 0...7 or 0...3
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const int n = 2 * K_QUANTS_PER_ITERATION; // 2 or 4
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const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
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const int in = il%2;
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const int l0 = n*(2*ir + in);
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const int q_offset = 32*im + l0;
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const int y_offset = 64*im + l0;
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uint16_t aux[4];
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const uint8_t * sc = (const uint8_t *)aux;
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#if K_QUANTS_PER_ITERATION == 2
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uint32_t q32[4];
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const uint8_t * q4 = (const uint8_t *)q32;
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#else
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uint16_t q16[4];
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const uint8_t * q4 = (const uint8_t *)q16;
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#endif
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float tmp = 0; // partial sum for thread in warp
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for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
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const int bi = ib0 + i;
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const float * y1 = yy + i*QK_K + y_offset;
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const float * y2 = y1 + 128;
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const sycl::half2 dm_val = dm_base[bi];
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const float dall = dm_val[0];
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const float dmin = dm_val[1];
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const uint16_t * a = (const uint16_t *)(scales_base + bi * K_SCALE_SIZE);
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aux[0] = a[im+0] & kmask1;
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aux[1] = a[im+2] & kmask1;
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aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
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aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
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#if K_QUANTS_PER_ITERATION == 2
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const uint32_t * q1 = (const uint32_t *)(qs_base + bi * (QK_K / 2) + q_offset);
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const uint32_t * q2 = q1 + 16;
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q32[0] = q1[0] & 0x0f0f0f0f;
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q32[1] = q1[0] & 0xf0f0f0f0;
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q32[2] = q2[0] & 0x0f0f0f0f;
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q32[3] = q2[0] & 0xf0f0f0f0;
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sycl::float4 s = {0.f, 0.f, 0.f, 0.f};
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float smin = 0;
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for (int l = 0; l < 4; ++l) {
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s.x() += y1[l] * q4[l + 0]; s.y() += y1[l + 32] * q4[l + 4];
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s.z() += y2[l] * q4[l + 8]; s.w() += y2[l + 32] * q4[l + 12];
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smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
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}
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tmp += dall * (s.x() * sc[0] + s.y() * sc[1] * 1.f / 16.f +
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s.z() * sc[4] + s.w() * sc[5] * 1.f / 16.f) -
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dmin * smin;
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#else
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const uint16_t * q1 = (const uint16_t *)(qs_base + bi * (QK_K / 2) + q_offset);
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const uint16_t * q2 = q1 + 32;
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q16[0] = q1[0] & 0x0f0f;
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q16[1] = q1[0] & 0xf0f0;
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q16[2] = q2[0] & 0x0f0f;
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q16[3] = q2[0] & 0xf0f0;
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float4 s = {0.f, 0.f, 0.f, 0.f};
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float smin = 0;
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for (int l = 0; l < 2; ++l) {
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s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+2];
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s.z += y2[l] * q4[l+4]; s.w += y2[l+32] * q4[l+6];
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smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
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}
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tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin;
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#endif
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}
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#else
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const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...15
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const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION);
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const int step = tid * K_QUANTS_PER_ITERATION;
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uint16_t aux16[2];
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const uint8_t * s = (const uint8_t *)aux16;
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float tmp = 0;
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for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
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const int bi = ib0 + i;
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const uint8_t * q = qs_base + bi * (QK_K / 2) + step;
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const float * y = yy + i*QK_K + step;
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const uint16_t * a = (const uint16_t *)(scales_base + bi * K_SCALE_SIZE);
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aux16[0] = a[0] & 0x0f0f;
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aux16[1] = (a[0] >> 4) & 0x0f0f;
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const sycl::half2 dm_val = dm_base[bi];
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const float d = (float)dm_val[0];
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const float m = (float)dm_val[1];
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float sum = 0.f;
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for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
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sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2])
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+ y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2])
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+ y[j+32] * (d * s[1] * (q[j+ 0] >> 4) - m * s[3])
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+ y[j+48] * (d * s[1] * (q[j+16] >> 4) - m * s[3]);
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}
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tmp += sum;
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}
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#endif
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// sum up partial sums and write back result
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#pragma unroll
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for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) {
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tmp +=
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dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
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}
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if (tid == 0) {
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dst[row] = tmp;
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}
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}
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/*
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DPCT1110:7: The total declared local variable size in device function
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dequantize_mul_mat_vec_q5_k exceeds 128 bytes and may cause high register
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@@ -864,6 +1020,129 @@ static void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const floa
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}
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}
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static void dequantize_mul_mat_vec_q6_k_reorder(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows,
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const sycl::nd_item<3> &item_ct1) {
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static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION");
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const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
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item_ct1.get_local_id(1);
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if (row > nrows) return;
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const int num_blocks_per_row = ncols / QK_K;
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const int ib0 = row*num_blocks_per_row;
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// SOA base pointers for the reordered layout:
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// [ql: nb * QK_K/2] [qh: nb * QK_K/4] [scales: nb * QK_K/16] [d: nb * sizeof(half)]
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const int nb = nrows * num_blocks_per_row;
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const uint8_t * ql_base = (const uint8_t *)vx;
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const uint8_t * qh_base = ql_base + (size_t)nb * (QK_K / 2);
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const int8_t * scales_base = (const int8_t *)(qh_base + (size_t)nb * (QK_K / 4));
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const sycl::half * d_base = (const sycl::half *)((const uint8_t *)scales_base + (size_t)nb * (QK_K / 16));
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#if QK_K == 256
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const int tid =
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item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...16
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const int ix =
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item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0, 1
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const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8
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const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128...
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const int in = tid - step*im; // 0...15 or 0...7
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#if K_QUANTS_PER_ITERATION == 1
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const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15
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const int is = 0;
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#else
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const int l0 = 4 * in; // 0, 4, 8, ..., 28
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const int is = in / 4;
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#endif
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const int ql_offset = 64*im + l0;
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const int qh_offset = 32*im + l0;
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const int s_offset = 8*im + is;
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const int y_offset = 128*im + l0;
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float tmp = 0; // partial sum for thread in warp
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for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
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const int bi = ib0 + i;
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const float * y = yy + i * QK_K + y_offset;
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const uint8_t * ql = ql_base + bi * (QK_K / 2) + ql_offset;
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const uint8_t * qh = qh_base + bi * (QK_K / 4) + qh_offset;
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const int8_t * s = scales_base + bi * (QK_K / 16) + s_offset;
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const float d = d_base[bi];
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#if K_QUANTS_PER_ITERATION == 1
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float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
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+ y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
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+ y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
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+ y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32)
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+ y[64] * s[4] * d * ((int8_t)((ql[ 0] >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32)
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+ y[80] * s[5] * d * ((int8_t)((ql[16] >> 4) | ((qh[16] & 0x30) >> 0)) - 32)
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+ y[96] * s[6] * d * ((int8_t)((ql[32] >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
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+y[112] * s[7] * d * ((int8_t)((ql[48] >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
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tmp += sum;
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#else
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float sum = 0;
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for (int l = 0; l < 4; ++l) {
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sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
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+ y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32)
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+ y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32)
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+ y[l+96] * s[6] * d * ((int8_t)((ql[l+32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
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}
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tmp += sum;
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#endif
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}
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#else
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const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...7
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const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION); // 0...3
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const int step = tid * K_QUANTS_PER_ITERATION;
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float tmp = 0; // partial sum for thread in warp
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for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) {
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const int bi = ib0 + i;
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const float * y = yy + i * QK_K + step;
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const uint8_t * ql = ql_base + bi * (QK_K / 2) + step;
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const uint8_t * qh = qh_base + bi * (QK_K / 4) + step;
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const int8_t * s = scales_base + bi * (QK_K / 16);
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const float d = d_base[bi];
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float sum = 0;
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for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
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sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32)
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+ y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32)
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+ y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >> 4) | ((qh[j] & 0x30) >> 0)) - 32)
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+ y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >> 4) | ((qh[j] & 0xc0) >> 2)) - 32);
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}
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tmp += sum;
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}
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#endif
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// sum up partial sums and write back result
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#pragma unroll
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for (int mask = QK_WARP_SIZE / 2; mask > 0; mask >>= 1) {
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tmp +=
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dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
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}
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if (tid == 0) {
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dst[row] = tmp;
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}
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}
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static void dequantize_mul_mat_vec_q4_0_sycl_reorder(const void *vx, const dfloat *y,
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float *dst, const int ncols,
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const int nrows,
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@@ -1167,6 +1446,38 @@ static void dequantize_mul_mat_vec_q6_K_sycl(const void *vx, const float *y,
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});
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}
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static void dequantize_mul_mat_vec_q4_K_sycl_reorder(const void *vx, const float *y,
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float *dst, const int ncols,
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const int nrows,
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dpct::queue_ptr stream) {
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GGML_ASSERT(ncols % QK_K == 0);
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const int ny = 2 / K_QUANTS_PER_ITERATION;
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const int block_num_y = (nrows + ny - 1) / ny;
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const sycl::range<3> block_nums(1, 1, block_num_y);
|
||||
const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE);
|
||||
stream->parallel_for(
|
||||
sycl::nd_range<3>(block_nums * block_dims, block_dims),
|
||||
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] {
|
||||
dequantize_mul_mat_vec_q4_k_reorder(vx, y, dst, ncols, nrows, item_ct1);
|
||||
});
|
||||
}
|
||||
|
||||
static void dequantize_mul_mat_vec_q6_K_sycl_reorder(const void *vx, const float *y,
|
||||
float *dst, const int ncols,
|
||||
const int nrows,
|
||||
dpct::queue_ptr stream) {
|
||||
GGML_ASSERT(ncols % QK_K == 0);
|
||||
const int ny = 2 / K_QUANTS_PER_ITERATION;
|
||||
const int block_num_y = (nrows + ny - 1) / ny;
|
||||
const sycl::range<3> block_nums(1, 1, block_num_y);
|
||||
const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE);
|
||||
stream->parallel_for(
|
||||
sycl::nd_range<3>(block_nums * block_dims, block_dims),
|
||||
[=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] {
|
||||
dequantize_mul_mat_vec_q6_k_reorder(vx, y, dst, ncols, nrows, item_ct1);
|
||||
});
|
||||
}
|
||||
|
||||
void ggml_sycl_op_dequantize_mul_mat_vec(
|
||||
ggml_backend_sycl_context & ctx,
|
||||
const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
|
||||
@@ -1235,8 +1546,7 @@ void ggml_sycl_op_dequantize_mul_mat_vec(
|
||||
case GGML_TYPE_Q4_K:
|
||||
if ((ggml_tensor_extra_gpu *) dst->src[0]->extra &&
|
||||
((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) {
|
||||
// reorder is currently not supported for dmmv
|
||||
GGML_ABORT("Unimplemented dequantize case case for q4_k reorder");
|
||||
dequantize_mul_mat_vec_q4_K_sycl_reorder(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
|
||||
} else {
|
||||
dequantize_mul_mat_vec_q4_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
|
||||
}
|
||||
@@ -1245,7 +1555,12 @@ void ggml_sycl_op_dequantize_mul_mat_vec(
|
||||
dequantize_mul_mat_vec_q5_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
|
||||
break;
|
||||
case GGML_TYPE_Q6_K:
|
||||
dequantize_mul_mat_vec_q6_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
|
||||
if ((ggml_tensor_extra_gpu *) dst->src[0]->extra &&
|
||||
((ggml_tensor_extra_gpu *) dst->src[0]->extra)->optimized_feature.reorder) {
|
||||
dequantize_mul_mat_vec_q6_K_sycl_reorder(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
|
||||
} else {
|
||||
dequantize_mul_mat_vec_q6_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream);
|
||||
}
|
||||
break;
|
||||
case GGML_TYPE_F16:
|
||||
convert_mul_mat_vec_f16_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream);
|
||||
|
||||
Reference in New Issue
Block a user