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| const uint32_t D_per_thread = D / D_split; | |
| const uint32_t cols_per_iter = WorkGroupSize / D_split; | |
| const uint32_t cols_per_thread = Bc / cols_per_iter; | |
| layout (binding = 0) readonly buffer Q {float data_q[];}; | |
| layout (binding = 0) readonly buffer QV4 {vec4 data_qv4[];}; | |
| layout (binding = 1) readonly buffer K {float16_t data_k[];}; | |
| layout (binding = 1) readonly buffer KV4 {f16vec4 data_kv4[];}; | |
| layout (binding = 2) readonly buffer V {float16_t data_v[];}; | |
| layout (binding = 2) readonly buffer VV4 {f16vec4 data_vv4[];}; | |
| layout (binding = 3) readonly buffer M {float16_t data_m[];}; | |
| // Store the output when doing grouped query attention. | |
| // Rows index by Q's dimension 2, and the first N rows are valid. | |
| D_TYPE perElemOpGqaStore(const in uint32_t r, const in uint32_t c, const in D_TYPE elem, const in uint32_t o_offset, const in uint32_t iq2, const in uint32_t N) | |
| { | |
| uint32_t offset = (iq2 + r) * D + c; | |
| data_o[o_offset + offset] = D_TYPE(elem); | |
| return elem; | |
| } | |
| shared FLOAT_TYPE tmpsh[WorkGroupSize]; | |
| shared vec4 tmpshv4[WorkGroupSize]; | |
| shared float masksh[Bc][Br]; | |
| shared vec4 Qf[Br][D / 4]; | |
| void main() { | |
| init_iq_shmem(gl_WorkGroupSize); | |
| init_indices(); | |
| const uint32_t tid = gl_LocalInvocationIndex; | |
| const uint32_t d_tid = gl_LocalInvocationIndex % D_split; | |
| const uint32_t col_tid = gl_LocalInvocationIndex / D_split; | |
| uint32_t q_offset = (iq2*p.nb02+iq3*p.nb03) / 4; | |
| [[unroll]] for (uint32_t idx = 0; idx < Br * D / 4; idx += gl_WorkGroupSize.x) { | |
| uint32_t d = (idx + tid) % (D / 4); | |
| uint32_t r = (idx + tid) / (D / 4); | |
| if (r < Br && d < D / 4 && | |
| i * Br + r < N) { | |
| Qf[r][d] = vec4(data_qv4[q_offset / 4 + (i * Br + r) * q_stride / 4 + d]) * p.scale; | |
| } | |
| } | |
| barrier(); | |
| vec4 Of[Br][D_per_thread / 4]; | |
| [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) { | |
| [[unroll]] for (uint32_t r = 0; r < Br; ++r) { | |
| Of[r][d] = vec4(0.0); | |
| } | |
| } | |
| float Lf[Br], Mf[Br]; | |
| // Use -FLT_MAX/2 rather than -inf to reduce the possibility of NaNs, e.g. when computing Mold-M. | |
| const float NEG_FLT_MAX_OVER_2 = uintBitsToFloat(0xFEFFFFFF); | |
| [[unroll]] for (uint32_t r = 0; r < Br; ++r) { | |
| Lf[r] = 0; | |
| Mf[r] = NEG_FLT_MAX_OVER_2; | |
| } | |
| float slope[Br]; | |
| [[unroll]] for (uint32_t r = 0; r < Br; ++r) { | |
| slope[r] = 1.0; | |
| } | |
| // ALiBi | |
| if (p.max_bias > 0.0f) { | |
| [[unroll]] for (uint32_t r = 0; r < Br; ++r) { | |
| slope[r] = perElemOpComputeSlope(r, col_tid, ACC_TYPE(0), iq2); | |
| } | |
| } | |
| uint32_t k_offset = (ik2*p.nb12 + ik3*p.nb13) / BLOCK_BYTE_SIZE; | |
| uint32_t v_offset = (iv2*p.nb22 + iv3*p.nb23) / BLOCK_BYTE_SIZE; | |
| uint32_t k_offset = (ik2*p.nb12 + ik3*p.nb13) / 2; | |
| uint32_t v_offset = (iv2*p.nb22 + iv3*p.nb23) / 2; | |
| [[dont_unroll]] | |
| for (uint32_t j = start_j; j < end_j; ++j) { | |
| float Sf[Br][cols_per_thread]; | |
| [[unroll]] for (uint32_t r = 0; r < Br; ++r) { | |
| [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { | |
| Sf[r][c] = 0.0; | |
| } | |
| } | |
| [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { | |
| [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) { | |
| uint coord = (j * Bc + c * cols_per_iter + col_tid) * k_stride * BLOCK_SIZE + 4 * (d * D_split + d_tid); | |
| uint ib = coord / BLOCK_SIZE; | |
| uint iqs = (coord % BLOCK_SIZE); | |
| vec4 K_Tf = dequantize4(ib, iqs, k_offset, BINDING_IDX_K); | |
| vec4 K_Tf = vec4(data_kv4[k_offset / 4 + (j * Bc + c * cols_per_iter + col_tid) * k_stride / 4 + d * D_split + d_tid]); | |
| [[unroll]] for (uint32_t r = 0; r < Br; ++r) { | |
| Sf[r][c] += dot(Qf[r][d * D_split + d_tid], K_Tf); | |
| } | |
| } | |
| } | |
| [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { | |
| // Compute sum across the D_split | |
| [[unroll]] for (uint s = D_split / 2; s > 0; s >>= 1) { | |
| [[unroll]] for (uint32_t r = 0; r < Br; ++r) { | |
| Sf[r][c] += subgroupShuffleXor(Sf[r][c], s); | |
| } | |
| } | |
| } | |
| if (p.logit_softcap != 0.0f) { | |
| [[unroll]] for (uint32_t r = 0; r < Br; ++r) { | |
| [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { | |
| Sf[r][c] = p.logit_softcap * tanh(Sf[r][c]); | |
| } | |
| } | |
| } | |
| if (p.mask != 0) { | |
| [[unroll]] for (uint32_t idx = 0; idx < Bc * Br; idx += gl_WorkGroupSize.x) { | |
| uint32_t c = (idx + tid) % Bc; | |
| uint32_t r = (idx + tid) / Bc; | |
| if (idx + tid < Bc * Br) { | |
| masksh[c][r] = float(data_m[(i * Br + r) * m_stride + (j * Bc + c)]); | |
| } | |
| } | |
| barrier(); | |
| [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { | |
| [[unroll]] for (uint32_t r = 0; r < Br; ++r) { | |
| float mvf = masksh[c * cols_per_iter + col_tid][r]; | |
| Sf[r][c] += slope[r]*mvf; | |
| } | |
| } | |
| barrier(); | |
| } | |
| float rowmaxf[Br], Pf[Br][cols_per_thread], rowsumf[Br], eMf[Br], Moldf[Br]; | |
| [[unroll]] for (uint32_t r = 0; r < Br; ++r) { | |
| rowmaxf[r] = Sf[r][0]; | |
| [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { | |
| rowmaxf[r] = max(rowmaxf[r], Sf[r][c]); | |
| } | |
| Moldf[r] = Mf[r]; | |
| // M = max(rowmax, Mold) | |
| // P = e^(S - M) | |
| // eM = e^(Mold - M) | |
| Mf[r] = max(rowmaxf[r], Moldf[r]); | |
| [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { | |
| Pf[r][c] = exp(Sf[r][c] - Mf[r]); | |
| } | |
| eMf[r] = exp(Moldf[r] - Mf[r]); | |
| // Compute sum across row of P | |
| rowsumf[r] = 0.0; | |
| [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { | |
| rowsumf[r] += Pf[r][c]; | |
| } | |
| Lf[r] = eMf[r]*Lf[r] + rowsumf[r]; | |
| } | |
| [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) { | |
| [[unroll]] for (uint32_t r = 0; r < Br; ++r) { | |
| Of[r][d] = eMf[r] * Of[r][d]; | |
| } | |
| } | |
| [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { | |
| [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) { | |
| uint coord = (j * Bc + c * cols_per_iter + col_tid) * v_stride * BLOCK_SIZE + 4 * (d * D_split + d_tid); | |
| uint ib = coord / BLOCK_SIZE; | |
| uint iqs = (coord % BLOCK_SIZE); | |
| vec4 Vf = dequantize4(ib, iqs, v_offset, BINDING_IDX_V); | |
| vec4 Vf = vec4(data_vv4[v_offset / 4 + (j * Bc + c * cols_per_iter + col_tid) * v_stride / 4 + d * D_split + d_tid]); | |
| [[unroll]] for (uint32_t r = 0; r < Br; ++r) { | |
| Of[r][d] += Pf[r][c] * Vf; | |
| } | |
| } | |
| } | |
| barrier(); | |
| } | |
| // reduce across threads | |
| [[unroll]] for (uint32_t r = 0; r < Br; ++r) { | |
| float rowmaxf, eMf; | |
| tmpsh[tid] = Mf[r]; | |
| // Compute max across the row | |
| barrier(); | |
| [[unroll]] for (int s = int(gl_WorkGroupSize.x) / 2; s >= D_split; s >>= 1) { | |
| if (tid < s) { | |
| tmpsh[tid] = max(tmpsh[tid], tmpsh[tid + s]); | |
| } | |
| barrier(); | |
| } | |
| rowmaxf = tmpsh[d_tid]; | |
| barrier(); | |
| float Moldf = Mf[r]; | |
| // M = max(rowmax, Mold) | |
| // eM = e^(Mold - M) | |
| Mf[r] = max(rowmaxf, Moldf); | |
| eMf = exp(Moldf - Mf[r]); | |
| Lf[r] = eMf*Lf[r]; | |
| tmpsh[tid] = Lf[r]; | |
| // Compute sum across the row | |
| barrier(); | |
| [[unroll]] for (int s = int(gl_WorkGroupSize.x) / 2; s >= D_split; s >>= 1) { | |
| if (tid < s) { | |
| tmpsh[tid] = tmpsh[tid] + tmpsh[tid + s]; | |
| } | |
| barrier(); | |
| } | |
| Lf[r] = tmpsh[d_tid]; | |
| barrier(); | |
| [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) { | |
| Of[r][d] = eMf * Of[r][d]; | |
| tmpshv4[tid] = Of[r][d]; | |
| barrier(); | |
| [[unroll]] for (int s = int(gl_WorkGroupSize.x) / 2; s >= D_split; s >>= 1) { | |
| if (tid < s) { | |
| Of[r][d] += tmpshv4[tid + s]; | |
| tmpshv4[tid] = Of[r][d]; | |
| } | |
| barrier(); | |
| } | |
| Of[r][d] = tmpshv4[d_tid]; | |
| barrier(); | |
| } | |
| } | |
| // If there is split_k, then the split_k resolve shader does the final | |
| // division by L. Store the intermediate O value and per-row m and L values. | |
| if (p.k_num > 1) { | |
| uint32_t o_offset = D * p.ne1 * split_k_index; | |
| [[unroll]] for (uint32_t r = 0; r < Br; ++r) { | |
| if (r < N) { | |
| [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) { | |
| [[unroll]] for (uint32_t comp = 0; comp < 4; ++comp) { | |
| perElemOpGqaStore(r, 4*(d * D_split + d_tid) + comp, Of[r][d][comp], o_offset, iq2, N); | |
| } | |
| } | |
| } | |
| } | |
| o_offset = D * p.ne1 * p.k_num + p.ne1 * split_k_index * 2; | |
| [[unroll]] for (uint32_t r = 0; r < Br; ++r) { | |
| if (r < N) { | |
| perElemOpStoreCol0(r, 0u, ACC_TYPE(Lf[r]), o_offset, iq2, N); | |
| perElemOpStoreCol0(r, 0u, ACC_TYPE(Mf[r]), o_offset + p.ne1, iq2, N); | |
| } | |
| } | |
| return; | |
| } | |
| float Lfrcp[Br]; | |
| [[unroll]] for (uint32_t r = 0; r < Br; ++r) { | |
| Lfrcp[r] = 1.0 / Lf[r]; | |
| } | |
| [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) { | |
| [[unroll]] for (uint32_t r = 0; r < Br; ++r) { | |
| Of[r][d] *= Lfrcp[r]; | |
| } | |
| } | |
| uint32_t o_offset = iq3*p.ne2*p.ne1; | |
| if (p.gqa_ratio > 1) { | |
| [[unroll]] for (uint32_t r = 0; r < Br; ++r) { | |
| if (r < N) { | |
| [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) { | |
| [[unroll]] for (uint32_t comp = 0; comp < 4; ++comp) { | |
| perElemOpGqaStore(r, 4*(d * D_split + d_tid) + comp, Of[r][d][comp], o_offset, iq2, N); | |
| } | |
| } | |
| } | |
| } | |
| } else { | |
| [[unroll]] for (uint32_t r = 0; r < Br; ++r) { | |
| if (i * Br + r < N) { | |
| [[unroll]] for (uint32_t d = 0; d < D_per_thread / 4; ++d) { | |
| [[unroll]] for (uint32_t comp = 0; comp < 4; ++comp) { | |
| data_o[o_offset + iq2 * D + (i * Br + r) * p.ne1 * D + 4*(d * D_split + d_tid) + comp] = D_TYPE(Of[r][d][comp]); | |
| } | |
| } | |
| } | |
| } | |
| } | |
| } | |