Merge branch 'master' into r1-toolcall

examples/server/utils.hpp

@@ -5,10 +5,6 @@
 #include "llama.h"
 #include "common/base64.hpp"
 
-#ifndef NDEBUG
-// crash the server in debug mode, otherwise send an http 500 error
-#define CPPHTTPLIB_NO_EXCEPTIONS 1
-#endif
 // increase max payload length to allow use of larger context size
 #define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576
 #include "httplib.h"

examples/server/webui/index.html

@@ -154,8 +154,6 @@
             placeholder="Type a message (Shift+Enter to add a new line)"
             v-model="inputMsg"
             @keydown.enter.exact.prevent="sendMessage"
-            @keydown.enter.shift.exact.prevent="inputMsg += '\n'"
-            :disabled="isGenerating"
             id="msg-input"
             dir="auto"
           ></textarea>

examples/server/webui/src/main.js

@@ -468,7 +468,10 @@ const mainApp = createApp({
       URL.revokeObjectURL(url);
     },
     async sendMessage() {
-      if (!this.inputMsg) return;
+      // prevent sending empty message
+      // also allow typing the message while generating, but does not allow sending it (to match UX/UI behavior of other chat apps)
+      if (!this.inputMsg || this.isGenerating) return;
+
       const currConvId = this.viewingConvId;
 
       StorageUtils.appendMsg(currConvId, {

ggml/src/ggml-cuda/common.cuh

@@ -176,6 +176,14 @@ static constexpr bool new_mma_available(const int cc) {
     return cc < GGML_CUDA_CC_OFFSET_AMD && cc >= GGML_CUDA_CC_TURING;
 }
 
+static constexpr __device__ int ggml_cuda_get_physical_warp_size() {
+#if defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
+    return __AMDGCN_WAVEFRONT_SIZE;
+#else
+    return 32;
+#endif // defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__)
+}
+
 [[noreturn]]
 static __device__ void no_device_code(
     const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) {

ggml/src/ggml-cuda/fattn-common.cuh

@@ -516,6 +516,12 @@ constexpr __device__ dequantize_1_f32_t get_dequantize_1_f32(ggml_type type_V) {
         nullptr;
 }
 
+// The HIP compiler for some reason complains that it can't unroll a loop because of the jt*ncols + j >= ne01 conditional.
+#ifdef __clang__
+#pragma clang diagnostic push
+#pragma clang diagnostic ignored "-Wpass-failed"
+#endif // __clang__
+
 template<int D, int ncols, int KQ_stride> // D == head size
 #if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
 __launch_bounds__(D, 1)
@@ -614,6 +620,10 @@ static __global__ void flash_attn_stream_k_fixup(
     }
 }
 
+#ifdef __clang__
+#pragma clang diagnostic pop
+#endif // __clang__
+
 template<int D, int parallel_blocks> // D == head size
 #if !(defined(GGML_USE_HIP) && defined(__HIP_PLATFORM_AMD__))
 __launch_bounds__(D, 1)

ggml/src/ggml-cuda/fattn-wmma-f16.cu

@@ -561,7 +561,7 @@ void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_ten
                     ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
                     break;
                 // case 256:
-                //     ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst);
+                //     ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, float>(ctx, dst);
                 //     break;
                 default:
                     GGML_ABORT("fatal error");

ggml/src/ggml-cuda/fattn.cu

@@ -235,7 +235,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
         return;
     }
 
-    if (!new_mma_available(cc)) {
+    if (!fp16_mma_available(cc)) {
         if (prec == GGML_PREC_DEFAULT) {
             if (Q->ne[1] <= 8) {
                 ggml_cuda_flash_attn_ext_vec_f16(ctx, dst);
@@ -265,6 +265,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst
     // The MMA implementation needs Turing or newer, use the old WMMA code for Volta:
     if (cc == GGML_CUDA_CC_VOLTA) {
         ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst);
+        return;
     }
 
     ggml_cuda_flash_attn_ext_mma_f16(ctx, dst);

ggml/src/ggml-cuda/mmv.cu

@@ -8,6 +8,7 @@ static __global__ void mul_mat_vec(
     const int64_t row       = blockIdx.x;
     const int64_t channel   = blockIdx.z;
     const int     tid       = threadIdx.x;
+    constexpr int warp_size = ggml_cuda_get_physical_warp_size();
 
     x   += (channel/channel_ratio)*stride_channel_x + row*stride_row;
     y   +=  channel               *stride_channel_y;
@@ -18,8 +19,8 @@ static __global__ void mul_mat_vec(
     extern __shared__ char data_mmv[];
     float * buf_iw = (float *) data_mmv;
 
-    if (block_size > WARP_SIZE) {
+    if (block_size > warp_size) {
-        if (tid < WARP_SIZE) {
+        if (tid < warp_size) {
             buf_iw[tid] = 0.0f;
         }
         __syncthreads();
@@ -67,16 +68,16 @@ static __global__ void mul_mat_vec(
         static_assert(std::is_same<T, void>::value, "unsupported type");
     }
 
-    sumf = warp_reduce_sum(sumf);
+    sumf = warp_reduce_sum<warp_size>(sumf);
 
-    if (block_size > WARP_SIZE) {
+    if (block_size > warp_size) {
-        buf_iw[tid/WARP_SIZE] = sumf;
+        buf_iw[tid/warp_size] = sumf;
         __syncthreads();
-        if (tid >= WARP_SIZE) {
+        if (tid >= warp_size) {
             return;
         }
         sumf = buf_iw[tid];
-        sumf = warp_reduce_sum(sumf);
+        sumf = warp_reduce_sum<warp_size>(sumf);
     }
 
     if (tid != 0) {
@@ -96,10 +97,19 @@ static void launch_mul_mat_vec_cuda(
     GGML_ASSERT(stride_row % 2 == 0);
     GGML_ASSERT(nchannels_y % nchannels_x == 0);
     const int64_t channel_ratio = nchannels_y / nchannels_x;
+    int device;
+    int warp_size;
 
-    int64_t block_size_best = WARP_SIZE;
+    CUDA_CHECK(cudaGetDevice(&device));
-    int64_t niter_best      = (ncols + 2*WARP_SIZE - 1) / (2*WARP_SIZE);
+    warp_size = ggml_cuda_info().devices[device].warp_size;
-    for (int64_t block_size = 2*WARP_SIZE; block_size <= 256; block_size += WARP_SIZE) {
+
+    int64_t block_size_best = warp_size;
+    int64_t niter_best      = (ncols + 2*warp_size - 1) / (2*warp_size);
+    int64_t max_block_size  = 256;
+    if(ggml_cuda_info().devices[device].cc > GGML_CUDA_CC_OFFSET_AMD && ggml_cuda_info().devices[device].cc < GGML_CUDA_CC_RDNA1) {
+        max_block_size = 128;
+    }
+    for (int64_t block_size = 2*warp_size; block_size <= max_block_size; block_size += warp_size) {
         const int64_t niter = (ncols + 2*block_size - 1) / (2*block_size);
         if (niter < niter_best) {
             niter_best      = niter;
@@ -107,7 +117,7 @@ static void launch_mul_mat_vec_cuda(
         }
     }
 
-    const int smem = WARP_SIZE*sizeof(float);
+    const int smem = warp_size*sizeof(float);
     const dim3 block_nums(nrows, 1, nchannels_y);
     const dim3 block_dims(block_size_best, 1, 1);
     switch (block_size_best) {

ggml/src/ggml-cuda/softmax.cu

@@ -18,7 +18,7 @@ __device__ float __forceinline__ t2f32<half>(half val) {
 #ifdef __clang__
 #pragma clang diagnostic push
 #pragma clang diagnostic ignored "-Wpass-failed"
-#endif
+#endif // __clang__
 template <bool use_shared, int ncols_template, int block_size_template, typename T>
 static __global__ void soft_max_f32(
         const float * x, const T * mask, float * dst, const int ncols_par, const int nrows_y,
@@ -126,7 +126,7 @@ static __global__ void soft_max_f32(
 }
 #ifdef __clang__
 #pragma clang diagnostic pop
-#endif
+#endif // __clang__
 
 static __global__ void soft_max_back_f32(
         const float * grad, const float * dstf, float * dst, const int ncols, const float scale) {

ggml/src/ggml-cuda/vendors/hip.h

@@ -1,5 +1,6 @@
 #pragma once
 
+#define HIP_ENABLE_WARP_SYNC_BUILTINS 1
 #include <hip/hip_runtime.h>
 #include <hipblas/hipblas.h>
 #include <hip/hip_fp16.h>
@@ -8,6 +9,7 @@
 // for rocblas_initialize()
 #include "rocblas/rocblas.h"
 #endif // __HIP_PLATFORM_AMD__
+
 #define CUBLAS_COMPUTE_16F HIPBLAS_R_16F
 #define CUBLAS_COMPUTE_32F HIPBLAS_R_32F
 #define CUBLAS_COMPUTE_32F_FAST_16F HIPBLAS_R_32F

scripts/sync-ggml.last

@@ -1 +1 @@
-32f0b85987396945afea2291d5f4c5862434292b
+498e0ecd2c4f9379439fd413805af10e8e9ff349