CUDA kernel for ggml_mul, norms in VRAM

Author: JohannesGaessler

ggml-cuda.cu

@@ -83,9 +83,19 @@ typedef struct {
 } block_q8_0;
 static_assert(sizeof(block_q8_0) == sizeof(float) + QK8_0, "wrong q8_0 block size/padding");
 
+#define CUDA_MUL_BLOCK_SIZE 256
 #define CUDA_DEQUANTIZE_BLOCK_SIZE 256
 #define CUDA_DMMV_BLOCK_SIZE 32 // dmmv = dequantize_mul_mat_vec
 
+static __global__ void mul_f32(const float * x, const float * y, float * dst, const int kx, const int ky) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= kx) {
+        return;
+    }
+    dst[i] = x[i] * y[i%ky];
+}
+
 static __device__ void dequantize_q4_0(const void * vx, const int ib, const int iqs, float & v0, float & v1){
     const block_q4_0 * x = (const block_q4_0 *) vx;
 
@@ -228,6 +238,11 @@ static __global__ void dequantize_mul_mat_vec(const void * vx, const float * y,
     }
 }
 
+static void mul_f32_cuda(const float * x, const float * y, float * dst, const int kx, const int ky, cudaStream_t stream) {
+    const int num_blocks = (kx + CUDA_MUL_BLOCK_SIZE - 1) / CUDA_MUL_BLOCK_SIZE;
+    mul_f32<<<num_blocks, CUDA_MUL_BLOCK_SIZE, 0, stream>>>(x, y, dst, kx, ky);
+}
+
 static void dequantize_row_q4_0_cuda(const void * vx, float * y, const int k, cudaStream_t stream) {
     const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE;
     dequantize_block<QK4_0, QR4_0, dequantize_q4_0><<<num_blocks, CUDA_DEQUANTIZE_BLOCK_SIZE, 0, stream>>>(vx, y, k);
@@ -467,6 +482,67 @@ static cudaError_t ggml_cuda_h2d_tensor_2d(void * dst, const struct ggml_tensor
     }
 }
 
+static void ggml_cuda_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src1->backend == GGML_BACKEND_CUDA);
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+    const int64_t ne03 = src0->ne[2];
+    const int64_t ne0 = ne00 * ne01 * ne02 * ne03;
+    const int64_t ne10 = src1->ne[0];
+    const int64_t ne11 = src1->ne[1];
+    const int64_t ne12 = src1->ne[2];
+    const int64_t ne13 = src1->ne[3];
+    const int nb2  = dst->nb[2];
+    const int nb3  = dst->nb[3];
+    size_t x_size, d_size;
+
+    float * d_X = (float *) ggml_cuda_pool_malloc(ne0 * sizeof(float), &x_size); // src0
+    float * d_Y = (float *) src1->data; // src1 is already on device, broadcasted.
+    float * d_D = (float *) ggml_cuda_pool_malloc(ne0 * sizeof(float), &d_size); // dst
+
+    for (int64_t i03 = 0; i03 < ne03; i03++) {
+        for (int64_t i02 = 0; i02 < ne02; i02++) {
+            const int i0 = i03*ne02 + i02;
+            float * c_X2 = d_X + i0*ne01*ne00;
+            float * c_D2 = d_D + i0*ne01*ne00;
+
+            cudaStream_t cudaStream = g_cudaStreams[i0 % GGML_CUDA_MAX_STREAMS];
+            cudaStream_t cudaStream2 = g_cudaStreams2[i0 % GGML_CUDA_MAX_STREAMS];
+            cudaEvent_t  cudaEvent = g_cudaEvents[i0 % GGML_CUDA_MAX_EVENTS];
+
+            // copy src0 to device
+            CUDA_CHECK(ggml_cuda_h2d_tensor_2d(c_X2, src0, i03, i02, cudaStream2));
+            CUDA_CHECK(cudaEventRecord(cudaEvent, cudaStream2));
+
+            // wait for data
+            CUDA_CHECK(cudaStreamWaitEvent(cudaStream, cudaEvent, 0));
+
+            for (int64_t i01 = 0; i01 < ne01; i01++) {
+                const int64_t i13 = i03%ne13;
+                const int64_t i12 = i02%ne12;
+                const int64_t i11 = i01%ne11;
+                const int i1 = i13*ne12*ne11 + i12*ne11 + i11;
+
+                float * c_X1 = c_X2 + i01*ne00;
+                float * c_Y = d_Y + i1*ne10;
+                float * c_D1 = c_D2 + i01*ne00;
+
+                // compute
+                mul_f32_cuda(c_X1, c_Y, c_D1, ne00, ne10, cudaStream);
+                CUDA_CHECK(cudaGetLastError());
+            }
+
+            // copy dst to host
+            float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3);
+            CUDA_CHECK(cudaMemcpyAsync(d, c_D2, sizeof(float)*ne00*ne01, cudaMemcpyDeviceToHost, cudaStream));
+        }
+    }
+    CUDA_CHECK(cudaDeviceSynchronize());
+    ggml_cuda_pool_free(d_X, x_size);
+    ggml_cuda_pool_free(d_D, d_size);
+}
+
 static void ggml_cuda_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     const int64_t ne00 = src0->ne[0];
     const int64_t ne01 = src0->ne[1];
@@ -724,6 +800,11 @@ static void ggml_cuda_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor
     ggml_cuda_pool_free(d_Q, q_size);
 }
 
+void ggml_cuda_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
+    GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
+    ggml_cuda_mul_f32(src0, src1, dst);
+}
+
 bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
     const int64_t ne10 = src1->ne[0];
 
@@ -797,14 +878,18 @@ void ggml_cuda_transform_tensor(ggml_tensor * tensor) {
     const size_t q_sz = ggml_type_size(type) * ne0 * ne1 * ne2 * ne3 / ggml_blck_size(type);
 
     size_t q_size;
-    char * d_Q = (char *) ggml_cuda_pool_malloc(q_sz, &q_size);
+    char * dst = (char *) ggml_cuda_pool_malloc(q_sz, &q_size);
 
     cudaStream_t cudaStream2 = g_cudaStreams2[0];
 
     // copy tensor to device
-    CUDA_CHECK(ggml_cuda_h2d_tensor_2d(d_Q, tensor, 0, 0, cudaStream2));
-    CUDA_CHECK(cudaDeviceSynchronize());
+    for (int64_t i3 = 0; i3 < ne3; i3++) {
+        for (int64_t i2 = 0; i2 < ne2; i2++) {
+            int i = i3*ne2 + i2;
+            CUDA_CHECK(ggml_cuda_h2d_tensor_2d(dst + i*ne0*ne1, tensor, i3, i2, cudaStream2));
+        }
+    }
 
-    tensor->data = d_Q;
+    tensor->data = dst;
     tensor->backend = GGML_BACKEND_CUDA;
 }

ggml-cuda.h

@@ -6,6 +6,7 @@ extern "C" {
 
 void   ggml_init_cublas(void);
 
+void   ggml_cuda_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
 bool   ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
 size_t ggml_cuda_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
 void   ggml_cuda_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize);

ggml.c

@@ -7961,6 +7961,14 @@ static void ggml_compute_forward_mul_f32(
     }
     const int ith = params->ith;
     const int nth = params->nth;
+#ifdef GGML_USE_CUBLAS
+    if (src1->backend == GGML_BACKEND_CUDA) {
+        if (ith == 0) {
+            ggml_cuda_mul(src0, src1, dst);
+        }
+        return;
+    }
+#endif
 
     const size_t nb00 = src0->nb[0];
     const size_t nb01 = src0->nb[1];

llama.cpp

@@ -1038,10 +1038,12 @@ static void llama_model_load_internal(
         for (int i = 0; i < n_gpu; ++i) {
             const auto & layer = model.layers[i];
 
+            ggml_cuda_transform_tensor(layer.attention_norm); vram_total += ggml_nbytes(layer.attention_norm);
             ggml_cuda_transform_tensor(layer.wq); vram_total += ggml_nbytes(layer.wq);
             ggml_cuda_transform_tensor(layer.wk); vram_total += ggml_nbytes(layer.wk);
             ggml_cuda_transform_tensor(layer.wv); vram_total += ggml_nbytes(layer.wv);
             ggml_cuda_transform_tensor(layer.wo); vram_total += ggml_nbytes(layer.wo);
+            ggml_cuda_transform_tensor(layer.ffn_norm); vram_total += ggml_nbytes(layer.ffn_norm);
             ggml_cuda_transform_tensor(layer.w1); vram_total += ggml_nbytes(layer.w1);
             ggml_cuda_transform_tensor(layer.w2); vram_total += ggml_nbytes(layer.w2);
             ggml_cuda_transform_tensor(layer.w3); vram_total += ggml_nbytes(layer.w3);