diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda.cu index 895ba479483e7..f940511980e59 100644 --- a/ggml/src/ggml-cuda.cu +++ b/ggml/src/ggml-cuda.cu @@ -34,6 +34,7 @@ #include "ggml-cuda/tsembd.cuh" #include "ggml-cuda/unary.cuh" #include "ggml-cuda/upscale.cuh" +#include "ggml-cuda/rwkv-wkv.cuh" #include #include @@ -2243,6 +2244,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg case GGML_UNARY_OP_HARDSWISH: ggml_cuda_op_hardswish(ctx, dst); break; + case GGML_UNARY_OP_EXP: + ggml_cuda_op_exp(ctx, dst); + break; default: return false; } @@ -2345,6 +2349,8 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg case GGML_OP_CROSS_ENTROPY_LOSS: ggml_cuda_cross_entropy_loss(ctx, dst); break; + case GGML_OP_RWKV_WKV: + ggml_cuda_op_rwkv_wkv(ctx, dst); case GGML_OP_CROSS_ENTROPY_LOSS_BACK: ggml_cuda_cross_entropy_loss_back(ctx, dst); break; @@ -2806,6 +2812,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_UNARY_OP_HARDSWISH: case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_TANH: + case GGML_UNARY_OP_EXP: return ggml_is_contiguous(op->src[0]); default: return false; @@ -2967,6 +2974,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_OP_ARANGE: case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_LEAKY_RELU: + case GGML_OP_RWKV_WKV: return true; case GGML_OP_FLASH_ATTN_EXT: #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) diff --git a/ggml/src/ggml-cuda/rwkv-wkv.cu b/ggml/src/ggml-cuda/rwkv-wkv.cu new file mode 100644 index 0000000000000..098e92d352181 --- /dev/null +++ b/ggml/src/ggml-cuda/rwkv-wkv.cu @@ -0,0 +1,89 @@ +#include "common.cuh" +#include "rwkv-wkv.cuh" + +static __global__ void rwkv_wkv_f32(const int B, const int T, const int C, const int H, const float * k, const float * v, const float * r, const float * tf, const float * td, const float * s, float * dst) { + const int tid = threadIdx.x; + const int bid = blockIdx.x; + + const int head_size = CUDA_WKV_BLOCK_SIZE; + const int batch_i = bid / H; + const int head_i = bid % H; + const int state_size = C * head_size; + const int n_seq_tokens = T / B; + + float state[head_size]; + __shared__ float _k[head_size], _r[head_size], _tf[head_size], _td[head_size]; + + #pragma unroll + for (int i = 0; i < head_size; i++) { + state[i] = s[batch_i * state_size + head_i * head_size * head_size + i * head_size + tid]; + } + + __syncthreads(); + _tf[tid] = tf[head_i * head_size + tid]; + __syncthreads(); + + for (int t = batch_i * n_seq_tokens * C + head_i * head_size + tid; t < (batch_i + 1) * n_seq_tokens * C + head_i * head_size + tid; t += C) { + __syncthreads(); + _k[tid] = k[t]; + _r[tid] = r[t]; + _td[tid] = td[t]; + __syncthreads(); + + const float _v = v[t]; + float y = 0; + for (int j = 0; j < head_size; j += 4) { + const float4& k = (float4&)(_k[j]); + const float4& r = (float4&)(_r[j]); + const float4& tf = (float4&)(_tf[j]); + const float4& td = (float4&)(_td[j]); + float4& s = (float4&)(state[j]); + float4 kv; + + kv.x = k.x * _v; + kv.y = k.y * _v; + kv.z = k.z * _v; + kv.w = k.w * _v; + + y += r.x * (tf.x * kv.x + s.x); + y += r.y * (tf.y * kv.y + s.y); + y += r.z * (tf.z * kv.z + s.z); + y += r.w * (tf.w * kv.w + s.w); + + s.x = s.x * td.x + kv.x; + s.y = s.y * td.y + kv.y; + s.z = s.z * td.z + kv.z; + s.w = s.w * td.w + kv.w; + } + dst[t] = y; + } + + #pragma unroll + for (int i = 0; i < head_size; i++) { + dst[T * C + batch_i * state_size + head_i * head_size * head_size + i * head_size + tid] = state[i]; + } +} + +void ggml_cuda_op_rwkv_wkv(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const float * k_d = (const float *)dst->src[0]->data; + const float * v_d = (const float *)dst->src[1]->data; + const float * r_d = (const float *)dst->src[2]->data; + const float * tf_d = (const float *)dst->src[3]->data; + const float * td_d = (const float *)dst->src[4]->data; + const float * s_d = (const float *)dst->src[5]->data; + + const int64_t B = dst->src[5]->ne[1]; + const int64_t T = dst->src[0]->ne[3]; + const int64_t C = dst->ne[0]; + const int64_t H = dst->src[0]->ne[2]; + + float * dst_d = (float *)dst->data; + + cudaStream_t stream = ctx.stream(); + + GGML_ASSERT(dst->src[5]->type == GGML_TYPE_F32); + GGML_ASSERT(C % H == 0); + GGML_ASSERT(C / H == CUDA_WKV_BLOCK_SIZE); + + rwkv_wkv_f32<<>>(B, T, C, H, k_d, v_d, r_d, tf_d, td_d, s_d, dst_d); +} diff --git a/ggml/src/ggml-cuda/rwkv-wkv.cuh b/ggml/src/ggml-cuda/rwkv-wkv.cuh new file mode 100644 index 0000000000000..13795247fbe12 --- /dev/null +++ b/ggml/src/ggml-cuda/rwkv-wkv.cuh @@ -0,0 +1,5 @@ +#include "common.cuh" + +#define CUDA_WKV_BLOCK_SIZE 64 + +void ggml_cuda_op_rwkv_wkv(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-cuda/unary.cu b/ggml/src/ggml-cuda/unary.cu index 163b5a8ffec6b..81fc92202f25a 100644 --- a/ggml/src/ggml-cuda/unary.cu +++ b/ggml/src/ggml-cuda/unary.cu @@ -95,6 +95,15 @@ static __global__ void hardswish_f32(const float * x, float * dst, const int k) dst[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); } +static __global__ void exp_f32(const float * x, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } + dst[i] = expf(x[i]); +} + static __global__ void leaky_relu_f32(const float * x, float * dst, const int k, const float negative_slope) { const int i = blockDim.x*blockIdx.x + threadIdx.x; if (i >= k) { @@ -189,6 +198,11 @@ static void hardswish_f32_cuda(const float * x, float * dst, const int k, cudaSt hardswish_f32<<>>(x, dst, k); } +static void exp_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_EXP_BLOCK_SIZE - 1) / CUDA_EXP_BLOCK_SIZE; + exp_f32<<>>(x, dst, k); +} + static void leaky_relu_f32_cuda(const float * x, float * dst, const int k, const float negative_slope, cudaStream_t stream) { const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE; leaky_relu_f32<<>>(x, dst, k, negative_slope); @@ -354,6 +368,20 @@ void ggml_cuda_op_hardswish(ggml_backend_cuda_context & ctx, ggml_tensor * dst) hardswish_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream); } +void ggml_cuda_op_exp(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; + const float * src0_d = (const float *)src0->data; + float * dst_d = (float *)dst->data; + cudaStream_t stream = ctx.stream(); + + GGML_ASSERT(ggml_is_contiguous(src0)); + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + exp_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream); +} + void ggml_cuda_op_leaky_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; const float * src0_d = (const float *)src0->data; diff --git a/ggml/src/ggml-cuda/unary.cuh b/ggml/src/ggml-cuda/unary.cuh index fe519f6a232df..c91936728bab1 100644 --- a/ggml/src/ggml-cuda/unary.cuh +++ b/ggml/src/ggml-cuda/unary.cuh @@ -8,6 +8,7 @@ #define CUDA_RELU_BLOCK_SIZE 256 #define CUDA_SIGMOID_BLOCK_SIZE 256 #define CUDA_HARDSIGMOID_BLOCK_SIZE 256 +#define CUDA_EXP_BLOCK_SIZE 256 #define CUDA_HARDSWISH_BLOCK_SIZE 256 #define CUDA_SQR_BLOCK_SIZE 256 #define CUDA_SQRT_BLOCK_SIZE 256 @@ -32,6 +33,8 @@ void ggml_cuda_op_sigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst); void ggml_cuda_op_hardsigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst); +void ggml_cuda_op_exp(ggml_backend_cuda_context & ctx, ggml_tensor * dst); + void ggml_cuda_op_hardswish(ggml_backend_cuda_context & ctx, ggml_tensor * dst); void ggml_cuda_op_leaky_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 889a199448a6d..efa88688cde75 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1543,6 +1543,36 @@ struct test_ssm_scan : public test_case { } }; +// GGML_OP_RWKV_WKV +struct test_rwkv_wkv : public test_case { + const ggml_type type; + + const int64_t head_count; + const int64_t head_size; + const int64_t n_seq_tokens; + const int64_t n_seqs; + + std::string vars() override { + return VARS_TO_STR5(type, head_count, head_size, n_seq_tokens, n_seqs); + } + + test_rwkv_wkv(ggml_type type = GGML_TYPE_F32, + int64_t head_count = 32, int64_t head_size = 64, int64_t n_seq_tokens = 32, int64_t n_seqs = 32) + : type(type), head_count(head_count), head_size(head_size), n_seq_tokens(n_seq_tokens), n_seqs(n_seqs) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + const int64_t n_tokens = n_seq_tokens * n_seqs; + ggml_tensor * r = ggml_new_tensor(ctx, type, 4, std::vector{ 1, head_size, head_count, n_tokens }.data()); + ggml_tensor * k = ggml_new_tensor(ctx, type, 4, std::vector{ head_size, 1, head_count, n_tokens }.data()); + ggml_tensor * v = ggml_new_tensor(ctx, type, 4, std::vector{ 1, head_size, head_count, n_tokens }.data()); + ggml_tensor * tf = ggml_new_tensor(ctx, type, 2, std::vector{ head_size, head_count }.data()); + ggml_tensor * td = ggml_new_tensor(ctx, type, 4, std::vector{ 1, head_size, head_count, n_tokens }.data()); + ggml_tensor * s = ggml_new_tensor(ctx, type, 2, std::vector{ head_size * head_size * head_count, n_seqs }.data()); + ggml_tensor * out = ggml_rwkv_wkv(ctx, k, v, r, tf, td, s); + return out; + } +}; + // GGML_OP_MUL_MAT struct test_mul_mat : public test_case { const ggml_type type_a; @@ -3337,6 +3367,11 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 16, 1024, 32, 4)); + test_cases.emplace_back(new test_rwkv_wkv(GGML_TYPE_F32, 32, 64, 1, 1)); + test_cases.emplace_back(new test_rwkv_wkv(GGML_TYPE_F32, 32, 64, 32, 1)); + test_cases.emplace_back(new test_rwkv_wkv(GGML_TYPE_F32, 32, 64, 32, 4)); + test_cases.emplace_back(new test_rwkv_wkv(GGML_TYPE_F32, 32, 64, 128, 4)); + #if 1 for (ggml_type type_a : base_types) { for (ggml_type type_b : {GGML_TYPE_F32, GGML_TYPE_F16}) {