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rwkv6: add wkv6 support for Vulkan backend (llama/10829)

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* rwkv_wkv6 vulkan shader

* RWKV_WKV6 Vulkan op tests passed

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* Apply code format changes

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>

* add [[unroll]] and remove unnecessary conditions

* add uma support

* fix erros in EditorConfig Checker

---------

Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
Co-authored-by: Molly Sophia <mollysophia379@gmail.com>
This commit is contained in:
Zhiyuan Li 2024-12-17 05:00:46 +08:00 committed by Georgi Gerganov
parent e22d38e4f2
commit a1ab9b5e91
3 changed files with 245 additions and 1 deletions
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157
ggml/src/ggml-vulkan/ggml-vulkan.cpp
View File

@ -245,6 +245,7 @@ struct vk_device_struct {
vk_pipeline pipeline_im2col_f32, pipeline_im2col_f32_f16;
vk_pipeline pipeline_timestep_embedding_f32;
vk_pipeline pipeline_pool2d_f32;
vk_pipeline pipeline_rwkv_wkv6_f32;
// [2][2][2] is for {f16acc,f32acc}x{large,small_rows}x{unaligned, aligned}
vk_pipeline pipeline_flash_attn_f32_f16_D64[GGML_TYPE_COUNT][2][2][2];
@ -528,6 +529,13 @@ struct vk_op_pool2d_push_constants {
int32_t p0; int32_t p1;
};
struct vk_op_rwkv_wkv6_push_constants {
uint32_t B;
uint32_t T;
uint32_t C;
uint32_t H;
};
// Allow pre-recording command buffers
struct vk_staging_memcpy {
vk_staging_memcpy(void * _dst, const void * _src, size_t _n) : dst(_dst), src(_src), n(_n) {}
@ -2014,6 +2022,8 @@ static void ggml_vk_load_shaders(vk_device& device) {
ggml_vk_create_pipeline(device, device->pipeline_pool2d_f32, "pool2d_f32", pool2d_f32_len, pool2d_f32_data, "main", 2, sizeof(vk_op_pool2d_push_constants), {512, 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_rwkv_wkv6_f32, "rwkv_wkv6_f32", rwkv_wkv6_f32_len, rwkv_wkv6_f32_data, "main", 7, sizeof(vk_op_rwkv_wkv6_push_constants), {1, 1, 1}, {device->subgroup_size}, 1);
for (auto &c : compiles) {
c.wait();
}
@ -5022,6 +5032,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
return ctx->device->pipeline_pool2d_f32;
}
return nullptr;
case GGML_OP_RWKV_WKV6:
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
return ctx->device->pipeline_rwkv_wkv6_f32;
}
return nullptr;
case GGML_OP_LEAKY_RELU:
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
return ctx->device->pipeline_leaky_relu_f32;
@ -5424,6 +5439,134 @@ static void ggml_vk_div(ggml_backend_vk_context * ctx, vk_context& subctx, const
}, dryrun);
}
static void ggml_vk_op_f32_rwkv6(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst, const vk_op_rwkv_wkv6_push_constants&& pc, bool dryrun = false) {
const ggml_tensor * k = dst->src[0];
const ggml_tensor * v = dst->src[1];
const ggml_tensor * r = dst->src[2];
const ggml_tensor * tf = dst->src[3];
const ggml_tensor * td = dst->src[4];
const ggml_tensor * state = dst->src[5];
GGML_ASSERT(!ggml_is_quantized(k->type));
GGML_ASSERT(!ggml_is_quantized(v->type));
GGML_ASSERT(!ggml_is_quantized(r->type));
GGML_ASSERT(!ggml_is_quantized(tf->type));
GGML_ASSERT(!ggml_is_quantized(td->type));
GGML_ASSERT(!ggml_is_quantized(state->type));
GGML_ASSERT(dst->buffer != nullptr);
vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, k, v, r, dst, GGML_OP_RWKV_WKV6);
GGML_ASSERT(pipeline != nullptr);
if (dryrun) {
ggml_pipeline_request_descriptor_sets(ctx->device, pipeline, 1);
return;
}
ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
ggml_backend_vk_buffer_context * k_buf_ctx = (ggml_backend_vk_buffer_context *)k->buffer->context;
ggml_backend_vk_buffer_context * v_buf_ctx = (ggml_backend_vk_buffer_context *)v->buffer->context;
ggml_backend_vk_buffer_context * r_buf_ctx = (ggml_backend_vk_buffer_context *)r->buffer->context;
ggml_backend_vk_buffer_context * tf_buf_ctx = (ggml_backend_vk_buffer_context *)tf->buffer->context;
ggml_backend_vk_buffer_context * td_buf_ctx = (ggml_backend_vk_buffer_context *)td->buffer->context;
ggml_backend_vk_buffer_context * state_buf_ctx = (ggml_backend_vk_buffer_context *)state->buffer->context;
ggml_vk_sync_buffers(subctx);
vk_buffer d_D, d_K, d_V, d_R, d_TF, d_TD, d_State;
uint64_t k_offset, v_offset, r_offset, tf_offset, td_offset, state_offset, dst_offset;
bool K_uma = false, V_uma = false, R_uma = false, TF_uma = false, TD_uma = false, STATE_uma = false, DST_uma = false;
if (ctx->device->uma) {
ggml_vk_host_get(ctx->device, k->data, d_K, k_offset);
ggml_vk_host_get(ctx->device, v->data, d_V, v_offset);
ggml_vk_host_get(ctx->device, r->data, d_R, r_offset);
ggml_vk_host_get(ctx->device, tf->data, d_TF, tf_offset);
ggml_vk_host_get(ctx->device, td->data, d_TD, td_offset);
ggml_vk_host_get(ctx->device, state->data, d_State, state_offset);
ggml_vk_host_get(ctx->device, dst->data, d_D, dst_offset);
K_uma = d_K != nullptr;
V_uma = d_V != nullptr;
R_uma = d_R != nullptr;
TF_uma = d_TF != nullptr;
TD_uma = d_TD != nullptr;
STATE_uma = d_State != nullptr;
DST_uma = d_D != nullptr;
}
if (!K_uma) {
d_K = k_buf_ctx->dev_buffer;
k_offset = vk_tensor_offset(k) + k->view_offs;
}
if (!V_uma) {
d_V = v_buf_ctx->dev_buffer;
v_offset = vk_tensor_offset(v) + v->view_offs;
}
if (!R_uma) {
d_R = r_buf_ctx->dev_buffer;
r_offset = vk_tensor_offset(r) + r->view_offs;
}
if (!TF_uma) {
d_TF = tf_buf_ctx->dev_buffer;
tf_offset = vk_tensor_offset(tf) + tf->view_offs;
}
if (!TD_uma) {
d_TD = td_buf_ctx->dev_buffer;
td_offset = vk_tensor_offset(td) + td->view_offs;
}
if (!STATE_uma) {
d_State = state_buf_ctx->dev_buffer;
state_offset = vk_tensor_offset(state) + state->view_offs;
}
if (!DST_uma) {
d_D = dst_buf_ctx->dev_buffer;
dst_offset = vk_tensor_offset(dst) + dst->view_offs;
}
const uint64_t k_size = ggml_nbytes(k);
const uint64_t v_size = ggml_nbytes(v);
const uint64_t r_size = ggml_nbytes(r);
const uint64_t tf_size = ggml_nbytes(tf);
const uint64_t td_size = ggml_nbytes(td);
const uint64_t state_size = ggml_nbytes(state);
const uint64_t dst_size = ggml_nbytes(dst);
std::array<uint32_t, 3> elements = {
(uint32_t)(pc.B * pc.H),
1,
1
};
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, {
vk_subbuffer{ d_K, k_offset, k_size },
vk_subbuffer{ d_V, v_offset, v_size },
vk_subbuffer{ d_R, r_offset, r_size },
vk_subbuffer{ d_TF, tf_offset, tf_size },
vk_subbuffer{ d_TD, td_offset, td_size },
vk_subbuffer{ d_State, state_offset, state_size },
vk_subbuffer{ d_D, dst_offset, dst_size }
}, sizeof(vk_op_rwkv_wkv6_push_constants), &pc, elements);
}
static void ggml_vk_rwkv_wkv6(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst, bool dryrun = false) {
const size_t seq_length = dst->src[0]->ne[3];
const size_t n_embed = dst->ne[0];
const size_t n_heads = dst->src[0]->ne[2];
const size_t n_seqs = dst->src[5]->ne[1];
ggml_vk_op_f32_rwkv6(
ctx, subctx, dst,
{
(uint32_t)n_seqs,
(uint32_t)seq_length,
(uint32_t)n_embed,
(uint32_t)n_heads,
},
dryrun
);
}
static void ggml_vk_concat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
int * op_params = (int *)dst->op_params;
@ -6569,6 +6712,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
case GGML_OP_IM2COL:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_POOL_2D:
case GGML_OP_RWKV_WKV6:
case GGML_OP_LEAKY_RELU:
case GGML_OP_FLASH_ATTN_EXT:
break;
@ -6768,6 +6912,11 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod
case GGML_OP_FLASH_ATTN_EXT:
ggml_vk_flash_attn(ctx, compute_ctx, src0, src1, src2, src3, node, dryrun);
break;
case GGML_OP_RWKV_WKV6:
ggml_vk_rwkv_wkv6(ctx, compute_ctx, node, dryrun);
break;
default:
return false;
@ -6848,6 +6997,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_tensor *
case GGML_OP_IM2COL:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_POOL_2D:
case GGML_OP_RWKV_WKV6:
case GGML_OP_LEAKY_RELU:
case GGML_OP_REPEAT:
buf = tensor->buffer;
@ -7724,6 +7874,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
case GGML_OP_IM2COL:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_POOL_2D:
case GGML_OP_RWKV_WKV6:
case GGML_OP_LEAKY_RELU:
return true;
default:
@ -8300,7 +8451,11 @@ static void ggml_vk_check_results_0(ggml_tensor * tensor) {
} else if (tensor->op == GGML_OP_LEAKY_RELU) {
const float * op_params = (const float *)tensor->op_params;
tensor_clone = ggml_leaky_relu(ggml_ctx, src0_clone, op_params[0], false);
} else {
} else if (tensor->op == GGML_OP_RWKV_WKV6) {
tensor_clone = ggml_rwkv_wkv6(ggml_ctx, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3],
tensor->src[4], tensor->src[5]);
}
else {
std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl;
GGML_ABORT("fatal error");
}

2
ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
View File

@ -479,6 +479,8 @@ void process_shaders() {
string_to_spv("pool2d_f32", "pool2d.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
string_to_spv("rwkv_wkv6_f32", "wkv6.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
for (auto &c : compiles) {
c.wait();
}

87
ggml/src/ggml-vulkan/vulkan-shaders/wkv6.comp Normal file
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@ -0,0 +1,87 @@
#version 450
#extension GL_EXT_control_flow_attributes : require
#define BLOCK_SIZE 64
layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
layout(push_constant) uniform Parameters {
uint B;
uint T;
uint C;
uint H;
};
layout(binding = 0) readonly buffer KBuf { A_TYPE k[]; };
layout(binding = 1) readonly buffer VBuf { A_TYPE v[]; };
layout(binding = 2) readonly buffer RBuf { A_TYPE r[]; };
layout(binding = 3) readonly buffer TimeFBuf { A_TYPE tf[]; };
layout(binding = 4) readonly buffer TimeDBuf { A_TYPE td[]; };
layout(binding = 5) readonly buffer StateBuf { A_TYPE state_in[]; };
layout(binding = 6) buffer DstBuf { A_TYPE dst[]; };
shared A_TYPE _k[BLOCK_SIZE], _r[BLOCK_SIZE], _tf[BLOCK_SIZE], _td[BLOCK_SIZE];
void main() {
const uint head_size = BLOCK_SIZE;
const uint batch_id = gl_WorkGroupID.x / H;
const uint head_id = gl_WorkGroupID.x % H;
const uint tid = gl_LocalInvocationID.x;
const uint state_size = C * head_size;
const uint n_seq_tokens = T / B;
if (batch_id >= B || head_id >= H) {
return;
}
A_TYPE state[BLOCK_SIZE];
[[unroll]] for (uint i = 0; i < head_size; i++) {
state[i] = state_in[batch_id * state_size + head_id * head_size * head_size
+ i * head_size + tid];
}
barrier();
_tf[tid] = tf[head_id * head_size + tid];
barrier();
const uint start_t = batch_id * n_seq_tokens * C + head_id * head_size + tid;
const uint end_t = (batch_id + 1) * n_seq_tokens * C + head_id * head_size + tid;
for (uint t = start_t; t < end_t; t += C) {
barrier();
_k[tid] = k[t];
_r[tid] = r[t];
_td[tid] = td[t];
barrier();
const A_TYPE v_val = v[t];
A_TYPE y = 0.0;
[[unroll]] for (uint j = 0; j < head_size; j += 4) {
vec4 k_vec = vec4(_k[j], _k[j+1], _k[j+2], _k[j+3]);
vec4 r_vec = vec4(_r[j], _r[j+1], _r[j+2], _r[j+3]);
vec4 tf_vec = vec4(_tf[j], _tf[j+1], _tf[j+2], _tf[j+3]);
vec4 td_vec = vec4(_td[j], _td[j+1], _td[j+2], _td[j+3]);
vec4 s_vec = vec4(state[j], state[j+1], state[j+2], state[j+3]);
vec4 kv = k_vec * v_val;
vec4 temp = tf_vec * kv + s_vec;
y += dot(r_vec, temp);
s_vec = s_vec * td_vec + kv;
state[j] = s_vec.x;
state[j+1] = s_vec.y;
state[j+2] = s_vec.z;
state[j+3] = s_vec.w;
}
dst[t] = y;
}
[[unroll]] for (uint i = 0; i < head_size; i++) {
dst[T * C + batch_id * state_size + head_id * head_size * head_size
+ i * head_size + tid] = state[i];
}
}