vbatts/llama.cpp
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Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
This commit is contained in:
Molly Sophia 2024-12-13 17:43:08 +08:00
parent 77fe4fd982
commit 60bbd4ebf1
3 changed files with 60 additions and 114 deletions
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97
ggml/src/ggml-vulkan/ggml-vulkan.cpp
View file

@ -524,15 +524,13 @@ struct vk_op_pool2d_push_constants {
int32_t p0; int32_t p1;
};
struct vk_op_rwkv_wkv6_push_constants {
uint32_t B; // Batch size (原n_seqs)
uint32_t T; // Sequence length
uint32_t C; // Total channels
uint32_t H; // Number of heads (原HEADS)
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) {}
@ -1952,19 +1950,7 @@ 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}, // work group
{device->subgroup_size},
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();
@ -5348,28 +5334,14 @@ 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];
template<typename PC>
static void ggml_vk_op_f32_rwkv6(
ggml_backend_vk_context * ctx,
vk_context& subctx,
ggml_tensor * dst,
const PC&& pc,
bool dryrun = false) {
// Get source tensors
const ggml_tensor * k = dst->src[0]; // keys
const ggml_tensor * v = dst->src[1]; // values
const ggml_tensor * r = dst->src[2]; // reset gates
const ggml_tensor * tf = dst->src[3]; // time first
const ggml_tensor * td = dst->src[4]; // time decay
const ggml_tensor * state = dst->src[5]; // states
VK_LOG_DEBUG("ggml_vk_op_f32_rwkv6(" << k << ", " << v << ", " << r << ", "
<< tf << ", " << td << ", " << state << ", " << dst << ")");
// Verify input types
GGML_ASSERT(!ggml_is_quantized(k->type));
GGML_ASSERT(!ggml_is_quantized(v->type));
GGML_ASSERT(!ggml_is_quantized(r->type));
@ -5378,7 +5350,6 @@ static void ggml_vk_op_f32_rwkv6(
GGML_ASSERT(!ggml_is_quantized(state->type));
GGML_ASSERT(dst->buffer != nullptr);
// Get pipeline
vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, k, v, r, dst, GGML_OP_RWKV_WKV6);
GGML_ASSERT(pipeline != nullptr);
@ -5387,7 +5358,6 @@ static void ggml_vk_op_f32_rwkv6(
return;
}
// Get buffer contexts
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;
@ -5396,7 +5366,6 @@ static void ggml_vk_op_f32_rwkv6(
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;
// Get device buffers
vk_buffer d_D = dst_buf_ctx->dev_buffer;
vk_buffer d_K = k_buf_ctx->dev_buffer;
vk_buffer d_V = v_buf_ctx->dev_buffer;
@ -5405,7 +5374,6 @@ static void ggml_vk_op_f32_rwkv6(
vk_buffer d_TD = td_buf_ctx->dev_buffer;
vk_buffer d_State = state_buf_ctx->dev_buffer;
// Calculate buffer offsets
const uint64_t k_offset = vk_tensor_offset(k);
const uint64_t v_offset = vk_tensor_offset(v);
const uint64_t r_offset = vk_tensor_offset(r);
@ -5414,7 +5382,6 @@ static void ggml_vk_op_f32_rwkv6(
const uint64_t state_offset = vk_tensor_offset(state);
const uint64_t dst_offset = vk_tensor_offset(dst);
// Calculate buffer sizes
const uint64_t k_size = ggml_nbytes(k);
const uint64_t v_size = ggml_nbytes(v);
const uint64_t r_size = ggml_nbytes(r);
@ -5423,14 +5390,12 @@ static void ggml_vk_op_f32_rwkv6(
const uint64_t state_size = ggml_nbytes(state);
const uint64_t dst_size = ggml_nbytes(dst);
// Set work elements based on tensor dimensions
std::array<uint32_t, 3> elements = {
(uint32_t)(pc.B*pc.H), // B * H workgroups
1, // 每个workgroup 64个线程
(uint32_t)(pc.B * pc.H),
1,
1
};
// Synchronize buffers and dispatch compute pipeline
ggml_vk_sync_buffers(subctx);
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, {
vk_subbuffer{ d_K, k_offset, k_size },
@ -5440,35 +5405,27 @@ static void ggml_vk_op_f32_rwkv6(
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(PC), &pc, elements);
}, 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) {
// Extract dimensions from tensors
const size_t T = dst->src[0]->ne[3]; // Sequence length
const size_t C = dst->ne[0]; // Channel dimension
const size_t HEADS = dst->src[0]->ne[2]; // Number of heads
const size_t n_seqs = dst->src[5]->ne[1]; // Batch size
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];
// Call implementation with push constants
ggml_vk_op_f32_rwkv6<vk_op_rwkv_wkv6_push_constants>(
ggml_vk_op_f32_rwkv6(
ctx, subctx, dst,
{
(uint32_t)n_seqs, // B
(uint32_t)T, // T
(uint32_t)C, // C
(uint32_t)HEADS, // H
(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;
@ -8344,10 +8301,10 @@ 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 if (tensor->op == GGML_OP_RWKV_WKV6) {
} 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

@ -479,7 +479,7 @@ 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"}, {"B_TYPE", "float"}, {"C_TYPE", "float"}, {"D_TYPE", "float"}, {"E_TYPE", "float"}, {"F_TYPE", "float"}, {"S_TYPE", "float"}}));
string_to_spv("rwkv_wkv6_f32", "wkv6.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
for (auto &c : compiles) {
c.wait();

75
ggml/src/ggml-vulkan/vulkan-shaders/wkv6.comp
View file

@ -1,96 +1,85 @@
#version 450
layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
#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; // Batch size
uint T; // Sequence length
uint C; // Total number of channels
uint H; // Number of heads
uint B;
uint T;
uint C;
uint H;
};
layout(set = 0, binding = 0) readonly buffer KBuf { float k[]; };
layout(set = 0, binding = 1) readonly buffer VBuf { float v[]; };
layout(set = 0, binding = 2) readonly buffer RBuf { float r[]; };
layout(set = 0, binding = 3) readonly buffer TimeFBuf { float tf[]; };
layout(set = 0, binding = 4) readonly buffer TimeDBuf { float td[]; };
layout(set = 0, binding = 5) readonly buffer StateBuf { float state_in[]; };
layout(set = 0, binding = 6) buffer DstBuf { float dst[]; };
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 float _k[64], _r[64], _tf[64], _td[64];
shared A_TYPE _k[BLOCK_SIZE], _r[BLOCK_SIZE], _tf[BLOCK_SIZE], _td[BLOCK_SIZE];
void main() {
const uint head_size = 64;
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 (tid >= head_size || batch_id >= B || head_id >= H) {
return;
}
// Load state
float state[64]; // Use fixed size matching head_size
A_TYPE state[BLOCK_SIZE];
for (uint i = 0; i < head_size; i++) {
state[i] = state_in[batch_id * state_size + head_id * head_size * head_size
state[i] = state_in[batch_id * state_size + head_id * head_size * head_size
+ i * head_size + tid];
}
_k[tid] = 0.0;
_r[tid] = 0.0;
_td[tid] = 0.0;
barrier();
_tf[tid] = tf[head_id * head_size + tid];
barrier();
// Main loop
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 float v_val = v[t];
float y = 0.0;
const A_TYPE v_val = v[t];
A_TYPE y = 0.0;
for (uint j = 0; j < head_size; j += 4) {
// Load values in blocks of 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]);
// Compute kv products
vec4 kv = k_vec * v_val;
// Accumulate results
vec4 temp = tf_vec * kv + s_vec;
y += dot(r_vec, temp);
// Update state
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;
}
// Write back state
for (uint i = 0; i < head_size; i++) {
dst[T * C + batch_id * state_size + head_id * head_size * head_size
dst[T * C + batch_id * state_size + head_id * head_size * head_size
+ i * head_size + tid] = state[i];
}
}
}