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sync and fix warning

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This commit is contained in:
Michael Yang 2024-10-11 16:56:50 -07:00
parent 12b9cac2ee
commit a795cbc934
8 changed files with 247 additions and 10 deletions
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4
llama/ggml-cuda.cu vendored
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@ -2269,6 +2269,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
case GGML_OP_PAD:
ggml_cuda_op_pad(ctx, dst);
break;
case GGML_OP_UNPAD:
ggml_cuda_op_unpad(ctx, dst);
break;
case GGML_OP_ARANGE:
ggml_cuda_op_arange(ctx, dst);
break;
@ -2923,6 +2926,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
case GGML_OP_GROUP_NORM:
case GGML_OP_UPSCALE:
case GGML_OP_PAD:
case GGML_OP_UNPAD:
case GGML_OP_ARANGE:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_LEAKY_RELU:

46
llama/ggml-cuda/pad.cu vendored
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@ -73,3 +73,49 @@ void ggml_cuda_op_pad(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], stream);
}
static __global__ void unpad_f32(const float * x, float * dst, const int ne0, const int ne00, const int ne01, const int ne02, const int ne03) {
// blockIdx.z: idx of ne2*ne3, aka ne02*ne03
// blockIdx.y: idx of ne1
// blockIDx.x: idx of ne0 / BLOCK_SIZE
int nidx = threadIdx.x + blockIdx.x * blockDim.x;
if (nidx >= ne0) {
return;
}
// operation
int offset_dst =
nidx +
blockIdx.y * ne0 +
blockIdx.z * ne0 * gridDim.y;
if (nidx < ne00 && blockIdx.y < ne01 && blockIdx.z < ne02*ne03) {
int offset_src =
nidx +
blockIdx.y * ne00 +
blockIdx.z * ne00 * ne01;
dst[offset_dst] = x[offset_src];
}
}
static void unpad_f32_cuda(const float * x, float * dst,
const int ne00, const int ne01, const int ne02, const int ne03,
const int ne0, const int ne1, const int ne2, const int ne3, cudaStream_t stream) {
int num_blocks = (ne0 + CUDA_unpad_BLOCK_SIZE - 1) / CUDA_unpad_BLOCK_SIZE;
dim3 gridDim(num_blocks, ne1, ne2*ne3);
unpad_f32<<<gridDim, CUDA_unpad_BLOCK_SIZE, 0, stream>>>(x, dst, ne0, ne00, ne01, ne02, ne03);
}
void ggml_cuda_op_unpad(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(src0->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors
unpad_f32_cuda(src0_d, dst_d,
src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], stream);
}

1
llama/ggml-cuda/pad.cuh vendored
View File

@ -29,3 +29,4 @@
#define CUDA_PAD_BLOCK_SIZE 256
void ggml_cuda_op_pad(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
void ggml_cuda_op_unpad(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

45
llama/ggml-metal.metal vendored
View File

@ -2055,6 +2055,51 @@ kernel void kernel_pad_f32(
}
}
kernel void kernel_unpad_f32(
device const char * src0,
device char * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant int64_t & ne03,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant uint64_t & nb03,
constant int64_t & ne0,
constant int64_t & ne1,
constant int64_t & ne2,
constant int64_t & ne3,
constant uint64_t & nb0,
constant uint64_t & nb1,
constant uint64_t & nb2,
constant uint64_t & nb3,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int64_t i3 = tgpig.z;
const int64_t i2 = tgpig.y;
const int64_t i1 = tgpig.x;
const int64_t i03 = i3;
const int64_t i02 = i2;
const int64_t i01 = i1;
device const float * src0_ptr = (device const float *) (src0 + i03*nb03 + i02*nb02 + i01*nb01);
device float * dst_ptr = (device float *) (dst + i3*nb3 + i2*nb2 + i1*nb1);
if (i1 < ne01 && i2 < ne02 && i3 < ne03) {
for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) {
if (i0 < ne00) {
dst_ptr[i0] = src0_ptr[i0];
}
}
return;
}
}
kernel void kernel_arange_f32(
device char * dst,
constant int64_t & ne0,

33
llama/ggml-metal_darwin_arm64.m vendored
View File

@ -210,6 +210,7 @@ enum ggml_metal_kernel_type {
GGML_METAL_KERNEL_TYPE_IM2COL_F32,
GGML_METAL_KERNEL_TYPE_UPSCALE_F32,
GGML_METAL_KERNEL_TYPE_PAD_F32,
GGML_METAL_KERNEL_TYPE_UNPAD_F32,
GGML_METAL_KERNEL_TYPE_ARANGE_F32,
GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32,
GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,
@ -677,6 +678,7 @@ static struct ggml_backend_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UNPAD_F32, unpad_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, timestep_embedding_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32, arange_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true);
@ -832,6 +834,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_context * ctx
return false;
case GGML_OP_UPSCALE:
case GGML_OP_PAD:
case GGML_OP_UNPAD:
case GGML_OP_ARANGE:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_ARGSORT:
@ -2695,6 +2698,36 @@ static enum ggml_status ggml_metal_graph_compute(
const int nth = MIN(1024, ne0);
[encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_UNPAD:
{
GGML_ASSERT(src0->type == GGML_TYPE_F32);
id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_UNPAD_F32].pipeline;
[encoder setComputePipelineState:pipeline];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
[encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5];
[encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6];
[encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7];
[encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8];
[encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9];
[encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10];
[encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11];
[encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12];
[encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13];
[encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14];
[encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15];
[encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16];
[encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17];
const int nth = MIN(1024, ne0);
[encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_ARANGE:

91
llama/ggml.c vendored
View File

@ -2981,7 +2981,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"CROSS_ENTROPY_LOSS_BACK",
};
static_assert(GGML_OP_COUNT == 79, "GGML_OP_COUNT != 79");
static_assert(GGML_OP_COUNT == 80, "GGML_OP_COUNT != 80");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@ -3074,7 +3074,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"cross_entropy_loss_back(x,y)",
};
static_assert(GGML_OP_COUNT == 79, "GGML_OP_COUNT != 79");
static_assert(GGML_OP_COUNT == 80, "GGML_OP_COUNT != 80");
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
@ -7302,6 +7302,32 @@ struct ggml_tensor * ggml_pad(
return result;
}
// ggml_unpad
struct ggml_tensor * ggml_unpad(
struct ggml_context * ctx,
struct ggml_tensor * a,
int p0, int p1, int p2, int p3) {
bool is_node = false;
if (a->grad) {
GGML_ABORT("fatal error"); // TODO: implement backward
is_node = true;
}
struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type,
a->ne[0] - p0,
a->ne[1] - p1,
a->ne[2] - p2,
a->ne[3] - p3);
result->op = GGML_OP_UNPAD;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = a;
return result;
}
// ggml_arange
struct ggml_tensor * ggml_arange(
@ -15776,6 +15802,58 @@ static void ggml_compute_forward_pad(
}
}
static void ggml_compute_forward_unpad_f32(
const struct ggml_compute_params *params,
struct ggml_tensor *dst) {
const struct ggml_tensor * src0 = dst->src[0];
GGML_ASSERT(src0->nb[0] == sizeof(float));
GGML_ASSERT( dst->nb[0] == sizeof(float));
const int ith = params->ith;
const int nth = params->nth;
GGML_TENSOR_UNARY_OP_LOCALS
float * dst_ptr = (float *) dst->data;
// TODO: optimize
for (int64_t i2 = 0; i2 < ne2; ++i2) {
for (int64_t i1 = ith; i1 < ne1; i1 += nth) {
for (int64_t i0 = 0; i0 < ne0; ++i0) {
for (int64_t i3 = 0; i3 < ne3; ++i3) {
const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0;
const float * src_ptr = (const float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
dst_ptr[dst_idx] = *src_ptr;
}
}
}
}
}
}
static void ggml_compute_forward_unpad(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
const struct ggml_tensor * src0 = dst->src[0];
switch (src0->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_unpad_f32(params, dst);
} break;
default:
{
GGML_ABORT("fatal error");
}
}
}
// ggml_compute_forward_arange
@ -17670,6 +17748,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_pad(params, tensor);
} break;
case GGML_OP_UNPAD:
{
ggml_compute_forward_unpad(params, tensor);
} break;
case GGML_OP_ARANGE:
{
ggml_compute_forward_arange(params, tensor);
@ -18692,6 +18774,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
{
GGML_ABORT("fatal error"); // TODO: not implemented
}
case GGML_OP_UNPAD:
{
GGML_ABORT("fatal error"); // TODO: not implemented
}
case GGML_OP_ARANGE:
{
GGML_ABORT("fatal error"); // TODO: not implemented
@ -19364,6 +19450,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
} break;
case GGML_OP_UPSCALE:
case GGML_OP_PAD:
case GGML_OP_UNPAD:
case GGML_OP_ARANGE:
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_ARGSORT:

10
llama/ggml.h vendored
View File

@ -527,6 +527,7 @@ extern "C" {
GGML_OP_POOL_2D_BACK,
GGML_OP_UPSCALE, // nearest interpolate
GGML_OP_PAD,
GGML_OP_UNPAD,
GGML_OP_ARANGE,
GGML_OP_TIMESTEP_EMBEDDING,
GGML_OP_ARGSORT,
@ -1823,6 +1824,15 @@ extern "C" {
int p2,
int p3);
// unpad each dimension: [x, ..., x, y, ..., y] -> [x, ..., x]
GGML_API struct ggml_tensor * ggml_unpad(
struct ggml_context * ctx,
struct ggml_tensor * a,
int p0,
int p1,
int p2,
int p3);
// Ref: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L151
// timesteps: [N,]
// return: [N, dim]

27
llm/patches/9999-unpad.patch
View File

@ -1,4 +1,4 @@
From d80c9e35f989b0da0edd0e5ddaf2a87cbf42b009 Mon Sep 17 00:00:00 2001
From eb77f6e32425e9f18914f3dec66faf4129c30301 Mon Sep 17 00:00:00 2001
From: Michael Yang <mxyng@pm.me>
Date: Fri, 11 Oct 2024 16:19:43 -0700
Subject: [PATCH] add unpad operator
@ -6,12 +6,12 @@ Subject: [PATCH] add unpad operator
---
ggml/include/ggml.h | 10 +++++
ggml/src/ggml-cuda.cu | 4 ++
ggml/src/ggml-cuda/pad.cu | 46 ++++++++++++++++++++
ggml/src/ggml-cuda/pad.cu | 46 +++++++++++++++++++
ggml/src/ggml-cuda/pad.cuh | 1 +
ggml/src/ggml-metal.m | 33 +++++++++++++++
ggml/src/ggml-metal.metal | 45 ++++++++++++++++++++
ggml/src/ggml.c | 87 +++++++++++++++++++++++++++++++++++++-
7 files changed, 224 insertions(+), 2 deletions(-)
ggml/src/ggml-metal.m | 33 ++++++++++++++
ggml/src/ggml-metal.metal | 45 +++++++++++++++++++
ggml/src/ggml.c | 91 +++++++++++++++++++++++++++++++++++++-
7 files changed, 228 insertions(+), 2 deletions(-)
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index 3fb68036..856937fc 100644
@ -248,7 +248,7 @@ index f323ab5f..a269fae8 100644
device char * dst,
constant int64_t & ne0,
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 6e2ebf28..b7599340 100644
index 6e2ebf28..a7e57120 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -2955,7 +2955,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
@ -372,7 +372,18 @@ index 6e2ebf28..b7599340 100644
case GGML_OP_ARANGE:
{
ggml_compute_forward_arange(params, tensor);
@@ -19338,6 +19420,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
@@ -18666,6 +18748,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
{
GGML_ABORT("fatal error"); // TODO: not implemented
}
+ case GGML_OP_UNPAD:
+ {
+ GGML_ABORT("fatal error"); // TODO: not implemented
+ }
case GGML_OP_ARANGE:
{
GGML_ABORT("fatal error"); // TODO: not implemented
@@ -19338,6 +19424,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
} break;
case GGML_OP_UPSCALE:
case GGML_OP_PAD: