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Commit 7001bc3e authored by TiagoMAntunes's avatar TiagoMAntunes
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Fixed indentation

parent 01d888bf
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Showing with 242 additions and 242 deletions
cuda/moe.cpp
View file @ 7001bc3e
......@@ -87,8 +87,8 @@ std::vector<torch::Tensor> moe_global_scatter(
size_t batch_size, size_t n_workers) {
CHECK_INPUT(input_buf);
return moe_cuda_global_scatter(input_buf,
local_expert_count, global_expert_count,
batch_size, n_workers);
local_expert_count, global_expert_count,
batch_size, n_workers);
}
std::vector<torch::Tensor> moe_global_gather(
......@@ -98,8 +98,8 @@ std::vector<torch::Tensor> moe_global_gather(
size_t batch_size, size_t n_workers) {
CHECK_INPUT(output_buf);
return moe_cuda_global_gather(output_buf,
local_expert_count, global_expert_count,
batch_size, n_workers);
local_expert_count, global_expert_count,
batch_size, n_workers);
}
......
cuda/moe_compute_kernel.cu
View file @ 7001bc3e
......@@ -19,32 +19,32 @@
template <typename scalar_t>
__global__
void generate_ptr_offset_kernel(size_t n, const scalar_t* base, size_t stride,
const long* offset, const scalar_t** ptrs) {
size_t idx = threadIdx.x + blockDim.x * blockIdx.x;
if (idx < n) {
ptrs[idx] = base + stride * offset[idx];
}
const long* offset, const scalar_t** ptrs) {
size_t idx = threadIdx.x + blockDim.x * blockIdx.x;
if (idx < n) {
ptrs[idx] = base + stride * offset[idx];
}
}
template <typename scalar_t>
__global__
void batch_scatter_kernel(size_t wid, const long* pos,
const scalar_t* inbuf, scalar_t* oubuf) {
inbuf += wid * pos[blockIdx.x];
oubuf += wid * blockIdx.x;
for (int i = threadIdx.x; i < wid; i += blockDim.x) {
oubuf[i] = inbuf[i];
}
const scalar_t* inbuf, scalar_t* oubuf) {
inbuf += wid * pos[blockIdx.x];
oubuf += wid * blockIdx.x;
for (int i = threadIdx.x; i < wid; i += blockDim.x) {
oubuf[i] = inbuf[i];
}
}
/*
This function is to be called with one block per each column
This function is to be called with one block per each column
*/
template <typename scalar_t>
__global__
void column_reduce(const scalar_t * matrix, scalar_t * result,
int m /* lines */, int n /* columns*/) {
int m /* lines */, int n /* columns*/) {
extern __shared__ float sdata[];
unsigned int tid = threadIdx.x; // line
unsigned int i = blockIdx.x + threadIdx.x * n; // get to idx th line
......@@ -73,296 +73,296 @@ void column_reduce(const scalar_t * matrix, scalar_t * result,
void moe_cuda_expert_count_impl(
const int* d_gate,
int* expert_count,
int* d_pos,
const size_t num_expert,
int* expert_count,
int* d_pos,
const size_t num_expert,
const size_t batch_size) {
int *gate = new int[batch_size];
int *expert_ptr = new int[num_expert];
memset(expert_count, 0, sizeof(int) * num_expert);
checkCudaErrors(cudaMemcpy(gate, d_gate, sizeof(int) * batch_size,
cudaMemcpyDeviceToHost));
for (int i = 0; i < batch_size; ++i) {
++expert_count[gate[i]];
}
expert_ptr[0] = 0;
for (int i = 1; i < num_expert; ++i) {
expert_ptr[i] = expert_ptr[i - 1] + expert_count[i - 1];
}
int *pos = new int[batch_size];
for (int i = 0; i < batch_size; ++i) {
pos[i] = expert_ptr[gate[i]]++;
}
for (int i = num_expert - 1; i > 0; --i) {
expert_ptr[i] = expert_ptr[i - 1];
}
expert_ptr[0] = 0;
checkCudaErrors(cudaMemcpy(d_pos, pos, sizeof(int) * batch_size,
cudaMemcpyHostToDevice));
delete [] gate;
delete [] expert_ptr;
int *expert_ptr = new int[num_expert];
memset(expert_count, 0, sizeof(int) * num_expert);
checkCudaErrors(cudaMemcpy(gate, d_gate, sizeof(int) * batch_size,
cudaMemcpyDeviceToHost));
for (int i = 0; i < batch_size; ++i) {
++expert_count[gate[i]];
}
expert_ptr[0] = 0;
for (int i = 1; i < num_expert; ++i) {
expert_ptr[i] = expert_ptr[i - 1] + expert_count[i - 1];
}
int *pos = new int[batch_size];
for (int i = 0; i < batch_size; ++i) {
pos[i] = expert_ptr[gate[i]]++;
}
for (int i = num_expert - 1; i > 0; --i) {
expert_ptr[i] = expert_ptr[i - 1];
}
expert_ptr[0] = 0;
checkCudaErrors(cudaMemcpy(d_pos, pos, sizeof(int) * batch_size,
cudaMemcpyHostToDevice));
delete [] gate;
delete [] expert_ptr;
}
template <typename scalar_t>
void moe_cuda_local_scatter_impl(
const scalar_t* input,
const long* d_pos,
scalar_t* input_buf,
const long batch_size,
const long in_feat,
CudaStreamManager* smgr) {
batch_scatter_kernel<scalar_t>
<<<batch_size, 256, 0, smgr->stream(0)>>>(in_feat, d_pos, input,
input_buf);
smgr->sync(1);
const long* d_pos,
scalar_t* input_buf,
const long batch_size,
const long in_feat,
CudaStreamManager* smgr) {
batch_scatter_kernel<scalar_t>
<<<batch_size, 256, 0, smgr->stream(0)>>>(in_feat, d_pos, input,
input_buf);
smgr->sync(1);
}
template <typename scalar_t>
__global__
void batch_gather_kernel(size_t wid, const long* pos,
const scalar_t* inbuf, scalar_t* oubuf) {
inbuf += wid * blockIdx.x;
oubuf += wid * pos[blockIdx.x];
for (int i = threadIdx.x; i < wid; i += blockDim.x) {
oubuf[i] = inbuf[i];
}
const scalar_t* inbuf, scalar_t* oubuf) {
inbuf += wid * blockIdx.x;
oubuf += wid * pos[blockIdx.x];
for (int i = threadIdx.x; i < wid; i += blockDim.x) {
oubuf[i] = inbuf[i];
}
}
template <typename scalar_t>
void moe_cuda_local_gather_impl(
const scalar_t* output_buf,
const long* d_pos,
scalar_t* output,
const size_t batch_size,
const size_t out_feat,
CudaStreamManager* smgr) {
batch_gather_kernel<scalar_t>
<<<batch_size, 256, 0, smgr->stream(0)>>>(out_feat, d_pos, output_buf,
output);
smgr->sync(1);
const long* d_pos,
scalar_t* output,
const size_t batch_size,
const size_t out_feat,
CudaStreamManager* smgr) {
batch_gather_kernel<scalar_t>
<<<batch_size, 256, 0, smgr->stream(0)>>>(out_feat, d_pos, output_buf,
output);
smgr->sync(1);
}
template <typename scalar_t>
void moe_cuda_forward_impl(
const scalar_t* input_buf,
const scalar_t* weight,
const long* expert_count,
const long* expert_count,
scalar_t* output_buf,
const bool has_bias,
const bool has_bias,
const size_t in_feat,
const size_t out_feat,
const size_t num_expert,
CudaStreamManager* smgr) {
scalar_t alpha = 1, beta = has_bias ? 1 : 0;
for (int i = 0, ptr = 0; i < num_expert; ++i) {
if (expert_count[i] == 0) {
continue;
}
// Use T(B) x T(A) = T(C) to produce row-major C
checkCudaErrors(cublasXgemm(
smgr->handle(i),
CUBLAS_OP_T,
CUBLAS_OP_N,
out_feat, expert_count[i], in_feat,
&alpha,
weight + i * in_feat * out_feat, in_feat,
input_buf + ptr * in_feat, in_feat,
&beta,
output_buf + out_feat * ptr, out_feat
));
ptr += expert_count[i];
}
smgr->sync(num_expert);
CudaStreamManager* smgr) {
scalar_t alpha = 1, beta = has_bias ? 1 : 0;
for (int i = 0, ptr = 0; i < num_expert; ++i) {
if (expert_count[i] == 0) {
continue;
}
// Use T(B) x T(A) = T(C) to produce row-major C
checkCudaErrors(cublasXgemm(
smgr->handle(i),
CUBLAS_OP_T,
CUBLAS_OP_N,
out_feat, expert_count[i], in_feat,
&alpha,
weight + i * in_feat * out_feat, in_feat,
input_buf + ptr * in_feat, in_feat,
&beta,
output_buf + out_feat * ptr, out_feat
));
ptr += expert_count[i];
}
smgr->sync(num_expert);
}
template <typename scalar_t>
void moe_cuda_backward_impl(
const scalar_t* grad_output_buf,
const scalar_t* input_buf,
const scalar_t* weight,
const long* expert_count,
const scalar_t* weight,
const long* expert_count,
scalar_t* grad_input_buf,
scalar_t* grad_weight,
scalar_t* grad_bias,
const bool has_bias,
scalar_t* grad_bias,
const bool has_bias,
const size_t batch_size,
const size_t in_feat,
const size_t out_feat,
const size_t num_expert,
CudaStreamManager* smgr) {
CudaStreamManager* smgr) {
scalar_t alpha = 1, beta = 0;
for (int i = 0, ptr = 0; i < num_expert; ++i) {
if (expert_count[i] == 0) {
cudaMemset(grad_weight + i * in_feat * out_feat, 0,
sizeof(scalar_t) * in_feat * out_feat);
cudaMemset(grad_bias + i * out_feat, 0, sizeof(scalar_t) * out_feat);
continue;
}
// Use T(B) x T(A) = T(C) to produce row-major C
// Backward input: g_i = w @ g_o
checkCudaErrors(cublasXgemm(
smgr->handle(i),
CUBLAS_OP_N,
CUBLAS_OP_N,
in_feat, expert_count[i], out_feat,
&alpha,
weight + i * in_feat * out_feat, in_feat,
grad_output_buf + ptr * out_feat, out_feat,
&beta,
grad_input_buf + in_feat * ptr, in_feat
));
// Backward weight: g_w = i @ g_o
checkCudaErrors(cublasXgemm(
smgr->handle(i),
CUBLAS_OP_N,
CUBLAS_OP_T,
in_feat, out_feat, expert_count[i],
&alpha,
input_buf + in_feat * ptr, in_feat,
grad_output_buf + ptr * out_feat, out_feat,
&beta,
grad_weight + i * in_feat * out_feat, in_feat
));
if (has_bias) {
column_reduce
<<<out_feat, 1024, sizeof(scalar_t)*1024, smgr->stream(0)>>>
(
grad_output_buf + ptr * out_feat,
grad_bias + i * out_feat,
expert_count[i],
out_feat
);
}
ptr += expert_count[i];
}
smgr->sync(num_expert);
for (int i = 0, ptr = 0; i < num_expert; ++i) {
if (expert_count[i] == 0) {
cudaMemset(grad_weight + i * in_feat * out_feat, 0,
sizeof(scalar_t) * in_feat * out_feat);
cudaMemset(grad_bias + i * out_feat, 0, sizeof(scalar_t) * out_feat);
continue;
}
// Use T(B) x T(A) = T(C) to produce row-major C
// Backward input: g_i = w @ g_o
checkCudaErrors(cublasXgemm(
smgr->handle(i),
CUBLAS_OP_N,
CUBLAS_OP_N,
in_feat, expert_count[i], out_feat,
&alpha,
weight + i * in_feat * out_feat, in_feat,
grad_output_buf + ptr * out_feat, out_feat,
&beta,
grad_input_buf + in_feat * ptr, in_feat
));
// Backward weight: g_w = i @ g_o
checkCudaErrors(cublasXgemm(
smgr->handle(i),
CUBLAS_OP_N,
CUBLAS_OP_T,
in_feat, out_feat, expert_count[i],
&alpha,
input_buf + in_feat * ptr, in_feat,
grad_output_buf + ptr * out_feat, out_feat,
&beta,
grad_weight + i * in_feat * out_feat, in_feat
));
if (has_bias) {
column_reduce
<<<out_feat, 1024, sizeof(scalar_t)*1024, smgr->stream(0)>>>
(
grad_output_buf + ptr * out_feat,
grad_bias + i * out_feat,
expert_count[i],
out_feat
);
}
ptr += expert_count[i];
}
smgr->sync(num_expert);
}
std::vector<torch::Tensor> moe_cuda_expert_count(
torch::Tensor gate,
size_t num_expert) {
const auto batch_size = gate.size(0);
auto ec_options = torch::TensorOptions().dtype(torch::kInt32);
auto expert_count = torch::empty(num_expert, ec_options);
auto pos_options = torch::TensorOptions()
.device(gate.device())
.dtype(torch::kInt32);
auto pos = torch::empty(batch_size, pos_options);
moe_cuda_expert_count_impl(
gate.data_ptr<int>(),
expert_count.data_ptr<int>(),
pos.data_ptr<int>(),
num_expert,
batch_size);
return {expert_count, pos};
torch::Tensor gate,
size_t num_expert) {
const auto batch_size = gate.size(0);
auto ec_options = torch::TensorOptions().dtype(torch::kInt32);
auto expert_count = torch::empty(num_expert, ec_options);
auto pos_options = torch::TensorOptions()
.device(gate.device())
.dtype(torch::kInt32);
auto pos = torch::empty(batch_size, pos_options);
moe_cuda_expert_count_impl(
gate.data_ptr<int>(),
expert_count.data_ptr<int>(),
pos.data_ptr<int>(),
num_expert,
batch_size);
return {expert_count, pos};
}
std::vector<torch::Tensor> moe_cuda_local_scatter(
torch::Tensor input,
torch::Tensor pos) {
auto smgr = getCudaStreamManager(input.device().index());
const auto batch_size = pos.size(0);
torch::Tensor pos) {
auto smgr = getCudaStreamManager(input.device().index());
const auto batch_size = pos.size(0);
const auto in_feat = input.size(1);
auto opt = torch::TensorOptions()
.dtype(input.dtype())
.device(input.device());
auto input_buf = torch::empty({batch_size, in_feat}, opt);
auto opt = torch::TensorOptions()
.dtype(input.dtype())
.device(input.device());
auto input_buf = torch::empty({batch_size, in_feat}, opt);
AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.scalar_type(), "moe_local_scatter_cuda",
([&] {
moe_cuda_local_scatter_impl<scalar_t>(
input.data_ptr<scalar_t>(),
pos.data_ptr<long>(),
input_buf.data_ptr<scalar_t>(),
batch_size,
in_feat,
smgr);
}));
return {input_buf,};
([&] {
moe_cuda_local_scatter_impl<scalar_t>(
input.data_ptr<scalar_t>(),
pos.data_ptr<long>(),
input_buf.data_ptr<scalar_t>(),
batch_size,
in_feat,
smgr);
}));
return {input_buf,};
}
std::vector<torch::Tensor> moe_cuda_local_gather(
torch::Tensor output_buf,
torch::Tensor pos) {
auto smgr = getCudaStreamManager(output_buf.device().index());
const auto batch_size = pos.size(0);
torch::Tensor output_buf,
torch::Tensor pos) {
auto smgr = getCudaStreamManager(output_buf.device().index());
const auto batch_size = pos.size(0);
const auto out_feat = output_buf.size(1);
auto opt = torch::TensorOptions()
.dtype(output_buf.dtype())
.device(output_buf.device());
auto output = torch::empty({batch_size, out_feat}, opt);
auto opt = torch::TensorOptions()
.dtype(output_buf.dtype())
.device(output_buf.device());
auto output = torch::empty({batch_size, out_feat}, opt);
AT_DISPATCH_FLOATING_TYPES_AND_HALF(output_buf.scalar_type(), "moe_local_gather_cuda",
([&] {
moe_cuda_local_gather_impl<scalar_t>(
output_buf.data_ptr<scalar_t>(),
pos.data_ptr<long>(),
output.data_ptr<scalar_t>(),
batch_size,
out_feat,
smgr);
}));
return {output,};
([&] {
moe_cuda_local_gather_impl<scalar_t>(
output_buf.data_ptr<scalar_t>(),
pos.data_ptr<long>(),
output.data_ptr<scalar_t>(),
batch_size,
out_feat,
smgr);
}));
return {output,};
}
std::vector<torch::Tensor> moe_cuda_forward(
torch::Tensor input_buf,
torch::Tensor expert_count,
torch::Tensor expert_count,
torch::Tensor weight,
at::optional<torch::Tensor> bias
) {
auto smgr = getCudaStreamManager(input_buf.device().index());
const auto batch_size = input_buf.size(0);
at::optional<torch::Tensor> bias
) {
auto smgr = getCudaStreamManager(input_buf.device().index());
const auto batch_size = input_buf.size(0);
const auto num_expert = weight.size(0);
const auto out_feat = weight.size(1);
const auto in_feat = weight.size(2);
#ifdef MOE_DEBUG
printf("[forward] expert=%ld, in_feat (d_model)=%ld, out_feat (d_ffn)=%ld\n",
num_expert, in_feat, out_feat);
num_expert, in_feat, out_feat);
#endif
torch::Tensor output;
if (bias.has_value()) {
output = bias.value().repeat_interleave(expert_count.to(bias.value().device()), 0);
} else{
auto out_options = torch::TensorOptions()
.device(input_buf.device())
.dtype(input_buf.dtype());
output = torch::empty({batch_size, out_feat}, out_options);
}
if (bias.has_value()) {
output = bias.value().repeat_interleave(expert_count.to(bias.value().device()), 0);
} else{
auto out_options = torch::TensorOptions()
.device(input_buf.device())
.dtype(input_buf.dtype());
output = torch::empty({batch_size, out_feat}, out_options);
}
AT_DISPATCH_FLOATING_TYPES_AND_HALF(input_buf.scalar_type(), "moe_forward_cuda",
([&] {
moe_cuda_forward_impl<scalar_t>(
input_buf.data_ptr<scalar_t>(),
weight.data_ptr<scalar_t>(),
expert_count.data_ptr<long>(),
output.data_ptr<scalar_t>(),
bias.has_value(),
in_feat,
out_feat,
num_expert,
smgr
);
([&] {
moe_cuda_forward_impl<scalar_t>(
input_buf.data_ptr<scalar_t>(),
weight.data_ptr<scalar_t>(),
expert_count.data_ptr<long>(),
output.data_ptr<scalar_t>(),
bias.has_value(),
in_feat,
out_feat,
num_expert,
smgr
);
}));
return {output, };
......@@ -371,11 +371,11 @@ std::vector<torch::Tensor> moe_cuda_forward(
std::vector<torch::Tensor> moe_cuda_backward(
torch::Tensor grad_output_buf, // [batch_size x out_feat]
torch::Tensor input_buf, // [batch_size x out_feat]
torch::Tensor expert_count,
torch::Tensor expert_count,
torch::Tensor weight, // [num_expert x out_feat x in_feat]
at::optional<torch::Tensor> bias
at::optional<torch::Tensor> bias
) {
auto smgr = getCudaStreamManager(input_buf.device().index());
auto smgr = getCudaStreamManager(input_buf.device().index());
const auto batch_size = input_buf.size(0);
const auto num_expert = weight.size(0);
const auto out_feat = weight.size(1);
......@@ -383,31 +383,31 @@ std::vector<torch::Tensor> moe_cuda_backward(
#ifdef MOE_DEBUG
printf("[backward] b=%ld, expert=%ld, in_feat (d_model)=%ld, "
"out_feat (d_ffn)=%ld\n",
batch_size, num_expert, in_feat, out_feat);
"out_feat (d_ffn)=%ld\n",
batch_size, num_expert, in_feat, out_feat);
#endif
auto grad_input_buf = grad_output_buf.new_empty({batch_size, in_feat});
auto grad_weight = grad_output_buf.new_empty({num_expert, out_feat, in_feat});
auto grad_bias = grad_output_buf.new_empty({num_expert, out_feat});
auto grad_bias = grad_output_buf.new_empty({num_expert, out_feat});
AT_DISPATCH_FLOATING_TYPES_AND_HALF(input_buf.scalar_type(), "moe_cuda_backward", ([&] {
moe_cuda_backward_impl<scalar_t>(
grad_output_buf.data_ptr<scalar_t>(),
input_buf.data_ptr<scalar_t>(),
weight.data_ptr<scalar_t>(),
expert_count.data_ptr<long>(),
expert_count.data_ptr<long>(),
grad_input_buf.data_ptr<scalar_t>(),
grad_weight.data_ptr<scalar_t>(),
grad_bias.data_ptr<scalar_t>(),
bias.has_value(),
grad_bias.data_ptr<scalar_t>(),
bias.has_value(),
batch_size,
in_feat,
out_feat,
num_expert,
smgr
smgr
);
}));
return {grad_input_buf, grad_weight, grad_bias};
return {grad_input_buf, grad_weight, grad_bias};
}
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