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1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 | #include <ATen/Functions.h>
#include <ATen/Tensor.h>
#include <ATen/mps/MPSStream.h>
#include <internal/metal-kernels.h>
#include <internal/metal.h>
#include <algorithm>
#include <cstddef>
#include <cstdint>
#include <cstring>
#include <utility>
#include <vector>
namespace {
class MetalBuffer {
public:
MetalBuffer() = default;
MetalBuffer(const MetalBuffer&) = delete;
MetalBuffer& operator=(const MetalBuffer&) = delete;
MetalBuffer(MetalBuffer&& other) noexcept
: buffer_(other.buffer_), has_value_(other.has_value_) {
other.buffer_ = {};
other.has_value_ = false;
}
MetalBuffer& operator=(MetalBuffer&& other) noexcept {
if (this != &other) {
reset();
buffer_ = other.buffer_;
has_value_ = other.has_value_;
other.buffer_ = {};
other.has_value_ = false;
}
return *this;
}
~MetalBuffer() {
reset();
}
gptoss_metal_buffer* get() {
return &buffer_;
}
const gptoss_metal_buffer* get() const {
return &buffer_;
}
void* ptr() const {
return buffer_.ptr;
}
size_t size_bytes() const {
return buffer_.size;
}
bool valid() const {
return has_value_;
}
void wrap(const gptoss_metal_device* device, size_t size, const void* data) {
reset();
TORCH_CHECK(gptoss_metal_buffer_wrap(device, size, data, &buffer_) == gptoss_status_success,
"metal_buffer_wrap failed");
has_value_ = true;
}
void wrap_mps_tensor(const gptoss_metal_device* device, const at::Tensor& tensor) {
reset();
TORCH_CHECK(tensor.is_contiguous(), "tensor must be contiguous for MPS wrap");
const size_t size_bytes = static_cast<size_t>(tensor.numel()) * tensor.element_size();
const size_t byte_offset = static_cast<size_t>(tensor.storage_offset()) * tensor.element_size();
const void* buffer_handle = tensor.storage().data();
TORCH_CHECK(
gptoss_metal_buffer_wrap_mtlbuffer(device, buffer_handle, size_bytes, byte_offset, &buffer_)
== gptoss_status_success,
"metal_buffer_wrap_mtlbuffer failed");
has_value_ = true;
}
void create(const gptoss_metal_device* device, size_t size, const void* data = nullptr) {
reset();
TORCH_CHECK(gptoss_metal_buffer_create(device, size, data, &buffer_) == gptoss_status_success,
"metal_buffer_create failed");
has_value_ = true;
}
void reset() {
if (has_value_) {
(void) gptoss_metal_buffer_release(&buffer_);
buffer_ = {};
has_value_ = false;
}
}
private:
gptoss_metal_buffer buffer_{};
bool has_value_ = false;
};
template <typename EncodeFn>
void run_metal_kernel(const char* kernel_symbol, EncodeFn&& encode_fn) {
gptoss_metal_device device{};
gptoss_metal_library library{};
gptoss_metal_function fn{};
gptoss_metal_command_queue cq{};
gptoss_metal_command_buffer cb{};
bool borrowed_queue = false;
bool borrowed_buffer = false;
at::mps::MPSStream* stream = nullptr;
auto cleanup = [&]() {
if (!borrowed_buffer) {
(void) gptoss_metal_command_buffer_release(&cb);
} else {
cb = {};
}
if (!borrowed_queue) {
(void) gptoss_metal_command_queue_release(&cq);
} else {
cq = {};
}
(void) gptoss_metal_function_release(&fn);
(void) gptoss_metal_library_release(&library);
(void) gptoss_metal_device_release(&device);
};
TORCH_CHECK(gptoss_metal_device_create_system_default(&device) == gptoss_status_success,
"device_create failed");
try {
TORCH_CHECK(gptoss_metal_library_create_default(&device, &library) == gptoss_status_success,
"library_create failed");
TORCH_CHECK(gptoss_metal_function_create(&library, kernel_symbol, &fn) == gptoss_status_success,
"function_create failed");
stream = at::mps::getCurrentMPSStream();
if (stream != nullptr) {
// stream->synchronize(at::mps::SyncType::COMMIT_AND_WAIT);
stream->endKernelCoalescing();
void* command_queue_obj = stream->commandQueue();
if (command_queue_obj != nullptr) {
TORCH_CHECK(
gptoss_metal_command_queue_wrap_existing(command_queue_obj, &cq) == gptoss_status_success,
"wrap_command_queue failed");
borrowed_queue = true;
void* command_buffer_obj = stream->commandBuffer();
if (command_buffer_obj != nullptr) {
TORCH_CHECK(
gptoss_metal_command_buffer_wrap_existing(command_buffer_obj, &cb) == gptoss_status_success,
"wrap_command_buffer failed");
borrowed_buffer = true;
}
}
}
if (!borrowed_queue) {
TORCH_CHECK(gptoss_metal_command_queue_create(&device, &cq) == gptoss_status_success,
"cq_create failed");
}
if (!borrowed_buffer) {
TORCH_CHECK(gptoss_metal_command_buffer_create(&cq, &cb) == gptoss_status_success,
"cb_create failed");
}
encode_fn(device, fn, cb);
if (borrowed_buffer) {
TORCH_CHECK(stream != nullptr, "stream must be valid when using borrowed command buffer");
stream->synchronize(at::mps::SyncType::COMMIT);
} else {
TORCH_CHECK(gptoss_metal_command_buffer_commit(&cb) == gptoss_status_success,
"commit failed");
TORCH_CHECK(gptoss_metal_command_buffer_wait_completion(&cb, nullptr) == gptoss_status_success,
"wait failed");
}
} catch (...) {
cleanup();
throw;
}
cleanup();
}
at::Tensor to_cpu_contiguous(const at::Tensor& tensor) {
if (tensor.device().is_cpu() && tensor.is_contiguous()) {
return tensor;
}
return tensor.contiguous().to(at::kCPU);
}
at::Tensor empty_cpu_like(const at::Tensor& tensor) {
return at::empty_like(tensor, tensor.options().device(at::kCPU)).contiguous();
}
void copy_back(at::Tensor& dst, const at::Tensor& src_cpu) {
dst.copy_(src_cpu.to(dst.device(), /*non_blocking=*/false, /*copy=*/true));
}
void create_control_buffer(const gptoss_metal_device* device, MetalBuffer& buffer) {
struct gptoss_control ctrl {0};
buffer.create(device, sizeof(ctrl), &ctrl);
}
template <typename LaunchFn>
void run_dense_matmul_bf16(const char* kernel_symbol,
LaunchFn&& launch_fn,
const at::Tensor& input,
const at::Tensor& weight_bf16,
const at::Tensor& bias_bf16,
at::Tensor& output)
{
TORCH_CHECK(input.dtype() == at::kFloat, "input must be float32");
TORCH_CHECK(weight_bf16.dtype() == at::kBFloat16, "weight must be bfloat16");
TORCH_CHECK(bias_bf16.dtype() == at::kBFloat16, "bias must be bfloat16");
TORCH_CHECK(output.dtype() == at::kFloat, "output must be float32");
TORCH_CHECK(input.dim() == 2, "input must be 2D");
TORCH_CHECK(weight_bf16.dim() == 2, "weight must be 2D");
TORCH_CHECK(bias_bf16.dim() == 1, "bias must be 1D");
TORCH_CHECK(output.dim() == 2, "output must be 2D");
const int64_t num_tokens = input.size(0);
const int64_t num_cols = input.size(1);
const int64_t num_rows = output.size(1);
TORCH_CHECK(output.size(0) == num_tokens,
"output first dimension must match number of tokens");
TORCH_CHECK(weight_bf16.size(0) == num_cols && weight_bf16.size(1) == num_rows,
"weight shape must be [num_cols, num_rows]");
TORCH_CHECK(bias_bf16.size(0) == num_rows,
"bias length must equal number of rows");
const bool use_mps = input.device().is_mps() && weight_bf16.device().is_mps() &&
bias_bf16.device().is_mps() && output.device().is_mps();
at::Tensor input_cpu;
at::Tensor weight_cpu;
at::Tensor bias_cpu;
at::Tensor out_cpu;
size_t in_bytes = 0;
size_t weight_bytes = 0;
size_t bias_bytes = 0;
size_t out_bytes = 0;
at::Tensor input_mps;
at::Tensor weight_mps;
at::Tensor bias_mps;
at::Tensor output_mps;
if (use_mps) {
input_mps = input.contiguous();
weight_mps = weight_bf16.transpose(0, 1).contiguous();
bias_mps = bias_bf16.contiguous();
output_mps = output.contiguous();
} else {
input_cpu = to_cpu_contiguous(input);
weight_cpu = weight_bf16.transpose(0, 1).contiguous().to(at::kCPU);
bias_cpu = to_cpu_contiguous(bias_bf16);
out_cpu = empty_cpu_like(output);
in_bytes = static_cast<size_t>(input_cpu.numel()) * input_cpu.element_size();
weight_bytes = static_cast<size_t>(weight_cpu.numel()) * weight_cpu.element_size();
bias_bytes = static_cast<size_t>(bias_cpu.numel()) * bias_cpu.element_size();
out_bytes = static_cast<size_t>(out_cpu.numel()) * out_cpu.element_size();
}
MetalBuffer input_buf;
MetalBuffer weight_buf;
MetalBuffer bias_buf;
MetalBuffer out_buf;
MetalBuffer control_buf;
run_metal_kernel(kernel_symbol, [&](const gptoss_metal_device& device,
const gptoss_metal_function& fn,
gptoss_metal_command_buffer& cb) {
if (use_mps) {
input_buf.wrap_mps_tensor(&device, input_mps);
weight_buf.wrap_mps_tensor(&device, weight_mps);
bias_buf.wrap_mps_tensor(&device, bias_mps);
out_buf.wrap_mps_tensor(&device, output_mps);
} else {
input_buf.wrap(&device, in_bytes, input_cpu.data_ptr());
weight_buf.wrap(&device, weight_bytes, weight_cpu.data_ptr());
bias_buf.wrap(&device, bias_bytes, bias_cpu.data_ptr());
out_buf.create(&device, out_bytes, nullptr);
}
create_control_buffer(&device, control_buf);
TORCH_CHECK(
launch_fn(
&cb, &fn,
input_buf.get(), 0,
weight_buf.get(), 0,
bias_buf.get(), 0,
out_buf.get(), 0,
control_buf.get(), 0,
static_cast<uint32_t>(num_tokens),
static_cast<uint32_t>(num_cols),
static_cast<uint32_t>(num_rows)) == gptoss_status_success,
"encode dense matmul failed");
});
if (use_mps) {
if (!output_mps.is_alias_of(output)) {
output.copy_(output_mps);
}
} else {
std::memcpy(out_cpu.data_ptr(), out_buf.ptr(), out_bytes);
copy_back(output, out_cpu);
}
}
} // namespace
void f32_bf16w_matmul_torch(const at::Tensor &input,
const at::Tensor &weight_bf16,
const at::Tensor &bias_bf16,
at::Tensor &output,
int64_t num_tokens, int64_t num_cols, int64_t num_rows, int64_t threadgroup_size)
{
TORCH_CHECK(input.dtype() == at::kFloat, "input must be float32");
TORCH_CHECK(weight_bf16.dtype() == at::kBFloat16, "weight must be bfloat16");
TORCH_CHECK(bias_bf16.dtype() == at::kBFloat16, "bias must be bfloat16");
TORCH_CHECK(output.dtype() == at::kFloat, "output must be float32");
TORCH_CHECK(input.dim() == 2, "input must be 2D");
TORCH_CHECK(weight_bf16.dim() == 2, "weight must be 2D");
TORCH_CHECK(bias_bf16.dim() == 1, "bias must be 1D");
TORCH_CHECK(output.dim() == 2, "output must be 2D");
TORCH_CHECK(input.size(0) == num_tokens && input.size(1) == num_cols,
"input shape must be [num_tokens, num_cols]");
TORCH_CHECK(weight_bf16.size(0) == num_cols && weight_bf16.size(1) == num_rows,
"weight shape must be [num_cols, num_rows]");
TORCH_CHECK(bias_bf16.size(0) == num_rows, "bias length must be num_rows");
TORCH_CHECK(output.size(0) == num_tokens && output.size(1) == num_rows,
"output shape must be [num_tokens, num_rows]");
const bool use_mps = input.device().is_mps() && weight_bf16.device().is_mps() &&
bias_bf16.device().is_mps() && output.device().is_mps();
at::Tensor input_cpu;
at::Tensor weight_cpu;
at::Tensor bias_cpu;
at::Tensor out_cpu;
size_t in_bytes = 0;
size_t wt_bytes = 0;
size_t bs_bytes = 0;
size_t out_bytes = 0;
at::Tensor input_mps;
at::Tensor weight_mps;
at::Tensor bias_mps;
at::Tensor output_mps;
if (use_mps) {
input_mps = input.contiguous();
weight_mps = weight_bf16.transpose(0, 1).contiguous();
bias_mps = bias_bf16.contiguous();
output_mps = output.contiguous();
} else {
input_cpu = to_cpu_contiguous(input);
weight_cpu = weight_bf16.transpose(0, 1).contiguous().to(at::kCPU);
bias_cpu = to_cpu_contiguous(bias_bf16);
out_cpu = empty_cpu_like(output);
in_bytes = static_cast<size_t>(num_tokens) * static_cast<size_t>(num_cols) * sizeof(float);
wt_bytes = static_cast<size_t>(num_rows) * static_cast<size_t>(num_cols) * sizeof(uint16_t);
bs_bytes = static_cast<size_t>(num_rows) * sizeof(uint16_t);
out_bytes = static_cast<size_t>(num_tokens) * static_cast<size_t>(num_rows) * sizeof(float);
}
MetalBuffer in_buf;
MetalBuffer wt_buf;
MetalBuffer bs_buf;
MetalBuffer out_buf;
MetalBuffer ctrl_buf;
run_metal_kernel("gptoss_f32_bf16w_matmul", [&](const gptoss_metal_device& device,
const gptoss_metal_function& fn,
gptoss_metal_command_buffer& cb) {
if (use_mps) {
in_buf.wrap_mps_tensor(&device, input_mps);
wt_buf.wrap_mps_tensor(&device, weight_mps);
bs_buf.wrap_mps_tensor(&device, bias_mps);
out_buf.wrap_mps_tensor(&device, output_mps);
} else {
in_buf.wrap(&device, in_bytes, input_cpu.data_ptr());
wt_buf.wrap(&device, wt_bytes, weight_cpu.data_ptr());
bs_buf.wrap(&device, bs_bytes, bias_cpu.data_ptr());
out_buf.create(&device, out_bytes, nullptr);
}
create_control_buffer(&device, ctrl_buf);
TORCH_CHECK(
gptoss_metal_command_buffer_encode_launch_f32_bf16w_matmul(
&cb, &fn, static_cast<size_t>(threadgroup_size),
in_buf.get(), 0,
wt_buf.get(), 0,
bs_buf.get(), 0,
out_buf.get(), 0,
ctrl_buf.get(), 0,
static_cast<uint32_t>(num_tokens),
static_cast<uint32_t>(num_cols),
static_cast<uint32_t>(num_rows)) == gptoss_status_success,
"encode failed");
});
if (use_mps) {
if (!output_mps.is_alias_of(output)) {
output.copy_(output_mps);
}
} else {
std::memcpy(out_cpu.data_ptr(), out_buf.ptr(), out_bytes);
copy_back(output, out_cpu);
}
}
void bf16_f32_embeddings_torch(const at::Tensor& token_ids,
const at::Tensor& weight_bf16,
at::Tensor& output,
int64_t threadgroup_size)
{
TORCH_CHECK(token_ids.dtype() == at::kInt || token_ids.dtype() == at::kLong,
"token_ids must be int32 or int64");
TORCH_CHECK(weight_bf16.dtype() == at::kBFloat16, "weight must be bfloat16");
TORCH_CHECK(output.dtype() == at::kFloat, "output must be float32");
TORCH_CHECK(token_ids.dim() == 1, "token_ids must be 1D");
TORCH_CHECK(weight_bf16.dim() == 2, "weight must be 2D");
TORCH_CHECK(output.dim() == 2, "output must be 2D");
const int64_t num_tokens = token_ids.size(0);
TORCH_CHECK(output.size(0) == num_tokens, "output first dimension must match num_tokens");
const int64_t num_channels = output.size(1);
TORCH_CHECK(num_channels % 4 == 0, "num_channels must be divisible by 4");
TORCH_CHECK(weight_bf16.size(1) == num_channels,
"weight second dimension must equal embedding dimension (num_channels)");
TORCH_CHECK(threadgroup_size >= 0, "threadgroup_size must be non-negative");
const bool use_mps = token_ids.device().is_mps() && weight_bf16.device().is_mps() &&
output.device().is_mps();
at::Tensor tokens_cpu;
at::Tensor weight_cpu;
at::Tensor out_cpu;
size_t token_bytes = 0;
size_t weight_bytes = 0;
size_t out_bytes = 0;
at::Tensor tokens_mps;
at::Tensor weight_mps;
at::Tensor output_mps;
if (use_mps) {
tokens_mps = (token_ids.dtype() == at::kInt ? token_ids : token_ids.to(at::kInt)).contiguous();
weight_mps = weight_bf16.contiguous();
output_mps = output.contiguous();
} else {
tokens_cpu = token_ids.dtype() == at::kInt
? to_cpu_contiguous(token_ids)
: token_ids.to(at::kInt).contiguous().to(at::kCPU);
weight_cpu = to_cpu_contiguous(weight_bf16);
out_cpu = empty_cpu_like(output);
token_bytes = static_cast<size_t>(num_tokens) * sizeof(uint32_t);
weight_bytes = static_cast<size_t>(weight_cpu.numel()) * weight_cpu.element_size();
out_bytes = static_cast<size_t>(out_cpu.numel()) * out_cpu.element_size();
}
MetalBuffer tokens_buf;
MetalBuffer weight_buf;
MetalBuffer out_buf;
MetalBuffer control_buf;
run_metal_kernel("gptoss_bf16_f32_embeddings", [&](const gptoss_metal_device& device,
const gptoss_metal_function& fn,
gptoss_metal_command_buffer& cb) {
if (use_mps) {
tokens_buf.wrap_mps_tensor(&device, tokens_mps);
weight_buf.wrap_mps_tensor(&device, weight_mps);
out_buf.wrap_mps_tensor(&device, output_mps);
} else {
tokens_buf.wrap(&device, token_bytes, tokens_cpu.data_ptr());
weight_buf.wrap(&device, weight_bytes, weight_cpu.data_ptr());
out_buf.create(&device, out_bytes, nullptr);
}
create_control_buffer(&device, control_buf);
TORCH_CHECK(
gptoss_metal_command_buffer_encode_launch_bf16_f32_embeddings(
&cb, &fn, static_cast<size_t>(threadgroup_size),
tokens_buf.get(), 0,
weight_buf.get(), 0,
out_buf.get(), 0,
control_buf.get(), 0,
static_cast<uint32_t>(num_tokens),
static_cast<uint32_t>(num_channels)) == gptoss_status_success,
"encode embeddings failed");
});
if (use_mps) {
if (!output_mps.is_alias_of(output)) {
output.copy_(output_mps);
}
} else {
std::memcpy(out_cpu.data_ptr(), out_buf.ptr(), out_bytes);
copy_back(output, out_cpu);
}
}
void f32_bf16w_rmsnorm_torch(const at::Tensor& input,
const at::Tensor& weight_bf16,
at::Tensor& output,
double epsilon)
{
TORCH_CHECK(input.dtype() == at::kFloat, "input must be float32");
TORCH_CHECK(weight_bf16.dtype() == at::kBFloat16, "weight must be bfloat16");
TORCH_CHECK(output.dtype() == at::kFloat, "output must be float32");
TORCH_CHECK(input.dim() == 2, "input must be 2D");
TORCH_CHECK(weight_bf16.dim() == 1, "weight must be 1D");
TORCH_CHECK(output.dim() == 2, "output must be 2D");
const int64_t num_tokens = input.size(0);
const int64_t num_channels = input.size(1);
TORCH_CHECK(output.size(0) == num_tokens && output.size(1) == num_channels,
"output shape must match input shape");
TORCH_CHECK(weight_bf16.size(0) == num_channels,
"weight length must equal number of channels");
TORCH_CHECK(num_channels % 4 == 0, "num_channels must be divisible by 4");
const bool use_mps = input.device().is_mps();
at::Tensor input_cpu;
at::Tensor weight_cpu;
at::Tensor out_cpu;
size_t in_bytes = 0;
size_t weight_bytes = 0;
size_t out_bytes = 0;
at::Tensor input_mps;
at::Tensor weight_mps;
at::Tensor output_mps;
if (use_mps) {
input_mps = input.contiguous();
weight_mps = weight_bf16.contiguous();
output_mps = output.contiguous();
} else {
input_cpu = to_cpu_contiguous(input);
weight_cpu = to_cpu_contiguous(weight_bf16);
out_cpu = empty_cpu_like(output);
in_bytes = static_cast<size_t>(input_cpu.numel()) * input_cpu.element_size();
weight_bytes = static_cast<size_t>(weight_cpu.numel()) * weight_cpu.element_size();
out_bytes = static_cast<size_t>(out_cpu.numel()) * out_cpu.element_size();
}
MetalBuffer input_buf;
MetalBuffer weight_buf;
MetalBuffer out_buf;
MetalBuffer control_buf;
run_metal_kernel("gptoss_f32_bf16w_rmsnorm", [&](const gptoss_metal_device& device,
const gptoss_metal_function& fn,
gptoss_metal_command_buffer& cb) {
if (use_mps) {
input_buf.wrap_mps_tensor(&device, input_mps);
weight_buf.wrap_mps_tensor(&device, weight_mps);
out_buf.wrap_mps_tensor(&device, output_mps);
} else {
input_buf.wrap(&device, in_bytes, input_cpu.data_ptr());
weight_buf.wrap(&device, weight_bytes, weight_cpu.data_ptr());
out_buf.create(&device, out_bytes, nullptr);
}
create_control_buffer(&device, control_buf);
TORCH_CHECK(
gptoss_metal_command_buffer_encode_launch_f32_bf16w_rmsnorm(
&cb, &fn,
input_buf.get(), 0,
weight_buf.get(), 0,
out_buf.get(), 0,
control_buf.get(), 0,
static_cast<uint32_t>(num_tokens),
static_cast<uint32_t>(num_channels),
static_cast<float>(epsilon)) == gptoss_status_success,
"encode rmsnorm failed");
});
if (use_mps) {
if (!output_mps.is_alias_of(output)) {
output.copy_(output_mps);
}
} else {
std::memcpy(out_cpu.data_ptr(), out_buf.ptr(), out_bytes);
copy_back(output, out_cpu);
}
}
void f32_bf16w_dense_matmul_qkv_torch(const at::Tensor& input,
const at::Tensor& weight_bf16,
const at::Tensor& bias_bf16,
at::Tensor& output)
{
run_dense_matmul_bf16(
"gptoss_f32_bf16w_dense_matmul_qkv",
gptoss_metal_command_buffer_encode_launch_f32_bf16w_dense_matmul_qkv,
input, weight_bf16, bias_bf16, output);
}
void f32_bf16w_dense_matmul_attn_output_torch(const at::Tensor& input,
const at::Tensor& weight_bf16,
const at::Tensor& bias_bf16,
at::Tensor& output)
{
run_dense_matmul_bf16(
"gptoss_f32_bf16w_dense_matmul_attn_output",
gptoss_metal_command_buffer_encode_launch_f32_bf16w_dense_matmul_attn_output,
input, weight_bf16, bias_bf16, output);
}
void f32_bf16w_dense_matmul_mlp_gate_torch(const at::Tensor& input,
const at::Tensor& weight_bf16,
const at::Tensor& bias_bf16,
at::Tensor& output)
{
run_dense_matmul_bf16(
"gptoss_f32_bf16w_dense_matmul_mlp_gate",
gptoss_metal_command_buffer_encode_launch_f32_bf16w_dense_matmul_mlp_gate,
input, weight_bf16, bias_bf16, output);
}
void f32_rope_torch(at::Tensor& activations,
double rope_base,
double interpolation_scale,
double yarn_offset,
double yarn_scale,
double yarn_multiplier,
int64_t num_tokens,
int64_t num_q_heads,
int64_t num_kv_heads,
int64_t attn_head_dim,
int64_t token_offset,
int64_t threadgroup_size)
{
TORCH_CHECK(activations.dtype() == at::kFloat, "activations must be float32");
TORCH_CHECK(num_tokens >= 0 && num_q_heads >= 0 && num_kv_heads >= 0 && attn_head_dim >= 0,
"shape parameters must be non-negative");
TORCH_CHECK(threadgroup_size >= 0, "threadgroup_size must be non-negative");
const bool use_mps = activations.device().is_mps();
at::Tensor activations_cpu;
at::Tensor activations_mps;
size_t activations_bytes = 0;
if (use_mps) {
activations_mps = activations.contiguous();
activations_bytes = static_cast<size_t>(activations_mps.numel()) * activations_mps.element_size();
} else {
activations_cpu = to_cpu_contiguous(activations);
activations_bytes = static_cast<size_t>(activations_cpu.numel()) * activations_cpu.element_size();
}
MetalBuffer activations_buf;
MetalBuffer control_buf;
run_metal_kernel("gptoss_f32_rope", [&](const gptoss_metal_device& device,
const gptoss_metal_function& fn,
gptoss_metal_command_buffer& cb) {
if (use_mps) {
activations_buf.wrap_mps_tensor(&device, activations_mps);
} else {
activations_buf.wrap(&device, activations_bytes, activations_cpu.data_ptr());
}
create_control_buffer(&device, control_buf);
TORCH_CHECK(
gptoss_metal_command_buffer_encode_launch_f32_rope(
&cb, &fn,
static_cast<size_t>(threadgroup_size),
activations_buf.get(), 0,
control_buf.get(), 0,
static_cast<float>(rope_base),
static_cast<float>(interpolation_scale),
static_cast<float>(yarn_offset),
static_cast<float>(yarn_scale),
static_cast<float>(yarn_multiplier),
static_cast<uint32_t>(num_tokens),
static_cast<uint32_t>(num_q_heads),
static_cast<uint32_t>(num_kv_heads),
static_cast<uint32_t>(attn_head_dim),
static_cast<uint32_t>(token_offset)) == gptoss_status_success,
"encode rope failed");
});
if (use_mps) {
if (!activations_mps.is_alias_of(activations)) {
activations.copy_(activations_mps);
}
} else {
copy_back(activations, activations_cpu);
}
}
void f32_bf16w_matmul_qkv_torch(const at::Tensor& input,
const at::Tensor& weight_bf16,
const at::Tensor& bias_bf16,
at::Tensor& output,
at::Tensor& kv_cache,
int64_t kv_cache_offset_bytes,
int64_t num_tokens,
int64_t num_cols,
int64_t num_q_heads,
int64_t num_kv_heads,
int64_t attn_head_dim,
int64_t token_offset,
int64_t max_tokens,
double rope_base,
double interpolation_scale,
double yarn_offset,
double yarn_scale,
double yarn_multiplier,
int64_t threadgroup_size)
{
TORCH_CHECK(input.dtype() == at::kFloat, "input must be float32");
TORCH_CHECK(weight_bf16.dtype() == at::kBFloat16, "weight must be bfloat16");
TORCH_CHECK(bias_bf16.dtype() == at::kBFloat16, "bias must be bfloat16");
TORCH_CHECK(output.dtype() == at::kFloat, "output must be float32");
TORCH_CHECK(kv_cache.dtype() == at::kFloat, "kv_cache must be float32");
TORCH_CHECK(input.dim() == 2, "input must be 2D");
TORCH_CHECK(weight_bf16.dim() == 2, "weight must be 2D");
TORCH_CHECK(bias_bf16.dim() == 1, "bias must be 1D");
TORCH_CHECK(output.dim() == 2, "output must be 2D");
TORCH_CHECK(num_tokens >= 0 && num_cols >= 0 && num_q_heads >= 0 && num_kv_heads >= 0 && attn_head_dim >= 0 && max_tokens >= 0,
"shape parameters must be non-negative");
TORCH_CHECK(threadgroup_size >= 0, "threadgroup_size must be non-negative");
TORCH_CHECK(kv_cache_offset_bytes >= 0, "kv_cache_offset_bytes must be non-negative");
TORCH_CHECK(input.size(0) == num_tokens && input.size(1) == num_cols,
"input shape must be [num_tokens, num_cols]");
const int64_t num_rows = (num_q_heads + 2 * num_kv_heads) * attn_head_dim;
TORCH_CHECK(output.size(0) == num_tokens && output.size(1) == num_rows,
"output shape must be [num_tokens, (num_q_heads + 2 * num_kv_heads) * attn_head_dim]");
TORCH_CHECK(weight_bf16.size(0) == num_cols && weight_bf16.size(1) == num_rows,
"weight shape must be [num_cols, (num_q_heads + 2 * num_kv_heads) * attn_head_dim]");
TORCH_CHECK(bias_bf16.size(0) == num_rows,
"bias length must equal output feature dimension");
const bool use_mps = input.device().is_mps() && weight_bf16.device().is_mps() &&
bias_bf16.device().is_mps() && output.device().is_mps() && kv_cache.device().is_mps();
at::Tensor input_cpu;
at::Tensor weight_cpu;
at::Tensor bias_cpu;
at::Tensor out_cpu;
at::Tensor kv_cpu;
size_t in_bytes = 0;
size_t weight_bytes = 0;
size_t bias_bytes = 0;
size_t out_bytes = 0;
size_t kv_bytes = 0;
at::Tensor input_mps;
at::Tensor weight_mps;
at::Tensor bias_mps;
at::Tensor out_mps;
at::Tensor kv_mps;
if (use_mps) {
input_mps = input.contiguous();
weight_mps = weight_bf16.transpose(0, 1).contiguous();
bias_mps = bias_bf16.contiguous();
out_mps = output.contiguous();
kv_mps = kv_cache.contiguous();
} else {
input_cpu = to_cpu_contiguous(input);
weight_cpu = weight_bf16.transpose(0, 1).contiguous().to(at::kCPU);
bias_cpu = to_cpu_contiguous(bias_bf16);
out_cpu = empty_cpu_like(output);
kv_cpu = to_cpu_contiguous(kv_cache);
in_bytes = static_cast<size_t>(input_cpu.numel()) * input_cpu.element_size();
weight_bytes = static_cast<size_t>(weight_cpu.numel()) * weight_cpu.element_size();
bias_bytes = static_cast<size_t>(bias_cpu.numel()) * bias_cpu.element_size();
out_bytes = static_cast<size_t>(out_cpu.numel()) * out_cpu.element_size();
kv_bytes = static_cast<size_t>(kv_cpu.numel()) * kv_cpu.element_size();
}
MetalBuffer input_buf;
MetalBuffer weight_buf;
MetalBuffer bias_buf;
MetalBuffer out_buf;
MetalBuffer kv_buf;
MetalBuffer control_buf;
run_metal_kernel("gptoss_f32_bf16w_matmul_qkv", [&](const gptoss_metal_device& device,
const gptoss_metal_function& fn,
gptoss_metal_command_buffer& cb) {
if (use_mps) {
input_buf.wrap_mps_tensor(&device, input_mps);
weight_buf.wrap_mps_tensor(&device, weight_mps);
bias_buf.wrap_mps_tensor(&device, bias_mps);
out_buf.wrap_mps_tensor(&device, out_mps);
kv_buf.wrap_mps_tensor(&device, kv_mps);
} else {
input_buf.wrap(&device, in_bytes, input_cpu.data_ptr());
weight_buf.wrap(&device, weight_bytes, weight_cpu.data_ptr());
bias_buf.wrap(&device, bias_bytes, bias_cpu.data_ptr());
out_buf.create(&device, out_bytes, nullptr);
kv_buf.wrap(&device, kv_bytes, kv_cpu.data_ptr());
}
create_control_buffer(&device, control_buf);
TORCH_CHECK(
gptoss_metal_command_buffer_encode_launch_f32_bf16w_matmul_qkv(
&cb, &fn,
static_cast<size_t>(threadgroup_size),
input_buf.get(), 0,
weight_buf.get(), 0,
bias_buf.get(), 0,
out_buf.get(), 0,
kv_buf.get(), static_cast<size_t>(kv_cache_offset_bytes),
control_buf.get(), 0,
static_cast<uint32_t>(num_tokens),
static_cast<uint32_t>(num_cols),
static_cast<uint32_t>(num_q_heads),
static_cast<uint32_t>(num_kv_heads),
static_cast<uint32_t>(attn_head_dim),
static_cast<uint32_t>(token_offset),
static_cast<uint32_t>(max_tokens),
static_cast<float>(rope_base),
static_cast<float>(interpolation_scale),
static_cast<float>(yarn_offset),
static_cast<float>(yarn_scale),
static_cast<float>(yarn_multiplier)) == gptoss_status_success,
"encode matmul_qkv failed");
});
if (use_mps) {
if (!out_mps.is_alias_of(output)) {
output.copy_(out_mps);
}
if (!kv_mps.is_alias_of(kv_cache)) {
kv_cache.copy_(kv_mps);
}
} else {
std::memcpy(out_cpu.data_ptr(), out_buf.ptr(), out_bytes);
copy_back(output, out_cpu);
copy_back(kv_cache, kv_cpu);
}
}
void f32_sdpa_torch(const at::Tensor& q,
int64_t q_offset_bytes,
const at::Tensor& kv,
int64_t kv_offset_bytes,
const at::Tensor& s_bf16,
int64_t s_offset_bytes,
at::Tensor& output,
int64_t output_offset_bytes,
int64_t window,
int64_t kv_stride,
int64_t num_q_tokens,
int64_t num_kv_tokens,
int64_t num_q_heads,
int64_t num_kv_heads,
int64_t head_dim)
{
TORCH_CHECK(q.dtype() == at::kFloat, "q must be float32");
TORCH_CHECK(kv.dtype() == at::kFloat, "kv must be float32");
TORCH_CHECK(s_bf16.dtype() == at::kBFloat16, "s must be bfloat16");
TORCH_CHECK(output.dtype() == at::kFloat, "output must be float32");
TORCH_CHECK(q_offset_bytes >= 0 && kv_offset_bytes >= 0 && s_offset_bytes >= 0 && output_offset_bytes >= 0,
"offsets must be non-negative");
TORCH_CHECK(window >= 0 && kv_stride >= 0 && num_q_tokens >= 0 && num_kv_tokens >= 0 && num_q_heads >= 0 && num_kv_heads >= 0 && head_dim >= 0,
"shape parameters must be non-negative");
const bool use_mps = q.device().is_mps() && kv.device().is_mps() &&
s_bf16.device().is_mps() && output.device().is_mps();
at::Tensor q_cpu;
at::Tensor kv_cpu;
at::Tensor s_cpu;
at::Tensor out_cpu;
size_t q_bytes = 0;
size_t kv_bytes = 0;
size_t s_bytes = 0;
size_t out_bytes = 0;
at::Tensor q_mps;
at::Tensor kv_mps;
at::Tensor s_mps;
at::Tensor out_mps;
if (use_mps) {
q_mps = q.contiguous();
kv_mps = kv.contiguous();
s_mps = s_bf16.contiguous();
out_mps = output.contiguous();
} else {
q_cpu = to_cpu_contiguous(q);
kv_cpu = to_cpu_contiguous(kv);
s_cpu = to_cpu_contiguous(s_bf16);
out_cpu = empty_cpu_like(output);
q_bytes = static_cast<size_t>(q_cpu.numel()) * q_cpu.element_size();
kv_bytes = static_cast<size_t>(kv_cpu.numel()) * kv_cpu.element_size();
s_bytes = static_cast<size_t>(s_cpu.numel()) * s_cpu.element_size();
out_bytes = static_cast<size_t>(out_cpu.numel()) * out_cpu.element_size();
}
MetalBuffer q_buf;
MetalBuffer kv_buf;
MetalBuffer s_buf;
MetalBuffer out_buf;
MetalBuffer control_buf;
run_metal_kernel("gptoss_f32_sdpa_q8_d64", [&](const gptoss_metal_device& device,
const gptoss_metal_function& fn,
gptoss_metal_command_buffer& cb) {
if (use_mps) {
q_buf.wrap_mps_tensor(&device, q_mps);
kv_buf.wrap_mps_tensor(&device, kv_mps);
s_buf.wrap_mps_tensor(&device, s_mps);
out_buf.wrap_mps_tensor(&device, out_mps);
} else {
q_buf.wrap(&device, q_bytes, q_cpu.data_ptr());
kv_buf.wrap(&device, kv_bytes, kv_cpu.data_ptr());
s_buf.wrap(&device, s_bytes, s_cpu.data_ptr());
out_buf.create(&device, out_bytes, nullptr);
}
create_control_buffer(&device, control_buf);
TORCH_CHECK(
gptoss_metal_command_buffer_encode_launch_f32_sdpa(
&cb, &fn,
q_buf.get(), static_cast<size_t>(q_offset_bytes),
kv_buf.get(), static_cast<size_t>(kv_offset_bytes),
s_buf.get(), static_cast<size_t>(s_offset_bytes),
out_buf.get(), static_cast<size_t>(output_offset_bytes),
control_buf.get(), 0,
static_cast<uint32_t>(window),
static_cast<uint32_t>(kv_stride),
static_cast<uint32_t>(num_q_tokens),
static_cast<uint32_t>(num_kv_tokens),
static_cast<uint32_t>(num_q_heads),
static_cast<uint32_t>(num_kv_heads),
static_cast<uint32_t>(head_dim)) == gptoss_status_success,
"encode sdpa failed");
});
if (use_mps) {
if (!out_mps.is_alias_of(output)) {
output.copy_(out_mps);
}
} else {
std::memcpy(out_cpu.data_ptr(), out_buf.ptr(), out_bytes);
copy_back(output, out_cpu);
}
}
void f32_topk_torch(const at::Tensor& scores,
at::Tensor& expert_ids,
at::Tensor& expert_scores,
int64_t num_tokens,
int64_t num_experts,
int64_t num_active_experts)
{
TORCH_CHECK(scores.dtype() == at::kFloat, "scores must be float32");
TORCH_CHECK(expert_ids.dtype() == at::kInt, "expert_ids must be int32");
TORCH_CHECK(expert_scores.dtype() == at::kFloat, "expert_scores must be float32");
TORCH_CHECK(num_tokens >= 0 && num_experts >= 0 && num_active_experts >= 0,
"shape parameters must be non-negative");
TORCH_CHECK(scores.size(0) == num_tokens,
"scores first dimension must match num_tokens");
TORCH_CHECK(scores.numel() == num_tokens * num_experts,
"scores must have num_tokens * num_experts elements");
TORCH_CHECK(expert_ids.numel() == num_tokens * num_active_experts,
"expert_ids must have num_tokens * num_active_experts elements");
TORCH_CHECK(expert_scores.numel() == num_tokens * num_active_experts,
"expert_scores must have num_tokens * num_active_experts elements");
const bool scores_use_mps = scores.device().is_mps();
at::Tensor scores_cpu;
at::Tensor scores_mps;
size_t score_bytes = 0;
if (scores_use_mps) {
scores_mps = scores.contiguous();
} else {
scores_cpu = to_cpu_contiguous(scores);
score_bytes = static_cast<size_t>(scores_cpu.numel()) * scores_cpu.element_size();
}
std::vector<gptoss_expert_prediction> predictions(static_cast<size_t>(num_tokens) * static_cast<size_t>(num_active_experts));
const size_t pred_bytes = predictions.size() * sizeof(gptoss_expert_prediction);
MetalBuffer score_buf;
MetalBuffer pred_buf;
MetalBuffer control_buf;
run_metal_kernel("gptoss_f32_topk_softmax_e128_k4", [&](const gptoss_metal_device& device,
const gptoss_metal_function& fn,
gptoss_metal_command_buffer& cb) {
if (scores_use_mps) {
score_buf.wrap_mps_tensor(&device, scores_mps);
} else {
score_buf.wrap(&device, score_bytes, scores_cpu.data_ptr());
}
pred_buf.wrap(&device, pred_bytes, predictions.data());
create_control_buffer(&device, control_buf);
TORCH_CHECK(
gptoss_metal_command_buffer_encode_launch_f32_topk(
&cb, &fn,
score_buf.get(), 0,
pred_buf.get(), 0,
control_buf.get(), 0,
static_cast<uint32_t>(num_tokens),
static_cast<uint32_t>(num_experts),
static_cast<uint32_t>(num_active_experts)) == gptoss_status_success,
"encode topk failed");
});
auto ids_cpu = expert_ids.to(at::kCPU).contiguous();
auto scores_out_cpu = expert_scores.to(at::kCPU).contiguous();
auto* ids_ptr = ids_cpu.data_ptr<int32_t>();
auto* scores_ptr = scores_out_cpu.data_ptr<float>();
const size_t total = predictions.size();
for (size_t i = 0; i < total; ++i) {
ids_ptr[i] = static_cast<int32_t>(predictions[i].expert_id);
scores_ptr[i] = predictions[i].score;
}
copy_back(expert_ids, ids_cpu);
copy_back(expert_scores, scores_out_cpu);
}
void expert_routing_metadata_torch(const at::Tensor& expert_ids,
const at::Tensor& expert_scores,
at::Tensor& expert_offsets,
at::Tensor& intra_expert_offsets,
int64_t num_tokens,
int64_t num_experts)
{
TORCH_CHECK(expert_ids.dtype() == at::kInt, "expert_ids must be int32");
TORCH_CHECK(expert_scores.dtype() == at::kFloat, "expert_scores must be float32");
TORCH_CHECK(expert_offsets.dtype() == at::kInt, "expert_offsets must be int32");
TORCH_CHECK(intra_expert_offsets.dtype() == at::kInt, "intra_expert_offsets must be int32");
TORCH_CHECK(num_tokens >= 0 && num_experts >= 0, "shape parameters must be non-negative");
TORCH_CHECK(expert_ids.numel() == num_tokens,
"expert_ids must have num_tokens elements");
TORCH_CHECK(expert_scores.numel() == num_tokens,
"expert_scores must have num_tokens elements");
TORCH_CHECK(intra_expert_offsets.numel() == num_tokens,
"intra_expert_offsets must have num_tokens elements");
TORCH_CHECK(expert_offsets.numel() == num_experts + 1,
"expert_offsets must have num_experts + 1 elements");
auto ids_cpu = to_cpu_contiguous(expert_ids);
auto scores_cpu = to_cpu_contiguous(expert_scores);
const bool use_mps = expert_offsets.device().is_mps() && intra_expert_offsets.device().is_mps();
at::Tensor offsets_cpu;
at::Tensor intra_offsets_cpu;
at::Tensor offsets_mps;
at::Tensor intra_offsets_mps;
size_t offsets_bytes = 0;
size_t intra_bytes = 0;
if (use_mps) {
offsets_mps = expert_offsets.contiguous();
intra_offsets_mps = intra_expert_offsets.contiguous();
} else {
offsets_cpu = to_cpu_contiguous(expert_offsets);
intra_offsets_cpu = to_cpu_contiguous(intra_expert_offsets);
offsets_bytes = static_cast<size_t>(offsets_cpu.numel()) * offsets_cpu.element_size();
intra_bytes = static_cast<size_t>(intra_offsets_cpu.numel()) * intra_offsets_cpu.element_size();
}
std::vector<gptoss_expert_prediction> predictions(static_cast<size_t>(num_tokens));
const auto* ids_ptr = ids_cpu.data_ptr<int32_t>();
const auto* scores_ptr = scores_cpu.data_ptr<float>();
for (int64_t i = 0; i < num_tokens; ++i) {
predictions[static_cast<size_t>(i)] = gptoss_expert_prediction {
.expert_id = static_cast<uint32_t>(ids_ptr[i]),
.score = scores_ptr[i],
};
}
const size_t pred_bytes = predictions.size() * sizeof(gptoss_expert_prediction);
MetalBuffer pred_buf;
MetalBuffer offsets_buf;
MetalBuffer intra_offsets_buf;
run_metal_kernel("gptoss_f32_expert_routing_metadata", [&](const gptoss_metal_device& device,
const gptoss_metal_function& fn,
gptoss_metal_command_buffer& cb) {
pred_buf.wrap(&device, pred_bytes, predictions.data());
if (use_mps) {
offsets_buf.wrap_mps_tensor(&device, offsets_mps);
intra_offsets_buf.wrap_mps_tensor(&device, intra_offsets_mps);
} else {
offsets_buf.wrap(&device, offsets_bytes, offsets_cpu.data_ptr());
intra_offsets_buf.wrap(&device, intra_bytes, intra_offsets_cpu.data_ptr());
}
TORCH_CHECK(
gptoss_metal_command_buffer_encode_launch_expert_routing_metadata(
&cb, &fn,
pred_buf.get(), 0,
offsets_buf.get(), 0,
intra_offsets_buf.get(), 0,
static_cast<uint32_t>(num_tokens),
static_cast<uint32_t>(num_experts)) == gptoss_status_success,
"encode expert_routing_metadata failed");
});
if (use_mps) {
if (!offsets_mps.is_alias_of(expert_offsets)) {
expert_offsets.copy_(offsets_mps);
}
if (!intra_offsets_mps.is_alias_of(intra_expert_offsets)) {
intra_expert_offsets.copy_(intra_offsets_mps);
}
} else {
copy_back(expert_offsets, offsets_cpu);
copy_back(intra_expert_offsets, intra_offsets_cpu);
}
}
void f32_scatter_torch(const at::Tensor& input,
const at::Tensor& expert_ids,
const at::Tensor& expert_scores,
const at::Tensor& expert_offsets,
const at::Tensor& intra_expert_offsets,
at::Tensor& output,
int64_t num_channels,
int64_t num_tokens,
int64_t num_active_experts)
{
TORCH_CHECK(input.dtype() == at::kFloat, "input must be float32");
TORCH_CHECK(expert_ids.dtype() == at::kInt, "expert_ids must be int32");
TORCH_CHECK(expert_scores.dtype() == at::kFloat, "expert_scores must be float32");
TORCH_CHECK(expert_offsets.dtype() == at::kInt, "expert_offsets must be int32");
TORCH_CHECK(intra_expert_offsets.dtype() == at::kInt, "intra_expert_offsets must be int32");
TORCH_CHECK(output.dtype() == at::kFloat, "output must be float32");
TORCH_CHECK(num_channels >= 0 && num_tokens >= 0 && num_active_experts >= 0,
"shape parameters must be non-negative");
TORCH_CHECK(input.numel() == static_cast<int64_t>(num_tokens / num_active_experts) * num_channels,
"input size mismatch");
TORCH_CHECK(expert_ids.numel() == num_tokens,
"expert_ids must have num_tokens elements");
TORCH_CHECK(expert_scores.numel() == num_tokens,
"expert_scores must have num_tokens elements");
TORCH_CHECK(intra_expert_offsets.numel() == num_tokens,
"intra_expert_offsets must have num_tokens elements");
TORCH_CHECK(output.numel() == num_tokens * num_channels / num_active_experts,
"output size mismatch");
const bool use_mps = input.device().is_mps() && expert_offsets.device().is_mps() &&
intra_expert_offsets.device().is_mps() && output.device().is_mps();
at::Tensor input_cpu;
at::Tensor expert_offsets_cpu;
at::Tensor intra_offsets_cpu;
at::Tensor output_cpu;
size_t input_bytes = 0;
size_t offsets_bytes = 0;
size_t intra_bytes = 0;
size_t output_bytes = 0;
at::Tensor input_mps;
at::Tensor expert_offsets_mps;
at::Tensor intra_offsets_mps;
at::Tensor output_mps;
if (use_mps) {
input_mps = input.contiguous();
expert_offsets_mps = expert_offsets.contiguous();
intra_offsets_mps = intra_expert_offsets.contiguous();
output_mps = output.contiguous();
} else {
input_cpu = to_cpu_contiguous(input);
expert_offsets_cpu = to_cpu_contiguous(expert_offsets);
intra_offsets_cpu = to_cpu_contiguous(intra_expert_offsets);
output_cpu = empty_cpu_like(output);
input_bytes = static_cast<size_t>(input_cpu.numel()) * input_cpu.element_size();
offsets_bytes = static_cast<size_t>(expert_offsets_cpu.numel()) * expert_offsets_cpu.element_size();
intra_bytes = static_cast<size_t>(intra_offsets_cpu.numel()) * intra_offsets_cpu.element_size();
output_bytes = static_cast<size_t>(output_cpu.numel()) * output_cpu.element_size();
}
std::vector<gptoss_expert_prediction> predictions(static_cast<size_t>(num_tokens));
const auto* ids_ptr = expert_ids.to(at::kCPU).contiguous().data_ptr<int32_t>();
const auto* scores_ptr = expert_scores.to(at::kCPU).contiguous().data_ptr<float>();
for (int64_t i = 0; i < num_tokens; ++i) {
predictions[static_cast<size_t>(i)] = gptoss_expert_prediction {
.expert_id = static_cast<uint32_t>(ids_ptr[i]),
.score = scores_ptr[i],
};
}
const size_t pred_bytes = predictions.size() * sizeof(gptoss_expert_prediction);
MetalBuffer input_buf;
MetalBuffer pred_buf;
MetalBuffer offsets_buf;
MetalBuffer intra_offsets_buf;
MetalBuffer output_buf;
run_metal_kernel("gptoss_f32_scatter_e4", [&](const gptoss_metal_device& device,
const gptoss_metal_function& fn,
gptoss_metal_command_buffer& cb) {
if (use_mps) {
input_buf.wrap_mps_tensor(&device, input_mps);
offsets_buf.wrap_mps_tensor(&device, expert_offsets_mps);
intra_offsets_buf.wrap_mps_tensor(&device, intra_offsets_mps);
output_buf.wrap_mps_tensor(&device, output_mps);
} else {
input_buf.wrap(&device, input_bytes, input_cpu.data_ptr());
offsets_buf.wrap(&device, offsets_bytes, expert_offsets_cpu.data_ptr());
intra_offsets_buf.wrap(&device, intra_bytes, intra_offsets_cpu.data_ptr());
output_buf.create(&device, output_bytes, nullptr);
}
pred_buf.wrap(&device, pred_bytes, predictions.data());
TORCH_CHECK(
gptoss_metal_command_buffer_encode_launch_f32_scatter(
&cb, &fn,
input_buf.get(), 0,
pred_buf.get(), 0,
offsets_buf.get(), 0,
intra_offsets_buf.get(), 0,
output_buf.get(), 0,
static_cast<uint32_t>(num_channels),
static_cast<uint32_t>(num_tokens / num_active_experts),
static_cast<uint32_t>(num_active_experts)) == gptoss_status_success,
"encode scatter failed");
});
if (use_mps) {
if (!output_mps.is_alias_of(output)) {
output.copy_(output_mps);
}
} else {
std::memcpy(output_cpu.data_ptr(), output_buf.ptr(), output_bytes);
copy_back(output, output_cpu);
}
}
void f32_bf16w_matmul_add_torch(const at::Tensor& input,
const at::Tensor& weight_bf16,
const at::Tensor& bias_bf16,
at::Tensor& output,
int64_t num_tokens,
int64_t num_cols,
int64_t num_rows,
int64_t threadgroup_size)
{
TORCH_CHECK(input.dtype() == at::kFloat, "input must be float32");
TORCH_CHECK(weight_bf16.dtype() == at::kBFloat16, "weight must be bfloat16");
TORCH_CHECK(bias_bf16.dtype() == at::kBFloat16, "bias must be bfloat16");
TORCH_CHECK(output.dtype() == at::kFloat, "output must be float32");
TORCH_CHECK(input.dim() == 2, "input must be 2D");
TORCH_CHECK(weight_bf16.dim() == 2, "weight must be 2D");
TORCH_CHECK(bias_bf16.dim() == 1, "bias must be 1D");
TORCH_CHECK(output.dim() == 2, "output must be 2D");
TORCH_CHECK(input.size(0) == num_tokens && input.size(1) == num_cols,
"input shape must be [num_tokens, num_cols]");
TORCH_CHECK(weight_bf16.size(0) == num_cols && weight_bf16.size(1) == num_rows,
"weight shape must be [num_cols, num_rows]");
TORCH_CHECK(bias_bf16.size(0) == num_rows,
"bias length must equal num_rows");
TORCH_CHECK(output.size(0) == num_tokens && output.size(1) == num_rows,
"output shape must be [num_tokens, num_rows]");
const bool use_mps = input.device().is_mps() && weight_bf16.device().is_mps() &&
bias_bf16.device().is_mps() && output.device().is_mps();
at::Tensor input_cpu;
at::Tensor weight_cpu;
at::Tensor bias_cpu;
at::Tensor out_cpu;
size_t in_bytes = 0;
size_t weight_bytes = 0;
size_t bias_bytes = 0;
size_t out_bytes = 0;
at::Tensor input_mps;
at::Tensor weight_mps;
at::Tensor bias_mps;
at::Tensor out_mps;
if (use_mps) {
input_mps = input.contiguous();
weight_mps = weight_bf16.transpose(0, 1).contiguous();
bias_mps = bias_bf16.contiguous();
out_mps = output.contiguous();
} else {
input_cpu = to_cpu_contiguous(input);
weight_cpu = weight_bf16.transpose(0, 1).contiguous().to(at::kCPU);
bias_cpu = to_cpu_contiguous(bias_bf16);
out_cpu = to_cpu_contiguous(output);
in_bytes = static_cast<size_t>(input_cpu.numel()) * input_cpu.element_size();
weight_bytes = static_cast<size_t>(weight_cpu.numel()) * weight_cpu.element_size();
bias_bytes = static_cast<size_t>(bias_cpu.numel()) * bias_cpu.element_size();
out_bytes = static_cast<size_t>(out_cpu.numel()) * out_cpu.element_size();
}
MetalBuffer input_buf;
MetalBuffer weight_buf;
MetalBuffer bias_buf;
MetalBuffer out_buf;
MetalBuffer control_buf;
run_metal_kernel("gptoss_f32_bf16w_matmul", [&](const gptoss_metal_device& device,
const gptoss_metal_function& fn,
gptoss_metal_command_buffer& cb) {
if (use_mps) {
input_buf.wrap_mps_tensor(&device, input_mps);
weight_buf.wrap_mps_tensor(&device, weight_mps);
bias_buf.wrap_mps_tensor(&device, bias_mps);
out_buf.wrap_mps_tensor(&device, out_mps);
} else {
input_buf.wrap(&device, in_bytes, input_cpu.data_ptr());
weight_buf.wrap(&device, weight_bytes, weight_cpu.data_ptr());
bias_buf.wrap(&device, bias_bytes, bias_cpu.data_ptr());
out_buf.create(&device, out_bytes, nullptr);
std::memcpy(out_buf.ptr(), out_cpu.data_ptr(), out_bytes);
}
create_control_buffer(&device, control_buf);
TORCH_CHECK(
gptoss_metal_command_buffer_encode_launch_f32_bf16w_matmul_add(
&cb, &fn,
static_cast<size_t>(threadgroup_size),
input_buf.get(), 0,
weight_buf.get(), 0,
bias_buf.get(), 0,
out_buf.get(), 0,
control_buf.get(), 0,
static_cast<uint32_t>(num_tokens),
static_cast<uint32_t>(num_cols),
static_cast<uint32_t>(num_rows)) == gptoss_status_success,
"encode matmul_add failed");
});
if (use_mps) {
if (!out_mps.is_alias_of(output)) {
output.copy_(out_mps);
}
} else {
std::memcpy(out_cpu.data_ptr(), out_buf.ptr(), out_bytes);
copy_back(output, out_cpu);
}
} |