Spaces:
Sleeping
Sleeping
File size: 20,963 Bytes
8b19012 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 |
/******************************************************************************
* Copyright (c) 2024, Tri Dao.
******************************************************************************/
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <torch/extension.h>
#include <vector>
#include "causal_conv1d.h"
#define CHECK_SHAPE(x, ...) TORCH_CHECK(x.sizes() == torch::IntArrayRef({__VA_ARGS__}), #x " must have shape (" #__VA_ARGS__ ")")
#define DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(ITYPE, NAME, ...) \
if (ITYPE == at::ScalarType::Half) { \
using input_t = at::Half; \
__VA_ARGS__(); \
} else if (ITYPE == at::ScalarType::BFloat16) { \
using input_t = at::BFloat16; \
__VA_ARGS__(); \
} else if (ITYPE == at::ScalarType::Float) { \
using input_t = float; \
__VA_ARGS__(); \
} else { \
AT_ERROR(#NAME, " not implemented for input type '", toString(ITYPE), "'"); \
}
#define DISPATCH_WTYPE_FLOAT_AND_HALF_AND_BF16(WTYPE, NAME, ...) \
if (WTYPE == at::ScalarType::Half) { \
using weight_t = at::Half; \
__VA_ARGS__(); \
} else if (WTYPE == at::ScalarType::BFloat16) { \
using weight_t = at::BFloat16; \
__VA_ARGS__(); \
} else if (WTYPE == at::ScalarType::Float) { \
using weight_t = float; \
__VA_ARGS__(); \
} else { \
AT_ERROR(#NAME, " not implemented for weight type '", toString(WTYPE), "'"); \
}
template<typename input_t, typename weight_t>
void causal_conv1d_fwd_cuda(ConvParamsBase ¶ms, cudaStream_t stream);
template <typename input_t, typename weight_t>
void causal_conv1d_channellast_fwd_cuda(ConvParamsBase ¶ms, cudaStream_t stream);
template<typename input_t, typename weight_t>
void causal_conv1d_bwd_cuda(ConvParamsBwd ¶ms, cudaStream_t stream);
template<typename input_t, typename weight_t>
void causal_conv1d_channellast_bwd_cuda(ConvParamsBwd ¶ms, cudaStream_t stream);
template<typename input_t, typename weight_t>
void causal_conv1d_update_cuda(ConvParamsBase ¶ms, cudaStream_t stream);
void set_conv_params_fwd(ConvParamsBase ¶ms,
// sizes
const size_t batch,
const size_t dim,
const size_t seqlen,
const size_t width,
// device pointers
const at::Tensor x,
const at::Tensor weight,
const at::Tensor out,
void* bias_ptr,
bool silu_activation) {
// Reset the parameters
memset(¶ms, 0, sizeof(params));
params.batch = batch;
params.dim = dim;
params.seqlen = seqlen;
params.width = width;
params.silu_activation = silu_activation;
// Set the pointers and strides.
params.x_ptr = x.data_ptr();
params.weight_ptr = weight.data_ptr();
params.bias_ptr = bias_ptr;
params.out_ptr = out.data_ptr();
// All stride are in elements, not bytes.
params.x_batch_stride = x.stride(0);
params.x_c_stride = x.stride(1);
params.x_l_stride = x.stride(-1);
params.weight_c_stride = weight.stride(0);
params.weight_width_stride = weight.stride(1);
params.out_batch_stride = out.stride(0);
params.out_c_stride = out.stride(1);
params.out_l_stride = out.stride(-1);
}
void set_conv_params_bwd(ConvParamsBwd ¶ms,
// sizes
const size_t batch,
const size_t dim,
const size_t seqlen,
const size_t width,
// device pointers
const at::Tensor x,
const at::Tensor weight,
void* bias_ptr,
const at::Tensor dout,
const at::Tensor dx,
const at::Tensor dweight,
void* dbias_ptr,
bool silu_activation) {
// Pass in "dout" instead of "out", we're not gonna use "out" at all.
set_conv_params_fwd(params, batch, dim, seqlen, width,
x, weight, dout, bias_ptr, silu_activation);
// Set the pointers and strides.
params.dout_ptr = dout.data_ptr();
params.dx_ptr = dx.data_ptr();
params.dweight_ptr = dweight.data_ptr();
params.dbias_ptr = dbias_ptr;
// All stride are in elements, not bytes.
params.dout_batch_stride = dout.stride(0);
params.dout_c_stride = dout.stride(1);
params.dout_l_stride = dout.stride(2);
params.dweight_c_stride = dweight.stride(0);
params.dweight_width_stride = dweight.stride(1);
params.dx_batch_stride = dx.stride(0);
params.dx_c_stride = dx.stride(1);
params.dx_l_stride = dx.stride(2);
}
at::Tensor
causal_conv1d_fwd(const at::Tensor &x, const at::Tensor &weight,
const c10::optional<at::Tensor> &bias_,
const c10::optional<at::Tensor> &seq_idx_,
const c10::optional<at::Tensor> &initial_states_,
c10::optional<at::Tensor> &final_states_out_,
bool silu_activation) {
auto input_type = x.scalar_type();
auto weight_type = weight.scalar_type();
TORCH_CHECK(input_type == at::ScalarType::Float || input_type == at::ScalarType::Half || input_type == at::ScalarType::BFloat16);
TORCH_CHECK(weight_type == at::ScalarType::Float || weight_type == at::ScalarType::Half || weight_type == at::ScalarType::BFloat16);
TORCH_CHECK(x.is_cuda());
TORCH_CHECK(weight.is_cuda());
const auto sizes = x.sizes();
const int batch_size = sizes[0];
const int dim = sizes[1];
const int seqlen = sizes[2];
const int width = weight.size(-1);
CHECK_SHAPE(x, batch_size, dim, seqlen);
CHECK_SHAPE(weight, dim, width);
TORCH_CHECK(x.stride(2) == 1 || x.stride(1) == 1);
const bool is_channel_last = x.stride(1) == 1 && x.stride(2) > 1;
if (is_channel_last) {
TORCH_CHECK(dim % 8 == 0, "causal_conv1d only supports channel dimension divisible by 8 for now");
TORCH_CHECK(x.stride(2) % 8 == 0 and x.stride(0) % 8 == 0, "causal_conv1d with channel last layout requires strides (x.stride(0) and x.stride(2)) to be multiples of 8");
}
TORCH_CHECK(width >= 2 && width <= 4, "causal_conv1d only supports width between 2 and 4");
if (bias_.has_value()) {
auto bias = bias_.value();
TORCH_CHECK(bias.scalar_type() == weight_type);
TORCH_CHECK(bias.is_cuda());
TORCH_CHECK(bias.stride(-1) == 1);
CHECK_SHAPE(bias, dim);
}
if (seq_idx_.has_value()) {
TORCH_CHECK(is_channel_last, "seq_idx is only supported for channel last layout");
auto seq_idx = seq_idx_.value();
TORCH_CHECK(seq_idx.scalar_type() == torch::kInt32);
TORCH_CHECK(seq_idx.is_cuda());
TORCH_CHECK(seq_idx.is_contiguous());
CHECK_SHAPE(seq_idx, batch_size, seqlen);
}
at::Tensor out = torch::empty_like(x);
ConvParamsBase params;
set_conv_params_fwd(params, batch_size, dim, seqlen, width, x, weight, out,
bias_.has_value() ? bias_.value().data_ptr() : nullptr,
silu_activation);
if (seq_idx_.has_value()) {
params.seq_idx_ptr = seq_idx_.value().data_ptr();
} else {
params.seq_idx_ptr = nullptr;
}
if (initial_states_.has_value()) {
TORCH_CHECK(is_channel_last, "initial_states is only supported for channel last layout");
auto initial_states = initial_states_.value();
TORCH_CHECK(initial_states.scalar_type() == input_type);
TORCH_CHECK(initial_states.is_cuda());
CHECK_SHAPE(initial_states, batch_size, dim, width - 1);
TORCH_CHECK(initial_states.stride(1) == 1);
params.initial_states_ptr = initial_states.data_ptr();
params.initial_states_batch_stride = initial_states.stride(0);
params.initial_states_c_stride = initial_states.stride(1);
params.initial_states_l_stride = initial_states.stride(2);
} else {
params.initial_states_ptr = nullptr;
}
if (final_states_out_.has_value()) {
TORCH_CHECK(is_channel_last, "final_states is only supported for channel last layout");
auto final_states = final_states_out_.value();
TORCH_CHECK(final_states.scalar_type() == input_type);
TORCH_CHECK(final_states.is_cuda());
CHECK_SHAPE(final_states, batch_size, dim, width - 1);
TORCH_CHECK(final_states.stride(1) == 1);
params.final_states_ptr = final_states.data_ptr();
params.final_states_batch_stride = final_states.stride(0);
params.final_states_c_stride = final_states.stride(1);
params.final_states_l_stride = final_states.stride(2);
} else {
params.final_states_ptr = nullptr;
}
// Otherwise the kernel will be launched from cuda:0 device
// Cast to char to avoid compiler warning about narrowing
at::cuda::CUDAGuard device_guard{(char)x.get_device()};
auto stream = at::cuda::getCurrentCUDAStream().stream();
DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(x.scalar_type(), "causal_conv1d_fwd", [&] {
DISPATCH_WTYPE_FLOAT_AND_HALF_AND_BF16(weight.scalar_type(), "causal_conv1d_fwd", [&] {
if (!is_channel_last) {
causal_conv1d_fwd_cuda<input_t, weight_t>(params, stream);
} else {
causal_conv1d_channellast_fwd_cuda<input_t, weight_t>(params, stream);
}
});
});
return out;
}
std::vector<at::Tensor>
causal_conv1d_bwd(const at::Tensor &x, const at::Tensor &weight,
const c10::optional<at::Tensor> &bias_,
at::Tensor &dout,
const c10::optional<at::Tensor> &seq_idx_,
const c10::optional<at::Tensor> &initial_states_,
const c10::optional<at::Tensor> &dfinal_states_,
c10::optional<at::Tensor> &dx_,
bool return_dinitial_states,
bool silu_activation) {
auto input_type = x.scalar_type();
auto weight_type = weight.scalar_type();
TORCH_CHECK(input_type == at::ScalarType::Float || input_type == at::ScalarType::Half || input_type == at::ScalarType::BFloat16);
TORCH_CHECK(weight_type == at::ScalarType::Float || weight_type == at::ScalarType::Half || weight_type == at::ScalarType::BFloat16);
TORCH_CHECK(x.is_cuda());
TORCH_CHECK(weight.is_cuda());
TORCH_CHECK(dout.is_cuda());
const auto sizes = x.sizes();
const int batch_size = sizes[0];
const int dim = sizes[1];
const int seqlen = sizes[2];
const int width = weight.size(-1);
TORCH_CHECK(width >= 2 && width <= 4, "causal_conv1d only supports width between 2 and 4");
CHECK_SHAPE(x, batch_size, dim, seqlen);
CHECK_SHAPE(weight, dim, width);
CHECK_SHAPE(dout, batch_size, dim, seqlen);
TORCH_CHECK(x.stride(2) == 1 || x.stride(1) == 1);
const bool is_channel_last = x.stride(1) == 1 && x.stride(2) > 1;
if (!is_channel_last && dout.stride(2) != 1) { dout = dout.contiguous(); }
if (is_channel_last && dout.stride(1) != 1) { dout = dout.transpose(-1, -2).contiguous().transpose(-1, -2); }
if (is_channel_last) {
TORCH_CHECK(dim % 8 == 0, "causal_conv1d only supports channel dimension divisible by 8 for now");
TORCH_CHECK(x.stride(2) % 8 == 0 and x.stride(0) % 8 == 0, "causal_conv1d with channel last layout requires strides (x.stride(0) and x.stride(2)) to be multiples of 8");
TORCH_CHECK(dout.stride(2) % 8 == 0 and dout.stride(0) % 8 == 0, "causal_conv1d with channel last layout requires strides (dout.stride(0) and dout.stride(2)) to be multiples of 8");
}
if (bias_.has_value()) {
auto bias = bias_.value();
TORCH_CHECK(bias.scalar_type() == weight_type);
TORCH_CHECK(bias.is_cuda());
TORCH_CHECK(bias.stride(-1) == 1);
CHECK_SHAPE(bias, dim);
}
if (seq_idx_.has_value()) {
TORCH_CHECK(is_channel_last, "seq_idx only supported for channel last layout");
auto seq_idx = seq_idx_.value();
TORCH_CHECK(seq_idx.scalar_type() == torch::kInt32);
TORCH_CHECK(seq_idx.is_cuda());
TORCH_CHECK(seq_idx.is_contiguous());
CHECK_SHAPE(seq_idx, batch_size, seqlen);
}
at::Tensor dx;
if (dx_.has_value()) {
dx = dx_.value();
TORCH_CHECK(dx.scalar_type() == input_type);
TORCH_CHECK(dx.is_cuda());
CHECK_SHAPE(dx, batch_size, dim, seqlen);
if (!is_channel_last) { TORCH_CHECK(dx.stride(2) == 1); }
if (is_channel_last) { TORCH_CHECK(dx.stride(1) == 1); }
} else {
dx = torch::empty_like(x);
}
// Otherwise the kernel will be launched from cuda:0 device
// Cast to char to avoid compiler warning about narrowing
at::cuda::CUDAGuard device_guard{(char)x.get_device()};
at::Tensor dweight = torch::zeros_like(weight, weight.options().dtype(at::kFloat));
at::Tensor dbias;
if (bias_.has_value()) { dbias = torch::zeros_like(bias_.value(), bias_.value().options().dtype(at::kFloat)); }
ConvParamsBwd params;
set_conv_params_bwd(params, batch_size, dim, seqlen, width,
x, weight, bias_.has_value() ? bias_.value().data_ptr() : nullptr,
dout, dx, dweight, bias_.has_value() ? dbias.data_ptr() : nullptr,
silu_activation);
if (seq_idx_.has_value()) {
params.seq_idx_ptr = seq_idx_.value().data_ptr();
} else {
params.seq_idx_ptr = nullptr;
}
if (initial_states_.has_value()) {
TORCH_CHECK(is_channel_last, "initial_states is only supported for channel last layout");
auto initial_states = initial_states_.value();
TORCH_CHECK(initial_states.scalar_type() == input_type);
TORCH_CHECK(initial_states.is_cuda());
CHECK_SHAPE(initial_states, batch_size, dim, width - 1);
TORCH_CHECK(initial_states.stride(1) == 1);
params.initial_states_ptr = initial_states.data_ptr();
params.initial_states_batch_stride = initial_states.stride(0);
params.initial_states_c_stride = initial_states.stride(1);
params.initial_states_l_stride = initial_states.stride(2);
} else {
params.initial_states_ptr = nullptr;
}
if (dfinal_states_.has_value()) {
TORCH_CHECK(is_channel_last, "dfinal_states is only supported for channel last layout");
auto dfinal_states = dfinal_states_.value();
TORCH_CHECK(dfinal_states.scalar_type() == input_type);
TORCH_CHECK(dfinal_states.is_cuda());
CHECK_SHAPE(dfinal_states, batch_size, dim, width - 1);
params.dfinal_states_ptr = dfinal_states.data_ptr();
params.dfinal_states_batch_stride = dfinal_states.stride(0);
params.dfinal_states_c_stride = dfinal_states.stride(1);
params.dfinal_states_l_stride = dfinal_states.stride(2);
} else {
params.dfinal_states_ptr = nullptr;
}
at::Tensor dinitial_states;
if (return_dinitial_states) {
dinitial_states = torch::empty({batch_size, width - 1, dim}, x.options()).transpose(1, 2);
TORCH_CHECK(dinitial_states.stride(1) == 1);
params.dinitial_states_ptr = dinitial_states.data_ptr();
params.dinitial_states_batch_stride = dinitial_states.stride(0);
params.dinitial_states_c_stride = dinitial_states.stride(1);
params.dinitial_states_l_stride = dinitial_states.stride(2);
} else {
params.dinitial_states_ptr = nullptr;
}
auto stream = at::cuda::getCurrentCUDAStream().stream();
DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(x.scalar_type(), "causal_conv1d_bwd", [&] {
DISPATCH_WTYPE_FLOAT_AND_HALF_AND_BF16(weight.scalar_type(), "causal_conv1d_bwd", [&] {
if (!is_channel_last) {
causal_conv1d_bwd_cuda<input_t, weight_t>(params, stream);
} else {
causal_conv1d_channellast_bwd_cuda<input_t, weight_t>(params, stream);
}
});
});
return {dx, dweight.to(weight.dtype()), bias_.has_value() ? dbias.to(bias_.value().dtype()) : dbias, dinitial_states};
}
at::Tensor
causal_conv1d_update(const at::Tensor &x,
const at::Tensor &conv_state,
const at::Tensor &weight,
const c10::optional<at::Tensor> &bias_,
bool silu_activation,
const c10::optional<at::Tensor> &cache_seqlens_
) {
auto input_type = x.scalar_type();
auto weight_type = weight.scalar_type();
TORCH_CHECK(input_type == at::ScalarType::Float || input_type == at::ScalarType::Half || input_type == at::ScalarType::BFloat16);
TORCH_CHECK(weight_type == at::ScalarType::Float || weight_type == at::ScalarType::Half || weight_type == at::ScalarType::BFloat16);
TORCH_CHECK(conv_state.scalar_type() == input_type);
TORCH_CHECK(x.is_cuda());
TORCH_CHECK(conv_state.is_cuda());
TORCH_CHECK(weight.is_cuda());
const auto sizes = x.sizes();
const int batch_size = sizes[0];
const int dim = sizes[1];
const int seqlen = sizes[2];
const int width = weight.size(-1);
const int conv_state_len = conv_state.size(2);
TORCH_CHECK(conv_state_len >= width - 1);
CHECK_SHAPE(x, batch_size, dim, seqlen);
CHECK_SHAPE(conv_state, batch_size, dim, conv_state_len);
CHECK_SHAPE(weight, dim, width);
TORCH_CHECK(width >= 2 && width <= 4, "causal_conv1d only supports width between 2 and 4");
if (bias_.has_value()) {
auto bias = bias_.value();
TORCH_CHECK(bias.scalar_type() == weight_type);
TORCH_CHECK(bias.is_cuda());
TORCH_CHECK(bias.stride(-1) == 1);
CHECK_SHAPE(bias, dim);
}
at::Tensor out = torch::empty_like(x);
ConvParamsBase params;
set_conv_params_fwd(params, batch_size, dim, seqlen, width, x, weight, out,
bias_.has_value() ? bias_.value().data_ptr() : nullptr,
silu_activation);
params.conv_state_ptr = conv_state.data_ptr();
params.conv_state_len = conv_state_len;
// All stride are in elements, not bytes.
params.conv_state_batch_stride = conv_state.stride(0);
params.conv_state_c_stride = conv_state.stride(1);
params.conv_state_l_stride = conv_state.stride(2);
if (cache_seqlens_.has_value()) {
auto cache_seqlens = cache_seqlens_.value();
TORCH_CHECK(cache_seqlens.scalar_type() == torch::kInt32);
TORCH_CHECK(cache_seqlens.is_cuda());
TORCH_CHECK(cache_seqlens.stride(-1) == 1);
CHECK_SHAPE(cache_seqlens, batch_size);
params.cache_seqlens = cache_seqlens.data_ptr<int32_t>();
} else {
params.cache_seqlens = nullptr;
}
// Otherwise the kernel will be launched from cuda:0 device
// Cast to char to avoid compiler warning about narrowing
at::cuda::CUDAGuard device_guard{(char)x.get_device()};
auto stream = at::cuda::getCurrentCUDAStream().stream();
DISPATCH_ITYPE_FLOAT_AND_HALF_AND_BF16(x.scalar_type(), "causal_conv1d_update", [&] {
DISPATCH_WTYPE_FLOAT_AND_HALF_AND_BF16(weight.scalar_type(), "causal_conv1d_update", [&] {
causal_conv1d_update_cuda<input_t, weight_t>(params, stream);
});
});
return out;
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("causal_conv1d_fwd", &causal_conv1d_fwd, "Causal conv1d forward");
m.def("causal_conv1d_bwd", &causal_conv1d_bwd, "Causal conv1d backward");
m.def("causal_conv1d_update", &causal_conv1d_update, "Causal conv1d update");
}
|