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Commit 6e14df49 authored by rohithkrn's avatar rohithkrn
Browse files

Merge remote-tracking branch 'rocm_up/master' into apex_amp_bfp16

parents c7fd532c 2d0f9cf2
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Showing with 1438 additions and 376 deletions
apex/amp/scaler.py
View file @ 6e14df49
......@@ -6,12 +6,18 @@ from itertools import product
def scale_check_overflow_python(model_grad, master_grad, scale, check_overflow=False):
# Exception handling for 18.04 compatibility
if check_overflow:
cpu_sum = float(model_grad.float().sum())
if model_grad.is_sparse:
cpu_sum = float(model_grad.float()._values().sum())
else:
cpu_sum = float(model_grad.float().sum())
if cpu_sum == float('inf') or cpu_sum == -float('inf') or cpu_sum != cpu_sum:
return True
if master_grad is not model_grad: # copy_ probably internally short-circuits this
master_grad.copy_(model_grad)
if model_grad.is_sparse:
master_grad.copy_(model_grad.to_dense())
else:
master_grad.copy_(model_grad)
if scale != 1.0:
master_grad.mul_(scale)
return False
......@@ -19,7 +25,10 @@ def scale_check_overflow_python(model_grad, master_grad, scale, check_overflow=F
def axpby_check_overflow_python(model_grad, stashed_grad, master_grad, a, b, check_overflow=False):
# Exception handling for 18.04 compatibility
if check_overflow:
cpu_sum = float(model_grad.float().sum())
if model_grad.is_sparse:
cpu_sum = float(model_grad.float()._values().sum())
else:
cpu_sum = float(model_grad.float().sum())
if cpu_sum == float('inf') or cpu_sum == -float('inf') or cpu_sum != cpu_sum:
return True
......
apex/contrib/csrc/optimizers/fused_adam_cuda_kernel.cu
View file @ 6e14df49
......@@ -14,6 +14,17 @@
#define BLOCK_SIZE 512
#define ILP 4
template<typename T>
__device__ __forceinline__ bool is_aligned(T* p){
return ((uint64_t)p) % (ILP*sizeof(T)) == 0;
}
template<typename T>
__device__ __forceinline__ void load_store(T* dst, T* src, int dst_offset, int src_offset){
typedef typename std::aligned_storage<ILP*sizeof(T), ILP*alignof(T)>::type LT;
((LT*)dst)[dst_offset] = ((LT*)src)[src_offset];
}
#include "type_shim.h"
typedef enum{
......@@ -99,24 +110,64 @@ struct AdamFunctor
T incoming_v[ILP];
T incoming_g[ILP];
for(int i_start = 0;
i_start < n && i_start < chunk_size;
i_start += blockDim.x*ILP) {
// to make things simple, we put aligned case in a different code path
if(n % ILP == 0 &&
chunk_size % ILP == 0 &&
is_aligned(p) &&
is_aligned(m) &&
is_aligned(v) &&
is_aligned(g) &&
is_aligned(p_copy))
{
for(int i_start = threadIdx.x; i_start*ILP < n && i_start*ILP < chunk_size; i_start += blockDim.x)
{
// load
GRAD_T tmp_g[ILP];
load_store(incoming_p, p, 0, i_start);
load_store(incoming_m, m, 0, i_start);
load_store(incoming_v, v, 0, i_start);
load_store(tmp_g, g, 0, i_start);
#pragma unroll
for(int ii = 0; ii < ILP; ii++) {
incoming_g[ii] = static_cast<T>(tmp_g[ii]);
T scaled_grad = incoming_g[ii]/grad_scale;
incoming_m[ii] = b1*incoming_m[ii] + (1-b1)*scaled_grad;
incoming_v[ii] = b2*incoming_v[ii] + (1-b2)*scaled_grad*scaled_grad;
float denom;
if (mode == ADAM_MODE_0)
denom = sqrtf(incoming_v[ii] + eps);
else // Mode 1
denom = sqrtf(incoming_v[ii]) + eps;
float update = (incoming_m[ii]/denom) + (decay*incoming_p[ii]);
incoming_p[ii] = incoming_p[ii] - (step_size*update);
if (DEPTH == 5) tmp_g[ii] = static_cast<GRAD_T>(incoming_p[ii]);
}
load_store(p, incoming_p, i_start, 0);
load_store(m, incoming_m, i_start, 0);
load_store(v, incoming_v, i_start, 0);
if (DEPTH == 5) load_store(p_copy, tmp_g, i_start, 0);
}
}
else
{
for(int i_start = 0;
i_start < n && i_start < chunk_size;
i_start += blockDim.x*ILP) {
#pragma unroll
#pragma unroll
for(int ii = 0; ii < ILP; ii++) {
incoming_p[ii] = 0;
incoming_m[ii] = 0;
incoming_v[ii] = 0;
incoming_g[ii] = 0;
incoming_p[ii] = 0;
incoming_m[ii] = 0;
incoming_v[ii] = 0;
incoming_g[ii] = 0;
int i = i_start + threadIdx.x + ii*blockDim.x;
if (i < n && i < chunk_size) {
incoming_p[ii] = p[i];
incoming_m[ii] = m[i];
incoming_v[ii] = v[i];
incoming_g[ii] = static_cast<T>(g[i]);
}
int i = i_start + threadIdx.x + ii*blockDim.x;
if (i < n && i < chunk_size) {
incoming_p[ii] = p[i];
incoming_m[ii] = m[i];
incoming_v[ii] = v[i];
incoming_g[ii] = static_cast<T>(g[i]);
}
}
// note for clarification to future michael:
......@@ -124,24 +175,25 @@ struct AdamFunctor
// the write loop, since writes just fire off once their LDGs arrive.
// Put another way, the STGs are dependent on the LDGs, but not on each other.
// There is still compute ILP benefit from unrolling the loop though.
#pragma unroll
#pragma unroll
for(int ii = 0; ii < ILP; ii++) {
int j = i_start + threadIdx.x + ii*blockDim.x;
int j = i_start + threadIdx.x + ii*blockDim.x;
if(j < n && j < chunk_size) {
T scaled_grad = incoming_g[ii]/grad_scale;
m[j] = b1*incoming_m[ii] + (1-b1)*scaled_grad;
v[j] = b2*incoming_v[ii] + (1-b2)*scaled_grad*scaled_grad;
float denom;
if (mode == ADAM_MODE_0)
denom = sqrtf(v[j] + eps);
else // Mode 1
denom = sqrtf(v[j]) + eps;
float update = (m[j]/denom) + (decay*incoming_p[ii]);
p[j] = incoming_p[ii] - (step_size*update);
if (DEPTH == 5) p_copy[j] = (GRAD_T) p[j];
}
if(j < n && j < chunk_size) {
T scaled_grad = incoming_g[ii]/grad_scale;
m[j] = b1*incoming_m[ii] + (1-b1)*scaled_grad;
v[j] = b2*incoming_v[ii] + (1-b2)*scaled_grad*scaled_grad;
float denom;
if (mode == ADAM_MODE_0)
denom = sqrtf(v[j] + eps);
else // Mode 1
denom = sqrtf(v[j]) + eps;
float update = (m[j]/denom) + (decay*incoming_p[ii]);
p[j] = incoming_p[ii] - (step_size*update);
if (DEPTH == 5) p_copy[j] = (GRAD_T) p[j];
}
}
}
}
}
};
......@@ -332,4 +384,3 @@ void fused_adam_cuda_mt(
}
THCudaCheck(cudaGetLastError());
}
apex/contrib/csrc/xentropy/xentropy_kernel.cu
View file @ 6e14df49
/**
* From PyTorch:
*
*
* Copyright (c) 2016- Facebook, Inc (Adam Paszke)
* Copyright (c) 2014- Facebook, Inc (Soumith Chintala)
* Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
......@@ -10,54 +10,54 @@
* Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
* Copyright (c) 2006 Idiap Research Institute (Samy Bengio)
* Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
*
*
* From Caffe2:
*
*
* Copyright (c) 2016-present, Facebook Inc. All rights reserved.
*
*
* All contributions by Facebook:
* Copyright (c) 2016 Facebook Inc.
*
*
* All contributions by Google:
* Copyright (c) 2015 Google Inc.
* All rights reserved.
*
*
* All contributions by Yangqing Jia:
* Copyright (c) 2015 Yangqing Jia
* All rights reserved.
*
*
* All contributions from Caffe:
* Copyright(c) 2013, 2014, 2015, the respective contributors
* All rights reserved.
*
*
* All other contributions:
* Copyright(c) 2015, 2016 the respective contributors
* All rights reserved.
*
*
* Caffe2 uses a copyright model similar to Caffe: each contributor holds
* copyright over their contributions to Caffe2. The project versioning records
* all such contribution and copyright details. If a contributor wants to further
* mark their specific copyright on a particular contribution, they should
* indicate their copyright solely in the commit message of the change when it is
* committed.
*
*
* All rights reserved.
*
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
*
* 1. Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
*
*
* 2. Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
*
* 3. Neither the names of Facebook, Deepmind Technologies, NYU, NEC Laboratories America
* and IDIAP Research Institute nor the names of its contributors may be
* used to endorse or promote products derived from this software without
* specific prior written permission.
*
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
......@@ -70,7 +70,6 @@
* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*/
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
......@@ -84,6 +83,8 @@
#include "type_shim.h"
#include "compat.h"
#define ALIGN_BYTES 16
using Tensor = at::Tensor;
using TensorList = at::TensorList;
using ScalarType = at::ScalarType;
......@@ -123,7 +124,7 @@ const int max_threads = 1024;
inline dim3 SoftMax_getBlockSize(int ILP, uint64_t dim_size) {
uint64_t block_size = 1;
uint64_t max_block_size = std::min(dim_size / ILP, static_cast<uint64_t>(max_threads));
while (block_size < max_block_size) block_size *= 2;
while (block_size < (max_block_size/2)) block_size *= 2;
// Launch at least a single warp - the kernel assumes that.
block_size = std::max(block_size, static_cast<uint64_t>(32));
return dim3(block_size);
......@@ -287,29 +288,40 @@ blockReduce(AccumT* smem,
template <template<typename, typename> class Reduction, int ILP, typename T, typename AccumT>
__device__ __forceinline__ AccumT
ilpReduce(T* data,
ilpReduce(int shift,
T* data,
int size,
const Reduction<T, AccumT>& r,
AccumT defaultVal)
{
typedef typename std::aligned_storage<ILP*sizeof(T), ILP*alignof(T)>::type LoadT;
AccumT threadVal = defaultVal;
int offset = threadIdx.x;
// shift and do 1
if(shift > 0){
data -= shift;
size += shift;
if(threadIdx.x >= shift){
threadVal = r(threadVal, data[offset]);
}
size -= blockDim.x;
data += blockDim.x;
}
int last = size % (ILP * blockDim.x);
// Body (unroll by ILP times)
for (; offset < size - last; offset += blockDim.x * ILP) {
T tmp[ILP];
T v[ILP];
LoadT* value = reinterpret_cast<LoadT*>(&v);
#pragma unroll
for (int j = 0; j < ILP; ++j)
tmp[j] = data[offset + j * blockDim.x];
for (; offset * ILP < (size - last); offset += blockDim.x) {
*value = reinterpret_cast<LoadT*>(data)[offset];
#pragma unroll
for (int j = 0; j < ILP; ++j)
threadVal = r(threadVal, tmp[j]);
for (int j = 0; j < ILP; ++j) {
threadVal = r(threadVal, v[j]);
}
}
offset = size - last + threadIdx.x;
// Epilogue
for (; offset < size; offset += blockDim.x)
threadVal = r(threadVal, data[offset]);
......@@ -319,7 +331,8 @@ ilpReduce(T* data,
template <template<typename, typename> class Reduction1, template<typename, typename> class Reduction2, int ILP, typename T, typename AccumT>
__device__ __forceinline__ void
ilpReduce(T* data,
ilpReduce(int shift,
T* data,
int size,
AccumT* reducVal1,
const Reduction1<T, AccumT>& r1,
......@@ -328,27 +341,38 @@ ilpReduce(T* data,
const Reduction2<T, AccumT>& r2,
AccumT defaultVal2)
{
typedef typename std::aligned_storage<ILP*sizeof(T), ILP*alignof(T)>::type LoadT;
AccumT threadVal1 = defaultVal1;
AccumT threadVal2 = defaultVal2;
int offset = threadIdx.x;
// shift and do 1
if(shift > 0){
data -= shift;
size += shift;
if(threadIdx.x >= shift){
threadVal1 = r1(threadVal1, data[offset]);
threadVal2 = r2(threadVal2, data[offset]);
}
size -= blockDim.x;
data += blockDim.x;
}
int last = size % (ILP * blockDim.x);
// Body (unroll by ILP times)
for (; offset < size - last; offset += blockDim.x * ILP) {
T tmp[ILP];
T v[ILP];
LoadT* value = reinterpret_cast<LoadT*>(&v);
#pragma unroll
for (int j = 0; j < ILP; ++j)
tmp[j] = data[offset + j * blockDim.x];
for (; offset * ILP < (size - last); offset += blockDim.x) {
*value = reinterpret_cast<LoadT*>(data)[offset];
#pragma unroll
for (int j = 0; j < ILP; ++j) {
threadVal1 = r1(threadVal1, tmp[j]);
threadVal2 = r2(threadVal2, tmp[j]);
threadVal1 = r1(threadVal1, v[j]);
threadVal2 = r2(threadVal2, v[j]);
}
}
offset = size - last + threadIdx.x;
// Epilogue
for (; offset < size; offset += blockDim.x) {
threadVal1 = r1(threadVal1, data[offset]);
......@@ -375,17 +399,19 @@ cunn_SoftMaxXEntropyForward(
// each block handles a sample in the mini-batch
input += blockIdx.x * classes;
//output += blockIdx.x * classes;
const int shift = ((uint64_t)input) % ALIGN_BYTES / sizeof(scalar_t);
int64_t label = labels[blockIdx.x];
// find the max and sum
accscalar_t threadMax, threadSum, max_k, sum_k;
ilpReduce<MaxFloat, AddFloat, ILP, scalar_t, accscalar_t>(
input, classes,
&threadMax, MaxFloat<scalar_t, accscalar_t>(),
-at::numeric_limits<accscalar_t>::max(),
&threadSum, AddFloat<scalar_t, accscalar_t>(),
static_cast<accscalar_t>(0));
shift, input, classes,
&threadMax, MaxFloat<scalar_t, accscalar_t>(),
-at::numeric_limits<accscalar_t>::max(),
&threadSum, AddFloat<scalar_t, accscalar_t>(),
static_cast<accscalar_t>(0));
blockReduce<Max, Add, accscalar_t>(
sdata,
&max_k, threadMax, Max<accscalar_t>(),
......@@ -393,9 +419,7 @@ cunn_SoftMaxXEntropyForward(
&sum_k, threadSum, Add<accscalar_t>(),
static_cast<accscalar_t>(0));
// reduce all values
accscalar_t threadExp = ilpReduce<SumExpFloat, ILP, scalar_t, accscalar_t>(
input, classes, SumExpFloat<scalar_t, accscalar_t>(max_k), static_cast<accscalar_t>(0));
accscalar_t threadExp = ilpReduce<SumExpFloat, ILP, scalar_t, accscalar_t>(shift, input, classes, SumExpFloat<scalar_t, accscalar_t>(max_k), static_cast<accscalar_t>(0));
accscalar_t sumAll = blockReduce<Add, accscalar_t>(
sdata, threadExp, Add<accscalar_t>(), static_cast<accscalar_t>(0));
......@@ -411,20 +435,16 @@ cunn_SoftMaxXEntropyForward(
}
}
template <int ILP, typename scalar_t, typename accscalar_t, typename outscalar_t, template<typename, typename, typename> class Epilogue>
__global__ void
cunn_SoftMaxXEntropyBackward(
scalar_t *gradInput,
scalar_t *logits,
outscalar_t *max_log_sum_exp,
outscalar_t *gradOutput,
int64_t *labels,
const float smoothing,
int classes)
template <int ILP, typename scalar_t, typename accscalar_t, typename outscalar_t>
__device__ __forceinline__ void
apply(scalar_t *gradInput,
scalar_t *logits,
outscalar_t *max_log_sum_exp,
outscalar_t *gradOutput,
int64_t *labels,
const float smoothing,
int classes)
{
gradInput += blockIdx.x * classes;
logits += blockIdx.x * classes;
accscalar_t smooth_positives = 1.0 - smoothing;
accscalar_t smooth_negatives = smoothing / classes;
accscalar_t tmpGradOutput = gradOutput[blockIdx.x];
......@@ -433,6 +453,7 @@ cunn_SoftMaxXEntropyBackward(
int offset = threadIdx.x;
int last = classes % (ILP * blockDim.x);
for (; offset < classes - last; offset += blockDim.x * ILP) {
accscalar_t tmpLogits[ILP];
......@@ -444,22 +465,112 @@ cunn_SoftMaxXEntropyBackward(
#pragma unroll
for (int j = 0; j < ILP; ++j)
gradInput[offset + j * blockDim.x] = tmpGradOutput * (
std::exp(tmpLogits[j] - coeff) - static_cast<accscalar_t>(
(offset + j * blockDim.x == label) ? 1 : 0) *
smooth_positives - smooth_negatives);
std::exp(tmpLogits[j] - coeff) - static_cast<accscalar_t>(
(offset + j * blockDim.x == label) ? 1 : 0) *
smooth_positives - smooth_negatives);
}
for (; offset < classes; offset += blockDim.x)
gradInput[offset] = tmpGradOutput * (std::exp(
static_cast<accscalar_t>(logits[offset]) - coeff) -
static_cast<accscalar_t>(logits[offset]) - coeff) -
static_cast<accscalar_t>((offset == label) ? 1 : 0) *
smooth_positives - smooth_negatives);
}
template <int ILP, typename scalar_t, typename accscalar_t, typename outscalar_t>
__device__ __forceinline__ void
aligned_apply(int shift,
scalar_t *gradInput,
scalar_t *logits,
outscalar_t *max_log_sum_exp,
outscalar_t *gradOutput,
int64_t *labels,
const float smoothing,
int classes)
{
accscalar_t smooth_positives = 1.0 - smoothing;
accscalar_t smooth_negatives = smoothing / classes;
accscalar_t tmpGradOutput = gradOutput[blockIdx.x];
int64_t label = labels[blockIdx.x];
accscalar_t coeff = max_log_sum_exp[blockIdx.x];
int offset = threadIdx.x;
// shift and do 1
if(shift > 0){
logits -= shift;
gradInput -= shift;
classes += shift;
if(threadIdx.x >= shift){
gradInput[offset] = tmpGradOutput * (std::exp(
static_cast<accscalar_t>(logits[offset]) - coeff) -
static_cast<accscalar_t>(((offset - shift) == label) ? 1 : 0) *
smooth_positives - smooth_negatives);
}
classes -= blockDim.x;
gradInput += blockDim.x;
logits += blockDim.x;
shift -= blockDim.x;
}
int last = classes % (ILP * blockDim.x);
typedef typename std::aligned_storage<ILP*sizeof(scalar_t), ILP*alignof(scalar_t)>::type LoadT;
// input
scalar_t v[ILP];
LoadT* value = reinterpret_cast<LoadT*>(&v);
// output
scalar_t r[ILP];
LoadT* result = reinterpret_cast<LoadT*>(&r);
for (; offset * ILP < (classes - last); offset += blockDim.x) {
*value = reinterpret_cast<LoadT*>(logits)[offset];
#pragma unroll
for (int j = 0; j < ILP; ++j) {
r[j] = tmpGradOutput * (std::exp(
static_cast<accscalar_t>(v[j]) - coeff) -
static_cast<accscalar_t>(((ILP * offset + j - shift) == label) ? 1 : 0) *
smooth_positives - smooth_negatives);
}
reinterpret_cast<LoadT*>(gradInput)[offset] = *result;
}
offset = classes - last + threadIdx.x;
for (; offset < classes; offset += blockDim.x)
gradInput[offset] = tmpGradOutput * (std::exp(
static_cast<accscalar_t>(logits[offset]) - coeff) -
static_cast<accscalar_t>(((offset - shift) == label) ? 1 : 0) *
smooth_positives - smooth_negatives);
}
template <int ILP, typename scalar_t, typename accscalar_t, typename outscalar_t, template<typename, typename, typename> class Epilogue>
__global__ void
cunn_SoftMaxXEntropyBackward(
scalar_t *gradInput,
scalar_t *logits,
outscalar_t *max_log_sum_exp,
outscalar_t *gradOutput,
int64_t *labels,
const float smoothing,
int classes)
{
gradInput += blockIdx.x * classes;
logits += blockIdx.x * classes;
// Do vectorized load/store when input/output have same alignment
const int shift = ((uint64_t)logits) % ALIGN_BYTES / sizeof(scalar_t);
const int shift_ = ((uint64_t)gradInput) % ALIGN_BYTES / sizeof(scalar_t);
if (shift == shift_){
aligned_apply<ILP, scalar_t, accscalar_t, outscalar_t>(shift, gradInput, logits, max_log_sum_exp, gradOutput, labels, smoothing, classes);
}
else {
apply<ILP, scalar_t, accscalar_t, outscalar_t>(gradInput, logits, max_log_sum_exp, gradOutput, labels, smoothing, classes);
}
}
template<template<typename, typename, typename> class Epilogue>
std::vector<Tensor> host_softmax_xentropy(
......@@ -495,13 +606,13 @@ std::vector<Tensor> host_softmax_xentropy(
// XXX: it assumes that inner_size == 1
TORCH_CHECK(inner_size == 1, "Currently only inner size 1 supported");
const int ILP = 2;
dim3 grid(outer_size);
dim3 block = SoftMax_getBlockSize(ILP, dim_size);
using namespace at;
DISPATCH_FLOAT_AND_HALF(input.scalar_type(), 0, "host_softmax_xentropy",
using accscalar_t = at::acc_type<scalar_t_0, true>;
const int ILP = sizeof(float4)/sizeof(scalar_t_0);
dim3 block = SoftMax_getBlockSize(ILP, dim_size);
if (!half_to_float) {
cunn_SoftMaxXEntropyForward<ILP, scalar_t_0, accscalar_t, scalar_t_0, Epilogue>
<<<grid, block, 2 * block.x * sizeof(accscalar_t), stream>>>(
......@@ -564,12 +675,12 @@ Tensor host_softmax_xentropy_backward(
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
TORCH_CHECK(inner_size == 1, "Currently only inner size 1 supported");
const int ILP = 2;
dim3 grid(outer_size);
dim3 block = SoftMax_getBlockSize(ILP, dim_size);
DISPATCH_FLOAT_AND_HALF(gI.scalar_type(), 0, "host_softmax_xentropy_backward",
using accscalar_t = acc_type<scalar_t_0, true>;
const int ILP = sizeof(float4)/sizeof(scalar_t_0);
dim3 block = SoftMax_getBlockSize(ILP, dim_size);
if (!half_to_float) {
cunn_SoftMaxXEntropyBackward<ILP, scalar_t_0, accscalar_t, scalar_t_0, Epilogue>
<<<grid, block, block.x * sizeof(accscalar_t), stream>>>(
......
apex/contrib/multihead_attn/self_multihead_attn_func.py
View file @ 6e14df49
......@@ -183,7 +183,7 @@ class SelfAttnFunc(torch.autograd.Function):
values_grads = torch.bmm(dropout_results.transpose(1,2), output_lin_grads, out=values_grads.transpose(0,1))
# Mask and Scaling for Dropout (not a publically documented op)
dropout_grads = torch._masked_scale(matmul2_dgrad1, dropout_mask, dropout_prob_t[0])
dropout_grads = torch._masked_scale(matmul2_dgrad1, dropout_mask, 1.0/(1.0-dropout_prob_t[0]))
# Softmax Grad (not a publically documented op)
softmax_grads = torch._softmax_backward_data(dropout_grads, softmax_results, -1, softmax_results)
......
apex/mlp/mlp.py
View file @ 6e14df49
......@@ -7,17 +7,19 @@ from .. import amp
class MlpFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, *args):
output = mlp_cuda.forward(args)
def forward(ctx, bias, activation, *args):
output = mlp_cuda.forward(bias, activation, args)
ctx.save_for_backward(*args)
ctx.outputs = output
ctx.bias = bias
ctx.activation = activation
return output[0]
@staticmethod
def backward(ctx, grad_o):
grads = mlp_cuda.backward(grad_o, ctx.outputs, ctx.saved_tensors)
grads = mlp_cuda.backward(ctx.bias, ctx.activation, grad_o, ctx.outputs, ctx.saved_tensors)
del ctx.outputs
return tuple(grads)
return (None, None, *grads)
mlp_function = amp.half_function(MlpFunction.apply)
......@@ -29,16 +31,21 @@ class MLP(torch.nn.Module):
bias (bool): Default True:
relu (bool): Default True
"""
def __init__(self, mlp_sizes, bias=True, relu=True):
if not (bias and relu):
raise TypeError("bias and relu must be both true.")
def __init__(self, mlp_sizes, bias=True, activation='relu'):
super(MLP, self).__init__()
self.num_layers = len(mlp_sizes) - 1
self.mlp_sizes = copy(mlp_sizes)
self.bias = bias
self.relu= relu
self.bias = 1 if bias else 0
if activation is 'none':
self.activation = 0
elif activation is 'relu':
self.activation = 1
elif activation is 'sigmoid':
self.activation = 2
else:
raise TypeError("activation must be relu or none.")
# ignoring bias = False now
self.weights = []
self.biases = []
for i in range(self.num_layers):
......@@ -46,10 +53,11 @@ class MLP(torch.nn.Module):
self.weights.append(w)
name = 'weight_{}'.format(i)
setattr(self, name, w)
b = torch.nn.Parameter(torch.empty(mlp_sizes[i+1]))
self.biases.append(b)
name = 'bias_{}'.format(i)
setattr(self, name, b)
if self.bias:
b = torch.nn.Parameter(torch.empty(mlp_sizes[i+1]))
self.biases.append(b)
name = 'bias_{}'.format(i)
setattr(self, name, b)
self.reset_parameters()
......@@ -58,13 +66,14 @@ class MLP(torch.nn.Module):
dimsum = weight.size(0) + weight.size(1)
std = math.sqrt(2. / float(dimsum))
nn.init.normal_(weight, 0., std)
for bias in self.biases:
std = math.sqrt(1. / float(bias.size(0)))
nn.init.normal_(bias, 0., std)
if self.bias:
for bias in self.biases:
std = math.sqrt(1. / float(bias.size(0)))
nn.init.normal_(bias, 0., std)
def forward(self, input):
return mlp_function(input, *self.weights, *self.biases)
return mlp_function(self.bias, self.activation, input, *self.weights, *self.biases)
def extra_repr(self):
s = F"MLP sizes: {self.mlp_sizes}, Bias={self.bias}, ReLU={self.relu}"
s = F"MLP sizes: {self.mlp_sizes}, Bias={self.bias}, activation={self.activation}"
return s
csrc/layer_norm_cuda_kernel.cu
View file @ 6e14df49
......@@ -172,8 +172,8 @@ void cuWelfordMuSigma2(
for (; l+7 < n2; l+=8*numx) {
for (int k = 0; k < 8; k+=2) {
float2 curr = __half22float2(*((__half2*)(lvals+l+k)));
cuWelfordOnlineSum(curr.x,mu,sigma2,count);
cuWelfordOnlineSum(curr.y,mu,sigma2,count);
cuWelfordOnlineSum<float>(curr.x,mu,sigma2,count);
cuWelfordOnlineSum<float>(curr.y,mu,sigma2,count);
}
}
for (; l < n2; ++l) {
......@@ -230,9 +230,15 @@ void cuWelfordMuSigma2(
template<typename U> U rsqrt(U v) {
return U(1) / sqrt(v);
}
#if defined __HIP_PLATFORM_HCC__
__device__ float rsqrt(float v) {
return rsqrtf(v);
}
#else
template<> float rsqrt(float v) {
return rsqrtf(v);
}
#endif
template<> double rsqrt(double v) {
return rsqrt(v);
}
......@@ -293,7 +299,7 @@ void cuApplyLayerNorm(
// 1) blockDim.x == warpSize
// 2) Tensors are contiguous
//
for (auto i1=blockIdx.y; i1 < n1; i1 += gridDim.y) {
for (int i1=blockIdx.y; i1 < n1; i1 += gridDim.y) {
SharedMemory<U> shared;
U* buf = shared.getPointer();
U mu,sigma2;
......@@ -531,7 +537,7 @@ void cuComputeGradInput(
const T* gamma,
T* grad_input)
{
for (auto i1=blockIdx.y; i1 < n1; i1 += gridDim.y) {
for (int i1=blockIdx.y; i1 < n1; i1 += gridDim.y) {
U sum_loss1 = U(0);
U sum_loss2 = U(0);
const U c_mean = mean[i1];
......
csrc/mlp.cpp
View file @ 6e14df49
......@@ -19,7 +19,9 @@ int mlp_fp(
int* output_features,
T** BPtr,
T* Y,
T* reserved_space);
T* reserved_space,
int use_bias,
int activation);
template <typename T>
int mlp_bp(
......@@ -35,11 +37,18 @@ int mlp_bp(
T* work_space,
T* dX,
T** dwPtr,
T** dbPtr);
T** dbPtr,
bool requires_grad,
int use_bias,
int activation);
std::vector<at::Tensor> mlp_forward(int use_bias, int activation, std::vector<at::Tensor> inputs) {
std::vector<at::Tensor> mlp_forward(std::vector<at::Tensor> inputs) {
// inputs contains (input, weights, biases)
auto num_layers = (inputs.size() - 1) / 2;
auto num_layers = inputs.size() - 1;
if (use_bias) {
// inputs contains (input, weights, biases)
num_layers /= 2;
}
auto batch_size = inputs[0].size(0);
auto input_features = inputs[0].size(1);
......@@ -60,7 +69,9 @@ std::vector<at::Tensor> mlp_forward(std::vector<at::Tensor> inputs) {
std::vector<scalar_t*> b_ptr;
for (int i = 0; i < num_layers; i++) {
w_ptr.push_back(inputs[i + 1].data_ptr<scalar_t>());
b_ptr.push_back(inputs[i + 1 + num_layers].data_ptr<scalar_t>());
if (use_bias) {
b_ptr.push_back(inputs[i + 1 + num_layers].data_ptr<scalar_t>());
}
}
auto result = mlp_fp<scalar_t>(
inputs[0].data_ptr<scalar_t>(),
......@@ -71,37 +82,48 @@ std::vector<at::Tensor> mlp_forward(std::vector<at::Tensor> inputs) {
output_features.data(),
b_ptr.data(),
out.data_ptr<scalar_t>(),
reserved_space.data_ptr<scalar_t>());
reserved_space.data_ptr<scalar_t>(),
use_bias,
activation);
});
return {out, reserved_space};
}
std::vector<at::Tensor> mlp_backward(
at::Tensor grad_o,
std::vector<at::Tensor> fprop_outputs,
std::vector<at::Tensor> inputs) {
// same code to get sizes and W pointers
auto num_layers = (inputs.size() - 1) / 2;
int use_bias,
int activation,
at::Tensor grad_o,
std::vector<at::Tensor> fprop_outputs,
std::vector<at::Tensor> inputs) {
auto num_layers = inputs.size() - 1;
if (use_bias) {
// inputs contains (input, weights, biases)
num_layers /= 2;
}
auto batch_size = inputs[0].size(0);
auto input_features = inputs[0].size(1);
// TODO: not creating empty tensor for it?
bool requires_grad = inputs[0].requires_grad();
std::vector<int> output_features;
for (int i = 0; i < num_layers; i++) {
output_features.push_back(inputs[i + 1].size(0));
}
// create outputs, length of inputs
// TODO: not create bias if not needed
std::vector<at::Tensor> outputs;
for (int i = 0; i < inputs.size(); i++) {
outputs.push_back(at::empty(inputs[i].sizes(), inputs[i].type())); // clone for testing now
}
AT_DISPATCH_FLOATING_TYPES_AND_HALF(inputs[0].type(), "mlp_forward", [&] {
AT_DISPATCH_FLOATING_TYPES_AND_HALF(inputs[0].type(), "mlp_backward", [&] {
std::vector<scalar_t*> w_ptr;
std::vector<scalar_t*> b_ptr;
for (int i = 0; i < num_layers; i++) {
w_ptr.push_back(inputs[i + 1].data_ptr<scalar_t>());
b_ptr.push_back(inputs[i + 1 + num_layers].data_ptr<scalar_t>());
}
std::vector<scalar_t*> outputs_ptr;
for (int i = 0; i < inputs.size(); i++) {
......@@ -127,7 +149,10 @@ std::vector<at::Tensor> mlp_backward(
work_space.data_ptr<scalar_t>(),
outputs_ptr[0],
outputs_ptr.data() + 1,
outputs_ptr.data() + 1 + num_layers);
outputs_ptr.data() + 1 + num_layers,
requires_grad,
use_bias,
activation);
});
return outputs;
......
csrc/mlp_cuda.cu
View file @ 6e14df49
This diff is collapsed.
csrc/multi_tensor_axpby_kernel.cu
View file @ 6e14df49
......@@ -13,6 +13,17 @@
#define BLOCK_SIZE 512
#define ILP 4
template<typename T>
__device__ __forceinline__ bool is_aligned(T* p){
return ((uint64_t)p) % (ILP*sizeof(T)) == 0;
}
template<typename T>
__device__ __forceinline__ void load_store(T* dst, T* src, int dst_offset, int src_offset){
typedef typename std::aligned_storage<ILP*sizeof(T), ILP*alignof(T)>::type LT;
((LT*)dst)[dst_offset] = ((LT*)src)[src_offset];
}
template<typename x_t, typename y_t, typename out_t>
struct AxpbyFunctor
{
......@@ -43,46 +54,74 @@ struct AxpbyFunctor
n -= chunk_idx*chunk_size;
// Non-divergent exit condition for __syncthreads, not necessary here
float xs[ILP];
float ys[ILP];
for(int i_start = 0;
i_start < n && i_start < chunk_size;
i_start += blockDim.x*ILP)
bool finite = true;
x_t r_x[ILP];
y_t r_y[ILP];
out_t r_out[ILP];
// to make things simple, we put aligned case in a different code path
if(n % ILP == 0 && chunk_size % ILP == 0 && is_aligned(x) && is_aligned(y) && is_aligned(out))
{
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
for(int i_start = threadIdx.x; i_start*ILP < n && i_start*ILP < chunk_size; i_start += blockDim.x)
{
xs[ii] = 0;
ys[ii] = 0;
int i = i_start + threadIdx.x + ii*blockDim.x;
if(i < n && i < chunk_size)
// load
load_store(r_x, x, 0 , i_start);
load_store(r_y, y, 0 , i_start);
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
xs[ii] = static_cast<float>(x[i]);
ys[ii] = static_cast<float>(y[i]);
r_out[ii] = a*static_cast<float>(r_x[ii]) + b*static_cast<float>(r_y[ii]);
if(arg_to_check == -1)
finite = finite && (isfinite(r_x[ii]) && isfinite(r_y[ii]));
if(arg_to_check == 0)
finite = finite && isfinite(r_x[ii]);
if(arg_to_check == 1)
finite = finite && isfinite(r_y[ii]);
}
// store
load_store(out, r_out, i_start , 0);
}
// see note in multi_tensor_scale_kernel.cu
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
}
else
{
// Non-divergent exit condition for __syncthreads, not necessary here
for(int i_start = 0; i_start < n && i_start < chunk_size; i_start += blockDim.x*ILP)
{
int i = i_start + threadIdx.x + ii*blockDim.x;
if(i < n && i < chunk_size)
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
out[i] = static_cast<out_t>(a*xs[ii] + b*ys[ii]);
bool finite = true;
r_x[ii] = 0;
r_y[ii] = 0;
int i = i_start + threadIdx.x + ii*blockDim.x;
if(i < n && i < chunk_size)
{
r_x[ii] = x[i];
r_y[ii] = y[i];
}
}
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
r_out[ii] = a*static_cast<float>(r_x[ii]) + b*static_cast<float>(r_y[ii]);
if(arg_to_check == -1)
finite = (isfinite(xs[ii]) && isfinite(ys[ii]));
finite = finite && (isfinite(r_x[ii]) && isfinite(r_y[ii]));
if(arg_to_check == 0)
finite = isfinite(xs[ii]);
finite = finite && isfinite(r_x[ii]);
if(arg_to_check == 1)
finite = isfinite(ys[ii]);
if(!finite)
*noop_gmem = 1; // Blindly fire off a write. These will race but that's ok.
finite = finite && isfinite(r_y[ii]);
}
// see note in multi_tensor_scale_kernel.cu
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
int i = i_start + threadIdx.x + ii*blockDim.x;
if(i < n && i < chunk_size)
out[i] = r_out[ii];
}
}
}
if(!finite)
*noop_gmem = 1; // Blindly fire off a write. These will race but that's ok.
}
};
......
csrc/multi_tensor_l2norm_kernel.cu
View file @ 6e14df49
......@@ -13,6 +13,17 @@
#define BLOCK_SIZE 512
#define ILP 4
template<typename T>
__device__ __forceinline__ bool is_aligned(T* p){
return ((uint64_t)p) % (ILP*sizeof(T)) == 0;
}
template<typename T>
__device__ __forceinline__ void load_store(T* dst, T* src, int dst_offset, int src_offset){
typedef typename std::aligned_storage<ILP*sizeof(T), ILP*alignof(T)>::type LT;
((LT*)dst)[dst_offset] = ((LT*)src)[src_offset];
}
template<typename x_t>
struct L2NormFunctor
{
......@@ -41,22 +52,44 @@ struct L2NormFunctor
__shared__ float s_vals[512];
float vals[ILP]; // = {0}; // this probably works too but I want to be sure...
x_t r_x[ILP];
for(int i = 0; i < ILP; i++)
{
vals[i] = 0.f;
r_x[i] = 0;
}
for(int i_start = 0; i_start < n && i_start < chunk_size; i_start += blockDim.x*ILP)
// to make things simple, we put aligned case in a different code path
if(n % ILP == 0 && chunk_size % ILP == 0 && is_aligned(x))
{
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
for(int i_start = threadIdx.x; i_start*ILP < n && i_start*ILP < chunk_size; i_start += blockDim.x)
{
int i = i_start + threadIdx.x + ii*blockDim.x;
if(i < n && i < chunk_size)
// load
load_store(r_x, x, 0 , i_start);
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
float next = static_cast<float>(x[i]);
float next = static_cast<float>(r_x[ii]);
vals[ii] += next*next;
}
}
}
else
{
for(int i_start = 0; i_start < n && i_start < chunk_size; i_start += blockDim.x*ILP)
{
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
int i = i_start + threadIdx.x + ii*blockDim.x;
if(i < n && i < chunk_size)
{
float next = static_cast<float>(x[i]);
vals[ii] += next*next;
}
}
}
}
float val = 0.f;
for(int i = 0; i < ILP; i++)
......@@ -104,22 +137,44 @@ struct MaxNormFunctor
__shared__ float s_vals[512];
float vals[ILP]; // = {0}; // this probably works too but I want to be sure...
x_t r_x[ILP];
for(int i = 0; i < ILP; i++)
{
vals[i] = 0.f;
r_x[i] = 0;
}
for(int i_start = 0; i_start < n && i_start < chunk_size; i_start += blockDim.x*ILP)
// to make things simple, we put aligned case in a different code path
if(n % ILP == 0 && chunk_size % ILP == 0 && is_aligned(x))
{
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
for(int i_start = threadIdx.x; i_start*ILP < n && i_start*ILP < chunk_size; i_start += blockDim.x)
{
int i = i_start + threadIdx.x + ii*blockDim.x;
if(i < n && i < chunk_size)
// load
load_store(r_x, x, 0 , i_start);
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
float next = static_cast<float>(x[i]);
float next = static_cast<float>(r_x[ii]);
vals[ii] = fmaxf(fabsf(vals[ii]), fabsf(next));
}
}
}
else
{
for(int i_start = 0; i_start < n && i_start < chunk_size; i_start += blockDim.x*ILP)
{
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
int i = i_start + threadIdx.x + ii*blockDim.x;
if(i < n && i < chunk_size)
{
float next = static_cast<float>(x[i]);
vals[ii] = fmaxf(fabsf(vals[ii]), fabsf(next));
}
}
}
}
float val = 0.f;
for(int i = 0; i < ILP; i++)
......
csrc/multi_tensor_lamb.cu
View file @ 6e14df49
......@@ -13,6 +13,17 @@
#define BLOCK_SIZE 512
#define ILP 4
template<typename T>
__device__ __forceinline__ bool is_aligned(T* p){
return ((uint64_t)p) % (ILP*sizeof(T)) == 0;
}
template<typename T>
__device__ __forceinline__ void load_store(T* dst, T* src, int dst_offset, int src_offset){
typedef typename std::aligned_storage<ILP*sizeof(T), ILP*alignof(T)>::type LT;
((LT*)dst)[dst_offset] = ((LT*)src)[src_offset];
}
typedef enum{
MOMENT_MODE_0 =0, // L2 regularization mode
MOMENT_MODE_1 =1 // Decoupled weight decay mode
......@@ -68,71 +79,149 @@ struct LAMBStage1Functor
n -= chunk_idx*chunk_size;
// see note in multi_tensor_scale_kernel.cu
for(int i_start = 0;
i_start < n && i_start < chunk_size;
i_start += blockDim.x*ILP)
MATH_T r_g[ILP];
MATH_T r_p[ILP];
MATH_T r_m[ILP];
MATH_T r_v[ILP];
// to make things simple, we put aligned case in a different code path
if(n % ILP == 0 &&
chunk_size % ILP == 0 &&
is_aligned(g) &&
is_aligned(p) &&
is_aligned(m) &&
is_aligned(v))
{
MATH_T r_g[ILP];
MATH_T r_p[ILP];
MATH_T r_m[ILP];
MATH_T r_v[ILP];
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
T l_g[ILP];
T l_p[ILP];
T l_m[ILP];
T l_v[ILP];
for(int i_start = threadIdx.x; i_start*ILP < n && i_start*ILP < chunk_size; i_start += blockDim.x)
{
int i = i_start + threadIdx.x + ii*blockDim.x;
if(i < n && i < chunk_size)
// load
load_store(l_g, g, 0, i_start);
if (decay != 0)
load_store(l_p, p, 0, i_start);
load_store(l_m, m, 0, i_start);
load_store(l_v, v, 0, i_start);
// unpack
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
r_g[ii] = g[i];
// special ?optimization? for lamb stage 1
r_g[ii] = l_g[ii];
if (decay == 0) {
r_p[ii] = MATH_T(0);
}
else {
r_p[ii] = p[i];
r_p[ii] = l_p[ii];
}
r_m[ii] = m[i];
r_v[ii] = v[i];
} else {
r_g[ii] = MATH_T(0);
r_p[ii] = MATH_T(0);
r_m[ii] = MATH_T(0);
r_v[ii] = MATH_T(0);
r_m[ii] = l_m[ii];
r_v[ii] = l_v[ii];
}
}
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
if (mode == MOMENT_MODE_0) {
MATH_T scaled_grad = r_g[ii] / clipped_global_grad_norm;
// L2 on scaled grad
scaled_grad = scaled_grad + decay*r_p[ii];
r_m[ii] = r_m[ii] * beta1 + beta3 * scaled_grad;
r_v[ii] = r_v[ii] * beta2 + (1-beta2) * scaled_grad * scaled_grad;
MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
MATH_T next_v_unbiased = r_v[ii] / beta2_correction;
MATH_T denom = sqrtf(next_v_unbiased) + epsilon;
r_p[ii] = next_m_unbiased / denom;
for(int ii = 0; ii < ILP; ii++)
{
if (mode == MOMENT_MODE_0) {
MATH_T scaled_grad = r_g[ii] / clipped_global_grad_norm;
// L2 on scaled grad
scaled_grad = scaled_grad + decay*r_p[ii];
r_m[ii] = r_m[ii] * beta1 + beta3 * scaled_grad;
r_v[ii] = r_v[ii] * beta2 + (1-beta2) * scaled_grad * scaled_grad;
MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
MATH_T next_v_unbiased = r_v[ii] / beta2_correction;
MATH_T denom = sqrtf(next_v_unbiased) + epsilon;
r_p[ii] = next_m_unbiased / denom;
}
else {
MATH_T scaled_grad = r_g[ii] / clipped_global_grad_norm;
r_m[ii] = r_m[ii] * beta1 + beta3 * scaled_grad;
r_v[ii] = r_v[ii] * beta2 + (1-beta2) * scaled_grad * scaled_grad;
MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
MATH_T next_v_unbiased = r_v[ii] / beta2_correction;
MATH_T denom = sqrtf(next_v_unbiased) + epsilon;
r_p[ii] = (next_m_unbiased/denom) + (decay*r_p[ii]);
}
}
else {
MATH_T scaled_grad = r_g[ii] / clipped_global_grad_norm;
r_m[ii] = r_m[ii] * beta1 + beta3 * scaled_grad;
r_v[ii] = r_v[ii] * beta2 + (1-beta2) * scaled_grad * scaled_grad;
MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
MATH_T next_v_unbiased = r_v[ii] / beta2_correction;
MATH_T denom = sqrtf(next_v_unbiased) + epsilon;
r_p[ii] = (next_m_unbiased/denom) + (decay*r_p[ii]);
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
l_p[ii] = r_p[ii];
l_m[ii] = r_m[ii];
l_v[ii] = r_v[ii];
}
// store
load_store(g, l_p, i_start, 0);
load_store(m, l_m, i_start, 0);
load_store(v, l_v, i_start, 0);
}
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
}
else
{
// see note in multi_tensor_scale_kernel.cu
for(int i_start = 0;
i_start < n && i_start < chunk_size;
i_start += blockDim.x*ILP)
{
int i = i_start + threadIdx.x + ii*blockDim.x;
if(i < n && i < chunk_size)
MATH_T r_g[ILP];
MATH_T r_p[ILP];
MATH_T r_m[ILP];
MATH_T r_v[ILP];
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
g[i] = r_p[ii];
m[i] = r_m[ii];
v[i] = r_v[ii];
int i = i_start + threadIdx.x + ii*blockDim.x;
if(i < n && i < chunk_size)
{
r_g[ii] = g[i];
// special ?optimization? for lamb stage 1
if (decay == 0) {
r_p[ii] = MATH_T(0);
}
else {
r_p[ii] = p[i];
}
r_m[ii] = m[i];
r_v[ii] = v[i];
} else {
r_g[ii] = MATH_T(0);
r_p[ii] = MATH_T(0);
r_m[ii] = MATH_T(0);
r_v[ii] = MATH_T(0);
}
}
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
if (mode == MOMENT_MODE_0) {
MATH_T scaled_grad = r_g[ii] / clipped_global_grad_norm;
// L2 on scaled grad
scaled_grad = scaled_grad + decay*r_p[ii];
r_m[ii] = r_m[ii] * beta1 + beta3 * scaled_grad;
r_v[ii] = r_v[ii] * beta2 + (1-beta2) * scaled_grad * scaled_grad;
MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
MATH_T next_v_unbiased = r_v[ii] / beta2_correction;
MATH_T denom = sqrtf(next_v_unbiased) + epsilon;
r_p[ii] = next_m_unbiased / denom;
}
else {
MATH_T scaled_grad = r_g[ii] / clipped_global_grad_norm;
r_m[ii] = r_m[ii] * beta1 + beta3 * scaled_grad;
r_v[ii] = r_v[ii] * beta2 + (1-beta2) * scaled_grad * scaled_grad;
MATH_T next_m_unbiased = r_m[ii] / beta1_correction;
MATH_T next_v_unbiased = r_v[ii] / beta2_correction;
MATH_T denom = sqrtf(next_v_unbiased) + epsilon;
r_p[ii] = (next_m_unbiased/denom) + (decay*r_p[ii]);
}
}
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
int i = i_start + threadIdx.x + ii*blockDim.x;
if(i < n && i < chunk_size)
{
g[i] = r_p[ii];
m[i] = r_m[ii];
v[i] = r_v[ii];
}
}
}
}
......@@ -173,34 +262,58 @@ struct LAMBStage2Functor
n -= chunk_idx*chunk_size;
for(int i_start = 0;
i_start < n && i_start < chunk_size;
i_start += blockDim.x*ILP)
// to make things simple, we put aligned case in a different code path
if(n % ILP == 0 &&
chunk_size % ILP == 0 &&
is_aligned(p) &&
is_aligned(update))
{
MATH_T r_p[ILP];
MATH_T r_update[ILP];
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
T r_p[ILP];
T r_update[ILP];
for(int i_start = threadIdx.x; i_start*ILP < n && i_start*ILP < chunk_size; i_start += blockDim.x)
{
int i = i_start + threadIdx.x + ii*blockDim.x;
if(i < n && i < chunk_size)
// load
load_store(r_p, p, 0, i_start);
load_store(r_update, update, 0, i_start);
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
r_p[ii] = p[i];
r_update[ii] = update[i];
r_p[ii] = static_cast<MATH_T>(r_p[ii]) - (ratio * static_cast<MATH_T>(r_update[ii]));
}
load_store(p, r_p, i_start, 0);
}
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
}
else
{
for(int i_start = 0;
i_start < n && i_start < chunk_size;
i_start += blockDim.x*ILP)
{
r_p[ii] = r_p[ii] - (ratio * r_update[ii]);
}
MATH_T r_p[ILP];
MATH_T r_update[ILP];
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
int i = i_start + threadIdx.x + ii*blockDim.x;
if(i < n && i < chunk_size)
for(int ii = 0; ii < ILP; ii++)
{
int i = i_start + threadIdx.x + ii*blockDim.x;
if(i < n && i < chunk_size)
{
r_p[ii] = p[i];
r_update[ii] = update[i];
}
}
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
r_p[ii] = r_p[ii] - (ratio * r_update[ii]);
}
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
p[i] = r_p[ii];
int i = i_start + threadIdx.x + ii*blockDim.x;
if(i < n && i < chunk_size)
{
p[i] = r_p[ii];
}
}
}
}
......
csrc/multi_tensor_scale_kernel.cu
View file @ 6e14df49
......@@ -15,6 +15,17 @@
#define BLOCK_SIZE 512
#define ILP 4
template<typename T>
__device__ __forceinline__ bool is_aligned(T* p){
return ((uint64_t)p) % (ILP*sizeof(T)) == 0;
}
template<typename T>
__device__ __forceinline__ void load_store(T* dst, T* src, int dst_offset, int src_offset){
typedef typename std::aligned_storage<ILP*sizeof(T), ILP*alignof(T)>::type LT;
((LT*)dst)[dst_offset] = ((LT*)src)[src_offset];
}
template<typename in_t, typename out_t>
struct ScaleFunctor
{
......@@ -34,44 +45,68 @@ struct ScaleFunctor
in_t* in = (in_t*)tl.addresses[0][tensor_loc];
in += chunk_idx*chunk_size;
out_t* out = (out_t*)tl.addresses[1][tensor_loc];
out += chunk_idx*chunk_size;
n -= chunk_idx*chunk_size;
// Non-divergent exit condition for __syncthreads, not necessary here
float incoming_vals[ILP];
for(int i_start = 0;
i_start < n && i_start < chunk_size;
i_start += blockDim.x*ILP)
bool finite = true;
in_t r_in[ILP];
out_t r_out[ILP];
// to make things simple, we put aligned case in a different code path
if(n % ILP == 0 && chunk_size % ILP == 0 && is_aligned(in) && is_aligned(out))
{
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
for(int i_start = threadIdx.x; i_start*ILP < n && i_start*ILP < chunk_size; i_start += blockDim.x)
{
incoming_vals[ii] = 0;
int i = i_start + threadIdx.x + ii*blockDim.x;
if(i < n && i < chunk_size)
incoming_vals[ii] = static_cast<float>(in[i]);
// load
load_store(r_in, in, 0 , i_start);
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
r_out[ii] = static_cast<float>(r_in[ii]) * scale;
finite = finite && isfinite(r_in[ii]);
}
// store
load_store(out, r_out, i_start, 0);
}
// note for clarification to future michael:
// From a pure memory dependency perspective, there's likely no point unrolling
// the write loop, since writes just fire off once their LDGs arrive.
// Put another way, the STGs are dependent on the LDGs, but not on each other.
// There is still compute ILP benefit from unrolling the loop though.
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
}
else
{
// Non-divergent exit condition for __syncthreads, not necessary here
for(int i_start = 0; i_start < n && i_start < chunk_size; i_start += blockDim.x*ILP)
{
int i = i_start + threadIdx.x + ii*blockDim.x;
if(i < n && i < chunk_size)
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
r_in[ii] = 0;
int i = i_start + threadIdx.x + ii*blockDim.x;
if(i < n && i < chunk_size)
r_in[ii] = in[i];
}
// note for clarification to future michael:
// From a pure memory dependency perspective, there's likely no point unrolling
// the write loop, since writes just fire off once their LDGs arrive.
// Put another way, the STGs are dependent on the LDGs, but not on each other.
// There is still compute ILP benefit from unrolling the loop though.
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
r_out[ii] = static_cast<float>(r_in[ii]) * scale;
finite = finite && isfinite(r_in[ii]);
}
#pragma unroll
for(int ii = 0; ii < ILP; ii++)
{
out[i] = static_cast<out_t>(incoming_vals[ii]*scale);
if(!isfinite(incoming_vals[ii]))
*noop_gmem = 1; // Blindly fire off a write. These will race but that's ok.
int i = i_start + threadIdx.x + ii*blockDim.x;
if(i < n && i < chunk_size)
out[i] = r_out[ii];
}
}
}
if(!finite)
*noop_gmem = 1; // Blindly fire off a write. These will race but that's ok.
}
};
......
setup.py
View file @ 6e14df49
......@@ -177,7 +177,13 @@ if "--cuda_ext" in sys.argv:
'-O3',
'--use_fast_math'] + version_dependent_macros}))
else:
print ("INFO: Skipping FusedLayerNorm extension.")
print ("INFO: Building FusedLayerNorm extension.")
ext_modules.append(
CUDAExtension(name='fused_layer_norm_cuda',
sources=['csrc/layer_norm_cuda.cpp',
'csrc/hip/layer_norm_hip_kernel.hip'],
extra_compile_args={'cxx' : ['-O3'] + version_dependent_macros,
'nvcc' : []}))
if not is_rocm_pytorch:
ext_modules.append(
......
tests/L0/run_mlp/test_mlp.py
View file @ 6e14df49
......@@ -51,6 +51,116 @@ class TestMLP(unittest.TestCase):
ref_mlp[0].bias.grad.detach().cpu().numpy(),
atol=1e-7, rtol=1e-5)
def test_no_bias(self):
for use_activation in ['none', 'relu', 'sigmoid']:
mlp = MLP(mlp_sizes, bias=False, activation=use_activation).cuda()
mlp_layers = []
for i in range(mlp.num_layers):
linear = nn.Linear(mlp_sizes[i], mlp_sizes[i + 1], bias=False)
mlp.weights[i].data.copy_(linear.weight)
mlp_layers.append(linear)
if use_activation == 'relu':
mlp_layers.append(nn.ReLU(inplace=True))
if use_activation == 'sigmoid':
mlp_layers.append(nn.Sigmoid())
ref_mlp = nn.Sequential(*mlp_layers).cuda()
test_input = torch.empty(batch_size, mlp_sizes[0], device="cuda").uniform_(-1., 1.).requires_grad_()
ref_input = test_input.clone().detach().requires_grad_()
mlp_out = mlp(test_input)
ref_out = ref_mlp(ref_input)
np.testing.assert_allclose(
mlp_out.detach().cpu().numpy(),
ref_out.detach().cpu().numpy(),
atol=1e-7, rtol=1e-5)
# Use mean value as scalar loss. Multiply 10 to make it big enough not zero out
mlp_out.mean().mul(10.).backward()
ref_out.mean().mul(10.).backward()
np.testing.assert_allclose(
test_input.grad.detach().cpu().numpy(),
ref_input.grad.detach().cpu().numpy(),
atol=0, rtol=100)
np.testing.assert_allclose(
mlp.weights[0].grad.detach().cpu().numpy(),
ref_mlp[0].weight.grad.detach().cpu().numpy(),
atol=1e-7, rtol=100)
def test_with_bias(self):
for use_activation in ['none', 'relu', 'sigmoid']:
mlp = MLP(mlp_sizes, bias=True, activation=use_activation).cuda()
mlp_layers = []
for i in range(mlp.num_layers):
linear = nn.Linear(mlp_sizes[i], mlp_sizes[i + 1], bias=True)
mlp.weights[i].data.copy_(linear.weight)
mlp.biases[i].data.copy_(linear.bias)
mlp_layers.append(linear)
if use_activation == 'relu':
mlp_layers.append(nn.ReLU(inplace=True))
if use_activation == 'sigmoid':
mlp_layers.append(nn.Sigmoid())
ref_mlp = nn.Sequential(*mlp_layers).cuda()
test_input = torch.empty(batch_size, mlp_sizes[0], device="cuda").uniform_(-1., 1.).requires_grad_()
ref_input = test_input.clone().detach().requires_grad_()
mlp_out = mlp(test_input)
ref_out = ref_mlp(ref_input)
np.testing.assert_allclose(
mlp_out.detach().cpu().numpy(),
ref_out.detach().cpu().numpy(),
atol=1e-7, rtol=1e-5)
# Use mean value as scalar loss. Multiply 10 to make it big enough not zero out
mlp_out.mean().mul(10.).backward()
ref_out.mean().mul(10.).backward()
np.testing.assert_allclose(
test_input.grad.detach().cpu().numpy(),
ref_input.grad.detach().cpu().numpy(),
atol=0, rtol=1)
np.testing.assert_allclose(
mlp.weights[0].grad.detach().cpu().numpy(),
ref_mlp[0].weight.grad.detach().cpu().numpy(),
atol=1e-7, rtol=1)
np.testing.assert_allclose(
mlp.biases[0].grad.detach().cpu().numpy(),
ref_mlp[0].bias.grad.detach().cpu().numpy(),
atol=1e-7, rtol=1e-5)
def test_no_grad(self):
mlp = MLP(mlp_sizes).cuda()
mlp_layers = []
for i in range(mlp.num_layers):
linear = nn.Linear(mlp_sizes[i], mlp_sizes[i + 1])
mlp.weights[i].data.copy_(linear.weight)
mlp.biases[i].data.copy_(linear.bias)
mlp_layers.append(linear)
mlp_layers.append(nn.ReLU(inplace=True))
ref_mlp = nn.Sequential(*mlp_layers).cuda()
test_input = torch.empty(batch_size, mlp_sizes[0], device="cuda").uniform_(-1., 1.)
ref_input = test_input.clone().detach()
mlp_out = mlp(test_input)
ref_out = ref_mlp(ref_input)
np.testing.assert_allclose(
mlp_out.detach().cpu().numpy(),
ref_out.detach().cpu().numpy(),
atol=1e-7, rtol=1e-5)
# Use mean value as scalar loss. Multiply 10 to make it big enough not zero out
mlp_out.mean().mul(10.).backward()
ref_out.mean().mul(10.).backward()
np.testing.assert_allclose(
mlp.weights[0].grad.detach().cpu().numpy(),
ref_mlp[0].weight.grad.detach().cpu().numpy(),
atol=1e-7, rtol=1e-5)
def test_performance_half(self):
mlp = MLP(mlp_sizes).cuda().half()
......
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