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Commit 876096d5 authored by Jared Casper's avatar Jared Casper
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Merge branch 'softmax_perf' into 'main' 

softmax data load/store optimization

See merge request ADLR/megatron-lm!249
parents 1a2cb60c 3b12ab15
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Showing with 177 additions and 78 deletions
megatron/fused_kernels/scaled_masked_softmax.h
View file @ 876096d5
......@@ -26,6 +26,21 @@
namespace {
template <typename Datatype, int ELEMENTS_PER_LDG>
__device__ __inline__ void copy_vector(Datatype *dst, const Datatype *src);
template <>
__device__ __inline__ void copy_vector<__half, 1>(__half *dst, const __half *src) { *dst = *src; }
template <>
__device__ __inline__ void copy_vector<__half, 4>(__half *dst, const __half *src) { *((float2*) dst) = *((float2*) src); }
template <>
__device__ __inline__ void copy_vector<uint8_t, 1>(uint8_t *dst, const uint8_t *src) { *dst = *src; }
template <>
__device__ __inline__ void copy_vector<uint8_t, 4>(uint8_t *dst, const uint8_t *src) {*((half2*) dst) = *((half2*) src); }
int log2_ceil(int value) {
int log2_value = 0;
while ((1 << log2_value) < value) ++log2_value;
......@@ -90,13 +105,14 @@ __global__ void scaled_masked_softmax_warp_forward(
constexpr int WARP_SIZE = (next_power_of_two < C10_WARP_SIZE) ? next_power_of_two : C10_WARP_SIZE;
constexpr int WARP_ITERATIONS = next_power_of_two / WARP_SIZE;
constexpr int WARP_BATCH = (next_power_of_two <= 128) ? 2 : 1;
constexpr int ELEMENTS_PER_LDG_STG = 4;
// blockDim/threadIdx = (WARP_SIZE, WARPS_PER_BLOCK, )
// gridDim/blockIdx = (seq_len, attn_heads, batches)
int first_batch = (blockDim.y * (blockIdx.x + gridDim.x * (blockIdx.y + gridDim.y * blockIdx.z))+ threadIdx.y) * WARP_BATCH;
int pad_first_batch = 0;
if (pad_batches != 1) { // bert style
pad_first_batch = (blockDim.y * (blockIdx.x + gridDim.x * blockIdx.z) + threadIdx.y) * WARP_BATCH;
pad_first_batch = (blockDim.y * (blockIdx.x + gridDim.x * blockIdx.z) + threadIdx.y) * WARP_BATCH;
} else { // gpt2 style
pad_first_batch = (blockDim.y * blockIdx.x + threadIdx.y) * WARP_BATCH;
}
......@@ -110,29 +126,40 @@ __global__ void scaled_masked_softmax_warp_forward(
// there might be multiple batches per warp. compute the index within the batch
int local_idx = threadIdx.x;
src += first_batch * element_count + local_idx;
dst += first_batch * element_count + local_idx;
mask += pad_first_batch * element_count + local_idx;
src += first_batch * element_count + ELEMENTS_PER_LDG_STG * local_idx;
dst += first_batch * element_count + ELEMENTS_PER_LDG_STG * local_idx;
mask += pad_first_batch * element_count + ELEMENTS_PER_LDG_STG * local_idx;
// load data from global memory
acc_t elements[WARP_BATCH][WARP_ITERATIONS];
input_t temp_data[ELEMENTS_PER_LDG_STG];
uint8_t temp_mask[ELEMENTS_PER_LDG_STG];
#pragma unroll
for (int i = 0; i < WARP_BATCH; ++i) {
int batch_element_count = (i >= local_batches) ? 0 : element_count;
#pragma unroll
for (int it = 0; it < WARP_ITERATIONS; ++it) {
int element_index = local_idx + it * WARP_SIZE;
int itr_idx = i*element_count+it*WARP_SIZE;
#pragma unroll
for (int it = 0; it < WARP_ITERATIONS; it+=ELEMENTS_PER_LDG_STG) {
int element_index = ELEMENTS_PER_LDG_STG * local_idx + it * WARP_SIZE;
if (element_index < batch_element_count) {
if (mask[itr_idx] != 1) {
elements[i][it] = (acc_t)src[itr_idx] * scale;
} else {
elements[i][it] = -10000.0;
}
int itr_idx = i*element_count+it*WARP_SIZE;
copy_vector<input_t, ELEMENTS_PER_LDG_STG>(temp_data, src + itr_idx);
copy_vector<uint8_t, ELEMENTS_PER_LDG_STG>(temp_mask, mask + itr_idx);
#pragma unroll
for (int element = 0; element < ELEMENTS_PER_LDG_STG; ++element) {
if (temp_mask[element] != 1) {
elements[i][it + element] = (acc_t)temp_data[element] * scale;
} else {
elements[i][it + element] = -10000.0;
}
}
} else {
elements[i][it] = -std::numeric_limits<acc_t>::infinity();
#pragma unroll
for (int element = 0; element < ELEMENTS_PER_LDG_STG; ++element) {
elements[i][it + element] = -std::numeric_limits<acc_t>::infinity();
}
}
}
}
......@@ -161,15 +188,20 @@ __global__ void scaled_masked_softmax_warp_forward(
warp_reduce<acc_t, WARP_BATCH, WARP_SIZE, Add>(sum);
// store result
output_t out[ELEMENTS_PER_LDG_STG];
#pragma unroll
for (int i = 0; i < WARP_BATCH; ++i) {
if (i >= local_batches)
break;
#pragma unroll
for (int it = 0; it < WARP_ITERATIONS; ++it) {
int element_index = local_idx + it * WARP_SIZE;
for (int it = 0; it < WARP_ITERATIONS; it+=ELEMENTS_PER_LDG_STG) {
int element_index = ELEMENTS_PER_LDG_STG * local_idx + it * WARP_SIZE;
if (element_index < element_count) {
dst[i*element_count+it*WARP_SIZE] = (output_t)(elements[i][it] / sum[i]);
#pragma unroll
for (int element = 0; element < ELEMENTS_PER_LDG_STG; ++element) {
out[element] = elements[i][it + element] / sum[i];
}
copy_vector<output_t, ELEMENTS_PER_LDG_STG>(dst + i * element_count + it * WARP_SIZE, out);
} else {
break;
}
......@@ -192,6 +224,7 @@ __global__ void scaled_masked_softmax_warp_backward(
constexpr int WARP_SIZE = (next_power_of_two < C10_WARP_SIZE) ? next_power_of_two : C10_WARP_SIZE;
constexpr int WARP_ITERATIONS = next_power_of_two / WARP_SIZE;
constexpr int WARP_BATCH = (next_power_of_two <= 128) ? 2 : 1;
constexpr int ELEMENTS_PER_LDG_STG = 4;
// blockDim/threadIdx = (WARP_SIZE, WARPS_PER_BLOCK, )
// gridDim/blockIdx = (seq_len, attn_heads, batches)
......@@ -207,36 +240,36 @@ __global__ void scaled_masked_softmax_warp_backward(
int local_idx = threadIdx.x;
// the first element to process by the current thread
int thread_offset = first_batch * element_count + local_idx;
int thread_offset = first_batch * element_count + ELEMENTS_PER_LDG_STG * local_idx;
grad += thread_offset;
output += thread_offset;
gradInput += thread_offset;
// load data from global memory
acc_t grad_reg[WARP_BATCH][WARP_ITERATIONS] { 0.0f };
acc_t output_reg[WARP_BATCH][WARP_ITERATIONS];
acc_t output_reg[WARP_BATCH][WARP_ITERATIONS] { 0.0f };
input_t temp_grad[ELEMENTS_PER_LDG_STG];
input_t temp_output[ELEMENTS_PER_LDG_STG];
#pragma unroll
for (int i = 0; i < WARP_BATCH; ++i) {
int batch_element_count = (i >= local_batches) ? 0 : element_count;
#pragma unroll
for (int it = 0; it < WARP_ITERATIONS; ++it) {
int element_index = local_idx + it * WARP_SIZE;
if (element_index < batch_element_count) {
output_reg[i][it] = output[i*element_count+it*WARP_SIZE];
} else {
output_reg[i][it] = acc_t(0);
}
}
#pragma unroll
for (int it = 0; it < WARP_ITERATIONS; ++it) {
int element_index = local_idx + it * WARP_SIZE;
if (element_index < batch_element_count) {
grad_reg[i][it] = (acc_t)grad[i*element_count+it*WARP_SIZE] * output_reg[i][it];
} else {
grad_reg[i][it] = acc_t(0);
}
for (int it = 0; it < WARP_ITERATIONS; it+=ELEMENTS_PER_LDG_STG) {
int element_index = ELEMENTS_PER_LDG_STG * local_idx + it * WARP_SIZE;
if (element_index < batch_element_count) {
copy_vector<input_t, ELEMENTS_PER_LDG_STG>(temp_grad, grad + i * element_count + it * WARP_SIZE);
copy_vector<input_t, ELEMENTS_PER_LDG_STG>(temp_output, output + i * element_count + it * WARP_SIZE);
#pragma unroll
for (int element = 0; element < ELEMENTS_PER_LDG_STG; ++element) {
output_reg[i][it + element] = (acc_t)temp_output[element];
}
#pragma unroll
for (int element = 0; element < ELEMENTS_PER_LDG_STG; ++element) {
grad_reg[i][it + element] = (acc_t)temp_grad[element] * output_reg[i][it + element];
}
}
}
}
......@@ -257,11 +290,16 @@ __global__ void scaled_masked_softmax_warp_backward(
if (i >= local_batches)
break;
#pragma unroll
for (int it = 0; it < WARP_ITERATIONS; ++it) {
int element_index = local_idx + it * WARP_SIZE;
for (int it = 0; it < WARP_ITERATIONS; it+=ELEMENTS_PER_LDG_STG) {
int element_index = ELEMENTS_PER_LDG_STG * local_idx + it * WARP_SIZE;
if (element_index < element_count) {
// compute gradients
gradInput[i*element_count+it*WARP_SIZE] = (output_t)(scale * (grad_reg[i][it] - output_reg[i][it] * sum[i]));
output_t out[ELEMENTS_PER_LDG_STG];
#pragma unroll
for (int element = 0; element < ELEMENTS_PER_LDG_STG; ++element) {
out[element] = (output_t)(scale * (grad_reg[i][it + element] - output_reg[i][it + element] * sum[i]));
}
copy_vector<output_t, ELEMENTS_PER_LDG_STG>(gradInput + i * element_count + it * WARP_SIZE, out);
}
}
}
......@@ -299,8 +337,8 @@ void dispatch_scaled_masked_softmax_forward(
constexpr int threads_per_block = 128;
int warps_per_block = (threads_per_block / warp_size);
int batches_per_block = warps_per_block * batches_per_warp;
TORCH_INTERNAL_ASSERT(query_seq_len%batches_per_block == 0);
int batches_per_block = warps_per_block * batches_per_warp;
TORCH_INTERNAL_ASSERT(query_seq_len%batches_per_block == 0);
dim3 blocks(query_seq_len/batches_per_block, attn_heads, batches);
dim3 threads(warp_size, warps_per_block, 1);
// Launch code would be more elegant if C++ supported FOR CONSTEXPR
......@@ -388,7 +426,7 @@ void dispatch_scaled_masked_softmax_backward(
constexpr int threads_per_block = 128;
int warps_per_block = (threads_per_block / warp_size);
int batches_per_block = warps_per_block * batches_per_warp;
int batches_per_block = warps_per_block * batches_per_warp;
int blocks = batch_count/batches_per_block;
dim3 threads(warp_size, warps_per_block, 1);
// Launch code would be more elegant if C++ supported FOR CONSTEXPR
......
megatron/fused_kernels/scaled_upper_triang_masked_softmax.h
View file @ 876096d5
......@@ -26,6 +26,31 @@
namespace {
template <typename Datatype, int ELEMENTS_PER_LDG>
__device__ __inline__ void copy_vector(Datatype *dst, const Datatype *src);
template <>
__device__ __inline__ void copy_vector<__half, 1>(__half *dst, const __half *src) { *dst = *src; }
template <>
__device__ __inline__ void copy_vector<__half, 4>(__half *dst, const __half *src) { *((float2*) dst) = *((float2*) src); }
template <>
__device__ __inline__ void copy_vector<uint8_t, 1>(uint8_t *dst, const uint8_t *src) { *dst = *src; }
template <>
__device__ __inline__ void copy_vector<uint8_t, 4>(uint8_t *dst, const uint8_t *src) {*((half2*) dst) = *((half2*) src); }
template <typename Datatype, int ELEMENTS_PER_LDG>
__device__ __inline__ void copy_zero_vector(Datatype *dst);
template <>
__device__ __inline__ void copy_zero_vector<__half, 1>(__half *dst) { *dst = 0.0; }
template <>
__device__ __inline__ void copy_zero_vector<__half, 4>(__half *dst) { *((float2*) dst) = make_float2(0.0f, 0.0f); }
int log2_ceil(int value) {
int log2_value = 0;
while ((1 << log2_value) < value) ++log2_value;
......@@ -73,7 +98,7 @@ __device__ __forceinline__ void warp_reduce(acc_t* sum) {
* Extended softmax (from native aten pytorch) with following additional features
* 1) input scaling
* 2) Implicit time (diagonal masking)
*/
*/
template <typename input_t, typename output_t, typename acc_t, int log2_elements>
__global__ void scaled_upper_triang_masked_softmax_warp_forward(
output_t *dst,
......@@ -89,10 +114,11 @@ __global__ void scaled_upper_triang_masked_softmax_warp_forward(
constexpr int WARP_SIZE = (next_power_of_two < C10_WARP_SIZE) ? next_power_of_two : C10_WARP_SIZE;
constexpr int WARP_ITERATIONS = next_power_of_two / WARP_SIZE;
constexpr int WARP_BATCH = (next_power_of_two <= 128) ? 2 : 1;
constexpr int ELEMENTS_PER_LDG_STG = 4;
int first_batch = (blockDim.y * blockIdx.y + threadIdx.y) * gridDim.x * WARP_BATCH + blockIdx.x;
int local_seq = blockIdx.x + 1;
int warp_iteration_limit = (local_seq + WARP_SIZE - 1)/WARP_SIZE;
int warp_iteration_limit = (local_seq + ELEMENTS_PER_LDG_STG * WARP_SIZE - 1)/ WARP_SIZE;
// micro_batch_size might not be a multiple of WARP_BATCH. Check how
// many batches have to computed within this WARP.
......@@ -103,22 +129,36 @@ __global__ void scaled_upper_triang_masked_softmax_warp_forward(
// there might be multiple batches per warp. compute the index within the batch
int local_idx = threadIdx.x;
src += first_batch * stride + local_idx;
dst += first_batch * stride + local_idx;
src += first_batch * stride + ELEMENTS_PER_LDG_STG * local_idx;
dst += first_batch * stride + ELEMENTS_PER_LDG_STG * local_idx;
// load data from global memory
acc_t elements[WARP_BATCH][WARP_ITERATIONS];
input_t temp_data[ELEMENTS_PER_LDG_STG];
#pragma unroll
for (int i = 0; i < WARP_BATCH; ++i) {
int batch_element_count = (i >= local_batches) ? 0 : local_seq;
#pragma unroll
for (int it = 0; it < WARP_ITERATIONS; ++it) {
int element_index = local_idx + it * WARP_SIZE;
#pragma unroll
for (int it = 0; it < WARP_ITERATIONS; it+=ELEMENTS_PER_LDG_STG) {
int element_index = ELEMENTS_PER_LDG_STG * local_idx + it * WARP_SIZE;
if (element_index < batch_element_count) {
elements[i][it] = (acc_t)src[i*element_count*stride+it*WARP_SIZE] * scale;
copy_vector<input_t, ELEMENTS_PER_LDG_STG>(temp_data, src + i*element_count*stride + it*WARP_SIZE);
#pragma unroll
for (int element = 0; element < ELEMENTS_PER_LDG_STG; ++element) {
if ((element_index + element) < batch_element_count) {
elements[i][it+element] = (acc_t)temp_data[element] * scale;
} else {
elements[i][it + element] = -std::numeric_limits<acc_t>::infinity();
}
}
} else {
elements[i][it] = -std::numeric_limits<acc_t>::infinity();
#pragma unroll
for (int element = 0; element < ELEMENTS_PER_LDG_STG; ++element) {
elements[i][it + element] = -std::numeric_limits<acc_t>::infinity();
}
}
}
}
......@@ -140,26 +180,37 @@ __global__ void scaled_upper_triang_masked_softmax_warp_forward(
for (int i = 0; i < WARP_BATCH; ++i) {
#pragma unroll
for (int it = 0; it < WARP_ITERATIONS; ++it) {
if (it < warp_iteration_limit) {
if (it < warp_iteration_limit) {
elements[i][it] = std::exp((elements[i][it] - max_value[i]));
sum[i] += elements[i][it];
}
}
}
}
warp_reduce<acc_t, WARP_BATCH, WARP_SIZE, Add>(sum);
// store result
output_t out[ELEMENTS_PER_LDG_STG];
#pragma unroll
for (int i = 0; i < WARP_BATCH; ++i) {
if (i >= local_batches)
break;
#pragma unroll
for (int it = 0; it < WARP_ITERATIONS; ++it) {
int element_index = local_idx + it * WARP_SIZE;
for (int it = 0; it < WARP_ITERATIONS; it+=ELEMENTS_PER_LDG_STG) {
int element_index = ELEMENTS_PER_LDG_STG * local_idx + it * WARP_SIZE;
if (element_index < local_seq) {
dst[i*element_count*stride+it*WARP_SIZE] = (output_t)(elements[i][it] / sum[i]);
#pragma unroll
for (int element = 0; element < ELEMENTS_PER_LDG_STG; ++element) {
if (element_index + element < local_seq) {
out[element] = elements[i][it + element] / sum[i];
} else {
out[element] = 0;
}
}
copy_vector<output_t, ELEMENTS_PER_LDG_STG>(dst + i * element_count * stride + it * WARP_SIZE, out);
} else if (element_index < element_count) {
dst[i*element_count*stride+it*WARP_SIZE] = 0;
copy_zero_vector<output_t, ELEMENTS_PER_LDG_STG>(dst + i * element_count * stride + it * WARP_SIZE);
} else {
break;
}
......@@ -183,6 +234,7 @@ __global__ void scaled_upper_triang_masked_softmax_warp_backward(
constexpr int WARP_SIZE = (next_power_of_two < C10_WARP_SIZE) ? next_power_of_two : C10_WARP_SIZE;
constexpr int WARP_ITERATIONS = next_power_of_two / WARP_SIZE;
constexpr int WARP_BATCH = (next_power_of_two <= 128) ? 2 : 1;
constexpr int ELEMENTS_PER_LDG_STG = 4;
int first_batch = (blockDim.y * blockIdx.y + threadIdx.y) * gridDim.x * WARP_BATCH + blockIdx.x;
int local_seq = blockIdx.x + 1;
......@@ -197,37 +249,41 @@ __global__ void scaled_upper_triang_masked_softmax_warp_backward(
int local_idx = threadIdx.x;
// the first element to process by the current thread
int thread_offset = first_batch * stride + local_idx;
int thread_offset = first_batch * stride + ELEMENTS_PER_LDG_STG * local_idx;
grad += thread_offset;
output += thread_offset;
gradInput += thread_offset;
// load data from global memory
acc_t grad_reg[WARP_BATCH][WARP_ITERATIONS] { 0.0f };
acc_t output_reg[WARP_BATCH][WARP_ITERATIONS];
acc_t output_reg[WARP_BATCH][WARP_ITERATIONS] { 0.0f };
input_t temp_grad[ELEMENTS_PER_LDG_STG];
input_t temp_output[ELEMENTS_PER_LDG_STG];
#pragma unroll
for (int i = 0; i < WARP_BATCH; ++i) {
int batch_element_count = (i >= local_batches) ? 0 : local_seq;
#pragma unroll
for (int it = 0; it < WARP_ITERATIONS; ++it) {
int element_index = local_idx + it * WARP_SIZE;
if (element_index < batch_element_count) {
output_reg[i][it] = output[i*element_count*stride+it*WARP_SIZE];
} else {
output_reg[i][it] = acc_t(0);
for (int it = 0; it < WARP_ITERATIONS; it+=ELEMENTS_PER_LDG_STG) {
int element_index = ELEMENTS_PER_LDG_STG * local_idx + it * WARP_SIZE;
if (element_index < batch_element_count) {
copy_vector<input_t, ELEMENTS_PER_LDG_STG>(temp_grad, grad + i * element_count * stride + it * WARP_SIZE);
copy_vector<input_t, ELEMENTS_PER_LDG_STG>(temp_output, output + i * element_count * stride + it * WARP_SIZE);
#pragma unroll
for (int element = 0; element < ELEMENTS_PER_LDG_STG; ++element) {
if (element_index + element < batch_element_count) {
output_reg[i][it + element] = (acc_t)temp_output[element];
}
}
#pragma unroll
for (int element = 0; element < ELEMENTS_PER_LDG_STG; ++element) {
if (element_index + element < batch_element_count) {
grad_reg[i][it + element] = (acc_t)temp_grad[element] * output_reg[i][it + element];
}
}
}
}
#pragma unroll
for (int it = 0; it < WARP_ITERATIONS; ++it) {
int element_index = local_idx + it * WARP_SIZE;
if (element_index < batch_element_count) {
grad_reg[i][it] = (acc_t)grad[i*element_count*stride+it*WARP_SIZE] * output_reg[i][it];
} else {
grad_reg[i][it] = acc_t(0);
}
}
}
acc_t sum[WARP_BATCH];
......@@ -247,11 +303,16 @@ __global__ void scaled_upper_triang_masked_softmax_warp_backward(
if (i >= local_batches)
break;
#pragma unroll
for (int it = 0; it < WARP_ITERATIONS; ++it) {
int element_index = local_idx + it * WARP_SIZE;
for (int it = 0; it < WARP_ITERATIONS; it+=ELEMENTS_PER_LDG_STG) {
int element_index = ELEMENTS_PER_LDG_STG * local_idx + it * WARP_SIZE;
if (element_index < element_count) {
// compute gradients
gradInput[i*element_count*stride+it*WARP_SIZE] = (output_t)(scale * (grad_reg[i][it] - output_reg[i][it] * sum[i]));
output_t out[ELEMENTS_PER_LDG_STG];
#pragma unroll
for (int element = 0; element < ELEMENTS_PER_LDG_STG; ++element) {
out[element] = (output_t)(scale * (grad_reg[i][it + element] - output_reg[i][it + element] * sum[i]));
}
copy_vector<output_t, ELEMENTS_PER_LDG_STG>(gradInput + i * element_count * stride + it * WARP_SIZE, out);
}
}
}
......
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