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CUTLASS 2.5

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This commit is contained in:
Andrew Kerr
2021-02-26 09:58:26 -05:00
parent ccb697bac7
commit 0e13748649
771 changed files with 15474 additions and 1715 deletions
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tools/profiler/src/conv2d_operation_profiler.h
+79 -26
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@@ -1,5 +1,5 @@
/***************************************************************************************************
* Copyright (c) 2017-2020, NVIDIA CORPORATION. All rights reserved.
* Copyright (c) 2017-2021, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted
* provided that the following conditions are met:
@@ -257,42 +257,95 @@ public:
/// host buffer for tensor c
std::vector<uint8_t> host_tensor_c;
//
// Methods
//
Conv2dWorkspace():
A(nullptr), B(nullptr), C(nullptr), Computed(nullptr), Reference(nullptr) { }
Conv2dWorkspace()
: A(nullptr),
B(nullptr),
C(nullptr),
Computed(nullptr),
Reference(nullptr) {}
// Returns stride vector for tensor A
std::vector<int> stride_a(library::ConvKind const &conv_kind) {
return {
configuration.layout_a(conv_kind).stride()[0],
configuration.layout_a(conv_kind).stride()[1],
configuration.layout_a(conv_kind).stride()[2]
};
// Set stride vector for tensor activations, filters, output
void set_stride_vector(Conv2dProblem const &problem,
library::ConvKind const &conv_kind,
library::LayoutTypeID const &layout_a,
library::LayoutTypeID const &layout_b,
library::LayoutTypeID const &layout_c) {
std::vector<int> stride_activations;
std::vector<int> stride_filters;
std::vector<int> stride_output;
// Strides for interleaved fprop
if (conv_kind == library::ConvKind::kFprop &&
((layout_a == library::LayoutTypeID::kTensorNC32HW32 &&
layout_b == library::LayoutTypeID::kTensorC32RSK32 &&
layout_c == library::LayoutTypeID::kTensorNC32HW32) ||
(layout_a == library::LayoutTypeID::kTensorNC64HW64 &&
layout_b == library::LayoutTypeID::kTensorC64RSK64 &&
layout_c == library::LayoutTypeID::kTensorNC64HW64))) {
int interleave =
(layout_a == library::LayoutTypeID::kTensorNC32HW32) ? 32 : 64;
stride_activations.push_back(int(problem.w) * interleave);
stride_activations.push_back(int(problem.w) * int(problem.h) *
interleave);
stride_activations.push_back(int(problem.h) * int(problem.w) *
int(problem.c));
stride_filters.push_back(int(problem.k) * interleave);
stride_filters.push_back(int(problem.k) * int(problem.s) * interleave);
stride_filters.push_back(int(problem.k) * int(problem.s) *
int(problem.r) * interleave);
stride_output.push_back(int(problem.q) * interleave);
stride_output.push_back(int(problem.q) * int(problem.p) * interleave);
stride_output.push_back(int(problem.q) * int(problem.p) *
int(problem.k));
} else {
// Strides for the rest cases
stride_activations.push_back(int(problem.c));
stride_activations.push_back(int(problem.w) * int(problem.c));
stride_activations.push_back(int(problem.h) * int(problem.w) *
int(problem.c));
stride_filters.push_back(int(problem.c));
stride_filters.push_back(int(problem.s) * int(problem.c));
stride_filters.push_back(int(problem.r) * int(problem.s) *
int(problem.c));
stride_output.push_back(int(problem.k));
stride_output.push_back(int(problem.q) * int(problem.k));
stride_output.push_back(int(problem.q) * int(problem.p) *
int(problem.k));
}
// Returns stride vector for tensor B
std::vector<int> stride_b(library::ConvKind const &conv_kind) {
switch (conv_kind) {
case library::ConvKind::kFprop:
configuration.stride_a = stride_activations;
configuration.stride_b = stride_filters;
configuration.stride_c = stride_output;
return {
configuration.layout_b(conv_kind).stride()[0],
configuration.layout_b(conv_kind).stride()[1],
configuration.layout_b(conv_kind).stride()[2]
};
}
break;
case library::ConvKind::kDgrad:
configuration.stride_a = stride_output;
configuration.stride_b = stride_filters;
configuration.stride_c = stride_activations;
// Returns stride vector for tensor C
std::vector<int> stride_c(library::ConvKind const &conv_kind) {
break;
case library::ConvKind::kWgrad:
configuration.stride_a = stride_output;
configuration.stride_b = stride_activations;
configuration.stride_c = stride_filters;
return {
configuration.layout_c(conv_kind).stride()[0],
configuration.layout_c(conv_kind).stride()[1],
configuration.layout_c(conv_kind).stride()[2]
};
break;
default:
throw std::runtime_error(
"Invalid Conv Operator (fprop, dgrad, wgrad)");
}
}
};
protected: