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#include <torch/csrc/jit/passes/autocast.h>
#include <ATen/autocast_mode.h>
#include <c10/core/ScalarType.h>
#include <c10/util/Exception.h>
#include <c10/util/Optional.h>
#include <torch/csrc/jit/ir/ir.h>
#include <torch/csrc/jit/jit_log.h>
#include <torch/csrc/jit/passes/cuda_graph_fuser.h>
#include <torch/csrc/jit/passes/quantization/helper.h>
#include <stack>
#include <unordered_set>
#include <vector>
namespace torch {
namespace jit {
namespace {
bool autocast_enabled = true;
struct AutocastContext {
bool gpu_enabled = false;
bool cpu_enabled = false;
c10::ScalarType gpu_scalar_type = c10::ScalarType::Undefined;
c10::ScalarType cpu_scalar_type = c10::ScalarType::Undefined;
operator bool() const {
return gpu_enabled || cpu_enabled;
}
};
struct AutocastScope {
Value* instance = nullptr;
AutocastContext context;
void stack(const AutocastContext& parent_context) {}
};
bool isAutocastNode(Value* value) {
const auto class_name = getModuleName(value);
return class_name.has_value() &&
(*class_name == "__torch__.torch.cuda.amp.autocast_mode.autocast" ||
*class_name == "__torch__.torch.cpu.amp.autocast_mode.autocast" ||
*class_name == "__torch__.torch.amp.autocast_mode.autocast");
}
// If we have an autocast instance, return it
//
// This is the pattern we're looking for (this is done after
// autocast.__init__() has been inlined)
//
// %4 : bool = prim::Constant[value=1]()
// %5 : __torch__.torch.cuda.amp.autocast_mode.autocast = prim::CreateObject()
// = prim::SetAttr[name="_enabled"](%5, %4)
//
// Notes:
// 1. There's no guarantee that the autocast instance is in the same block
// as the prim::Enter() node
// 2. `prim::SetAttr` must follow `prim::CreateObject()` in the same block,
// but there might be other nodes in between
//
c10::optional<AutocastScope> parseAutocast(
Value* value,
const AutocastContext& context) {
if (!isAutocastNode(value)) {
// Not an autocast...
return c10::nullopt;
}
if (value->node()->kind() == prim::CreateObject) {
AutocastScope scope;
scope.instance = value;
scope.context = context;
c10::optional<bool> enabled;
std::string device;
c10::ScalarType dtype = c10::ScalarType::Undefined;
for (Use use : value->uses()) {
// TODO: support runtime flag
if (use.user->kind() == prim::SetAttr &&
use.user->s(attr::name) == "_enabled") {
// Search for `prim::SetAttr[name="_enabled"]`
auto ret = constant_as<bool>(use.user->input(1));
TORCH_CHECK(
ret.has_value(), "Autocast _enabled argument must be a constant");
enabled = ret.value();
} else if (
use.user->kind() == prim::SetAttr &&
use.user->s(attr::name) == "device") {
// Search for `prim::SetAttr[name="device"]`
auto ret = constant_as<std::string>(use.user->input(1));
TORCH_CHECK(
ret.has_value(), "Autocast device argument must be a constant");
device = ret.value();
} else if (
use.user->kind() == prim::SetAttr &&
use.user->s(attr::name) == "fast_dtype") {
// Search for `prim::SetAttr[name="fast_dtype"]`
auto ret = constant_as<c10::ScalarType>(use.user->input(1));
TORCH_CHECK(
ret.has_value() && ret.value() != c10::ScalarType::Undefined,
"Autocast dtype argument must be a constant and defined");
dtype = ret.value();
}
}
TORCH_CHECK(enabled.has_value(), "Autocast missing _enabled attribute");
TORCH_CHECK(
dtype != c10::ScalarType::Undefined,
"Autocast missing fast_dtype attribute");
TORCH_CHECK(!device.empty(), "Autocast missing device attribute");
if (device == "cuda") {
scope.context.gpu_enabled = enabled.value();
scope.context.gpu_scalar_type = dtype;
} else if (device == "cpu") {
scope.context.cpu_enabled = enabled.value();
scope.context.cpu_scalar_type = dtype;
} else {
TORCH_INTERNAL_ASSERT(
false, "unrecognized device for autocast pass: ", device);
}
return scope;
} else {
// We only support simple and static autocast expressions. For example,
// the following should report an error (since the autocast would not
// work as expected)
//
// autocast_on = autocast(enabled=True)
// autocast_off = autocast(enabled=False)
// with autocast_on if condition else autocast_off:
// ...
//
// TODO: better error message
//
AT_ERROR("Unsupported autocast syntax");
}
return c10::nullopt;
}
void castTensorInputs(
Node* node,
Symbol cast_op,
const AutocastContext& context) {
if (!context) {
return;
}
const auto graph = node->owningGraph();
std::unordered_set<Value*> casted_inputs;
// need to also keep the inputs in order, otherwise tracing fails
// sanity checks because casting ops are inserted in random order
std::vector<Value*> casted_inputs_ordered;
for (auto input : node->inputs()) {
// TODO: update cast_op signature to take dynamic context flags
auto input_tensor_type = input->type()->cast<TensorType>();
if (input_tensor_type && input->node()->kind() != cast_op) {
auto has_inserted = casted_inputs.insert(input);
if (has_inserted.second) {
casted_inputs_ordered.push_back(input);
}
}
}
WithInsertPoint insert_point(node);
for (auto input : casted_inputs_ordered) {
if (cast_op == aten::_autocast_to_full_precision) {
const auto new_input = graph->insert(
cast_op,
{input,
graph->insertConstant(IValue(context.gpu_enabled)),
graph->insertConstant(IValue(context.cpu_enabled))});
node->replaceInputWith(input, new_input);
} else if (cast_op == aten::_autocast_to_reduced_precision) {
const auto new_input = graph->insert(
cast_op,
{input,
graph->insertConstant(IValue(context.gpu_enabled)),
graph->insertConstant(IValue(context.cpu_enabled)),
graph->insertConstant(IValue(context.gpu_scalar_type)),
graph->insertConstant(IValue(context.cpu_scalar_type))});
node->replaceInputWith(input, new_input);
} else {
TORCH_INTERNAL_ASSERT(
false, "unrecognized cast_op symbol: ", cast_op.toQualString());
}
}
}
bool hasExplicitDtypeArgument(Node* node) {
if (node->hasNamedInput("dtype")) {
Value* dtype_arg = node->namedInput("dtype");
return dtype_arg->type()->kind() != TypeKind::NoneType;
}
return false;
}
void castInputsToWidestType(Node* node, const AutocastContext& context) {
if (!context) {
return;
}
// Figure out the widest type
// (really, just looking for any float32 inputs)
//
// TODO: revisit this (do we need to consider float64 types?)
//
for (auto input : node->inputs()) {
if (auto tensor_type = input->type()->cast<TensorType>()) {
const auto dtype = tensor_type->scalarType();
if (!dtype.has_value() || *dtype == at::ScalarType::Float) {
castTensorInputs(node, aten::_autocast_to_full_precision, context);
return;
}
}
}
}
// Users can call torch.is_autocast_enabled() or is_autocast_cpu_enabled() to
// determine whether autocasting is enabled. With JIT-scripted functions, we
// actually need to return true if eager autocast OR jit autocast are enabled.
//
// In the case where JIT autocast is enabled, we replace
// %x : bool = aten::is_autocast_enabled()
// with a constant "True".
//
// More context on eager vs JIT autocasting:
//
// Autocasting actually has two settings: eager autocasting, and JIT
// autocasting. Eager autocasting is the thread-local setting that turns on
// the relevant bit in the dispatcher settings. JIT autocasting is the pass
// implemented in this file, which makes changes to the graph to insert casting
// ops in order to achieve the same behavior as eager autocasting.
//
// If eager autocasting is enabled at the time when a JIT-scripted function is
// invoked, then autocasting will occur regardless of what the JIT-autocasting
// settings are.
void updateAutocastEnabledCheck(Node* node, bool is_jit_enabled) {
if (!is_jit_enabled) {
return;
}
auto graph = node->owningGraph();
WithInsertPoint insert_point(node);
Value* true_constant = graph->insertConstant(IValue(true));
node->output()->replaceAllUsesWith(true_constant);
node->destroy();
}
// [Note: implicit type promotion in Autocast]
//
// Casting policy below mostly follows pytorch/aten/src/ATen/autocast.cpp, with
// a few exceptions, e.g. `aten::add`, which is needed to be put to promotion
// list for JIT autocast.
// The reason is that in eager amp, some binary ops promote inputs implicitly
// inside the operation, e.g. `aten::add` with fp16 & fp32 inputs would both be
// casted to fp32. In backward, autograd would cast dgrad to match their
// scalar_type in forward graph. So inputs with mismatched scalar_type would
// get the different dgrad.
// While in JIT, autodiff doesn't do this, so implicit cast is not visible to
// autodiff and backward dgrad for mismatched inputs would ended up with dgrads
// in the same scalar_type. This has caused downstream operations, which
// expects dgrad to be the same scalar type to throw mismatch error.
//
// TODO: Use the list from AMP eager directly
void handleBlock(Block* block, AutocastContext initial_state) {
std::stack<AutocastScope> autocast_stack;
c10::optional<bool> incompatible_amp = c10::nullopt;
// The current autocast enabled/disabled state
auto current_state = [&] {
return autocast_stack.empty() ? initial_state
: autocast_stack.top().context;
};
for (Node* node : block->nodes()) {
switch (node->kind()) {
case prim::CallFunction:
// TODO: limit it only to amp related node;
if (current_state() == initial_state) {
// if the current autocasting state is the same as the global state,
// then autocasting will be done correctly on subsequent method and
// function calls
if (current_state()) {
castTensorInputs(
node, aten::_autocast_to_full_precision, current_state());
}
break;
}
TORCH_INTERNAL_ASSERT(
!incompatible_amp.has_value() || incompatible_amp.value(),
"Calls are not expected with AMP & JIT");
incompatible_amp = true;
break;
case prim::CallMethod:
// TODO: limit it only to amp related node;
if (current_state() == initial_state) {
// if the current autocasting state is the same as the global state,
// then autocasting will be done correctly on subsequent method and
// function calls
if (current_state()) {
castTensorInputs(
node, aten::_autocast_to_full_precision, current_state());
}
break;
}
if (auto class_type = node->input(0)->type()->cast<ClassType>()) {
const auto& name = node->s(attr::name);
const auto& function = class_type->getMethod(name);
if (!function.isGraphFunction()) {
TORCH_INTERNAL_ASSERT(
!incompatible_amp.has_value() || incompatible_amp.value(),
"Calls are not expected with AMP & JIT");
incompatible_amp = true;
}
} else {
TORCH_INTERNAL_ASSERT(
!incompatible_amp.has_value() || incompatible_amp.value(),
"Unexpected prim::CallMethod form with AMP & JIT");
incompatible_amp = true;
}
break;
case prim::Enter:
if (auto autocast_scope =
parseAutocast(node->input(), current_state())) {
if (node->hasUses()) {
// TODO: better error message
AT_ERROR("`with autocast() as ...` is not supported");
}
TORCH_INTERNAL_ASSERT(
!incompatible_amp.has_value() || !incompatible_amp.value(),
"Unsupported case by AMP & JIT");
incompatible_amp = false;
autocast_stack.push(*autocast_scope);
}
break;
case prim::Exit:
if (isAutocastNode(node->input(0))) {
TORCH_INTERNAL_ASSERT(!autocast_stack.empty());
TORCH_INTERNAL_ASSERT(autocast_stack.top().instance == node->input());
TORCH_INTERNAL_ASSERT(
!incompatible_amp.has_value() || !incompatible_amp.value(),
"Unsupported case by AMP & JIT");
incompatible_amp = false;
autocast_stack.pop();
}
break;
case aten::is_autocast_enabled:
updateAutocastEnabledCheck(node, current_state().gpu_enabled);
break;
case aten::is_autocast_cpu_enabled:
updateAutocastEnabledCheck(node, current_state().cpu_enabled);
break;
// CastPolicy::fp16 (cast all inputs to float16)
case aten::_convolution:
case aten::conv1d:
case aten::conv2d:
case aten::conv3d:
case aten::conv_tbc:
case aten::conv_transpose1d:
case aten::convolution:
case aten::cudnn_convolution:
case aten::cudnn_convolution_transpose:
case aten::prelu:
case aten::addmm:
case aten::addmv:
case aten::addr:
case aten::matmul:
case aten::mm:
case aten::mv:
case aten::linear:
case aten::addbmm:
case aten::baddbmm:
case aten::bmm:
case aten::chain_matmul:
case aten::_thnn_fused_lstm_cell:
case aten::_thnn_fused_gru_cell:
case aten::lstm_cell:
case aten::gru_cell:
case aten::rnn_tanh_cell:
case aten::rnn_relu_cell:
if (!node->schema().is_mutable()) {
castTensorInputs(
node, aten::_autocast_to_reduced_precision, current_state());
}
break;
// CastPolicy::fp32 (cast all inputs to float32)
case aten::native_layer_norm:
case aten::acos:
case aten::asin:
case aten::cosh:
case aten::erfinv:
case aten::exp:
case aten::expm1:
case aten::log:
case aten::log10:
case aten::log2:
case aten::log1p:
case aten::reciprocal:
case aten::rsqrt:
case aten::sinh:
case aten::tan:
case aten::pow:
case aten::softplus:
case aten::gelu:
case aten::layer_norm:
case aten::group_norm:
case aten::frobenius_norm:
case aten::nuclear_norm:
case aten::cosine_similarity:
case aten::cosine_embedding_loss:
case aten::nll_loss:
case aten::nll_loss2d:
case aten::hinge_embedding_loss:
case aten::kl_div:
case aten::l1_loss:
case aten::smooth_l1_loss:
case aten::mse_loss:
case aten::margin_ranking_loss:
case aten::multilabel_margin_loss:
case aten::soft_margin_loss:
case aten::triplet_margin_loss:
case aten::multi_margin_loss:
case aten::binary_cross_entropy_with_logits:
case aten::dist:
case aten::pdist:
case aten::cdist:
case aten::renorm:
case aten::logsumexp:
if (!node->schema().is_mutable()) {
castTensorInputs(
node, aten::_autocast_to_full_precision, current_state());
}
break;
// CastPolicy::fp32_set_opt_dtype
case aten::prod:
case aten::log_softmax:
case aten::cumprod:
case aten::cumsum:
case aten::sum:
if (!node->schema().is_mutable() && !hasExplicitDtypeArgument(node)) {
castTensorInputs(
node, aten::_autocast_to_full_precision, current_state());
}
break;
// cast softmax to fp32 only on GPU
case aten::softmax:
if (!node->schema().is_mutable() && !hasExplicitDtypeArgument(node)) {
auto context = current_state();
context.cpu_enabled = false;
castTensorInputs(node, aten::_autocast_to_full_precision, context);
}
break;
// CastPolicy::promote (promote inputs to the widest type)
case aten::addcdiv:
case aten::addcmul:
case aten::atan2:
case aten::bilinear:
case aten::cat:
case aten::cross:
case aten::dot:
case aten::equal:
case aten::index_put:
case aten::stack:
case aten::tensordot:
// add, sub, mul, div were added to autocast jit, because aten implicit
// type promotion is not visible to JIT and could cause dtype mismatch on
// backward
// see [Note: implicit type promotion in Autocast]
case aten::add:
case aten::sub:
case aten::mul:
case aten::div:
if (!node->schema().is_mutable()) {
castInputsToWidestType(node, current_state());
}
break;
// Banned in autocast, see binary_cross_entropy_banned()
case aten::binary_cross_entropy:
if (current_state()) {
AT_ERROR("Unsafe to autocast");
}
}
// process sub-blocks, if any
for (Block* sub_block : node->blocks()) {
handleBlock(sub_block, current_state());
}
}
// Sanity check: make sure there's no unbalanced transition
TORCH_INTERNAL_ASSERT(autocast_stack.empty());
}
} // namespace
bool setAutocastMode(bool value) {
auto old_value = autocast_enabled;
autocast_enabled = value;
return old_value;
}
bool autocastEnabled() {
return autocast_enabled;
}
void Autocast(const std::shared_ptr<Graph>& graph) {
GRAPH_DUMP("\nBefore Autocast: ", graph);
if (autocastEnabled()) {
AutocastContext init = {
at::autocast::is_enabled(),
at::autocast::is_cpu_enabled(),
at::autocast::get_autocast_gpu_dtype(),
at::autocast::get_autocast_cpu_dtype()};
handleBlock(graph->block(), init);
}
GRAPH_DUMP("\nAfter Autocast: ", graph);
}
} // namespace jit
} // namespace torch
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