Revert D25719980: [pytorch][PR] Accept input tensor with 0-dim batch size for MultiLabelMarginLoss
Test Plan: revert-hammer
Differential Revision:
D25719980 (https://github.com/pytorch/pytorch/commit/6b56b71e61e14bf4de5b371f0d8f2f2029065b31)
Original commit changeset: 83414bad37c0
fbshipit-source-id: 27eddd711a2b9e0adbc08bfab12100562e63ac21
diff --git a/aten/src/ATen/native/LossMulti.h b/aten/src/ATen/native/LossMulti.h
deleted file mode 100644
index 4282c34..0000000
--- a/aten/src/ATen/native/LossMulti.h
+++ /dev/null
@@ -1,72 +0,0 @@
-#include <ATen/ATen.h>
-#include <ATen/Dispatch.h>
-#include <ATen/AccumulateType.h>
-
-#pragma once
-
-namespace at { namespace native {
-namespace {
- static void multilabel_margin_loss_shape_check(
- int64_t& nframe,
- int64_t& dim,
- const int64_t& ndims,
- TensorArg& target_arg,
- const Tensor& input,
- const Tensor& target) {
- bool valid_inputs = (ndims == 2 && input.size(1) != 0) || (ndims == 1 && input.size(0) != 0) || ndims == 0;
- TORCH_CHECK(
- valid_inputs,
- "Expected non-empty vector or matrix with optional 0-dim batch size, but got: ",
- input.sizes());
-
- if (ndims <= 1) {
- nframe = 1;
- dim = ndims == 0 ? 1 : input.size(0);
- TORCH_CHECK(
- valid_inputs && target.dim() <= 1 && target.numel() == dim,
- "inconsistent size ",
- target.sizes(),
- " for ",
- target_arg);
- } else {
- nframe = input.size(0);
- dim = input.size(1);
- TORCH_CHECK(
- valid_inputs && target.dim() == 2 && target.size(0) == nframe &&
- target.size(1) == dim,
- "inconsistent size ",
- target.sizes(),
- " for ",
- target_arg);
- }
- }
-
- static void multi_margin_loss_shape_check(
- int64_t& nframe,
- int64_t& dim,
- const int64_t& ndims,
- TensorArg& target_arg,
- const Tensor& input,
- const Tensor& target) {
- bool valid_inputs = (ndims == 2 && input.size(1) != 0) || (ndims == 1 && input.size(0) != 0) || ndims == 0;
- if (ndims <= 1) {
- nframe = 1;
- dim = ndims == 0 ? 1 : input.size(0);
- } else {
- nframe = input.size(0);
- dim = input.size(1);
- }
-
- TORCH_CHECK(
- valid_inputs,
- "Expected non-empty vector or matrix with optional 0-dim batch size, but got: ",
- input.sizes());
- TORCH_CHECK(
- valid_inputs && target.dim() <= 1 && target.numel() == nframe,
- "inconsistent target size, got: ",
- target.sizes());
- }
-
-
-} // anonymous namespace
-}} // namespace at::native
diff --git a/aten/src/ATen/native/LossMultiLabelMargin.cpp b/aten/src/ATen/native/LossMultiLabelMargin.cpp
index 3cd0f46..9582bf6 100644
--- a/aten/src/ATen/native/LossMultiLabelMargin.cpp
+++ b/aten/src/ATen/native/LossMultiLabelMargin.cpp
@@ -2,7 +2,6 @@
#include <ATen/AccumulateType.h>
#include <ATen/Dispatch.h>
#include <ATen/TensorUtils.h>
-#include <ATen/native/LossMulti.h>
namespace at {
namespace native {
@@ -40,7 +39,6 @@
}
}
}
-
return sum;
}
@@ -102,32 +100,34 @@
Tensor& is_target,
int64_t reduction) {
auto target_arg = TensorArg(target, "target", 2);
+
+ const auto ndims = input.dim();
+
+ TORCH_CHECK(
+ input.numel() > 0 && ndims <= 2,
+ "non-empty vector or matrix expected, got size: ",
+ input.sizes());
+
int64_t nframe, dim;
- const int64_t ndims = input.dim();
if (ndims <= 1) {
nframe = 1;
dim = ndims == 0 ? 1 : input.size(0);
- }
- else {
+ TORCH_CHECK(
+ target.numel() > 0 && target.dim() <= 1 && target.numel() == dim,
+ "inconsistent size ",
+ target.sizes(),
+ " for ",
+ target_arg);
+ } else {
nframe = input.size(0);
dim = input.size(1);
- }
- multilabel_margin_loss_shape_check(nframe, dim, ndims, target_arg, input, target);
-
- // special case target.dim() <= 1: produce scalar output for scalar inputs
- // even if reduction == Reduction::None
- if (reduction != Reduction::None || target.dim() <= 1) {
- output.resize_({});
- } else {
- output.resize_({nframe});
- }
-
- is_target.resize_as_(target);
- TORCH_CHECK(is_target.is_contiguous(), "is_target must be contiguous");
- is_target.zero_();
-
- if (input.numel() == 0) {
- return;
+ TORCH_CHECK(
+ target.numel() > 0 && target.dim() == 2 && target.size(0) == nframe &&
+ target.size(1) == dim,
+ "inconsistent size ",
+ target.sizes(),
+ " for ",
+ target_arg);
}
TORCH_CHECK(
@@ -138,6 +138,18 @@
auto input_contiguous = input.contiguous();
auto target_contiguous = target.contiguous();
+ is_target.resize_as_(target);
+ TORCH_CHECK(is_target.is_contiguous(), "is_target must be contiguous");
+ is_target.zero_();
+
+ // special case target.dim() <= 1: produce scalar output for scalar inputs
+ // even if reduction == Reduction::None
+ if (reduction != Reduction::None || target.dim() <= 1) {
+ output.resize_({});
+ } else {
+ output.resize_({nframe});
+ }
+
AT_DISPATCH_FLOATING_TYPES(
input.scalar_type(), "multilabel_margin_loss_forward_out_frame", [&] {
multilabel_margin_loss_forward_out_frame<scalar_t>(
@@ -220,22 +232,39 @@
const Tensor& target,
int64_t reduction,
const Tensor& is_target) {
- int64_t nframe, dim;
CheckedFrom c = "multilabel_margin_loss_backward_cpu_template";
auto target_arg = TensorArg(target, "target", 3);
auto is_target_arg = TensorArg(is_target, "is_target", 5);
- const int64_t ndims = input.dim();
- multilabel_margin_loss_shape_check(nframe, dim, ndims, target_arg, input, target);
- checkSameSize(c, target_arg, is_target_arg);
+ const auto ndims = input.dim();
- grad_input.resize_as_(input);
- if (grad_input.numel() == 0) {
- return;
+ TORCH_CHECK(
+ input.numel() > 0 && ndims <= 2,
+ "non-empty vector or matrix expected, got size: ",
+ input.sizes());
+
+ int64_t nframe, dim;
+ if (ndims <= 1) {
+ nframe = 1;
+ dim = ndims == 0 ? 1 : input.size(0);
+ TORCH_CHECK(
+ target.numel() > 0 && target.dim() <= 1 && target.numel() == dim,
+ "inconsistent size ",
+ target.sizes(),
+ " for ",
+ target_arg);
+ } else {
+ nframe = input.size(0);
+ dim = input.size(1);
+ TORCH_CHECK(
+ target.numel() > 0 && target.dim() == 2 && target.size(0) == nframe &&
+ target.size(1) == dim,
+ "inconsistent size ",
+ target.sizes(),
+ " for ",
+ target_arg);
}
-
- TORCH_CHECK(grad_input.is_contiguous(), "grad_input must be contiguous");
- grad_input.zero_();
+ checkSameSize(c, target_arg, is_target_arg);
TORCH_CHECK(
target.min().item<int64_t>() >= -1, target_arg, " is out of range");
@@ -246,6 +275,10 @@
auto target_contiguous = target.contiguous();
auto is_target_contiguous = is_target.contiguous();
+ grad_input.resize_as_(input);
+ TORCH_CHECK(grad_input.is_contiguous(), "grad_input must be contiguous");
+ grad_input.zero_();
+
AT_DISPATCH_FLOATING_TYPES(
input.scalar_type(), "multilabel_margin_loss_backward_out_frame", [&] {
multilabel_margin_loss_backward_out_frame<scalar_t>(
diff --git a/aten/src/ATen/native/LossMultiMargin.cpp b/aten/src/ATen/native/LossMultiMargin.cpp
index db18d1f..48446a9 100644
--- a/aten/src/ATen/native/LossMultiMargin.cpp
+++ b/aten/src/ATen/native/LossMultiMargin.cpp
@@ -1,7 +1,6 @@
#include <ATen/ATen.h>
#include <ATen/Dispatch.h>
#include <ATen/AccumulateType.h>
-#include <ATen/native/LossMulti.h>
namespace at {
namespace native {
@@ -94,13 +93,27 @@
Scalar margin,
const Tensor& weight,
int64_t reduction) {
- int64_t nframe, dim;
const auto ndims = input.dim();
- auto target_arg = TensorArg(target, "target", 2);
+ TORCH_CHECK(
+ input.numel() > 0 && ndims <= 2,
+ "non-empty vector or matrix expected, got size: ",
+ input.sizes());
TORCH_CHECK(p == 1 || p == 2, "only p == 1 and p == 2 supported");
- multi_margin_loss_shape_check(nframe, dim, ndims, target_arg, input, target);
+ int64_t nframe, dim;
+ if (ndims <= 1) {
+ nframe = 1;
+ dim = ndims == 0 ? 1 : input.size(0);
+ } else {
+ nframe = input.size(0);
+ dim = input.size(1);
+ }
+
+ TORCH_CHECK(
+ target.numel() > 0 && target.dim() <= 1 && target.numel() == nframe,
+ "inconsistent target size, got: ",
+ target.sizes());
// produce a scalar output for 1d input
if (reduction == Reduction::None && target.dim() > 0) {
@@ -108,9 +121,6 @@
} else {
output.resize_({});
}
- if (input.numel() == 0) {
- return;
- }
auto input_contiguous = input.contiguous();
auto target_contiguous = target.contiguous();
@@ -202,13 +212,28 @@
Scalar margin,
const Tensor& weight,
int64_t reduction) {
- int64_t nframe, dim;
- auto target_arg = TensorArg(target, "target", 2);
const auto ndims = input.dim();
+ TORCH_CHECK(
+ input.numel() > 0 && ndims <= 2,
+ "non-empty vector or matrix expected, got size: ",
+ input.sizes());
TORCH_CHECK(p == 1 || p == 2, "only p == 1 and p == 2 supported");
- multi_margin_loss_shape_check(nframe, dim, ndims, target_arg, input, target);
+ int64_t nframe, dim;
+ if (ndims <= 1) {
+ nframe = 1;
+ dim = ndims == 0 ? 1 : input.size(0);
+ } else {
+ nframe = input.size(0);
+ dim = input.size(1);
+ }
+
+ TORCH_CHECK(
+ target.numel() > 0 && target.dim() <= 1 && target.numel() == nframe,
+ "inconsistent target size, got: ",
+ target.sizes());
+
grad_input.resize_as_(input);
TORCH_CHECK(grad_input.is_contiguous(), "grad_input must be contiguous");
diff --git a/aten/src/THCUNN/generic/MultiLabelMarginCriterion.cu b/aten/src/THCUNN/generic/MultiLabelMarginCriterion.cu
index 6e8d9bc..ab8d2cb 100644
--- a/aten/src/THCUNN/generic/MultiLabelMarginCriterion.cu
+++ b/aten/src/THCUNN/generic/MultiLabelMarginCriterion.cu
@@ -3,30 +3,21 @@
#else
static inline void THNN_(MultiLabelMarginCriterion_shapeCheck)(
- THCState *state,
- THCTensor *input, THCTensor *target) {
- int64_t ndims = input->dim();
- bool valid_inputs = (ndims == 2 && input->size(1) != 0) || (ndims == 1 && input->size(0) != 0) || ndims == 0;
- TORCH_CHECK(
- valid_inputs,
- "Expected non-empty vector or matrix with optional 0-dim batch size, but got: ",
- input->sizes());
-
- if (ndims <= 1) {
+ THCState *state,
+ THCTensor *input, THCTensor *target) {
+ if (input->dim() <= 1) {
int dim = input->dim() == 0 ? 1 : input->size(0);
int target_size = target->dim() == 0 ? 1 : target->size(0);
-
- TORCH_CHECK(valid_inputs && target->dim() <= 1 && target->numel() == dim,
- "inconsistent target size: ", target->sizes(), " for input of size: ", input->sizes());
- } else if (ndims == 2) {
+ TORCH_CHECK(!target->is_empty() && (target->dim() <= 1) && (target_size == dim),
+ "inconsistent target size: ", target->sizes(), " for input of size: ", input->sizes());
+ } else if (input->dim() == 2) {
int nframe = input->size(0);
int dim = input->size(1);
-
- TORCH_CHECK(
- valid_inputs && target->dim() == 2 && target->size(0) == nframe && target->size(1) == dim,
- "inconsistent target size: ", target->sizes(), " for input of size: ", input->sizes());
+ TORCH_CHECK(!target->is_empty() && (target->dim() == 2)
+ && (target->size(0) == nframe) && (target->size(1) == dim),
+ "inconsistent target size: ", target->sizes(), " for input of size: ", input->sizes());
} else {
- TORCH_CHECK(false, "Expected input of ndims <= 2, but got ndims: ", ndims);
+ TORCH_CHECK(false, "non-empty vector or matrix expected, got size: ", input->sizes());
}
}
@@ -40,9 +31,6 @@
int64_t reduction)
{
THNN_(MultiLabelMarginCriterion_shapeCheck)(state, input, target);
- if (input->numel() == 0) {
- return;
- }
input = THCTensor_(newContiguous)(state, input);
target = THCIndexTensor_(newContiguous)(state, target);
istarget = THCTensor_(newContiguous)(state, istarget);
@@ -112,8 +100,7 @@
}
}
else {
- TORCH_CHECK(false, "Expected 2D input with optional zero batch dim, or 1D input with non-zero dims, but got sizes: ",
- input->sizes());
+ TORCH_INTERNAL_ASSERT(false, "non-empty vector or matrix expected (shouldn't get here)");
}
THCTensor_(free)(state, input);
@@ -130,17 +117,11 @@
THCTensor *istarget,
int64_t reduction)
{
- THNN_(MultiLabelMarginCriterion_shapeCheck)(state, input, target);
input = THCTensor_(newContiguous)(state, input);
- THCTensor_(resizeAs)(state, gradInput, input);
- if (input->numel() == 0) {
- THCTensor_(free)(state, input);
- return;
- }
-
target = THCIndexTensor_(newContiguous)(state, target);
istarget = THCTensor_(newContiguous)(state, istarget);
gradOutput = THCTensor_(newContiguous)(state, gradOutput);
+ THCTensor_(resizeAs)(state, gradInput, input);
if(gradInput->dim() <= 1)
{
@@ -168,11 +149,10 @@
{
int nframe = gradInput->size(0);
int dim = gradInput->size(1);
- THArgCheck((input->size(1) != 0) && (target->dim() == 2) && (target->size(0) == nframe)
+ THArgCheck(!target->is_empty() && (target->dim() == 2) && (target->size(0) == nframe)
&& (target->size(1) == dim), 3, "inconsistent target size");
- THArgCheck((istarget->dim() == 2) && (istarget->size(0) == nframe)
+ THArgCheck(!istarget->is_empty() && (istarget->dim() == 2) && (istarget->size(0) == nframe)
&& (istarget->size(1) == dim), 3, "inconsistent isTarget size");
-
dim3 blocks(gradInput->size(0));
dim3 threads(MULTILABELMARGIN_THREADS);
@@ -188,8 +168,7 @@
reduction != at::Reduction::None);
}
else {
- TORCH_CHECK(false, "Expected 2D input with optional zero batch dim, or 1D input with non-zero dims, but got sizes: ",
- gradInput->sizes());
+ AT_ERROR("non-empty vector or matrix expected, got size: ", gradInput->sizes());
}
THCudaCheck(cudaGetLastError());
diff --git a/aten/src/THCUNN/generic/MultiMarginCriterion.cu b/aten/src/THCUNN/generic/MultiMarginCriterion.cu
index 129413f..f2df150 100644
--- a/aten/src/THCUNN/generic/MultiMarginCriterion.cu
+++ b/aten/src/THCUNN/generic/MultiMarginCriterion.cu
@@ -2,30 +2,6 @@
#define THC_GENERIC_FILE "THCUNN/generic/MultiMarginCriterion.cu"
#else
-static inline void THNN_(MultiMarginCriterion_shapeCheck)(
- THCState *state,
- THCTensor *input, THCTensor *target) {
- int64_t nframe, dim;
- int64_t ndims = input->dim();
- bool valid_inputs = (ndims == 2 && input->size(1) != 0) || (ndims == 1 && input->size(0) != 0) || ndims == 0;
- if (ndims <= 1) {
- nframe = 1;
- dim = ndims == 0 ? 1 : input->size(0);
- } else {
- nframe = input->size(0);
- dim = input->size(1);
- }
-
- TORCH_CHECK(
- valid_inputs,
- "Expected non-empty vector or matrix with optional 0-dim batch size, but got: ",
- input->sizes());
- TORCH_CHECK(
- valid_inputs && target->dim() <= 1 && target->numel() == nframe,
- "inconsistent target size, got: ",
- target->sizes());
-}
-
// TODO: improve error messages
void THNN_(MultiMarginCriterion_updateOutput)(
THCState *state,
@@ -37,10 +13,6 @@
THCTensor *weights,
accreal margin_)
{
- THNN_(MultiMarginCriterion_shapeCheck)(state, input, target);
- if (input->numel() == 0) {
- return;
- }
scalar_t margin = ScalarConvert<accreal, scalar_t>::to(margin_);
THCUNN_assertSameGPU(state, 2, input, target);
input = THCTensor_(newContiguous)(state, input);
@@ -87,8 +59,7 @@
else if (input->dim() == 2)
{
int nframe = input->size(0);
- // allow zero-dim target for 2D input.
- THArgCheck((input->size(1) != 0) && (THTensor_nDimensionLegacyNoScalars(target) == 1) && (THTensor_sizeLegacyNoScalars(target, 0) == nframe), 3,
+ THArgCheck(!target->is_empty() && (THTensor_nDimensionLegacyNoScalars(target) == 1) && (THTensor_sizeLegacyNoScalars(target, 0) == nframe), 3,
"inconsistent target size");
dim3 blocks(input->size(0));
dim3 threads(MULTIMARGIN_THREADS);
@@ -159,8 +130,7 @@
}
else
{
- TORCH_CHECK(false, "Expected 2D input with optional zero batch dim, or 1D input with non-zero dims, but got sizes: ",
- input->sizes());
+ AT_ERROR("non-empty vector or matrix expected, got sizes: ", input->sizes());
}
THCTensor_(free)(state, input);
@@ -179,17 +149,11 @@
THCTensor *weights,
accreal margin_)
{
- THNN_(MultiMarginCriterion_shapeCheck)(state, input, target);
- input = THCTensor_(newContiguous)(state, input);
- THCTensor_(resizeAs)(state, gradInput, input);
- if (input->numel() == 0) {
- THCTensor_(free)(state, input);
- return;
- }
scalar_t margin = ScalarConvert<accreal, scalar_t>::to(margin_);
THCUNN_assertSameGPU(state, 3, input, gradInput, target);
+ input = THCTensor_(newContiguous)(state, input);
gradOutput = THCTensor_(newContiguous)(state, gradOutput);
-
+ THCTensor_(resizeAs)(state, gradInput, input);
if(weights)
weights = THCTensor_(newContiguous)(state, weights);
@@ -231,7 +195,7 @@
else if (input->dim() == 2)
{
int nframe = gradInput->size(0);
- THArgCheck((input->size(1) != 0) && (THTensor_nDimensionLegacyNoScalars(target) == 1) && (THTensor_sizeLegacyNoScalars(target, 0) == nframe), 3,
+ THArgCheck(!target->is_empty() && (THTensor_nDimensionLegacyNoScalars(target) == 1) && (THTensor_sizeLegacyNoScalars(target, 0) == nframe), 3,
"inconsistent target size");
dim3 blocks(gradInput->size(0));
dim3 threads(MULTIMARGIN_THREADS);
@@ -268,8 +232,7 @@
}
else
{
- TORCH_CHECK(false, "Expected 2D input with optional zero batch dim, or 1D input with non-zero dims, but got sizes: ",
- input->sizes());
+ AT_ERROR("non-empty vector or matrix expected, got ", input->sizes());
}
THCTensor_(free)(state, input);
diff --git a/test/test_nn.py b/test/test_nn.py
index ef9ea4c..386ba36 100644
--- a/test/test_nn.py
+++ b/test/test_nn.py
@@ -10864,35 +10864,6 @@
inp = torch.randn(3, 0, 10, 10, device=device)
mod(inp)
-
- @onlyOnCPUAndCUDA
- @dtypes(torch.float, torch.double)
- def test_MarginLoss_empty(self, device, dtype):
- for mod, x, y in [
- (torch.nn.MultiMarginLoss().to(device),
- torch.randn(0, 10, requires_grad=True, device=device, dtype=dtype),
- torch.ones(0, device=device).type(torch.long)),
- (torch.nn.MultiLabelMarginLoss().to(device),
- torch.randn(0, 10, requires_grad=True, device=device, dtype=dtype),
- torch.ones(0, 10, device=device).type(torch.long))]:
-
- out = mod(x, y)
- out.sum().backward()
-
- self.assertEqual(x, torch.zeros_like(x))
- self.assertEqual(x.grad, torch.zeros_like(x))
-
- with self.assertRaisesRegex(RuntimeError, 'Expected'):
- x = torch.randn(0, requires_grad=True, device=device, dtype=dtype)
- y = torch.ones(10, device=device).type(torch.long)
- mod(x, y)
-
- with self.assertRaisesRegex(RuntimeError, 'Expected'):
- x = torch.randn(10, 0, requires_grad=True, device=device, dtype=dtype)
- y = torch.ones(10, 0, device=device).type(torch.long)
- mod(x, y)
-
-
@onlyOnCPUAndCUDA
def test_Unfold_empty(self, device):
inp = torch.randn(0, 3, 3, 4, device=device)
