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import torch
from torch.testing._internal.common_utils import TEST_WITH_ROCM
class AutocastTestLists(object):
def _rnn_cell_args(self, n, num_chunks, is_lstm, dev, dtype):
input = (torch.randn((n, n), device=dev, dtype=torch.float32),)
hx = ((torch.randn((n, n), device=dev, dtype=torch.float32),
torch.randn((n, n), device=dev, dtype=torch.float32)) if is_lstm else
torch.randn((n, n), device=dev, dtype=torch.float32),)
weights = (torch.randn((num_chunks * n, n), device=dev, dtype=torch.float32), # weight_ih
torch.randn((num_chunks * n, n), device=dev, dtype=torch.float32), # weight_hh
torch.randn((num_chunks * n), device=dev, dtype=torch.float32), # bias_ih
torch.randn((num_chunks * n), device=dev, dtype=torch.float32)) # bias_hh
# returns args as a tuple
return input + hx + weights
# Supplies ops and arguments for test_autocast_* in test/test_cuda.py
def __init__(self, dev):
super().__init__()
n = 8
# Utility arguments, created as one-element tuples
pointwise0_fp16 = (torch.randn(n, dtype=torch.float16, device=dev),)
pointwise1_fp16 = (torch.randn(n, dtype=torch.float16, device=dev),)
pointwise2_fp16 = (torch.randn(n, dtype=torch.float16, device=dev),)
mat0_fp16 = (torch.randn((n, n), dtype=torch.float16, device=dev),)
mat1_fp16 = (torch.randn((n, n), dtype=torch.float16, device=dev),)
mat2_fp16 = (torch.randn((n, n), dtype=torch.float16, device=dev),)
dimsets = ((n, n, n), (n, n, n, n), (n, n, n, n, n))
conv_args_fp32 = [(torch.randn(dimset, dtype=torch.float32, device=dev),
torch.randn(dimset, dtype=torch.float32, device=dev))
for dimset in dimsets]
bias_fp32 = (torch.randn((n,), dtype=torch.float32, device=dev),)
element0_fp32 = (torch.randn(1, dtype=torch.float32, device=dev),)
pointwise0_fp32 = (torch.randn(n, dtype=torch.float32, device=dev),)
pointwise1_fp32 = (torch.randn(n, dtype=torch.float32, device=dev),)
mat0_fp32 = (torch.randn((n, n), dtype=torch.float32, device=dev),)
mat1_fp32 = (torch.randn((n, n), dtype=torch.float32, device=dev),)
mat2_fp32 = (torch.randn((n, n), dtype=torch.float32, device=dev),)
mat3_fp32 = (torch.randn((n, n), dtype=torch.float32, device=dev),)
# The lists below organize ops that autocast needs to test.
# self.list_name corresponds to test_autocast_list_name in test/test_cuda.py.
# Each op is associated with a tuple of valid arguments.
# In addition, cudnn conv ops are not supported on ROCm and hence will
# be skipped by passing TEST_WITH_ROCM flag to those ops in self.torch_fp16 list.
# Some ops implement built-in type promotion. These don't need autocasting,
# but autocasting relies on their promotion, so we include tests to double-check.
self.torch_expect_builtin_promote = [
("eq", pointwise0_fp32 + pointwise1_fp16, torch.bool),
("ge", pointwise0_fp32 + pointwise1_fp16, torch.bool),
("gt", pointwise0_fp32 + pointwise1_fp16, torch.bool),
("le", pointwise0_fp32 + pointwise1_fp16, torch.bool),
("lt", pointwise0_fp32 + pointwise1_fp16, torch.bool),
("ne", pointwise0_fp32 + pointwise1_fp16, torch.bool),
("add", pointwise0_fp32 + pointwise1_fp16, torch.float32),
("div", pointwise0_fp32 + pointwise1_fp16, torch.float32),
("mul", pointwise0_fp32 + pointwise1_fp16, torch.float32),
("cat", (pointwise0_fp16 + pointwise1_fp32,), torch.float32),
("equal", pointwise0_fp32 + pointwise1_fp16, torch.float32),
("stack", (pointwise0_fp16 + pointwise1_fp32,), torch.float32),
]
self.methods_expect_builtin_promote = [
("__eq__", pointwise0_fp32 + pointwise1_fp16, torch.bool),
("__ge__", pointwise0_fp32 + pointwise1_fp16, torch.bool),
("__gt__", pointwise0_fp32 + pointwise1_fp16, torch.bool),
("__le__", pointwise0_fp32 + pointwise1_fp16, torch.bool),
("__lt__", pointwise0_fp32 + pointwise1_fp16, torch.bool),
("__ne__", pointwise0_fp32 + pointwise1_fp16, torch.bool),
("__add__", pointwise0_fp32 + pointwise1_fp16, torch.float32),
("__div__", pointwise0_fp32 + pointwise1_fp16, torch.float32),
("__mul__", pointwise0_fp32 + pointwise1_fp16, torch.float32),
]
# The remaining lists organize ops that autocast treats explicitly.
self.torch_fp16 = [
# deprecated _convolution
("_convolution", conv_args_fp32[1] + bias_fp32 + ((1, 1), (0, 0), (1, 1), False,
(0, 0), 1, False, True, True)),
# the current _convolution
("_convolution", conv_args_fp32[1] + bias_fp32 + ((1, 1), (0, 0), (1, 1), False,
(0, 0), 1, False, True, True, True)),
("conv1d", conv_args_fp32[0]),
("conv2d", conv_args_fp32[1]),
("conv3d", conv_args_fp32[2]),
("conv_tbc", conv_args_fp32[0] + bias_fp32),
("conv_transpose1d", conv_args_fp32[0]),
("conv_transpose2d", conv_args_fp32[1]),
("conv_transpose3d", conv_args_fp32[2]),
("convolution", conv_args_fp32[1] + bias_fp32 + ((1, 1), (0, 0), (1, 1), False, (0, 0), 1)),
("cudnn_convolution", conv_args_fp32[1] + ((0, 0), (1, 1), (1, 1), 1, False, True, True), TEST_WITH_ROCM),
("cudnn_convolution_transpose", conv_args_fp32[1] + ((0, 0), (0, 0), (1, 1),
(1, 1), 1, False, True, True), TEST_WITH_ROCM),
("prelu", pointwise0_fp32 + element0_fp32),
("addmm", mat1_fp32 + mat2_fp32 + mat3_fp32),
("addmv", pointwise0_fp32 + mat2_fp32 + pointwise1_fp32),
("addr", mat0_fp32 + pointwise0_fp32 + pointwise1_fp32),
("matmul", mat0_fp32 + mat1_fp32),
("einsum", "bkhd,bqhd->bqkh", mat0_fp32 + mat1_fp32),
("mm", mat0_fp32 + mat1_fp32),
("mv", mat0_fp32 + pointwise0_fp32),
("chain_matmul", mat0_fp32 + mat1_fp32 + mat2_fp32),
("addbmm", mat0_fp32 + (torch.randn((n, n, n), device=dev, dtype=torch.float32),
torch.randn((n, n, n), device=dev, dtype=torch.float32))),
("baddbmm", (torch.randn((n, n, n), device=dev, dtype=torch.float32),
torch.randn((n, n, n), device=dev, dtype=torch.float32),
torch.randn((n, n, n), device=dev, dtype=torch.float32))),
("bmm", (torch.randn((n, n, n), device=dev, dtype=torch.float32),
torch.randn((n, n, n), device=dev, dtype=torch.float32))),
# _thnn_fused_lstm_cell and _thnn_fused_gru_cell are not Python-exposed as far as I can tell.
# ("_thnn_fused_lstm_cell", mat0_fp32 + mat1_fp32 + mat2_fp32 + pointwise0_fp32 + pointwise1_fp32),
# ("_thnn_fused_gru_cell", mat0_fp32 + mat1_fp32 + mat2_fp32 + pointwise0_fp32 + pointwise1_fp32),
("lstm_cell", self._rnn_cell_args(n, num_chunks=4, is_lstm=True, dev=dev, dtype=torch.float32)),
("gru_cell", self._rnn_cell_args(n, num_chunks=3, is_lstm=False, dev=dev, dtype=torch.float32)),
("rnn_tanh_cell", self._rnn_cell_args(n, num_chunks=1, is_lstm=False, dev=dev, dtype=torch.float32)),
("rnn_relu_cell", self._rnn_cell_args(n, num_chunks=1, is_lstm=False, dev=dev, dtype=torch.float32)),
]
self.torch_fp32 = [
("acos", (pointwise0_fp16[0].clamp(-.9, 0.9),)),
("asin", (pointwise0_fp16[0].clamp(-.9, 0.9),)),
("cosh", pointwise0_fp16),
("erfinv", (pointwise0_fp16[0].clamp(-.9, .9),)),
("exp", pointwise0_fp16),
("expm1", pointwise0_fp16),
("log", (pointwise0_fp16[0].clamp(0.1, 100.0),)),
("log10", (pointwise0_fp16[0].clamp(0.1, 100.0),)),
("log2", (pointwise0_fp16[0].clamp(0.1, 100.0),)),
("log1p", (pointwise0_fp16[0].clamp(-0.9, 100.0),)),
("reciprocal", pointwise0_fp16),
("rsqrt", (pointwise0_fp16[0].clamp(0.0, 100.0),)),
("sinh", pointwise0_fp16),
("tan", (pointwise0_fp16[0].clamp(-3.1 / 2, 3.1 / 2),)),
("pow", ((pointwise0_fp16[0] + 1.).clamp(0.0, 100.0),) + pointwise1_fp16),
("pow", ((pointwise0_fp16[0] + 1.).clamp(0.0, 100.0),) + (1.7,)),
# ("pow", (1.7,) + pointwise0_fp16), # This variant has a backend, but is not documented in the API.
("softmax", pointwise0_fp16 + (0,)),
("log_softmax", pointwise0_fp16 + (0,)),
("layer_norm", pointwise0_fp16 + ((pointwise0_fp16[0].numel(),),)),
("group_norm", mat0_fp16 + (1,)),
("norm", pointwise0_fp16),
("norm", pointwise0_fp16, {"dim": 0}),
# these need magma
# ("norm", mat0_fp16, {"p": "nuc"}),
# ("norm", mat0_fp16, {"p": "nuc", "dim": 0}),
("norm", pointwise0_fp16, {"p": 1}),
("norm", pointwise0_fp16, {"p": 1, "dim": 0}),
("cosine_similarity", mat0_fp16 + mat1_fp16),
("poisson_nll_loss", mat0_fp16 + mat1_fp16 + (True, False, 1.e-8, torch.nn._reduction.get_enum('mean'))),
("cosine_embedding_loss", (torch.tensor([[1, 2, 3]], device=dev, dtype=torch.float16),
torch.tensor([[1, 3, 4]], device=dev, dtype=torch.float16),
torch.tensor([1], device=dev, dtype=torch.int))),
("hinge_embedding_loss", mat0_fp16 + (torch.ones(n, device=dev, dtype=torch.int),)),
("kl_div", mat0_fp16 + (torch.rand((n, n), device=dev, dtype=torch.float16),)),
("margin_ranking_loss", mat0_fp16 + mat1_fp16 + (torch.ones((n,), device=dev, dtype=torch.float16),)),
("triplet_margin_loss", mat0_fp16 + mat1_fp16 + mat2_fp16),
("binary_cross_entropy_with_logits", mat0_fp16 + (torch.rand((n, n), device=dev, dtype=torch.float16),)),
("cumprod", pointwise0_fp16 + (0,)),
("cumsum", pointwise0_fp16 + (0,)),
("dist", pointwise0_fp16 + pointwise1_fp16),
("pdist", mat0_fp16),
("cdist", mat0_fp16 + mat1_fp16),
("prod", pointwise0_fp16),
("prod", pointwise0_fp16 + (0,)),
("renorm", mat0_fp16 + (2, 0, 1.0)),
("sum", pointwise0_fp16),
("sum", mat0_fp16 + (1,)),
("logsumexp", mat0_fp16 + (1,)),
]
self.torch_need_autocast_promote = [
("addcdiv", pointwise0_fp32 + pointwise1_fp16 + (pointwise2_fp16[0].clamp(0.1, 100),)),
("addcmul", pointwise0_fp32 + pointwise1_fp16 + pointwise2_fp16),
("atan2", pointwise0_fp32 + (pointwise1_fp16[0].clamp(0.1, 100),)),
("bilinear", (torch.randn((1, 2), dtype=torch.float16, device=dev),
torch.randn((1, 2), dtype=torch.float32, device=dev),
torch.randn((1, 2, 2), dtype=torch.float16, device=dev),
torch.randn((1,), dtype=torch.float32, device=dev))),
("cross", (torch.randn(3, dtype=torch.float32, device=dev),
torch.randn(3, dtype=torch.float16, device=dev))),
("dot", pointwise0_fp16 + pointwise1_fp32),
("grid_sampler", (torch.randn((2, 3, 33, 22), dtype=torch.float16, device=dev),
torch.randn((2, 22, 11, 2), dtype=torch.float32, device=dev),
0, 0, False)),
("index_put", pointwise0_fp32 + ((torch.tensor([1], device=dev, dtype=torch.long),),
torch.randn(1, device=dev, dtype=torch.float16))),
("index_put", pointwise0_fp16 + ((torch.tensor([1], device=dev, dtype=torch.long),),
torch.randn(1, device=dev, dtype=torch.float32))),
("tensordot", (torch.randn((2, 2, 2), dtype=torch.float32, device=dev),
torch.randn((2, 2, 2), dtype=torch.float16, device=dev))),
("scatter_add", (torch.zeros(2, 2, 2, dtype=torch.float32, device=dev),
0,
torch.randint(0, 2, (2, 2, 2), device=dev),
torch.randn((2, 2, 2), dtype=torch.float16, device=dev))),
("scatter_add", (torch.zeros(2, 2, 2, dtype=torch.float16, device=dev),
0,
torch.randint(0, 2, (2, 2, 2), device=dev),
torch.randn((2, 2, 2), dtype=torch.float32, device=dev))),
]
self.nn_fp16 = [
("linear", mat0_fp32 + mat1_fp32 + mat2_fp32),
]
self.nn_fp32 = [
("softplus", pointwise0_fp16),
("nll_loss", (torch.rand((n, n), device=dev, dtype=torch.float),
torch.zeros((n,), device=dev, dtype=torch.long))),
("nll_loss2d", (torch.rand((n, n, n, n), device=dev, dtype=torch.half),
torch.zeros((n, n, n), device=dev, dtype=torch.long))),
("l1_loss", mat0_fp16 + mat1_fp16),
("smooth_l1_loss", mat0_fp16 + mat1_fp16),
("mse_loss", mat0_fp16 + mat1_fp16),
("multilabel_margin_loss", mat0_fp16 + (torch.ones((n, n), device=dev, dtype=torch.long),)),
("soft_margin_loss", mat0_fp16 + (torch.ones((n, n), device=dev, dtype=torch.long),)),
("multi_margin_loss", mat0_fp16 + (torch.ones((n,), device=dev, dtype=torch.long),)),
]
self.linalg_fp16 = [
("linalg_multi_dot", (mat0_fp32 + mat1_fp32 + mat2_fp32,)),
]
self.methods_fp16 = [
("__matmul__", mat0_fp32 + mat1_fp32)
]
self.methods_fp32 = [
("__pow__", (torch.rand(n, device=dev, dtype=torch.float16), 1.5)),
]
self.banned = [
("binary_cross_entropy", (torch.rand((n, n), device=dev, dtype=torch.float32),
torch.rand((n, n), device=dev, dtype=torch.float32)), torch._C._nn),
]
class AutocastCPUTestLists(object):
# Supplies ops and arguments for test_autocast_* in test/test_cpu.py
def __init__(self, dev):
super().__init__()
n = 8
# Utility arguments, created as one-element tuples
pointwise0_bf16 = (torch.randn(n, dtype=torch.bfloat16, device=dev),)
pointwise1_bf16 = (torch.randn(n, dtype=torch.bfloat16, device=dev),)
pointwise2_bf16 = (torch.randn(n, dtype=torch.bfloat16, device=dev),)
mat0_bf16 = (torch.randn((n, n), dtype=torch.bfloat16, device=dev),)
mat1_bf16 = (torch.randn((n, n), dtype=torch.bfloat16, device=dev),)
mat2_bf16 = (torch.randn((n, n), dtype=torch.bfloat16, device=dev),)
dummy_dimsets = ((n,), (n, n), (n, n, n), (n, n, n, n), (n, n, n, n, n))
dummy_bf16 = [(torch.randn(dimset, dtype=torch.bfloat16, device=dev),)
for dimset in dummy_dimsets]
dimsets = ((n, n, n), (n, n, n, n), (n, n, n, n, n))
conv_args_bf16 = [(torch.randn(dimset, dtype=torch.bfloat16, device=dev),
torch.randn(dimset, dtype=torch.bfloat16, device=dev))
for dimset in dimsets]
conv_args_fp32 = [(torch.randn(dimset, dtype=torch.float32, device=dev),
torch.randn(dimset, dtype=torch.float32, device=dev))
for dimset in dimsets]
bias_fp32 = (torch.randn((n,), dtype=torch.float32, device=dev),)
element0_fp32 = (torch.randn(1, dtype=torch.float32, device=dev),)
pointwise0_fp32 = (torch.randn(n, dtype=torch.float32, device=dev),)
pointwise1_fp32 = (torch.randn(n, dtype=torch.float32, device=dev),)
mat0_fp32 = (torch.randn((n, n), dtype=torch.float32, device=dev),)
mat1_fp32 = (torch.randn((n, n), dtype=torch.float32, device=dev),)
mat2_fp32 = (torch.randn((n, n), dtype=torch.float32, device=dev),)
mat3_fp32 = (torch.randn((n, n), dtype=torch.float32, device=dev),)
dummy_fp32 = [(torch.randn(dimset, dtype=torch.float32, device=dev),)
for dimset in dummy_dimsets]
# The lists below organize ops that autocast needs to test.
# self.list_name corresponds to test_autocast_list_name in test/test_cpu.py.
# Each op is associated with a tuple of valid arguments.
# Some ops implement built-in type promotion. These don't need autocasting,
# but autocasting relies on their promotion, so we include tests to double-check.
self.torch_expect_builtin_promote = [
("eq", pointwise0_fp32 + pointwise1_bf16, torch.bool),
("ge", pointwise0_fp32 + pointwise1_bf16, torch.bool),
("gt", pointwise0_fp32 + pointwise1_bf16, torch.bool),
("le", pointwise0_fp32 + pointwise1_bf16, torch.bool),
("lt", pointwise0_fp32 + pointwise1_bf16, torch.bool),
("ne", pointwise0_fp32 + pointwise1_bf16, torch.bool),
("add", pointwise0_fp32 + pointwise1_bf16, torch.float32),
("div", pointwise0_fp32 + pointwise1_bf16, torch.float32),
("mul", pointwise0_fp32 + pointwise1_bf16, torch.float32),
]
self.methods_expect_builtin_promote = [
("__eq__", pointwise0_fp32 + pointwise1_bf16, torch.bool),
("__ge__", pointwise0_fp32 + pointwise1_bf16, torch.bool),
("__gt__", pointwise0_fp32 + pointwise1_bf16, torch.bool),
("__le__", pointwise0_fp32 + pointwise1_bf16, torch.bool),
("__lt__", pointwise0_fp32 + pointwise1_bf16, torch.bool),
("__ne__", pointwise0_fp32 + pointwise1_bf16, torch.bool),
("__add__", pointwise0_fp32 + pointwise1_bf16, torch.float32),
("__div__", pointwise0_fp32 + pointwise1_bf16, torch.float32),
("__mul__", pointwise0_fp32 + pointwise1_bf16, torch.float32),
]
# The remaining lists organize ops that autocast treats explicitly.
self.torch_bf16 = [
("conv1d", conv_args_fp32[0]),
("conv2d", conv_args_fp32[1]),
("conv3d", conv_args_fp32[2]),
("bmm", (torch.randn((n, n, n), device=dev, dtype=torch.float32),
torch.randn((n, n, n), device=dev, dtype=torch.float32))),
("mm", mat0_fp32 + mat1_fp32),
("matmul", mat0_fp32 + mat1_fp32),
("baddbmm", (torch.randn((n, n, n), device=dev, dtype=torch.float32),
torch.randn((n, n, n), device=dev, dtype=torch.float32),
torch.randn((n, n, n), device=dev, dtype=torch.float32))),
("addmm", mat1_fp32 + mat2_fp32 + mat3_fp32),
("addbmm", mat0_fp32 + (torch.randn((n, n, n), device=dev, dtype=torch.float32),
torch.randn((n, n, n), device=dev, dtype=torch.float32))),
("conv_tbc", (torch.randn((10, 7, 3), device=dev, dtype=torch.float32),
torch.randn((5, 3, 5), device=dev, dtype=torch.float32),
torch.randn(5, device=dev, dtype=torch.float32),
0)),
]
self.torch_fp32 = [
("conv_transpose1d", conv_args_bf16[0]),
("conv_transpose2d", conv_args_bf16[1]),
("conv_transpose3d", conv_args_bf16[2]),
("poisson_nll_loss", mat0_bf16 + mat1_bf16 + (True, False, 1.e-8, torch.nn._reduction.get_enum('mean'))),
("cosine_embedding_loss", (torch.tensor([[1, 2, 3]], device=dev, dtype=torch.bfloat16),
torch.tensor([[1, 3, 4]], device=dev, dtype=torch.bfloat16),
torch.tensor([1], device=dev, dtype=torch.int))),
("hinge_embedding_loss", mat0_bf16 + (torch.ones(n, device=dev, dtype=torch.int),)),
("margin_ranking_loss", mat0_bf16 + mat1_bf16 + (torch.ones((n,), device=dev, dtype=torch.bfloat16),)),
("triplet_margin_loss", mat0_bf16 + mat1_bf16 + mat2_bf16),
("binary_cross_entropy_with_logits", mat0_bf16 + (torch.rand((n, n), device=dev, dtype=torch.bfloat16),)),
]
self.nn_bf16 = [
("linear", mat0_fp32 + mat1_fp32, {}),
]
self.nn_fp32 = [
("avg_pool3d", dummy_bf16[3], {"kernel_size": (3, 3, 3), "stride": (1, 1, 1)}),
("binary_cross_entropy", (torch.rand((n, n), device=dev, dtype=torch.bfloat16),) +
(torch.rand((n, n), device=dev, dtype=torch.bfloat16),)),
("reflection_pad1d", dummy_bf16[2], {"padding": (3, 3)}),
("nll_loss", (torch.rand((n, n), device=dev, dtype=torch.bfloat16),
torch.zeros((n,), device=dev, dtype=torch.long))),
("nll_loss2d", (torch.rand((n, n, n, n), device=dev, dtype=torch.bfloat16),
torch.zeros((n, n, n), device=dev, dtype=torch.long))),
("l1_loss", mat0_bf16 + mat1_bf16),
("smooth_l1_loss", mat0_bf16 + mat1_bf16),
("mse_loss", mat0_bf16 + mat1_bf16),
("multilabel_margin_loss", mat0_bf16 + (torch.ones((n, n), device=dev, dtype=torch.long),)),
("soft_margin_loss", mat0_bf16 + (torch.ones((n, n), device=dev, dtype=torch.long),)),
("multi_margin_loss", mat0_bf16 + (torch.ones((n,), device=dev, dtype=torch.long),)),
("huber_loss", mat0_bf16 + mat1_bf16),
]
self.torch_need_autocast_promote = [
("cat", (pointwise0_bf16 + pointwise1_fp32,)),
("stack", (pointwise0_bf16 + pointwise1_fp32,)),
]
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