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[ROCm] Remove HIPBLASLT_ALLOW_TF32 from codebase (#162998)

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A few UT failures are caused by `HIPBLASLT_ALLOW_TF32`

Fixes #157094
Fixes #157093
Fixes #157092
Fixes #157091
Fixes #157064
Fixes #157063
Fixes #157062
Fixes #157061
Fixes #157042
Fixes #157041
Fixes #157039
Fixes #157004

Pull Request resolved: https://github.com/pytorch/pytorch/pull/162998
Approved by: https://github.com/jeffdaily

Co-authored-by: Jeff Daily <jeff.daily@amd.com>
This commit is contained in:
Xinya Zhang
2025-09-18 13:53:48 +00:00
committed by PyTorch MergeBot
parent 14f8d86136
commit e769026bcb
10 changed files with 48 additions and 196 deletions
Download Patch File Download Diff File Expand all files Collapse all files

21
aten/src/ATen/Context.cpp
View File

@ -180,7 +180,7 @@ void Context::setUserEnabledNNPACK(bool e) {
}
bool Context::allowTF32CuDNN(const std::string& op) const {
if (op.size() == 0){
if (op.empty()){
bool allow_tf32_rnn = float32Precision("cuda", "rnn") == "tf32";
bool allow_tf32_conv = float32Precision("cuda", "conv") == "tf32";
TORCH_CHECK(
@ -281,9 +281,6 @@ bool Context::userEnabledOverrideableSDP() const {
static constexpr const auto cublas_config_var_name = "CUBLAS_WORKSPACE_CONFIG";
static constexpr const std::array<const char*, 2> cublas_deterministic_configs = {":4096:8", ":16:8"};
#ifdef USE_ROCM
static constexpr const auto hipblaslt_allow_tf32 = "HIPBLASLT_ALLOW_TF32";
#endif
bool Context::checkCuBLASConfigDeterministic() {
// If using CUDA 10.2 or greater, need to make sure CuBLAS workspace config
@ -343,12 +340,6 @@ void Context::setImmediateMiopen(bool b) {
}
bool Context::allowTF32CuBLAS() const {
#ifdef USE_ROCM
const auto allow_tf32 = c10::utils::check_env(hipblaslt_allow_tf32);
if (allow_tf32 != true) {
return false;
}
#endif
bool legacy_allow_tf32 = float32_matmul_precision != at::Float32MatmulPrecision::HIGHEST;
bool allow_tf32_new = float32Precision("cuda", "matmul") == "tf32";
TORCH_CHECK(
@ -362,14 +353,6 @@ bool Context::allowTF32CuBLAS() const {
}
void Context::setAllowTF32CuBLAS(bool b) {
#ifdef USE_ROCM
const auto allow_tf32 = c10::utils::check_env(hipblaslt_allow_tf32);
if (allow_tf32 != true) {
C10_LOG_FIRST_N(INFO, 10) << "torch.backends.cuda.matmul.allow_tf32 is not supported on ROCm by default. "
<< "Please set environment variable HIPBLASLT_ALLOW_TF32=1 to enable it.";
return;
}
#endif
float32_matmul_precision = b ? at::Float32MatmulPrecision::HIGH : at::Float32MatmulPrecision::HIGHEST;
setFloat32Precision("cuda", "matmul", b ? "tf32" : "ieee");
}
@ -443,7 +426,7 @@ void Context::setFloat32Precision(const std::string& backend, const std::string&
std::string msg;
auto iterp = _fp32_precisions.find(backend);
TORCH_CHECK(iterp != _fp32_precisions.end());
for (auto p : iterp->second) {
for (const auto& p : iterp->second) {
msg += p;
msg += " ";
}

62
test/dynamo/test_graph_region_tracker.py
View File

@ -1,6 +1,5 @@
# Owner(s): ["module: dynamo"]
import contextlib
import os
import torch
import torch.fx
@ -196,21 +195,6 @@ class GraphRegionTrackerTests(TestCase):
)
def test_mismatched_global_state(self):
@contextlib.contextmanager
def _hip_allow_tf32():
# for HIP/AMDGPU, tf32 is behind a flag because the TF32 support is new
# and only for MI300+
hip_allow_tf32 = os.environ.get("HIPBLASLT_ALLOW_TF32", None)
os.environ["HIPBLASLT_ALLOW_TF32"] = "1"
try:
yield
finally:
if hip_allow_tf32 is not None:
os.environ["HIPBLASLT_ALLOW_TF32"] = hip_allow_tf32
else:
del os.environ["HIPBLASLT_ALLOW_TF32"]
def inner_fn(x, y):
x1 = x * 1
y1 = y + 1
@ -251,31 +235,29 @@ class GraphRegionTrackerTests(TestCase):
def reset_default_dtype():
torch.set_default_dtype(old_dtype)
tf32_ctx = _hip_allow_tf32 if torch.version.hip else contextlib.nullcontext
with tf32_ctx():
for ctx in [
lambda: torch.set_grad_enabled(False),
torch.autograd.grad_mode.inference_mode,
lambda: torch.autograd.graph.disable_saved_tensors_hooks(
"This is not supported"
),
# lambda: torch.set_num_threads(2), : Unsupported
(set_default_dtype_bfloat16, reset_default_dtype),
(
lambda: torch.use_deterministic_algorithms(True),
lambda: torch.use_deterministic_algorithms(False),
),
# (lambda: torch.use_deterministic_algorithms(True, warn_only=True),
# lambda: torch.use_deterministic_algorithms(False)), : Unsupported
create_toggle_fns("allow_bf16_reduced_precision_reduction"),
create_toggle_fns("allow_fp16_reduced_precision_reduction"),
create_toggle_fns("allow_tf32"),
]:
self.assertExpectedInline(
self.get_result(fn, torch.rand(10, 10), torch.ones(10, 20), ctx),
"""[[['x1_2', 'y1_2', 'sum_3', 'o0'], ['x1_3', 'y1_3', 'sum_4', 'o2']], \
for ctx in [
lambda: torch.set_grad_enabled(False),
torch.autograd.grad_mode.inference_mode,
lambda: torch.autograd.graph.disable_saved_tensors_hooks(
"This is not supported"
),
# lambda: torch.set_num_threads(2), : Unsupported
(set_default_dtype_bfloat16, reset_default_dtype),
(
lambda: torch.use_deterministic_algorithms(True),
lambda: torch.use_deterministic_algorithms(False),
),
# (lambda: torch.use_deterministic_algorithms(True, warn_only=True),
# lambda: torch.use_deterministic_algorithms(False)), : Unsupported
create_toggle_fns("allow_bf16_reduced_precision_reduction"),
create_toggle_fns("allow_fp16_reduced_precision_reduction"),
create_toggle_fns("allow_tf32"),
]:
self.assertExpectedInline(
self.get_result(fn, torch.rand(10, 10), torch.ones(10, 20), ctx),
"""[[['x1_2', 'y1_2', 'sum_3', 'o0'], ['x1_3', 'y1_3', 'sum_4', 'o2']], \
[['x1', 'y1', 'sum_1', 'o4'], ['x1_1', 'y1_1', 'sum_2', 'o5']]]""",
)
)
def test_mutation_tracking_simple(self):
def fn(x, y, z):

55
test/dynamo/test_misc.py
View File

@ -8478,43 +8478,24 @@ utils_device.CURRENT_DEVICE == None""".split("\n"):
def fn(x):
return x + 1
import contextlib
@contextlib.contextmanager
def _hip_allow_tf32():
# for HIP/AMDGPU, tf32 is behind a flag because the TF32 support is new
# and only for MI300+
hip_allow_tf32 = os.environ.get("HIPBLASLT_ALLOW_TF32", None)
os.environ["HIPBLASLT_ALLOW_TF32"] = "1"
try:
yield
finally:
if hip_allow_tf32 is not None:
os.environ["HIPBLASLT_ALLOW_TF32"] = hip_allow_tf32
else:
del os.environ["HIPBLASLT_ALLOW_TF32"]
tf32_ctx = _hip_allow_tf32 if torch.version.hip else contextlib.nullcontext
with tf32_ctx():
initial_state = read_state()
y = torch.randn(10)
try:
for round in range(3):
for i in range(len(initial_state)):
new_state = [False] * len(initial_state)
new_state[i] = True
write_state(new_state)
assert read_state() == new_state
last_state.clear()
fn(y)
assert last_state == new_state
if round == 0:
assert cnt == i + 1
else:
assert cnt == len(initial_state)
finally:
write_state(initial_state)
initial_state = read_state()
y = torch.randn(10)
try:
for round in range(3):
for i in range(len(initial_state)):
new_state = [False] * len(initial_state)
new_state[i] = True
write_state(new_state)
assert read_state() == new_state
last_state.clear()
fn(y)
assert last_state == new_state
if round == 0:
assert cnt == i + 1
else:
assert cnt == len(initial_state)
finally:
write_state(initial_state)
def test_grad_state_mutated(self):
prior = torch.is_grad_enabled()

3
test/inductor/test_flex_decoding.py
View File

@ -43,9 +43,6 @@ if IS_WINDOWS and IS_CI:
Tolerances = namedtuple("Tolerances", ["atol", "rtol"])
# In MI300, HIPBLASLT_ALLOW_TF32=1 is used to enable tf32 for matmul.
# In the current test, HIPBLASLT_ALLOW_TF32 is not set, according to the
# logic of allowTF32CuBLAS(), set float32_matmul_precision to highest.
if torch.version.hip:
torch.set_float32_matmul_precision("highest")
else:

3
test/inductor/test_padding.py
View File

@ -109,9 +109,6 @@ class TestCaseBase(TestCase):
if HAS_GPU:
cls.prior_float32_matmul_precision = torch.get_float32_matmul_precision()
cls.prior_default_device = torch.get_default_device()
# In MI300, HIPBLASLT_ALLOW_TF32=1 is used to enable tf32 for matmul.
# In the current test, HIPBLASLT_ALLOW_TF32 is not set, according to the
# logic of allowTF32CuBLAS(), set float32_matmul_precision to highest.
if torch.version.hip:
torch.set_float32_matmul_precision("highest")
else:

52
test/test_cuda.py
View File

@ -759,53 +759,7 @@ print(t.is_pinned())
torch._C._cuda_clearCublasWorkspaces()
@contextlib.contextmanager
def _hip_allow_tf32(self):
# for HIP/AMDGPU, tf32 is behind a flag because the TF32 support is new
# and only for MI300+
hip_allow_tf32 = os.environ.get("HIPBLASLT_ALLOW_TF32", None)
os.environ["HIPBLASLT_ALLOW_TF32"] = "1"
try:
yield
finally:
if hip_allow_tf32 is not None:
os.environ["HIPBLASLT_ALLOW_TF32"] = hip_allow_tf32
else:
del os.environ["HIPBLASLT_ALLOW_TF32"]
@unittest.skipIf(not TEST_WITH_ROCM, "not relevant for CUDA testing")
def test_hipblaslt_allow_tf32(self):
tf32_ctx = self._hip_allow_tf32
with tf32_ctx():
os.environ["HIPBLASLT_ALLOW_TF32"] = "0"
# Save original value of allow_tf32
orig = torch.backends.cuda.matmul.allow_tf32
# If allow_tf32 variable is declared as static in aten/src/ATen/Context.cpp
# then matmul.allow_tf32 will return False after this point even if
# HIP_BLASLT_ALLOW_TF32 is set to 1 and matmul.allow_tf32 is changed.
os.environ["HIPBLASLT_ALLOW_TF32"] = "1"
# Toggle torch.backends.cuda.matmul.allow_tf32 couple of times.
torch.backends.cuda.matmul.allow_tf32 = not orig
test1 = torch.backends.cuda.matmul.allow_tf32
torch.backends.cuda.matmul.allow_tf32 = orig
test2 = torch.backends.cuda.matmul.allow_tf32
self.assertNotEqual(test1, test2)
# Restore original value of allow_tf32
torch.backends.cuda.matmul.allow_tf32 = orig
def test_cublas_allow_tf32_get_set(self):
"""
We only turn on TF32 for MI300 with a special env var. This is because TF32
is only available in MI300+ and is in experimental mode (hipblaslt support
is current WIP)
"""
tf32_ctx = self._hip_allow_tf32 if torch.version.hip else contextlib.nullcontext
with tf32_ctx():
self._test_cublas_allow_tf32_get_set_inner()
def _test_cublas_allow_tf32_get_set_inner(self):
skip_tf32_cublas = "TORCH_ALLOW_TF32_CUBLAS_OVERRIDE" in os.environ and int(
os.environ["TORCH_ALLOW_TF32_CUBLAS_OVERRIDE"]
)
@ -820,12 +774,6 @@ print(t.is_pinned())
torch.backends.cuda.matmul.allow_tf32 = orig
def test_float32_matmul_precision_get_set(self):
tf32_ctx = self._hip_allow_tf32 if torch.version.hip else contextlib.nullcontext
with tf32_ctx():
self._test_float32_matmul_precision_get_set_inner()
def _test_float32_matmul_precision_get_set_inner(self):
orig = torch.get_float32_matmul_precision()
skip_tf32_cublas = "TORCH_ALLOW_TF32_CUBLAS_OVERRIDE" in os.environ and int(
os.environ["TORCH_ALLOW_TF32_CUBLAS_OVERRIDE"]

30
test/test_linalg.py
View File

@ -109,22 +109,6 @@ def get_tunableop_untuned_filename():
return untuned_filename
class TestLinalg(TestCase):
@contextlib.contextmanager
def _hip_allow_tf32(self):
# for HIP/AMDGPU, tf32 is behind a flag because the TF32 support is new
# and only for MI300+. Environment variable will be removed in the future.
import os
hip_allow_tf32 = os.environ.get("HIPBLASLT_ALLOW_TF32", None)
os.environ["HIPBLASLT_ALLOW_TF32"] = "1"
try:
yield
finally:
if hip_allow_tf32 is not None:
os.environ["HIPBLASLT_ALLOW_TF32"] = hip_allow_tf32
else:
del os.environ["HIPBLASLT_ALLOW_TF32"]
def setUp(self):
super().setUp()
torch.backends.cuda.matmul.allow_tf32 = False
@ -5542,13 +5526,8 @@ class TestLinalg(TestCase):
@runOnRocmArch(MI300_ARCH)
@dtypes(torch.float)
def test_tf32_tunableop(self, device, dtype):
# Test TunableOp with TF32. Supported by hipblasLT on MI300+.
# for HIP/AMDGPU, tf32 is behind a flag because the TF32 support is new
# and only for MI300+. Eventually this flag will go away.
tf32_ctx = self._hip_allow_tf32 if torch.version.hip else contextlib.nullcontext
try:
with self._tunableop_ctx(), tf32_ctx():
with self._tunableop_ctx():
torch.backends.cuda.matmul.allow_tf32 = True
torch.cuda.tunable.set_rotating_buffer_size(0)
@ -5611,13 +5590,8 @@ class TestLinalg(TestCase):
# This test is the offline version of test_tf32_tunableop
import os
# Test TunableOp with TF32. Supported by hipblasLT on MI300+.
# for HIP/AMDGPU, tf32 is behind a flag because the TF32 support is new
# and only for MI300+. Eventually this flag will go away.
tf32_ctx = self._hip_allow_tf32 if torch.version.hip else contextlib.nullcontext
try:
with self._tunableop_ctx(), tf32_ctx():
with self._tunableop_ctx():
torch.backends.cuda.matmul.allow_tf32 = True
ordinal = torch.cuda.current_device()
torch.cuda.tunable.set_rotating_buffer_size(0)

7
test/test_transformers.py
View File

@ -51,7 +51,6 @@ from torch.testing._internal.common_cuda import (
PLATFORM_SUPPORTS_CUDNN_ATTENTION,
tf32_on_and_off,
tf32_enabled,
ROCM_VERSION,
)
if TEST_FAIRSEQ:
@ -340,7 +339,7 @@ class TestTransformers(NNTestCase):
l1_bool = nn.L1Loss()(test_train_bool[:, 0:2, :], test_eval_bool[:, 0:2, :]).item()
self.assertTrue(l1_bool < 1e-4, "Eval/Train difference in pad_mask BOOL")
@tf32_on_and_off(0.001, only_if=(not TEST_WITH_ROCM or ROCM_VERSION < (7, 0)))
@tf32_on_and_off(0.001)
@parametrize("attn_mask_dim", [2, 3, None])
@parametrize("key_padding_mask_dim", [2, None])
@parametrize("mask_dtype", [torch.bool, torch.float32])
@ -524,7 +523,7 @@ class TestTransformers(NNTestCase):
slowpath_output = slowpath_output.masked_fill(src_key_padding_mask.unsqueeze(-1), 0)
self.assertEqual(fastpath_output_expanded, slowpath_output)
@tf32_on_and_off(0.001, only_if=(not TEST_WITH_ROCM or ROCM_VERSION < (7, 0)))
@tf32_on_and_off(0.001)
@parametrize("with_no_grad", [True, False])
@parametrize("training", [True, False])
@parametrize("enable_nested_tensor", [False])
@ -1110,7 +1109,7 @@ class TestTransformers(NNTestCase):
return_all_hiddens=False,
)[0]
@tf32_on_and_off(0.003, only_if=(not TEST_WITH_ROCM or ROCM_VERSION < (7, 0)))
@tf32_on_and_off(0.003)
@parametrize("input_dim,attn_mask_dim,is_causal",
[(3, None, False), (3, 2, False), (3, 2, True), (3, 3, False), (3, 3, True),
(4, None, False), (4, 2, False), (4, 2, True), (4, 4, False), (4, 4, True)],

1
torch/cuda/tunable.py
View File

@ -591,7 +591,6 @@ def _process_single_offline_gemm(untuned_gemm_line: str, gpu_id: int) -> None:
transA = layout[1] == "T"
dtype = dtype_dict.get(data_type)
if data_type == "tf32":
# User must still set HIPBLASLT_ALLOW_TF32=1
torch.backends.cuda.matmul.allow_tf32 = True
else:
torch.backends.cuda.matmul.allow_tf32 = False

10
torch/testing/_internal/common_cuda.py
View File

@ -181,9 +181,6 @@ def tf32_off():
@contextlib.contextmanager
def tf32_on(self, tf32_precision=1e-5):
if torch.version.hip:
hip_allow_tf32 = os.environ.get("HIPBLASLT_ALLOW_TF32", None)
os.environ["HIPBLASLT_ALLOW_TF32"] = "1"
old_allow_tf32_matmul = torch.backends.cuda.matmul.allow_tf32
old_precision = self.precision
try:
@ -192,11 +189,6 @@ def tf32_on(self, tf32_precision=1e-5):
with torch.backends.cudnn.flags(enabled=None, benchmark=None, deterministic=None, allow_tf32=True):
yield
finally:
if torch.version.hip:
if hip_allow_tf32 is not None:
os.environ["HIPBLASLT_ALLOW_TF32"] = hip_allow_tf32
else:
del os.environ["HIPBLASLT_ALLOW_TF32"]
torch.backends.cuda.matmul.allow_tf32 = old_allow_tf32_matmul
self.precision = old_precision
@ -246,7 +238,7 @@ def tf32_enabled():
# if device is specified, it will check if device is cuda
# if dtype is specified, it will check if dtype is float32 or complex64
# tf32 and fp32 are different only when all the three checks pass
def tf32_on_and_off(tf32_precision=1e-5, only_if=True):
def tf32_on_and_off(tf32_precision=1e-5, *, only_if=True):
def with_tf32_disabled(self, function_call):
with tf32_off():
function_call()