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Commit 2a64c9a6 authored by tabuchixiangcai3's avatar tabuchixiangcai3
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[DCU]Fix memory overflow and test-didistributed in L1_pytorch_istributed_unittest 


Signed-off-by: tabuchixiangcai3's avatarTangao <2205747538@qq.com>
parent a26a0c30
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Showing with 108 additions and 87 deletions
tests/pytorch/debug/run_distributed.py
View file @ 2a64c9a6
......@@ -16,7 +16,7 @@ import transformer_engine
import transformer_engine_torch as tex
import nvdlfw_inspect.api as debug_api
from transformer_engine.debug import set_weight_tensor_tp_group_reduce
from transformer_engine.pytorch.fp8 import FP8GlobalStateManager
from torch.utils.cpp_extension import IS_HIP_EXTENSION
from test_numerics import (
_emulate_linear,
......@@ -45,8 +45,6 @@ FEATURE_DIRS = None
all_boolean = [True, False]
TEST_NR = 0
fp8_available, _ = FP8GlobalStateManager.is_fp8_available()
def _get_tensors(parallel_mode, weight_seed=SEED, data_seed=SEED, tp_size=None, tp_rank=None):
if tp_size is None:
......@@ -72,13 +70,23 @@ def _get_tensors(parallel_mode, weight_seed=SEED, data_seed=SEED, tp_size=None,
def _init_model(weight, parallel_mode=None, tp_group=None, name="linear"):
model = transformer_engine.pytorch.Linear(
if IS_HIP_EXTENSION:
model = transformer_engine.pytorch.Linear(
IN_SIZE,
OUT_SIZE,
name=name,
bias=False,
parallel_mode=parallel_mode,
tp_group=(tp_group or NCCL_WORLD if parallel_mode else None),
)
)
else:
model = transformer_engine.pytorch.Linear(
IN_SIZE,
OUT_SIZE,
name=name,
parallel_mode=parallel_mode,
tp_group=(tp_group or NCCL_WORLD if parallel_mode else None),
)
with torch.no_grad():
model.weight.copy_(weight)
return model
......@@ -223,7 +231,7 @@ def run_debug_test(func):
return wrapper
CONFIG_LOG_TEST_DISTRIBUTED_FP8 = """log_distributed:
CONFIG_LOG_TEST_DISTRIBUTED = """log_distributed:
layers:
layer_types: [linear]
enabled:
......@@ -243,27 +251,11 @@ CONFIG_LOG_TEST_DISTRIBUTED_FP8 = """log_distributed:
end_step: 1
"""
CONFIG_LOG_TEST_DISTRIBUTED_NO_FP8 = """log_distributed:
layers:
layer_types: [linear]
enabled:
True
transformer_engine:
LogTensorStats:
enabled: True
tensors: [activation, gradient, weight, output, wgrad, dgrad]
stats: [min, max, mean, std, l1_norm, l2_norm, cur_amax, dynamic_range]
start_step : 0
end_step: 1
"""
def _prepare_config_test_log_distributed(config_file):
if WORLD_RANK != 0:
return
config_file.write(
CONFIG_LOG_TEST_DISTRIBUTED_FP8 if fp8_available else CONFIG_LOG_TEST_DISTRIBUTED_NO_FP8
)
config_file.write(CONFIG_LOG_TEST_DISTRIBUTED)
config_file.flush()
......@@ -364,40 +356,6 @@ def test_log_distributed(parallel_mode, gather_weight, **kwargs):
set_weight_tensor_tp_group_reduce(True) # reset
@run_debug_test
def sanity_test_log_quantized_stats(parallel_mode, gather_weight, **kwargs):
from test_log import LOG_QUANTIZED_CONFIG
kwargs["config_file"].write(LOG_QUANTIZED_CONFIG)
kwargs["config_file"].flush()
_init_debug(kwargs["config_file"].name, kwargs["log_dir"], FEATURE_DIRS)
set_weight_tensor_tp_group_reduce(gather_weight)
if WORLD_SIZE % 2 != 0:
return # skip
TP_SIZE = WORLD_SIZE // 2
DP_SIZE = 2
TP_RANK = WORLD_RANK % TP_SIZE
DP_RANK = (WORLD_RANK - TP_RANK) // TP_SIZE
debug_api.set_tensor_reduction_group(NCCL_WORLD)
x, weight = _get_tensors(
parallel_mode,
weight_seed=TP_RANK * 1234,
data_seed=DP_RANK * 1234,
tp_size=TP_SIZE,
tp_rank=TP_RANK,
)
tp_group_ranks = [i for i in range(DP_RANK * TP_SIZE, (DP_RANK + 1) * TP_SIZE)]
tp_group = dist.new_group(ranks=tp_group_ranks)
model = _init_model(weight, parallel_mode=parallel_mode, tp_group=tp_group)
_run_forward_backward(x, model, parallel_mode=parallel_mode, group=tp_group)
set_weight_tensor_tp_group_reduce(True) # reset
@run_debug_test
def test_log_expert_parallel(**kwargs):
"""
......@@ -413,13 +371,13 @@ def test_log_expert_parallel(**kwargs):
) # data parallel
model = _init_model(weight, parallel_mode=None, name="linear1")
model1 = _init_model(weight, parallel_mode=None, name="linear2")
with transformer_engine.pytorch.fp8_autocast(enabled=fp8_available, fp8_recipe=FP8_RECIPE):
with transformer_engine.pytorch.fp8_autocast(enabled=True, fp8_recipe=FP8_RECIPE):
y1 = model(x)
y2 = model1(x)
y = y1 + y2
y.sum().backward()
debug_api.step()
with transformer_engine.pytorch.fp8_autocast(enabled=fp8_available, fp8_recipe=FP8_RECIPE):
with transformer_engine.pytorch.fp8_autocast(enabled=True, fp8_recipe=FP8_RECIPE):
y = model(x)
if WORLD_RANK != 0:
y = y + model1(x)
......@@ -580,6 +538,9 @@ def test_fake_quant_fp8(
"dgrad_fp8": not (dgrad_weight or dgrad_grad),
"wgrad_fp8": not (wgrad_grad or wgrad_input),
}
if IS_HIP_EXTENSION:
if fp8_kwargs["fprop_fp8"] or fp8_kwargs["dgrad_fp8"] or fp8_kwargs["wgrad_fp8"]:
return # Output type 32 (FP32) does not support int8 simulation.
if WORLD_RANK == 0:
fake_quant_fp8_create_config(
fprop_inp,
......@@ -667,30 +628,40 @@ if __name__ == "__main__":
random.seed(SEED)
_init_distributed()
test_log_expert_parallel()
for parallel_mode in ["column", "row"]:
for gather_weight in [True, False]:
test_log_distributed(parallel_mode, gather_weight)
if IS_HIP_EXTENSION:
# Output type 32 (FP32) does not support int8 simulation.
pass
else:
test_log_expert_parallel()
for parallel_mode in ["column", "row"]:
for gather_weight in [True, False]:
test_log_distributed(parallel_mode, gather_weight)
if fp8_available:
for parallel_mode in ["row", "column"]:
test_disable_fp8_layer(parallel_mode)
for parallel_mode in ["row", "column"]:
test_disable_fp8_layer(parallel_mode)
# test_disable_fp8_gemms
if IS_HIP_EXTENSION:
# Output type 32 (FP32) does not support int8 simulation.
pass
else:
# test_disable_fp8_gemms
_run_test_with_combinations(
test_disable_fp8_gemms, all_boolean, num_repeat=3, extra_args=["column", "row"]
)
# test_fake_quant_fp8
dtype_options = [tex.DType.kFloat8E4M3, tex.DType.kFloat8E5M2, None]
_run_test_with_combinations(
test_fake_quant_fp8,
dtype_options,
num_repeat=6,
extra_args=["column", "row"],
sample_size=20,
)
# test_fake_quant_fp8
dtype_options = [tex.DType.kFloat8E4M3, tex.DType.kFloat8E5M2, None]
_run_test_with_combinations(
test_fake_quant_fp8,
dtype_options,
num_repeat=6,
extra_args=["column", "row"],
sample_size=20,
)
if IS_HIP_EXTENSION:
# Output type 32 (FP32) does not support int8 simulation.
pass
else:
_run_test_with_combinations(
test_per_tensor_scaling,
all_boolean,
......
tests/pytorch/distributed/run_numerics.py
View file @ 2a64c9a6
......@@ -509,7 +509,7 @@ def _test_linear(parallel_mode=None, sequence_parallel=False, **kwargs):
def test_linear():
"""Run linear layer tests with various configurations."""
kwargs_list = [
base_kwargs_list = [
{},
{"bias": False},
{"init_method": _constant},
......@@ -517,11 +517,17 @@ def test_linear():
{"return_bias": True},
{"params_dtype": torch.float16},
{"delay_wgrad_compute": True},
{"save_original_input": True},
]
#TODO:The blockwise recipe does not currently support calculations with bias set to true.
"""
For AMD platforms, when the quantization recipe is fp8_block_scaling, iterate through base_kwargs_list,
and if the bias value is not set in kwargs or the bias value is true, set bias to false.
"""
if IS_HIP_EXTENSION and QUANTIZATION == "fp8_block_scaling":
kwargs_list = [kwargs for kwargs in base_kwargs_list if kwargs.get("bias", True) is False]
else:
kwargs_list = base_kwargs_list
for kwargs in kwargs_list:
if kwargs.get("save_original_input", False) and QUANTIZATION == "fp8":
continue
for parallel_mode in ["column", "row"]:
for sequence_parallel in [False, True]:
_test_linear(parallel_mode, sequence_parallel, **kwargs)
......@@ -688,7 +694,7 @@ def _test_layernorm_linear(parallel_mode=None, sequence_parallel=False, **kwargs
def test_layernorm_linear():
kwargs_list = [
base_kwargs_list = [
{},
{"bias": False},
{"init_method": _constant},
......@@ -699,6 +705,15 @@ def test_layernorm_linear():
{"return_layernorm_output": True},
{"delay_wgrad_compute": True},
]
#TODO:The blockwise recipe does not currently support calculations with bias set to true.
"""
For AMD platforms, when the quantization recipe is fp8_block_scaling, iterate through base_kwargs_list,
and if the bias value is not set in kwargs or the bias value is true, set bias to false.
"""
if IS_HIP_EXTENSION and QUANTIZATION == "fp8_block_scaling":
kwargs_list = [kwargs for kwargs in base_kwargs_list if kwargs.get("bias", True) is False]
else:
kwargs_list = base_kwargs_list
for kwargs in kwargs_list:
for parallel_mode in ["column"]:
for sequence_parallel in [False, True]:
......@@ -793,7 +808,7 @@ def _test_layernorm_mlp(set_parallel_mode=None, sequence_parallel=False, **kwarg
def test_layernorm_mlp():
kwargs_list = [
base_kwargs_list = [
{},
{"init_method": _constant},
{"output_layer_init_method": _constant},
......@@ -807,7 +822,15 @@ def test_layernorm_mlp():
{"return_layernorm_output": True},
{"delay_wgrad_compute": True},
]
#TODO:The blockwise recipe does not currently support calculations with bias set to true.
"""
For AMD platforms, when the quantization recipe is fp8_block_scaling, iterate through base_kwargs_list,
and if the bias value is not set in kwargs or the bias value is true, set bias to false.
"""
if IS_HIP_EXTENSION and QUANTIZATION == "fp8_block_scaling":
kwargs_list = [kwargs for kwargs in base_kwargs_list if kwargs.get("bias", True) is False]
else:
kwargs_list = base_kwargs_list
for kwargs in kwargs_list:
for set_parallel_mode in [True]:
for sequence_parallel in [False, True]:
......@@ -882,7 +905,7 @@ def _test_transformer_layer_parallel(sequence_parallel=False, **kwargs):
def test_transformer_layer():
kwargs_list = [
base_kwargs_list = [
{},
{"num_gqa_groups": 4},
{"init_method": _constant},
......@@ -902,6 +925,15 @@ def test_transformer_layer():
{"fuse_qkv_params": True},
{"activation": "relu"},
]
#TODO:The blockwise recipe does not currently support calculations with bias set to true.
"""
For AMD platforms, when the quantization recipe is fp8_block_scaling, iterate through base_kwargs_list,
and if the bias value is not set in kwargs or the bias value is true, set bias to false.
"""
if IS_HIP_EXTENSION and QUANTIZATION == "fp8_block_scaling":
kwargs_list = [kwargs for kwargs in base_kwargs_list if kwargs.get("bias", True) is False]
else:
kwargs_list = base_kwargs_list
for kwargs in kwargs_list:
for sequence_parallel in [False, True]:
......
tests/pytorch/distributed/test_numerics.py
View file @ 2a64c9a6
......@@ -9,7 +9,8 @@ from pathlib import Path
import pytest
import torch
from transformer_engine.pytorch.fp8 import FP8GlobalStateManager
from torch.utils.cpp_extension import IS_HIP_EXTENSION
import transformer_engine as te
"""
Distributed numerics tests
......@@ -61,4 +62,15 @@ def test_distributed(quantization):
pytest.skip(reason_for_no_mxfp8)
if quantization == "fp8_block_scaling" and not fp8_block_scaling_available:
pytest.skip(reason_for_no_fp8_block_scaling)
if IS_HIP_EXTENSION and quantization == "fp8_block_scaling":
import importlib
ori_int8_sim_fp8 = os.environ.get("NVTE_INT8_SIM_FP8", "None")
os.environ["NVTE_INT8_SIM_FP8"] = "1"
importlib.reload(te.pytorch.fp8)
_run_test(quantization)
if IS_HIP_EXTENSION and quantization == "fp8_block_scaling":
if ori_int8_sim_fp8 is None or ori_int8_sim_fp8 == "None":
os.environ["NVTE_INT8_SIM_FP8"] = "0"
else:
del os.environ["NVTE_INT8_SIM_FP8"]
importlib.reload(te.pytorch.fp8)
transformer_engine/common/transpose/transpose.cu
View file @ 2a64c9a6
......@@ -217,7 +217,13 @@ void transpose(const Tensor &input, const Tensor &noop, Tensor *output_, cudaStr
// Choose between runtime-compiled or statically-compiled kernel
const bool aligned = (row_length % THREADS_PER_WARP == 0 && num_rows % THREADS_PER_WARP == 0);
if (aligned && rtc::is_enabled()) { // Runtime-compiled tuned kernel
//TODO:Using RTC may cause kernel crashes. Therefore, set use_rtc to true to avoid using RTC and resolve the kernel crash issue.
#ifdef USE_ROCM
const bool use_rtc = false;
#else
const bool use_rtc = true;
#endif
if (aligned && rtc::is_enabled() && use_rtc) { // Runtime-compiled tuned kernel
// Pick kernel config
std::vector<KernelConfig> kernel_configs;
kernel_configs.reserve(16);
......
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