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Commit 3a040217 authored by wenjh's avatar wenjh
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Merge branch 'fix_develop2.8_zc' into 'develop_v2.8' 

[DCU]Fix some bugs

See merge request dcutoolkit/deeplearing/TransformerEngine!56
parents a622988a f7c66e28
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qa/L0_pytorch_unittest/test.sh
View file @ 3a040217
......@@ -48,6 +48,7 @@ NVTE_FLASH_ATTN=0 python3 -m pytest --tb=auto --junitxml=$XML_LOG_DIR/pytest_tes
python3 -m pytest --tb=auto --junitxml=$XML_LOG_DIR/pytest_test_attention.xml $TE_PATH/tests/pytorch/attention/test_attention.py || test_fail "test_attention.py"
python3 -m pytest --tb=auto --junitxml=$XML_LOG_DIR/pytest_test_kv_cache.xml $TE_PATH/tests/pytorch/attention/test_kv_cache.py || test_fail "test_kv_cache.py"
python3 -m pytest --tb=auto --junitxml=$XML_LOG_DIR/pytest_test_hf_integration.xml $TE_PATH/tests/pytorch/test_hf_integration.py || test_fail "test_hf_integration.py"
mkdir -p $TE_PATH/artifacts/tests/pytorch/test_checkpoint && python $TE_PATH/tests/pytorch/test_checkpoint.py --save-checkpoint all --checkpoint-dir $TE_PATH/artifacts/tests/pytorch/test_checkpoint/
NVTE_TEST_CHECKPOINT_ARTIFACT_PATH=$TE_PATH/artifacts/tests/pytorch/test_checkpoint python3 -m pytest --tb=auto --junitxml=$XML_LOG_DIR/pytest_test_checkpoint.xml $TE_PATH/tests/pytorch/test_checkpoint.py || test_fail "test_checkpoint.py"
python3 -m pytest --tb=auto --junitxml=$XML_LOG_DIR/pytest_test_fused_router.xml $TE_PATH/tests/pytorch/test_fused_router.py || test_fail "test_fused_router.py"
......
tests/pytorch/attention/test_attention.py
View file @ 3a040217
......@@ -260,21 +260,22 @@ def test_dpa_checkpoint(dtype, model_configs, model):
model_configs_mla = {
#TODO:FlashAttention on ROCm only support MLA with head_dim_qk = head_dim_v
# test: b, h, hg, dqk, sq, skv, p, mask, bias # attn , backend
"mla_1_0": ModelConfig(8, 128, 16, 64, head_dim_v=128), # self , 0
"mla_1_1": ModelConfig(4, 128, 16, 64, max_seqlen_kv=256, head_dim_v=128), # cross, 0
"mla_1_2": ModelConfig(4, 128, 16, 192, max_seqlen_kv=256, head_dim_v=128), # cross, 0
"mla_2_0": ModelConfig(2, 2048, 24, 128, attn_mask_type="causal", head_dim_v=64), # self , 1
"mla_2_1": ModelConfig(
1, 2048, 24, 128, max_seqlen_kv=4096, attn_mask_type="causal", head_dim_v=64
), # cross, 1
"mla_2_2": ModelConfig(
1, 2048, 24, 192, max_seqlen_kv=4096, attn_mask_type="causal", head_dim_v=128
), # cross, 1
"mla_3_0": ModelConfig(8, 1, 16, 128, max_seqlen_kv=2048, head_dim_v=64), # inference
"mla_3_1": ModelConfig(8, 1, 16, 256, max_seqlen_kv=2048, head_dim_v=128), # inference
"mla_3_2": ModelConfig(8, 1, 16, 192, max_seqlen_kv=2048, head_dim_v=128), # inference
"mla_3_3": ModelConfig(8, 1, 16, 160, max_seqlen_kv=2048, head_dim_v=128), # inference
# "mla_1_0": ModelConfig(8, 128, 16, 64, head_dim_v=128), # self , 0
# "mla_1_1": ModelConfig(4, 128, 16, 64, max_seqlen_kv=256, head_dim_v=128), # cross, 0
# "mla_1_2": ModelConfig(4, 128, 16, 192, max_seqlen_kv=256, head_dim_v=128), # cross, 0
# "mla_2_0": ModelConfig(2, 2048, 24, 128, attn_mask_type="causal", head_dim_v=64), # self , 1
# "mla_2_1": ModelConfig(
# 1, 2048, 24, 128, max_seqlen_kv=4096, attn_mask_type="causal", head_dim_v=64
# ), # cross, 1
# "mla_2_2": ModelConfig(
# 1, 2048, 24, 192, max_seqlen_kv=4096, attn_mask_type="causal", head_dim_v=128
# ), # cross, 1
# "mla_3_0": ModelConfig(8, 1, 16, 128, max_seqlen_kv=2048, head_dim_v=64), # inference
# "mla_3_1": ModelConfig(8, 1, 16, 256, max_seqlen_kv=2048, head_dim_v=128), # inference
# "mla_3_2": ModelConfig(8, 1, 16, 192, max_seqlen_kv=2048, head_dim_v=128), # inference
# "mla_3_3": ModelConfig(8, 1, 16, 160, max_seqlen_kv=2048, head_dim_v=128), # inference
"mla_3_4": ModelConfig(8, 1, 16, 160, max_seqlen_kv=2048, head_dim_v=160), # inference
}
......
tests/pytorch/test_cuda_graphs.py
View file @ 3a040217
......@@ -28,9 +28,9 @@ if IS_HIP_EXTENSION:
from functools import cache
# Check if FP8 is supported.
fp8_available, _ = FP8GlobalStateManager.is_fp8_available()
fp8_block_scaling_available, _ = FP8GlobalStateManager.is_fp8_block_scaling_available()
mxfp8_available, _ = FP8GlobalStateManager.is_mxfp8_available()
fp8_available, reason_for_no_fp8 = FP8GlobalStateManager.is_fp8_available()
fp8_block_scaling_available, reason_for_no_fp8_block_scaling = FP8GlobalStateManager.is_fp8_block_scaling_available()
mxfp8_available, reason_for_no_mxfp8 = FP8GlobalStateManager.is_mxfp8_available()
# Reset RNG states.
reset_rng_states()
......@@ -310,6 +310,12 @@ def test_make_graphed_callables(
pytest.skip("FP8 needed for FP8 parameters.")
if fp8 and fp8_recipe.float8_block_scaling() and module == "linear_op":
pytest.skip("Module not yet supported for float8_block_scaling with CUDA graphs")
if fp8 and not fp8_available:
pytest.skip(reason_for_no_fp8)
if fp8 and fp8_recipe.float8_block_scaling() and not fp8_block_scaling_available:
pytest.skip(reason_for_no_fp8_block_scaling)
if fp8 and fp8_recipe.mxfp8() and not mxfp8_available:
pytest.skip(reason_for_no_mxfp8)
# Run model with different CUDA graph settings.
model_config = model_configs[model_config]
......
tests/pytorch/test_float8_blockwise_gemm_exact.py
View file @ 3a040217
......@@ -6,7 +6,6 @@ import pytest
import torch
import transformer_engine as te
import transformer_engine_torch as tex
from transformer_engine.pytorch.utils import use_lightop_w8a8
from transformer_engine.pytorch.constants import TE_DType
from transformer_engine.pytorch.fp8 import (FP8GlobalStateManager, blockwise_fp8_block_len, int8_simulation_fp8)
from transformer_engine.pytorch.tensor.float8_blockwise_tensor import (
......
tests/pytorch/test_fusible_ops.py
View file @ 3a040217
......@@ -2084,7 +2084,8 @@ class TestFusedOps:
quantized_weight: bool = False,
) -> None:
"""Forward GEMM + scale + add"""
if IS_HIP_EXTENSION and scale != 1:
pytest.skip("alpha must be 1.0 for hip")
# Make input and weight shapes consistent
out_features, in_features = weight_shape
in_shape = list(in_shape)[:-1] + [in_features]
......@@ -2469,7 +2470,8 @@ class TestFusedOps:
quantized_weight: bool = False,
) -> None:
"""Backward dgrad GEMM + scale"""
if IS_HIP_EXTENSION and scale != 1:
pytest.skip("alpha must be 1.0 for hip")
# Make input and weight shapes consistent
out_features, in_features = weight_shape
in_shape = list(in_shape)[:-1] + [in_features]
......
tests/pytorch/test_numerics.py
View file @ 3a040217
......@@ -56,7 +56,7 @@ from utils import ModelConfig, reset_rng_states, get_available_attention_backend
# Only run FP8 tests on supported devices.
fp8_available, reason_for_no_fp8 = FP8GlobalStateManager.is_fp8_available()
mxfp8_available, reason_for_no_mxfp8 = FP8GlobalStateManager.is_mxfp8_available()
fp8_block_scaling_available, _ = FP8GlobalStateManager.is_fp8_block_scaling_available()
fp8_block_scaling_available, reason_for_no_fp8_block_scaling = FP8GlobalStateManager.is_fp8_block_scaling_available()
sm_80plus = get_device_compute_capability() >= (8, 0)
......@@ -606,6 +606,13 @@ def _test_e2e_selective_recompute(
def test_gpt_selective_activation_recompute(dtype, bs, model, fp8, recipe, fp8_model_params):
if fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
pytest.skip("FP8 parameters are not supported in debug mode.")
if recipe.float8_block_scaling() and not fp8_block_scaling_available:
pytest.skip(reason_for_no_fp8_block_scaling)
if fp8 and not fp8_available:
pytest.skip(reason_for_no_fp8)
if recipe.mxfp8() and not mxfp8_available:
pytest.skip(reason_for_no_mxfp8)
config = model_configs[model]
......@@ -714,8 +721,15 @@ def _test_e2e_full_recompute(
def test_gpt_full_activation_recompute(
dtype, bs, model, fp8, recipe, fp8_model_params, use_reentrant
):
if fp8 and not fp8_available:
pytest.skip(reason_for_no_fp8)
if recipe.mxfp8() and not mxfp8_available:
pytest.skip(reason_for_no_mxfp8)
if fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
pytest.skip("FP8 parameters are not supported in debug mode.")
if recipe.float8_block_scaling() and not fp8_block_scaling_available:
pytest.skip(reason_for_no_fp8_block_scaling)
config = model_configs[model]
......@@ -1301,9 +1315,14 @@ def test_linear_accuracy_save_original_input(dtype, model, recipe):
fuse_wgrad_accumulation = True
fp8_model_params = False
fp8 = recipe is not None
if fp8 and not fp8_available:
pytest.skip(reason_for_no_fp8)
if fp8 and recipe.delayed():
pytest.skip("DelayedScaling recipe is not supported with save_original_input")
if fp8 and recipe.mxfp8() and not mxfp8_available:
pytest.skip(reason_for_no_mxfp8)
if fp8 and recipe.float8_block_scaling() and not fp8_block_scaling_available:
pytest.skip(reason_for_no_fp8_block_scaling)
config = model_configs[model]
if config.max_seqlen_q % 16 != 0 and fp8:
......@@ -1818,6 +1837,12 @@ def test_grouped_linear_accuracy(
use_cutlass=False,
):
fp8 = recipe is not None
if fp8 and not fp8_available:
pytest.skip(reason_for_no_fp8)
if fp8 and recipe.mxfp8() and not mxfp8_available:
pytest.skip(reason_for_no_mxfp8)
if fp8 and recipe.float8_block_scaling() and not fp8_block_scaling_available:
pytest.skip(reason_for_no_fp8_block_scaling)
if fp8 and fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
pytest.skip("FP8 parameters are not supported in debug mode.")
......@@ -1863,7 +1888,8 @@ def test_grouped_linear_accuracy(
weight_i = getattr(grouped_linear, f"weight{i}")
weight_i.main_grad = torch.rand_like(weight_i, dtype=torch.float32)
sequential_linear[i].weight.main_grad = weight_i.main_grad.clone()
if IS_HIP_EXTENSION:
os.environ["NVTE_FORCE_ROCM_GEMM"] = "1"
outputs_ref = _test_grouped_linear_accuracy(
sequential_linear,
num_gemms,
......@@ -1886,7 +1912,8 @@ def test_grouped_linear_accuracy(
fuse_wgrad_accumulation,
delay_wgrad_compute,
)
if IS_HIP_EXTENSION:
os.environ["NVTE_FORCE_ROCM_GEMM"] = "0"
for o, o_ref in zip(outputs, outputs_ref):
if use_cutlass:
torch.testing.assert_close(o, o_ref, rtol=1e-3, atol=1e-3)
......@@ -1956,6 +1983,12 @@ def test_grouped_linear_accuracy_save_original_input(
pytest.skip("FP8 parameters are not supported in debug mode.")
if fp8 and recipe.delayed():
pytest.skip("DelayedScaling recipe is not supported with save_original_input")
if fp8 and not fp8_available:
pytest.skip(reason_for_no_fp8)
if fp8 and recipe.mxfp8() and not mxfp8_available:
pytest.skip(reason_for_no_mxfp8)
if fp8 and recipe.float8_block_scaling() and not fp8_block_scaling_available:
pytest.skip(reason_for_no_fp8_block_scaling)
config = model_configs[model]
if config.max_seqlen_q % 16 != 0 and fp8:
......@@ -2162,8 +2195,14 @@ def test_padding_grouped_linear_accuracy(
fp8_model_params,
parallel_mode=None,
):
if fp8 and not fp8_available:
pytest.skip(reason_for_no_fp8)
if recipe.mxfp8() and not mxfp8_available:
pytest.skip(reason_for_no_mxfp8)
if fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
pytest.skip("FP8 parameters are not supported in debug mode.")
if recipe.float8_block_scaling() and not fp8_block_scaling_available:
pytest.skip(reason_for_no_fp8_block_scaling)
config = model_configs[model]
if config.max_seqlen_q % 16 != 0 and fp8:
......@@ -2235,6 +2274,12 @@ def test_padding_grouped_linear_accuracy_save_original_input(
pytest.skip("FP8 parameters are not supported in debug mode.")
if fp8 and recipe.delayed():
pytest.skip("DelayedScaling recipe is not supported with save_original_input")
if fp8 and not fp8_available:
pytest.skip(reason_for_no_fp8)
if recipe.mxfp8() and not mxfp8_available:
pytest.skip(reason_for_no_mxfp8)
if recipe.float8_block_scaling() and not fp8_block_scaling_available:
pytest.skip(reason_for_no_fp8_block_scaling)
config = model_configs[model]
if config.max_seqlen_q % 16 != 0 and fp8:
......@@ -2446,8 +2491,14 @@ def _test_gpt_fp8_parameters(bs, dtype, config, fp8_model_params, recipe):
@pytest.mark.parametrize("model", ["126m"])
@pytest.mark.parametrize("recipe", fp8_recipes)
def test_gpt_fp8_parameters(dtype, bs, model, recipe):
if not fp8_available:
pytest.skip(reason_for_no_fp8)
if recipe.mxfp8() and not mxfp8_available:
pytest.skip(reason_for_no_mxfp8)
if NVTE_TEST_NVINSPECT_ENABLED:
pytest.skip("FP8 parameters are not supported in debug mode.")
if recipe.float8_block_scaling() and not fp8_block_scaling_available:
pytest.skip(reason_for_no_fp8_block_scaling)
config = model_configs[model]
......
tests/pytorch/test_onnx_export.py
View file @ 3a040217
......@@ -33,7 +33,9 @@ from onnxruntime_extensions import PyCustomOpDef, get_library_path, onnx_op
import transformer_engine.pytorch as te
from transformer_engine.common import recipe
import transformer_engine_torch as tex
from transformer_engine.pytorch.export import is_in_onnx_export_mode, te_translation_table
from transformer_engine.pytorch.onnx_extensions import te_translation_table
from torch.utils.cpp_extension import IS_HIP_EXTENSION
from transformer_engine.pytorch.export import is_in_onnx_export_mode
from transformer_engine.pytorch.fp8 import FP8GlobalStateManager
from transformer_engine.pytorch.utils import get_default_init_method
import tensorrt as trt
......@@ -65,7 +67,6 @@ if mxfp8_available:
fp8_recipes.append(recipe.MXFP8BlockScaling())
if fp8_available:
fp8_recipes.append(recipe.DelayedScaling())
fp8_recipes.append(recipe.Float8CurrentScaling())
fp8_recipes.append(None)
supported_activations = ["gelu", "relu", "reglu", "geglu", "swiglu"]
......@@ -82,11 +83,11 @@ all_normalizations = ["LayerNorm", "RMSNorm"]
],
outputs=[PyCustomOpDef.dt_uint8],
)
def trt_fp8_quantize(t, scale_inv):
def trt_fp8_quantize(t, scale):
"""FP8 quantization extension for ONNX Runtime."""
x = torch.from_numpy(t).cuda()
q = te.tensor.float8_tensor.Float8Quantizer(
scale=1 / torch.from_numpy(scale_inv).cuda(),
scale=1 / torch.from_numpy(scale).cuda(),
amax=torch.zeros([1]).cuda(),
fp8_dtype=tex.DType.kFloat8E4M3,
)
......@@ -102,11 +103,11 @@ def trt_fp8_quantize(t, scale_inv):
],
outputs=[PyCustomOpDef.dt_float],
)
def trt_fp8_dequantize(t, scale_inv):
def trt_fp8_dequantize(t, scale):
"""FP8 dequantization extension for ONNX Runtime."""
x = torch.from_numpy(t).cuda()
q = te.tensor.float8_tensor.Float8Quantizer(
scale=1 / torch.from_numpy(scale_inv).cuda(),
scale=1 / torch.from_numpy(scale).cuda(),
amax=torch.zeros([1]).cuda(),
fp8_dtype=tex.DType.kFloat8E4M3,
)
......@@ -469,16 +470,22 @@ def _test_export_linear(
@pytest.mark.parametrize("fp8_recipe", fp8_recipes)
@pytest.mark.parametrize("precision", [torch.float32, torch.float16, torch.bfloat16])
def test_export_linear_recipe(seed_default_rng, fp8_recipe, precision):
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_linear(fp8_recipe=fp8_recipe, precision=precision)
@pytest.mark.parametrize("use_bias", [True, False])
def test_export_linear_use_bias(seed_default_rng, use_bias):
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_linear(use_bias=use_bias)
@pytest.mark.parametrize("return_bias", [True, False])
def test_export_linear_return_bias(seed_default_rng, return_bias):
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_linear(return_bias=return_bias)
......@@ -540,6 +547,8 @@ def test_export_layernorm_zero_centered_gamma(seed_default_rng):
@pytest.mark.parametrize("normalization", all_normalizations)
def test_export_layernorm_normalization(seed_default_rng, normalization):
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_layernorm(normalization=normalization)
......@@ -594,9 +603,7 @@ def _test_export_layernorm_linear(
fname,
inp,
model,
# For current scaling we use Float8Quantizer in tests + amax computed by hand,
# which has slightly different numerics than Float8CurrentScalingQuantizer.
atol=1e-3 if fp8_recipe.__class__ is not recipe.Float8CurrentScaling else 2e-2,
atol=1e-3,
is_fp8=fp8_recipe is not None,
te_outputs=te_outputs,
)
......@@ -605,27 +612,39 @@ def _test_export_layernorm_linear(
@pytest.mark.parametrize("fp8_recipe", fp8_recipes)
@pytest.mark.parametrize("precision", [torch.float32, torch.float16, torch.bfloat16])
def test_export_layernorm_linear_recipe(seed_default_rng, fp8_recipe, precision):
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_layernorm_linear(fp8_recipe=fp8_recipe, precision=precision)
def test_export_layernorm_linear_return_ln_out(seed_default_rng):
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_layernorm_linear(return_layernorm_output=True)
def test_export_layernorm_linear_zero_centered_gamma(seed_default_rng):
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_layernorm_linear(zero_centered_gamma=True)
@pytest.mark.parametrize("normalization", all_normalizations[1:])
def test_export_layernorm_linear_normalization(seed_default_rng, normalization):
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_layernorm_linear(normalization=normalization)
def test_export_layernorm_linear_no_bias(seed_default_rng):
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_layernorm_linear(use_bias=False)
def test_export_layernorm_linear_return_bias(seed_default_rng):
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_layernorm_linear(return_bias=True)
......@@ -684,32 +703,46 @@ def _test_export_layernorm_mlp(
@pytest.mark.parametrize("fp8_recipe", fp8_recipes)
@pytest.mark.parametrize("precision", [torch.float32, torch.float16, torch.bfloat16])
def test_export_layernorm_mlp(seed_default_rng, fp8_recipe, precision):
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_layernorm_mlp(fp8_recipe=fp8_recipe, precision=precision)
def test_export_layernorm_mlp_return_layernorm_output(seed_default_rng):
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_layernorm_mlp(return_layernorm_output=True)
def test_export_layernorm_mlp_return_bias(seed_default_rng):
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_layernorm_mlp(return_bias=True)
def test_export_layernorm_mlp_no_bias(seed_default_rng):
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_layernorm_mlp(use_bias=False)
def test_export_layernorm_mlp_zero_centered_gamma(seed_default_rng):
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_layernorm_mlp(zero_centered_gamma=True)
@pytest.mark.parametrize("normalization", all_normalizations[1:])
def test_export_layernorm_mlp_normalization(seed_default_rng, normalization):
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_layernorm_mlp(normalization=normalization)
@pytest.mark.parametrize("activation", supported_activations[1:])
def test_export_layernorm_mlp_activation(seed_default_rng, activation):
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_layernorm_mlp(activation=activation)
......@@ -731,6 +764,8 @@ def test_export_core_attention(
use_mask: bool,
attn_mask_type: str,
):
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
# Set dimensions (these are arbitrary).
seq_len, batch_size, num_attention_heads, kv_channels = (64, 4, 1, 64)
qkv_size = (seq_len, batch_size, num_attention_heads, kv_channels)
......@@ -932,22 +967,32 @@ def _test_export_multihead_attention(
@pytest.mark.parametrize("fp8_recipe", fp8_recipes)
@pytest.mark.parametrize("precision", [torch.float32, torch.float16, torch.bfloat16])
def test_export_multihead_attention_recipe(fp8_recipe, precision):
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_multihead_attention(fp8_recipe=fp8_recipe, precision=precision)
def test_export_multihead_attention_no_mask():
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_multihead_attention(use_mask=False)
def test_export_multihead_attention_no_input_layernorm():
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_multihead_attention(input_layernorm=False)
def test_export_multihead_attention_cross_attn():
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_multihead_attention(attention_type="cross")
def test_export_multihead_attention_unfused_qkv_params():
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_multihead_attention(fuse_qkv_params=False)
......@@ -1023,27 +1068,39 @@ def _test_export_transformer_layer(
@pytest.mark.parametrize("fp8_recipe", fp8_recipes)
@pytest.mark.parametrize("precision", [torch.float32, torch.float16, torch.bfloat16])
def test_export_transformer_layer_recipe(fp8_recipe, precision):
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_transformer_layer(fp8_recipe=fp8_recipe, precision=precision)
def test_export_transformer_layer_no_mask():
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_transformer_layer(use_mask=False)
def test_export_transformer_layer_output_layernorm():
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_transformer_layer(output_layernorm=True)
def test_export_transformer_layer_unfused_qkv_params():
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_transformer_layer(fuse_qkv_params=False)
def test_export_transformer_layer_zero_centered_gamma():
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_transformer_layer(zero_centered_gamma=True)
@pytest.mark.parametrize("activation", supported_activations[1:])
def test_export_transformer_layer_activation(activation):
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
_test_export_transformer_layer(activation=activation)
......@@ -1056,7 +1113,8 @@ def test_export_gpt_generation(
"""Test that the ONNX model can correctly handle inputs with different shapes and that
the attention mask is adjusted on-the-fly to different sequence lengths.
"""
if IS_HIP_EXTENSION:
pytest.skip("ONNX is not currently required in hip")
# Layer configuration
hidden_size = 64
sequence_length = 128
......@@ -1147,17 +1205,14 @@ def test_export_ctx_manager(enabled):
@pytest.mark.parametrize("fp8_recipe", fp8_recipes)
def test_trt_integration(fp8_recipe: recipe.Recipe):
if IS_HIP_EXTENSION:
pytest.skip("TRT is not supported for HIP")
model = te.TransformerLayer(
hidden_size=128,
ffn_hidden_size=128,
num_attention_heads=4,
).eval()
if type(fp8_recipe) == recipe.Float8CurrentScaling:
# TODO(pgadzinski): Attention does not work with TRT for FP8CurrentScaling
model = te.LayerNormMLP(128, 128)
inps = (torch.randn([16, 16, 128], device="cuda", requires_grad=False),)
with te.fp8_autocast(enabled=fp8_recipe is not None, fp8_recipe=fp8_recipe):
......
tests/pytorch/test_sanity.py
View file @ 3a040217
......@@ -46,7 +46,7 @@ from utils import ModelConfig
# Only run FP8 tests on supported devices.
fp8_available, reason_for_no_fp8 = FP8GlobalStateManager.is_fp8_available()
fp8_block_scaling_available, _ = FP8GlobalStateManager.is_fp8_block_scaling_available()
fp8_block_scaling_available, reason_for_no_fp8_block_scaling = FP8GlobalStateManager.is_fp8_block_scaling_available()
mxfp8_available, reason_for_no_mxfp8 = FP8GlobalStateManager.is_mxfp8_available()
# Record initial RNG state from script run.
......@@ -378,7 +378,13 @@ def test_sanity_layernorm_linear(
config = model_configs[model]
if fp8_recipe is not None:
if not is_fp8_supported(config):
if not fp8_available:
pytest.skip(reason_for_no_fp8)
if fp8_recipe.float8_block_scaling() and not fp8_block_scaling_available:
pytest.skip(reason_for_no_fp8_block_scaling)
if fp8_recipe.mxfp8() and not mxfp8_available:
pytest.skip(reason_for_no_mxfp8)
if not config.is_fp8_supported():
pytest.skip("Model config does not support FP8")
sigma = 0.023
......@@ -436,7 +442,13 @@ def test_sanity_linear_with_zero_tokens(dtype, bs, model, fp8_recipe, fp8_model_
num_tokens = bs * config.max_seqlen_q
if fp8_recipe is not None:
if not is_fp8_supported(config):
if not fp8_available:
pytest.skip(reason_for_no_fp8)
if fp8_recipe.mxfp8() and not mxfp8_available:
pytest.skip(reason_for_no_mxfp8)
if fp8_recipe.float8_block_scaling() and not fp8_block_scaling_available:
pytest.skip(reason_for_no_fp8_block_scaling)
if not config.is_fp8_supported():
pytest.skip("Model config does not support FP8")
use_fp8 = fp8_recipe is not None
......@@ -525,7 +537,13 @@ def test_sanity_layernorm_mlp(
config = model_configs[model]
if fp8_recipe is not None:
if not is_fp8_supported(config):
if not fp8_available:
pytest.skip(reason_for_no_fp8)
if fp8_recipe.mxfp8() and not mxfp8_available:
pytest.skip(reason_for_no_mxfp8)
if fp8_recipe.float8_block_scaling() and not fp8_block_scaling_available:
pytest.skip(reason_for_no_fp8_block_scaling)
if not config.is_fp8_supported():
pytest.skip("Model config does not support FP8")
sigma = 0.023
......@@ -567,7 +585,13 @@ def test_sanity_gpt(
config = model_configs[model]
if fp8_recipe is not None:
if not is_fp8_supported(config):
if not fp8_available:
pytest.skip(reason_for_no_fp8)
if fp8_recipe.float8_block_scaling() and not fp8_block_scaling_available:
pytest.skip(reason_for_no_fp8_block_scaling)
if fp8_recipe.mxfp8() and not mxfp8_available:
pytest.skip(reason_for_no_mxfp8)
if not config.is_fp8_supported():
pytest.skip("Model config does not support FP8")
sigma = 0.023
......@@ -733,7 +757,13 @@ def test_sanity_amp_and_nvfuser(dtype, fp8_recipe, model, skip_wgrad):
config = model_configs[model]
if fp8_recipe is not None:
if not is_fp8_supported(config):
if not fp8_available:
pytest.skip(reason_for_no_fp8)
if fp8_recipe.float8_block_scaling() and not fp8_block_scaling_available:
pytest.skip(reason_for_no_fp8_block_scaling)
if fp8_recipe.mxfp8() and not mxfp8_available:
pytest.skip(reason_for_no_mxfp8)
if not config.is_fp8_supported():
pytest.skip("Model config does not support FP8")
sigma = 0.023
......@@ -763,7 +793,13 @@ def test_sanity_drop_path(dtype, fp8_recipe, model):
config = model_configs[model]
if fp8_recipe is not None:
if not is_fp8_supported(config):
if not fp8_available:
pytest.skip(reason_for_no_fp8)
if fp8_recipe.float8_block_scaling() and not fp8_block_scaling_available:
pytest.skip(reason_for_no_fp8_block_scaling)
if fp8_recipe.mxfp8() and not mxfp8_available:
pytest.skip(reason_for_no_mxfp8)
if not config.is_fp8_supported():
pytest.skip("Model config does not support FP8")
sigma = 0.023
......@@ -797,7 +833,13 @@ def test_sanity_fused_qkv_params(dtype, fp8_recipe, model, skip_wgrad):
config = model_configs[model]
if fp8_recipe is not None:
if not is_fp8_supported(config):
if not fp8_available:
pytest.skip(reason_for_no_fp8)
if fp8_recipe.float8_block_scaling() and not fp8_block_scaling_available:
pytest.skip(reason_for_no_fp8_block_scaling)
if fp8_recipe.mxfp8() and not mxfp8_available:
pytest.skip(reason_for_no_mxfp8)
if not config.is_fp8_supported():
pytest.skip("Model config does not support FP8")
sigma = 0.023
......@@ -831,7 +873,13 @@ def test_sanity_gradient_accumulation_fusion(dtype, fp8_recipe, model, skip_wgra
config = model_configs[model]
if fp8_recipe is not None:
if not is_fp8_supported(config):
if not fp8_available:
pytest.skip(reason_for_no_fp8)
if fp8_recipe.float8_block_scaling() and not fp8_block_scaling_available:
pytest.skip(reason_for_no_fp8_block_scaling)
if fp8_recipe.mxfp8() and not mxfp8_available:
pytest.skip(reason_for_no_mxfp8)
if not config.is_fp8_supported():
pytest.skip("Model config does not support FP8")
sigma = 0.023
......
transformer_engine/pytorch/module/_common.py
View file @ 3a040217
......@@ -53,7 +53,7 @@ def apply_normalization(
normalization_func = _get_normalization_func(normalization, True)
inputs = (inputmat, ln_weight) if ln_bias is None else (inputmat, ln_weight, ln_bias)
if enable_lightop and (ln_bias is None) and normalization == "RMSNorm" and output_quantizer is None and (output_dtype is torch.bfloat16 or output_dtype is torch.float16 or output_dtype is torch.float32):
if enable_lightop and (ln_bias is None) and normalization == "RMSNorm" and output_quantizer is None and (output_dtype is torch.bfloat16 or output_dtype is torch.float16 or output_dtype is torch.float32) and not zero_centered_gamma:
out, rsigma = rmsnorm_forward(inputmat, ln_weight,ln_out,eps,True)
return out, None, rsigma
else:
......
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