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ksivaman merged 5 commits into from
Mar 11, 2023
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deprecate qk layer scaling and fp32 softmax args #90

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145064e
deprecate qk layer scaling and fp32 softmax args
ksivaman Mar 10, 2023
2f3f263
Merge branch 'main' into deprecate_args
ksivaman Mar 10, 2023
ba85b46
apply QK layer scaling for fp16 training
ksivaman Mar 10, 2023
80edaa0
address review comments
ksivaman Mar 11, 2023
20932da
Merge branch 'main' into deprecate_args
ksivaman Mar 11, 2023
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18 changes: 2 additions & 16 deletions tests/pytorch/test_onnx_export.py
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Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -756,16 +756,10 @@ def test_export_layernorm_mlp(
(torch.float16, True, "padding"), # calls ScaledMaskedSoftmax
(torch.float16, False, "padding"), # calls ScaledSoftmax
])
@pytest.mark.parametrize("attention_softmax_in_fp32",
[True, False])
@pytest.mark.parametrize("apply_query_key_layer_scaling",
[True, False])
def test_export_core_attention(
precision: torch.dtype,
use_mask: bool,
attn_mask_type: str,
attention_softmax_in_fp32: bool,
apply_query_key_layer_scaling: bool,
):
# Set dimensions (these are arbitrary).
kv_channels = 64
Expand All@@ -784,11 +778,9 @@ def test_export_core_attention(
input_names.append("attention_mask")
inp = (query_layer, key_layer, value_layer, attention_mask)

sm_prec_str = "_sm-fp32" if attention_softmax_in_fp32 else "_sm-fp16"
qk_scaling_str = "_qk-scaling" if apply_query_key_layer_scaling else ""
mask_str = get_attn_mask_str(use_mask, attn_mask_type)
high_prec_str = dtype2str(precision)
fname = f"te.core_attention{mask_str}{qk_scaling_str}{sm_prec_str}{high_prec_str}.onnx"
fname = f"te.core_attention{mask_str}{high_prec_str}.onnx"

if attn_mask_type is None:
attn_mask_type = 'causal'
Expand All@@ -798,8 +790,6 @@ def test_export_core_attention(
kv_channels=kv_channels,
attention_dropout=0.5,
attn_mask_type=attn_mask_type,
attention_softmax_in_fp32=attention_softmax_in_fp32,
apply_query_key_layer_scaling=apply_query_key_layer_scaling,
).to(device='cuda')
do_export(model,
inp,
Expand DownExpand Up@@ -911,7 +901,6 @@ def test_export_multihead_attention(
])
@pytest.mark.parametrize("precision", [torch.float32, torch.float16])
@pytest.mark.parametrize("fuse_qkv_params", [False, True])
@pytest.mark.parametrize("apply_query_key_layer_scaling", [True, False])
@pytest.mark.parametrize("zero_centered_gamma", [False, True])
def test_export_transformer_layer(
use_fp8: bool,
Expand All@@ -920,7 +909,6 @@ def test_export_transformer_layer(
output_layernorm: bool,
precision: torch.dtype,
fuse_qkv_params: bool,
apply_query_key_layer_scaling: bool,
zero_centered_gamma: bool
):
# Skip FP8 tests on non-hopper devices
Expand All@@ -946,10 +934,9 @@ def test_export_transformer_layer(

fp8_str = "_fp8" if use_fp8 else ""
fuse_qkv_params_str = "_fused-qkv" if fuse_qkv_params else ""
qk_scaling_str = "_qk-scaling" if apply_query_key_layer_scaling else ""
high_prec_str = dtype2str(precision)
attn_mask_str = get_attn_mask_str(use_mask, attn_mask_type)
fname = f"te.transformer_layer{fp8_str}{attn_mask_str}{fuse_qkv_params_str}{qk_scaling_str}{high_prec_str}.onnx"
fname = f"te.transformer_layer{fp8_str}{attn_mask_str}{fuse_qkv_params_str}{high_prec_str}.onnx"

model = te.TransformerLayer(
hidden_size,
Expand All@@ -959,7 +946,6 @@ def test_export_transformer_layer(
output_layernorm=output_layernorm,
params_dtype=precision,
fuse_qkv_params=fuse_qkv_params,
apply_query_key_layer_scaling=apply_query_key_layer_scaling,
zero_centered_gamma=zero_centered_gamma).to(device='cuda')
do_export(model, inp, fname, use_fp8)
if not use_fp8:
Expand Down
36 changes: 19 additions & 17 deletions transformer_engine/pytorch/softmax.py
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Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -4,7 +4,7 @@

"""Fused scaled masked softmax functions"""
import os
from typing import Callable, Tuple, Union
from typing import Callable, Tuple, Union, Optional
import torch
from torch import nn
import torch._C._onnx as _C_onnx
Expand DownExpand Up@@ -198,15 +198,13 @@ class FusedScaleMaskSoftmax(nn.Module):
attn_mask_type: attention mask type (pad or causal)
mask_func: mask function to be applied.
softmax_in_fp32: if true, softmax in performed at fp32 precision.
scale: scaling factor used in input tensor scaling.
"""

def __init__(
self,
attn_mask_type: str,
mask_func: Callable,
softmax_in_fp32: bool,
scale: float,
softmax_in_fp32: bool = True,
Comment thread
ptrendx marked this conversation as resolved.
) -> None:
super().__init__()
self.attn_mask_type = attn_mask_type
Expand All@@ -215,23 +213,27 @@ def __init__(
)
self.mask_func = mask_func
self.softmax_in_fp32 = softmax_in_fp32
self.scale = scale

assert (
self.scale is None or softmax_in_fp32
), "softmax should be in fp32 when scaled"

def forward(self, inp: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
def forward(
self,
inp: torch.Tensor,
mask: torch.Tensor,
scale: Optional[float] = None,
) -> torch.Tensor:
"""FusedScaleMaskSoftmax fprop"""
# [b, np, sq, sk]
assert inp.dim() == 4
self.input_in_fp16 = inp.dtype == torch.float16
self.input_in_bf16 = inp.dtype == torch.bfloat16
self.input_in_float16 = self.input_in_fp16 or self.input_in_bf16

assert (
scale is None or self.softmax_in_fp32
), "softmax should be in fp32 when scaled"

if self.is_kernel_available(*inp.size()):
return self.forward_fused_softmax(inp, mask)
return self.forward_torch_softmax(inp, mask)
return self.forward_fused_softmax(inp, mask, scale)
return self.forward_torch_softmax(inp, mask, scale)

def is_kernel_available(self, b: int, np: int, sq: int, sk: int) -> bool:
"""Check FusedScaleMaskSoftmax kernel availability based on size"""
Expand All@@ -256,11 +258,11 @@ def is_kernel_available(self, b: int, np: int, sq: int, sk: int) -> bool:
return False

def forward_fused_softmax(
self, inp: torch.Tensor, mask: torch.Tensor
self, inp: torch.Tensor, mask: torch.Tensor, scale: Optional[float] = None
) -> torch.Tensor:
"""Fused masked softmax kernel"""
b, np, sq, sk = inp.size()
scale = self.scale if self.scale is not None else 1.0
scale = 1.0 if scale is None else scale

if self.attn_mask_type == "causal":
assert sq == sk, "causal mask is only for self attention"
Expand All@@ -275,14 +277,14 @@ def forward_fused_softmax(
return ScaledSoftmax.apply(inp, scale)

def forward_torch_softmax(
self, inp: torch.Tensor, mask: torch.Tensor
self, inp: torch.Tensor, mask: torch.Tensor, scale: Optional[float] = None
) -> torch.Tensor:
"""Framework softmax"""
if self.input_in_float16 and self.softmax_in_fp32:
inp = inp.float()

if self.scale is not None:
inp = inp * self.scale
if scale is not None:
inp = inp * scale

if self.attn_mask_type == "causal":
mask = _get_default_causal_mask(inp.size()[2])
Expand Down
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deprecate qk layer scaling and fp32 softmax args by ksivaman · Pull Request #90 · NVIDIA/TransformerEngine · GitHub
Skip to content
deprecate qk layer scaling and fp32 softmax args#90

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 Merged

ksivaman
merged 5 commits into

from

Mar 11, 2023
 Merged


deprecate qk layer scaling and fp32 softmax args
#90
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145064e

deprecate qk layer scaling and fp32 softmax args


ksivaman Mar 10, 2023
2f3f263

Merge branch 'main' into deprecate_args


ksivaman Mar 10, 2023
ba85b46

apply QK layer scaling for fp16 training


ksivaman Mar 10, 2023
80edaa0

address review comments


ksivaman Mar 11, 2023
20932da

Merge branch 'main' into deprecate_args


ksivaman Mar 11, 2023

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18 changes: 2 additions & 16 deletions
tests/pytorch/test_onnx_export.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -756,16 +756,10 @@ def test_export_layernorm_mlp(
 (torch.float16, True, "padding"), # calls ScaledMaskedSoftmax
 (torch.float16, False, "padding"), # calls ScaledSoftmax
])
@pytest.mark.parametrize("attention_softmax_in_fp32",
 [True, False])
@pytest.mark.parametrize("apply_query_key_layer_scaling",
 [True, False])
def test_export_core_attention(
 precision: torch.dtype,
 use_mask: bool,
 attn_mask_type: str,
 attention_softmax_in_fp32: bool,
 apply_query_key_layer_scaling: bool,
):
 # Set dimensions (these are arbitrary).
 kv_channels = 64
Expand All@@ -784,11 +778,9 @@ def test_export_core_attention(
 input_names.append("attention_mask")
 inp = (query_layer, key_layer, value_layer, attention_mask)

 sm_prec_str = "_sm-fp32" if attention_softmax_in_fp32 else "_sm-fp16"
 qk_scaling_str = "_qk-scaling" if apply_query_key_layer_scaling else ""
 mask_str = get_attn_mask_str(use_mask, attn_mask_type)
 high_prec_str = dtype2str(precision)
 fname = f"te.core_attention{mask_str}{qk_scaling_str}{sm_prec_str}{high_prec_str}.onnx"
 fname = f"te.core_attention{mask_str}{high_prec_str}.onnx"

 if attn_mask_type is None:
 attn_mask_type = 'causal'
Expand All@@ -798,8 +790,6 @@ def test_export_core_attention(
 kv_channels=kv_channels,
 attention_dropout=0.5,
 attn_mask_type=attn_mask_type,
 attention_softmax_in_fp32=attention_softmax_in_fp32,
 apply_query_key_layer_scaling=apply_query_key_layer_scaling,
 ).to(device='cuda')
 do_export(model,
 inp,
Expand DownExpand Up@@ -911,7 +901,6 @@ def test_export_multihead_attention(
])
@pytest.mark.parametrize("precision", [torch.float32, torch.float16])
@pytest.mark.parametrize("fuse_qkv_params", [False, True])
@pytest.mark.parametrize("apply_query_key_layer_scaling", [True, False])
@pytest.mark.parametrize("zero_centered_gamma", [False, True])
def test_export_transformer_layer(
 use_fp8: bool,
Expand All@@ -920,7 +909,6 @@ def test_export_transformer_layer(
 output_layernorm: bool,
 precision: torch.dtype,
 fuse_qkv_params: bool,
 apply_query_key_layer_scaling: bool,
 zero_centered_gamma: bool
):
 # Skip FP8 tests on non-hopper devices
Expand All@@ -946,10 +934,9 @@ def test_export_transformer_layer(

 fp8_str = "_fp8" if use_fp8 else ""
 fuse_qkv_params_str = "_fused-qkv" if fuse_qkv_params else ""
 qk_scaling_str = "_qk-scaling" if apply_query_key_layer_scaling else ""
 high_prec_str = dtype2str(precision)
 attn_mask_str = get_attn_mask_str(use_mask, attn_mask_type)
 fname = f"te.transformer_layer{fp8_str}{attn_mask_str}{fuse_qkv_params_str}{qk_scaling_str}{high_prec_str}.onnx"
 fname = f"te.transformer_layer{fp8_str}{attn_mask_str}{fuse_qkv_params_str}{high_prec_str}.onnx"

 model = te.TransformerLayer(
 hidden_size,
Expand All@@ -959,7 +946,6 @@ def test_export_transformer_layer(
 output_layernorm=output_layernorm,
 params_dtype=precision,
 fuse_qkv_params=fuse_qkv_params,
 apply_query_key_layer_scaling=apply_query_key_layer_scaling,
 zero_centered_gamma=zero_centered_gamma).to(device='cuda')
 do_export(model, inp, fname, use_fp8)
 if not use_fp8:
Expand Down

36 changes: 19 additions & 17 deletions
transformer_engine/pytorch/softmax.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -4,7 +4,7 @@

"""Fused scaled masked softmax functions"""
import os
from typing import Callable, Tuple, Union
from typing import Callable, Tuple, Union, Optional
import torch
from torch import nn
import torch._C._onnx as _C_onnx
Expand DownExpand Up@@ -198,15 +198,13 @@ class FusedScaleMaskSoftmax(nn.Module):
 attn_mask_type: attention mask type (pad or causal)
 mask_func: mask function to be applied.
 softmax_in_fp32: if true, softmax in performed at fp32 precision.
 scale: scaling factor used in input tensor scaling.
 """

 def __init__(
 self,
 attn_mask_type: str,
 mask_func: Callable,
 softmax_in_fp32: bool,
 scale: float,
 softmax_in_fp32: bool = True,
Comment thread
ptrendx marked this conversation as resolved.

 ) -> None:
 super().__init__()
 self.attn_mask_type = attn_mask_type
Expand All@@ -215,23 +213,27 @@ def __init__(
 )
 self.mask_func = mask_func
 self.softmax_in_fp32 = softmax_in_fp32
 self.scale = scale

 assert (
 self.scale is None or softmax_in_fp32
 ), "softmax should be in fp32 when scaled"

 def forward(self, inp: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
 def forward(
 self,
 inp: torch.Tensor,
 mask: torch.Tensor,
 scale: Optional[float] = None,
 ) -> torch.Tensor:
 """FusedScaleMaskSoftmax fprop"""
 # [b, np, sq, sk]
 assert inp.dim() == 4
 self.input_in_fp16 = inp.dtype == torch.float16
 self.input_in_bf16 = inp.dtype == torch.bfloat16
 self.input_in_float16 = self.input_in_fp16 or self.input_in_bf16

 assert (
 scale is None or self.softmax_in_fp32
 ), "softmax should be in fp32 when scaled"

 if self.is_kernel_available(*inp.size()):
 return self.forward_fused_softmax(inp, mask)
 return self.forward_torch_softmax(inp, mask)
 return self.forward_fused_softmax(inp, mask, scale)
 return self.forward_torch_softmax(inp, mask, scale)

 def is_kernel_available(self, b: int, np: int, sq: int, sk: int) -> bool:
 """Check FusedScaleMaskSoftmax kernel availability based on size"""
Expand All@@ -256,11 +258,11 @@ def is_kernel_available(self, b: int, np: int, sq: int, sk: int) -> bool:
 return False

 def forward_fused_softmax(
 self, inp: torch.Tensor, mask: torch.Tensor
 self, inp: torch.Tensor, mask: torch.Tensor, scale: Optional[float] = None
 ) -> torch.Tensor:
 """Fused masked softmax kernel"""
 b, np, sq, sk = inp.size()
 scale = self.scale if self.scale is not None else 1.0
 scale = 1.0 if scale is None else scale

 if self.attn_mask_type == "causal":
 assert sq == sk, "causal mask is only for self attention"
Expand All@@ -275,14 +277,14 @@ def forward_fused_softmax(
 return ScaledSoftmax.apply(inp, scale)

 def forward_torch_softmax(
 self, inp: torch.Tensor, mask: torch.Tensor
 self, inp: torch.Tensor, mask: torch.Tensor, scale: Optional[float] = None
 ) -> torch.Tensor:
 """Framework softmax"""
 if self.input_in_float16 and self.softmax_in_fp32:
 inp = inp.float()

 if self.scale is not None:
 inp = inp * self.scale
 if scale is not None:
 inp = inp * scale

 if self.attn_mask_type == "causal":
 mask = _get_default_causal_mask(inp.size()[2])
Expand Down
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deprecate qk layer scaling and fp32 softmax args by ksivaman · Pull Request #90 · NVIDIA/TransformerEngine · GitHub
Skip to content
deprecate qk layer scaling and fp32 softmax args#90

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 Merged

ksivaman
merged 5 commits into

from

Mar 11, 2023
 Merged


deprecate qk layer scaling and fp32 softmax args
#90
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145064e

deprecate qk layer scaling and fp32 softmax args


ksivaman Mar 10, 2023
2f3f263

Merge branch 'main' into deprecate_args


ksivaman Mar 10, 2023
ba85b46

apply QK layer scaling for fp16 training


ksivaman Mar 10, 2023
80edaa0

address review comments


ksivaman Mar 11, 2023
20932da

Merge branch 'main' into deprecate_args


ksivaman Mar 11, 2023

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18 changes: 2 additions & 16 deletions
tests/pytorch/test_onnx_export.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -756,16 +756,10 @@ def test_export_layernorm_mlp(
 (torch.float16, True, "padding"), # calls ScaledMaskedSoftmax
 (torch.float16, False, "padding"), # calls ScaledSoftmax
])
@pytest.mark.parametrize("attention_softmax_in_fp32",
 [True, False])
@pytest.mark.parametrize("apply_query_key_layer_scaling",
 [True, False])
def test_export_core_attention(
 precision: torch.dtype,
 use_mask: bool,
 attn_mask_type: str,
 attention_softmax_in_fp32: bool,
 apply_query_key_layer_scaling: bool,
):
 # Set dimensions (these are arbitrary).
 kv_channels = 64
Expand All@@ -784,11 +778,9 @@ def test_export_core_attention(
 input_names.append("attention_mask")
 inp = (query_layer, key_layer, value_layer, attention_mask)

 sm_prec_str = "_sm-fp32" if attention_softmax_in_fp32 else "_sm-fp16"
 qk_scaling_str = "_qk-scaling" if apply_query_key_layer_scaling else ""
 mask_str = get_attn_mask_str(use_mask, attn_mask_type)
 high_prec_str = dtype2str(precision)
 fname = f"te.core_attention{mask_str}{qk_scaling_str}{sm_prec_str}{high_prec_str}.onnx"
 fname = f"te.core_attention{mask_str}{high_prec_str}.onnx"

 if attn_mask_type is None:
 attn_mask_type = 'causal'
Expand All@@ -798,8 +790,6 @@ def test_export_core_attention(
 kv_channels=kv_channels,
 attention_dropout=0.5,
 attn_mask_type=attn_mask_type,
 attention_softmax_in_fp32=attention_softmax_in_fp32,
 apply_query_key_layer_scaling=apply_query_key_layer_scaling,
 ).to(device='cuda')
 do_export(model,
 inp,
Expand DownExpand Up@@ -911,7 +901,6 @@ def test_export_multihead_attention(
])
@pytest.mark.parametrize("precision", [torch.float32, torch.float16])
@pytest.mark.parametrize("fuse_qkv_params", [False, True])
@pytest.mark.parametrize("apply_query_key_layer_scaling", [True, False])
@pytest.mark.parametrize("zero_centered_gamma", [False, True])
def test_export_transformer_layer(
 use_fp8: bool,
Expand All@@ -920,7 +909,6 @@ def test_export_transformer_layer(
 output_layernorm: bool,
 precision: torch.dtype,
 fuse_qkv_params: bool,
 apply_query_key_layer_scaling: bool,
 zero_centered_gamma: bool
):
 # Skip FP8 tests on non-hopper devices
Expand All@@ -946,10 +934,9 @@ def test_export_transformer_layer(

 fp8_str = "_fp8" if use_fp8 else ""
 fuse_qkv_params_str = "_fused-qkv" if fuse_qkv_params else ""
 qk_scaling_str = "_qk-scaling" if apply_query_key_layer_scaling else ""
 high_prec_str = dtype2str(precision)
 attn_mask_str = get_attn_mask_str(use_mask, attn_mask_type)
 fname = f"te.transformer_layer{fp8_str}{attn_mask_str}{fuse_qkv_params_str}{qk_scaling_str}{high_prec_str}.onnx"
 fname = f"te.transformer_layer{fp8_str}{attn_mask_str}{fuse_qkv_params_str}{high_prec_str}.onnx"

 model = te.TransformerLayer(
 hidden_size,
Expand All@@ -959,7 +946,6 @@ def test_export_transformer_layer(
 output_layernorm=output_layernorm,
 params_dtype=precision,
 fuse_qkv_params=fuse_qkv_params,
 apply_query_key_layer_scaling=apply_query_key_layer_scaling,
 zero_centered_gamma=zero_centered_gamma).to(device='cuda')
 do_export(model, inp, fname, use_fp8)
 if not use_fp8:
Expand Down

36 changes: 19 additions & 17 deletions
transformer_engine/pytorch/softmax.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -4,7 +4,7 @@

"""Fused scaled masked softmax functions"""
import os
from typing import Callable, Tuple, Union
from typing import Callable, Tuple, Union, Optional
import torch
from torch import nn
import torch._C._onnx as _C_onnx
Expand DownExpand Up@@ -198,15 +198,13 @@ class FusedScaleMaskSoftmax(nn.Module):
 attn_mask_type: attention mask type (pad or causal)
 mask_func: mask function to be applied.
 softmax_in_fp32: if true, softmax in performed at fp32 precision.
 scale: scaling factor used in input tensor scaling.
 """

 def __init__(
 self,
 attn_mask_type: str,
 mask_func: Callable,
 softmax_in_fp32: bool,
 scale: float,
 softmax_in_fp32: bool = True,
Comment thread
ptrendx marked this conversation as resolved.

 ) -> None:
 super().__init__()
 self.attn_mask_type = attn_mask_type
Expand All@@ -215,23 +213,27 @@ def __init__(
 )
 self.mask_func = mask_func
 self.softmax_in_fp32 = softmax_in_fp32
 self.scale = scale

 assert (
 self.scale is None or softmax_in_fp32
 ), "softmax should be in fp32 when scaled"

 def forward(self, inp: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
 def forward(
 self,
 inp: torch.Tensor,
 mask: torch.Tensor,
 scale: Optional[float] = None,
 ) -> torch.Tensor:
 """FusedScaleMaskSoftmax fprop"""
 # [b, np, sq, sk]
 assert inp.dim() == 4
 self.input_in_fp16 = inp.dtype == torch.float16
 self.input_in_bf16 = inp.dtype == torch.bfloat16
 self.input_in_float16 = self.input_in_fp16 or self.input_in_bf16

 assert (
 scale is None or self.softmax_in_fp32
 ), "softmax should be in fp32 when scaled"

 if self.is_kernel_available(*inp.size()):
 return self.forward_fused_softmax(inp, mask)
 return self.forward_torch_softmax(inp, mask)
 return self.forward_fused_softmax(inp, mask, scale)
 return self.forward_torch_softmax(inp, mask, scale)

 def is_kernel_available(self, b: int, np: int, sq: int, sk: int) -> bool:
 """Check FusedScaleMaskSoftmax kernel availability based on size"""
Expand All@@ -256,11 +258,11 @@ def is_kernel_available(self, b: int, np: int, sq: int, sk: int) -> bool:
 return False

 def forward_fused_softmax(
 self, inp: torch.Tensor, mask: torch.Tensor
 self, inp: torch.Tensor, mask: torch.Tensor, scale: Optional[float] = None
 ) -> torch.Tensor:
 """Fused masked softmax kernel"""
 b, np, sq, sk = inp.size()
 scale = self.scale if self.scale is not None else 1.0
 scale = 1.0 if scale is None else scale

 if self.attn_mask_type == "causal":
 assert sq == sk, "causal mask is only for self attention"
Expand All@@ -275,14 +277,14 @@ def forward_fused_softmax(
 return ScaledSoftmax.apply(inp, scale)

 def forward_torch_softmax(
 self, inp: torch.Tensor, mask: torch.Tensor
 self, inp: torch.Tensor, mask: torch.Tensor, scale: Optional[float] = None
 ) -> torch.Tensor:
 """Framework softmax"""
 if self.input_in_float16 and self.softmax_in_fp32:
 inp = inp.float()

 if self.scale is not None:
 inp = inp * self.scale
 if scale is not None:
 inp = inp * scale

 if self.attn_mask_type == "causal":
 mask = _get_default_causal_mask(inp.size()[2])
Expand Down
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deprecate qk layer scaling and fp32 softmax args by ksivaman · Pull Request #90 · NVIDIA/TransformerEngine · GitHub
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deprecate qk layer scaling and fp32 softmax args#90

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ksivaman
merged 5 commits into

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Mar 11, 2023
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deprecate qk layer scaling and fp32 softmax args
#90
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145064e

deprecate qk layer scaling and fp32 softmax args


ksivaman Mar 10, 2023
2f3f263

Merge branch 'main' into deprecate_args


ksivaman Mar 10, 2023
ba85b46

apply QK layer scaling for fp16 training


ksivaman Mar 10, 2023
80edaa0

address review comments


ksivaman Mar 11, 2023
20932da

Merge branch 'main' into deprecate_args


ksivaman Mar 11, 2023

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18 changes: 2 additions & 16 deletions
tests/pytorch/test_onnx_export.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -756,16 +756,10 @@ def test_export_layernorm_mlp(
 (torch.float16, True, "padding"), # calls ScaledMaskedSoftmax
 (torch.float16, False, "padding"), # calls ScaledSoftmax
])
@pytest.mark.parametrize("attention_softmax_in_fp32",
 [True, False])
@pytest.mark.parametrize("apply_query_key_layer_scaling",
 [True, False])
def test_export_core_attention(
 precision: torch.dtype,
 use_mask: bool,
 attn_mask_type: str,
 attention_softmax_in_fp32: bool,
 apply_query_key_layer_scaling: bool,
):
 # Set dimensions (these are arbitrary).
 kv_channels = 64
Expand All@@ -784,11 +778,9 @@ def test_export_core_attention(
 input_names.append("attention_mask")
 inp = (query_layer, key_layer, value_layer, attention_mask)

 sm_prec_str = "_sm-fp32" if attention_softmax_in_fp32 else "_sm-fp16"
 qk_scaling_str = "_qk-scaling" if apply_query_key_layer_scaling else ""
 mask_str = get_attn_mask_str(use_mask, attn_mask_type)
 high_prec_str = dtype2str(precision)
 fname = f"te.core_attention{mask_str}{qk_scaling_str}{sm_prec_str}{high_prec_str}.onnx"
 fname = f"te.core_attention{mask_str}{high_prec_str}.onnx"

 if attn_mask_type is None:
 attn_mask_type = 'causal'
Expand All@@ -798,8 +790,6 @@ def test_export_core_attention(
 kv_channels=kv_channels,
 attention_dropout=0.5,
 attn_mask_type=attn_mask_type,
 attention_softmax_in_fp32=attention_softmax_in_fp32,
 apply_query_key_layer_scaling=apply_query_key_layer_scaling,
 ).to(device='cuda')
 do_export(model,
 inp,
Expand DownExpand Up@@ -911,7 +901,6 @@ def test_export_multihead_attention(
])
@pytest.mark.parametrize("precision", [torch.float32, torch.float16])
@pytest.mark.parametrize("fuse_qkv_params", [False, True])
@pytest.mark.parametrize("apply_query_key_layer_scaling", [True, False])
@pytest.mark.parametrize("zero_centered_gamma", [False, True])
def test_export_transformer_layer(
 use_fp8: bool,
Expand All@@ -920,7 +909,6 @@ def test_export_transformer_layer(
 output_layernorm: bool,
 precision: torch.dtype,
 fuse_qkv_params: bool,
 apply_query_key_layer_scaling: bool,
 zero_centered_gamma: bool
):
 # Skip FP8 tests on non-hopper devices
Expand All@@ -946,10 +934,9 @@ def test_export_transformer_layer(

 fp8_str = "_fp8" if use_fp8 else ""
 fuse_qkv_params_str = "_fused-qkv" if fuse_qkv_params else ""
 qk_scaling_str = "_qk-scaling" if apply_query_key_layer_scaling else ""
 high_prec_str = dtype2str(precision)
 attn_mask_str = get_attn_mask_str(use_mask, attn_mask_type)
 fname = f"te.transformer_layer{fp8_str}{attn_mask_str}{fuse_qkv_params_str}{qk_scaling_str}{high_prec_str}.onnx"
 fname = f"te.transformer_layer{fp8_str}{attn_mask_str}{fuse_qkv_params_str}{high_prec_str}.onnx"

 model = te.TransformerLayer(
 hidden_size,
Expand All@@ -959,7 +946,6 @@ def test_export_transformer_layer(
 output_layernorm=output_layernorm,
 params_dtype=precision,
 fuse_qkv_params=fuse_qkv_params,
 apply_query_key_layer_scaling=apply_query_key_layer_scaling,
 zero_centered_gamma=zero_centered_gamma).to(device='cuda')
 do_export(model, inp, fname, use_fp8)
 if not use_fp8:
Expand Down

36 changes: 19 additions & 17 deletions
transformer_engine/pytorch/softmax.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -4,7 +4,7 @@

"""Fused scaled masked softmax functions"""
import os
from typing import Callable, Tuple, Union
from typing import Callable, Tuple, Union, Optional
import torch
from torch import nn
import torch._C._onnx as _C_onnx
Expand DownExpand Up@@ -198,15 +198,13 @@ class FusedScaleMaskSoftmax(nn.Module):
 attn_mask_type: attention mask type (pad or causal)
 mask_func: mask function to be applied.
 softmax_in_fp32: if true, softmax in performed at fp32 precision.
 scale: scaling factor used in input tensor scaling.
 """

 def __init__(
 self,
 attn_mask_type: str,
 mask_func: Callable,
 softmax_in_fp32: bool,
 scale: float,
 softmax_in_fp32: bool = True,
Comment thread
ptrendx marked this conversation as resolved.

 ) -> None:
 super().__init__()
 self.attn_mask_type = attn_mask_type
Expand All@@ -215,23 +213,27 @@ def __init__(
 )
 self.mask_func = mask_func
 self.softmax_in_fp32 = softmax_in_fp32
 self.scale = scale

 assert (
 self.scale is None or softmax_in_fp32
 ), "softmax should be in fp32 when scaled"

 def forward(self, inp: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
 def forward(
 self,
 inp: torch.Tensor,
 mask: torch.Tensor,
 scale: Optional[float] = None,
 ) -> torch.Tensor:
 """FusedScaleMaskSoftmax fprop"""
 # [b, np, sq, sk]
 assert inp.dim() == 4
 self.input_in_fp16 = inp.dtype == torch.float16
 self.input_in_bf16 = inp.dtype == torch.bfloat16
 self.input_in_float16 = self.input_in_fp16 or self.input_in_bf16

 assert (
 scale is None or self.softmax_in_fp32
 ), "softmax should be in fp32 when scaled"

 if self.is_kernel_available(*inp.size()):
 return self.forward_fused_softmax(inp, mask)
 return self.forward_torch_softmax(inp, mask)
 return self.forward_fused_softmax(inp, mask, scale)
 return self.forward_torch_softmax(inp, mask, scale)

 def is_kernel_available(self, b: int, np: int, sq: int, sk: int) -> bool:
 """Check FusedScaleMaskSoftmax kernel availability based on size"""
Expand All@@ -256,11 +258,11 @@ def is_kernel_available(self, b: int, np: int, sq: int, sk: int) -> bool:
 return False

 def forward_fused_softmax(
 self, inp: torch.Tensor, mask: torch.Tensor
 self, inp: torch.Tensor, mask: torch.Tensor, scale: Optional[float] = None
 ) -> torch.Tensor:
 """Fused masked softmax kernel"""
 b, np, sq, sk = inp.size()
 scale = self.scale if self.scale is not None else 1.0
 scale = 1.0 if scale is None else scale

 if self.attn_mask_type == "causal":
 assert sq == sk, "causal mask is only for self attention"
Expand All@@ -275,14 +277,14 @@ def forward_fused_softmax(
 return ScaledSoftmax.apply(inp, scale)

 def forward_torch_softmax(
 self, inp: torch.Tensor, mask: torch.Tensor
 self, inp: torch.Tensor, mask: torch.Tensor, scale: Optional[float] = None
 ) -> torch.Tensor:
 """Framework softmax"""
 if self.input_in_float16 and self.softmax_in_fp32:
 inp = inp.float()

 if self.scale is not None:
 inp = inp * self.scale
 if scale is not None:
 inp = inp * scale

 if self.attn_mask_type == "causal":
 mask = _get_default_causal_mask(inp.size()[2])
Expand Down
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deprecate qk layer scaling and fp32 softmax args by ksivaman · Pull Request #90 · NVIDIA/TransformerEngine · GitHub
Skip to content
deprecate qk layer scaling and fp32 softmax args#90

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 Merged

ksivaman
merged 5 commits into

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Mar 11, 2023
 Merged


deprecate qk layer scaling and fp32 softmax args
#90
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145064e

deprecate qk layer scaling and fp32 softmax args


ksivaman Mar 10, 2023
2f3f263

Merge branch 'main' into deprecate_args


ksivaman Mar 10, 2023
ba85b46

apply QK layer scaling for fp16 training


ksivaman Mar 10, 2023
80edaa0

address review comments


ksivaman Mar 11, 2023
20932da

Merge branch 'main' into deprecate_args


ksivaman Mar 11, 2023

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18 changes: 2 additions & 16 deletions
tests/pytorch/test_onnx_export.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -756,16 +756,10 @@ def test_export_layernorm_mlp(
 (torch.float16, True, "padding"), # calls ScaledMaskedSoftmax
 (torch.float16, False, "padding"), # calls ScaledSoftmax
])
@pytest.mark.parametrize("attention_softmax_in_fp32",
 [True, False])
@pytest.mark.parametrize("apply_query_key_layer_scaling",
 [True, False])
def test_export_core_attention(
 precision: torch.dtype,
 use_mask: bool,
 attn_mask_type: str,
 attention_softmax_in_fp32: bool,
 apply_query_key_layer_scaling: bool,
):
 # Set dimensions (these are arbitrary).
 kv_channels = 64
Expand All@@ -784,11 +778,9 @@ def test_export_core_attention(
 input_names.append("attention_mask")
 inp = (query_layer, key_layer, value_layer, attention_mask)

 sm_prec_str = "_sm-fp32" if attention_softmax_in_fp32 else "_sm-fp16"
 qk_scaling_str = "_qk-scaling" if apply_query_key_layer_scaling else ""
 mask_str = get_attn_mask_str(use_mask, attn_mask_type)
 high_prec_str = dtype2str(precision)
 fname = f"te.core_attention{mask_str}{qk_scaling_str}{sm_prec_str}{high_prec_str}.onnx"
 fname = f"te.core_attention{mask_str}{high_prec_str}.onnx"

 if attn_mask_type is None:
 attn_mask_type = 'causal'
Expand All@@ -798,8 +790,6 @@ def test_export_core_attention(
 kv_channels=kv_channels,
 attention_dropout=0.5,
 attn_mask_type=attn_mask_type,
 attention_softmax_in_fp32=attention_softmax_in_fp32,
 apply_query_key_layer_scaling=apply_query_key_layer_scaling,
 ).to(device='cuda')
 do_export(model,
 inp,
Expand DownExpand Up@@ -911,7 +901,6 @@ def test_export_multihead_attention(
])
@pytest.mark.parametrize("precision", [torch.float32, torch.float16])
@pytest.mark.parametrize("fuse_qkv_params", [False, True])
@pytest.mark.parametrize("apply_query_key_layer_scaling", [True, False])
@pytest.mark.parametrize("zero_centered_gamma", [False, True])
def test_export_transformer_layer(
 use_fp8: bool,
Expand All@@ -920,7 +909,6 @@ def test_export_transformer_layer(
 output_layernorm: bool,
 precision: torch.dtype,
 fuse_qkv_params: bool,
 apply_query_key_layer_scaling: bool,
 zero_centered_gamma: bool
):
 # Skip FP8 tests on non-hopper devices
Expand All@@ -946,10 +934,9 @@ def test_export_transformer_layer(

 fp8_str = "_fp8" if use_fp8 else ""
 fuse_qkv_params_str = "_fused-qkv" if fuse_qkv_params else ""
 qk_scaling_str = "_qk-scaling" if apply_query_key_layer_scaling else ""
 high_prec_str = dtype2str(precision)
 attn_mask_str = get_attn_mask_str(use_mask, attn_mask_type)
 fname = f"te.transformer_layer{fp8_str}{attn_mask_str}{fuse_qkv_params_str}{qk_scaling_str}{high_prec_str}.onnx"
 fname = f"te.transformer_layer{fp8_str}{attn_mask_str}{fuse_qkv_params_str}{high_prec_str}.onnx"

 model = te.TransformerLayer(
 hidden_size,
Expand All@@ -959,7 +946,6 @@ def test_export_transformer_layer(
 output_layernorm=output_layernorm,
 params_dtype=precision,
 fuse_qkv_params=fuse_qkv_params,
 apply_query_key_layer_scaling=apply_query_key_layer_scaling,
 zero_centered_gamma=zero_centered_gamma).to(device='cuda')
 do_export(model, inp, fname, use_fp8)
 if not use_fp8:
Expand Down

36 changes: 19 additions & 17 deletions
transformer_engine/pytorch/softmax.py

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Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -4,7 +4,7 @@

"""Fused scaled masked softmax functions"""
import os
from typing import Callable, Tuple, Union
from typing import Callable, Tuple, Union, Optional
import torch
from torch import nn
import torch._C._onnx as _C_onnx
Expand DownExpand Up@@ -198,15 +198,13 @@ class FusedScaleMaskSoftmax(nn.Module):
 attn_mask_type: attention mask type (pad or causal)
 mask_func: mask function to be applied.
 softmax_in_fp32: if true, softmax in performed at fp32 precision.
 scale: scaling factor used in input tensor scaling.
 """

 def __init__(
 self,
 attn_mask_type: str,
 mask_func: Callable,
 softmax_in_fp32: bool,
 scale: float,
 softmax_in_fp32: bool = True,
Comment thread
ptrendx marked this conversation as resolved.

 ) -> None:
 super().__init__()
 self.attn_mask_type = attn_mask_type
Expand All@@ -215,23 +213,27 @@ def __init__(
 )
 self.mask_func = mask_func
 self.softmax_in_fp32 = softmax_in_fp32
 self.scale = scale

 assert (
 self.scale is None or softmax_in_fp32
 ), "softmax should be in fp32 when scaled"

 def forward(self, inp: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
 def forward(
 self,
 inp: torch.Tensor,
 mask: torch.Tensor,
 scale: Optional[float] = None,
 ) -> torch.Tensor:
 """FusedScaleMaskSoftmax fprop"""
 # [b, np, sq, sk]
 assert inp.dim() == 4
 self.input_in_fp16 = inp.dtype == torch.float16
 self.input_in_bf16 = inp.dtype == torch.bfloat16
 self.input_in_float16 = self.input_in_fp16 or self.input_in_bf16

 assert (
 scale is None or self.softmax_in_fp32
 ), "softmax should be in fp32 when scaled"

 if self.is_kernel_available(*inp.size()):
 return self.forward_fused_softmax(inp, mask)
 return self.forward_torch_softmax(inp, mask)
 return self.forward_fused_softmax(inp, mask, scale)
 return self.forward_torch_softmax(inp, mask, scale)

 def is_kernel_available(self, b: int, np: int, sq: int, sk: int) -> bool:
 """Check FusedScaleMaskSoftmax kernel availability based on size"""
Expand All@@ -256,11 +258,11 @@ def is_kernel_available(self, b: int, np: int, sq: int, sk: int) -> bool:
 return False

 def forward_fused_softmax(
 self, inp: torch.Tensor, mask: torch.Tensor
 self, inp: torch.Tensor, mask: torch.Tensor, scale: Optional[float] = None
 ) -> torch.Tensor:
 """Fused masked softmax kernel"""
 b, np, sq, sk = inp.size()
 scale = self.scale if self.scale is not None else 1.0
 scale = 1.0 if scale is None else scale

 if self.attn_mask_type == "causal":
 assert sq == sk, "causal mask is only for self attention"
Expand All@@ -275,14 +277,14 @@ def forward_fused_softmax(
 return ScaledSoftmax.apply(inp, scale)

 def forward_torch_softmax(
 self, inp: torch.Tensor, mask: torch.Tensor
 self, inp: torch.Tensor, mask: torch.Tensor, scale: Optional[float] = None
 ) -> torch.Tensor:
 """Framework softmax"""
 if self.input_in_float16 and self.softmax_in_fp32:
 inp = inp.float()

 if self.scale is not None:
 inp = inp * self.scale
 if scale is not None:
 inp = inp * scale

 if self.attn_mask_type == "causal":
 mask = _get_default_causal_mask(inp.size()[2])
Expand Down
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deprecate qk layer scaling and fp32 softmax args by ksivaman · Pull Request #90 · NVIDIA/TransformerEngine · GitHub
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deprecate qk layer scaling and fp32 softmax args#90

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deprecate qk layer scaling and fp32 softmax args
#90
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deprecate qk layer scaling and fp32 softmax args


ksivaman Mar 10, 2023
2f3f263

Merge branch 'main' into deprecate_args


ksivaman Mar 10, 2023
ba85b46

apply QK layer scaling for fp16 training


ksivaman Mar 10, 2023
80edaa0

address review comments


ksivaman Mar 11, 2023
20932da

Merge branch 'main' into deprecate_args


ksivaman Mar 11, 2023

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18 changes: 2 additions & 16 deletions
tests/pytorch/test_onnx_export.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -756,16 +756,10 @@ def test_export_layernorm_mlp(
 (torch.float16, True, "padding"), # calls ScaledMaskedSoftmax
 (torch.float16, False, "padding"), # calls ScaledSoftmax
])
@pytest.mark.parametrize("attention_softmax_in_fp32",
 [True, False])
@pytest.mark.parametrize("apply_query_key_layer_scaling",
 [True, False])
def test_export_core_attention(
 precision: torch.dtype,
 use_mask: bool,
 attn_mask_type: str,
 attention_softmax_in_fp32: bool,
 apply_query_key_layer_scaling: bool,
):
 # Set dimensions (these are arbitrary).
 kv_channels = 64
Expand All@@ -784,11 +778,9 @@ def test_export_core_attention(
 input_names.append("attention_mask")
 inp = (query_layer, key_layer, value_layer, attention_mask)

 sm_prec_str = "_sm-fp32" if attention_softmax_in_fp32 else "_sm-fp16"
 qk_scaling_str = "_qk-scaling" if apply_query_key_layer_scaling else ""
 mask_str = get_attn_mask_str(use_mask, attn_mask_type)
 high_prec_str = dtype2str(precision)
 fname = f"te.core_attention{mask_str}{qk_scaling_str}{sm_prec_str}{high_prec_str}.onnx"
 fname = f"te.core_attention{mask_str}{high_prec_str}.onnx"

 if attn_mask_type is None:
 attn_mask_type = 'causal'
Expand All@@ -798,8 +790,6 @@ def test_export_core_attention(
 kv_channels=kv_channels,
 attention_dropout=0.5,
 attn_mask_type=attn_mask_type,
 attention_softmax_in_fp32=attention_softmax_in_fp32,
 apply_query_key_layer_scaling=apply_query_key_layer_scaling,
 ).to(device='cuda')
 do_export(model,
 inp,
Expand DownExpand Up@@ -911,7 +901,6 @@ def test_export_multihead_attention(
])
@pytest.mark.parametrize("precision", [torch.float32, torch.float16])
@pytest.mark.parametrize("fuse_qkv_params", [False, True])
@pytest.mark.parametrize("apply_query_key_layer_scaling", [True, False])
@pytest.mark.parametrize("zero_centered_gamma", [False, True])
def test_export_transformer_layer(
 use_fp8: bool,
Expand All@@ -920,7 +909,6 @@ def test_export_transformer_layer(
 output_layernorm: bool,
 precision: torch.dtype,
 fuse_qkv_params: bool,
 apply_query_key_layer_scaling: bool,
 zero_centered_gamma: bool
):
 # Skip FP8 tests on non-hopper devices
Expand All@@ -946,10 +934,9 @@ def test_export_transformer_layer(

 fp8_str = "_fp8" if use_fp8 else ""
 fuse_qkv_params_str = "_fused-qkv" if fuse_qkv_params else ""
 qk_scaling_str = "_qk-scaling" if apply_query_key_layer_scaling else ""
 high_prec_str = dtype2str(precision)
 attn_mask_str = get_attn_mask_str(use_mask, attn_mask_type)
 fname = f"te.transformer_layer{fp8_str}{attn_mask_str}{fuse_qkv_params_str}{qk_scaling_str}{high_prec_str}.onnx"
 fname = f"te.transformer_layer{fp8_str}{attn_mask_str}{fuse_qkv_params_str}{high_prec_str}.onnx"

 model = te.TransformerLayer(
 hidden_size,
Expand All@@ -959,7 +946,6 @@ def test_export_transformer_layer(
 output_layernorm=output_layernorm,
 params_dtype=precision,
 fuse_qkv_params=fuse_qkv_params,
 apply_query_key_layer_scaling=apply_query_key_layer_scaling,
 zero_centered_gamma=zero_centered_gamma).to(device='cuda')
 do_export(model, inp, fname, use_fp8)
 if not use_fp8:
Expand Down

36 changes: 19 additions & 17 deletions
transformer_engine/pytorch/softmax.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -4,7 +4,7 @@

"""Fused scaled masked softmax functions"""
import os
from typing import Callable, Tuple, Union
from typing import Callable, Tuple, Union, Optional
import torch
from torch import nn
import torch._C._onnx as _C_onnx
Expand DownExpand Up@@ -198,15 +198,13 @@ class FusedScaleMaskSoftmax(nn.Module):
 attn_mask_type: attention mask type (pad or causal)
 mask_func: mask function to be applied.
 softmax_in_fp32: if true, softmax in performed at fp32 precision.
 scale: scaling factor used in input tensor scaling.
 """

 def __init__(
 self,
 attn_mask_type: str,
 mask_func: Callable,
 softmax_in_fp32: bool,
 scale: float,
 softmax_in_fp32: bool = True,
Comment thread
ptrendx marked this conversation as resolved.

 ) -> None:
 super().__init__()
 self.attn_mask_type = attn_mask_type
Expand All@@ -215,23 +213,27 @@ def __init__(
 )
 self.mask_func = mask_func
 self.softmax_in_fp32 = softmax_in_fp32
 self.scale = scale

 assert (
 self.scale is None or softmax_in_fp32
 ), "softmax should be in fp32 when scaled"

 def forward(self, inp: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
 def forward(
 self,
 inp: torch.Tensor,
 mask: torch.Tensor,
 scale: Optional[float] = None,
 ) -> torch.Tensor:
 """FusedScaleMaskSoftmax fprop"""
 # [b, np, sq, sk]
 assert inp.dim() == 4
 self.input_in_fp16 = inp.dtype == torch.float16
 self.input_in_bf16 = inp.dtype == torch.bfloat16
 self.input_in_float16 = self.input_in_fp16 or self.input_in_bf16

 assert (
 scale is None or self.softmax_in_fp32
 ), "softmax should be in fp32 when scaled"

 if self.is_kernel_available(*inp.size()):
 return self.forward_fused_softmax(inp, mask)
 return self.forward_torch_softmax(inp, mask)
 return self.forward_fused_softmax(inp, mask, scale)
 return self.forward_torch_softmax(inp, mask, scale)

 def is_kernel_available(self, b: int, np: int, sq: int, sk: int) -> bool:
 """Check FusedScaleMaskSoftmax kernel availability based on size"""
Expand All@@ -256,11 +258,11 @@ def is_kernel_available(self, b: int, np: int, sq: int, sk: int) -> bool:
 return False

 def forward_fused_softmax(
 self, inp: torch.Tensor, mask: torch.Tensor
 self, inp: torch.Tensor, mask: torch.Tensor, scale: Optional[float] = None
 ) -> torch.Tensor:
 """Fused masked softmax kernel"""
 b, np, sq, sk = inp.size()
 scale = self.scale if self.scale is not None else 1.0
 scale = 1.0 if scale is None else scale

 if self.attn_mask_type == "causal":
 assert sq == sk, "causal mask is only for self attention"
Expand All@@ -275,14 +277,14 @@ def forward_fused_softmax(
 return ScaledSoftmax.apply(inp, scale)

 def forward_torch_softmax(
 self, inp: torch.Tensor, mask: torch.Tensor
 self, inp: torch.Tensor, mask: torch.Tensor, scale: Optional[float] = None
 ) -> torch.Tensor:
 """Framework softmax"""
 if self.input_in_float16 and self.softmax_in_fp32:
 inp = inp.float()

 if self.scale is not None:
 inp = inp * self.scale
 if scale is not None:
 inp = inp * scale

 if self.attn_mask_type == "causal":
 mask = _get_default_causal_mask(inp.size()[2])
Expand Down
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var __re = new RegExp('^' + ".*" + '
deprecate qk layer scaling and fp32 softmax args by ksivaman · Pull Request #90 · NVIDIA/TransformerEngine · GitHub
Skip to content
deprecate qk layer scaling and fp32 softmax args#90

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 Merged

ksivaman
merged 5 commits into

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Mar 11, 2023
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deprecate qk layer scaling and fp32 softmax args
#90
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145064e

deprecate qk layer scaling and fp32 softmax args


ksivaman Mar 10, 2023
2f3f263

Merge branch 'main' into deprecate_args


ksivaman Mar 10, 2023
ba85b46

apply QK layer scaling for fp16 training


ksivaman Mar 10, 2023
80edaa0

address review comments


ksivaman Mar 11, 2023
20932da

Merge branch 'main' into deprecate_args


ksivaman Mar 11, 2023

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18 changes: 2 additions & 16 deletions
tests/pytorch/test_onnx_export.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -756,16 +756,10 @@ def test_export_layernorm_mlp(
 (torch.float16, True, "padding"), # calls ScaledMaskedSoftmax
 (torch.float16, False, "padding"), # calls ScaledSoftmax
])
@pytest.mark.parametrize("attention_softmax_in_fp32",
 [True, False])
@pytest.mark.parametrize("apply_query_key_layer_scaling",
 [True, False])
def test_export_core_attention(
 precision: torch.dtype,
 use_mask: bool,
 attn_mask_type: str,
 attention_softmax_in_fp32: bool,
 apply_query_key_layer_scaling: bool,
):
 # Set dimensions (these are arbitrary).
 kv_channels = 64
Expand All@@ -784,11 +778,9 @@ def test_export_core_attention(
 input_names.append("attention_mask")
 inp = (query_layer, key_layer, value_layer, attention_mask)

 sm_prec_str = "_sm-fp32" if attention_softmax_in_fp32 else "_sm-fp16"
 qk_scaling_str = "_qk-scaling" if apply_query_key_layer_scaling else ""
 mask_str = get_attn_mask_str(use_mask, attn_mask_type)
 high_prec_str = dtype2str(precision)
 fname = f"te.core_attention{mask_str}{qk_scaling_str}{sm_prec_str}{high_prec_str}.onnx"
 fname = f"te.core_attention{mask_str}{high_prec_str}.onnx"

 if attn_mask_type is None:
 attn_mask_type = 'causal'
Expand All@@ -798,8 +790,6 @@ def test_export_core_attention(
 kv_channels=kv_channels,
 attention_dropout=0.5,
 attn_mask_type=attn_mask_type,
 attention_softmax_in_fp32=attention_softmax_in_fp32,
 apply_query_key_layer_scaling=apply_query_key_layer_scaling,
 ).to(device='cuda')
 do_export(model,
 inp,
Expand DownExpand Up@@ -911,7 +901,6 @@ def test_export_multihead_attention(
])
@pytest.mark.parametrize("precision", [torch.float32, torch.float16])
@pytest.mark.parametrize("fuse_qkv_params", [False, True])
@pytest.mark.parametrize("apply_query_key_layer_scaling", [True, False])
@pytest.mark.parametrize("zero_centered_gamma", [False, True])
def test_export_transformer_layer(
 use_fp8: bool,
Expand All@@ -920,7 +909,6 @@ def test_export_transformer_layer(
 output_layernorm: bool,
 precision: torch.dtype,
 fuse_qkv_params: bool,
 apply_query_key_layer_scaling: bool,
 zero_centered_gamma: bool
):
 # Skip FP8 tests on non-hopper devices
Expand All@@ -946,10 +934,9 @@ def test_export_transformer_layer(

 fp8_str = "_fp8" if use_fp8 else ""
 fuse_qkv_params_str = "_fused-qkv" if fuse_qkv_params else ""
 qk_scaling_str = "_qk-scaling" if apply_query_key_layer_scaling else ""
 high_prec_str = dtype2str(precision)
 attn_mask_str = get_attn_mask_str(use_mask, attn_mask_type)
 fname = f"te.transformer_layer{fp8_str}{attn_mask_str}{fuse_qkv_params_str}{qk_scaling_str}{high_prec_str}.onnx"
 fname = f"te.transformer_layer{fp8_str}{attn_mask_str}{fuse_qkv_params_str}{high_prec_str}.onnx"

 model = te.TransformerLayer(
 hidden_size,
Expand All@@ -959,7 +946,6 @@ def test_export_transformer_layer(
 output_layernorm=output_layernorm,
 params_dtype=precision,
 fuse_qkv_params=fuse_qkv_params,
 apply_query_key_layer_scaling=apply_query_key_layer_scaling,
 zero_centered_gamma=zero_centered_gamma).to(device='cuda')
 do_export(model, inp, fname, use_fp8)
 if not use_fp8:
Expand Down

36 changes: 19 additions & 17 deletions
transformer_engine/pytorch/softmax.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -4,7 +4,7 @@

"""Fused scaled masked softmax functions"""
import os
from typing import Callable, Tuple, Union
from typing import Callable, Tuple, Union, Optional
import torch
from torch import nn
import torch._C._onnx as _C_onnx
Expand DownExpand Up@@ -198,15 +198,13 @@ class FusedScaleMaskSoftmax(nn.Module):
 attn_mask_type: attention mask type (pad or causal)
 mask_func: mask function to be applied.
 softmax_in_fp32: if true, softmax in performed at fp32 precision.
 scale: scaling factor used in input tensor scaling.
 """

 def __init__(
 self,
 attn_mask_type: str,
 mask_func: Callable,
 softmax_in_fp32: bool,
 scale: float,
 softmax_in_fp32: bool = True,
Comment thread
ptrendx marked this conversation as resolved.

 ) -> None:
 super().__init__()
 self.attn_mask_type = attn_mask_type
Expand All@@ -215,23 +213,27 @@ def __init__(
 )
 self.mask_func = mask_func
 self.softmax_in_fp32 = softmax_in_fp32
 self.scale = scale

 assert (
 self.scale is None or softmax_in_fp32
 ), "softmax should be in fp32 when scaled"

 def forward(self, inp: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
 def forward(
 self,
 inp: torch.Tensor,
 mask: torch.Tensor,
 scale: Optional[float] = None,
 ) -> torch.Tensor:
 """FusedScaleMaskSoftmax fprop"""
 # [b, np, sq, sk]
 assert inp.dim() == 4
 self.input_in_fp16 = inp.dtype == torch.float16
 self.input_in_bf16 = inp.dtype == torch.bfloat16
 self.input_in_float16 = self.input_in_fp16 or self.input_in_bf16

 assert (
 scale is None or self.softmax_in_fp32
 ), "softmax should be in fp32 when scaled"

 if self.is_kernel_available(*inp.size()):
 return self.forward_fused_softmax(inp, mask)
 return self.forward_torch_softmax(inp, mask)
 return self.forward_fused_softmax(inp, mask, scale)
 return self.forward_torch_softmax(inp, mask, scale)

 def is_kernel_available(self, b: int, np: int, sq: int, sk: int) -> bool:
 """Check FusedScaleMaskSoftmax kernel availability based on size"""
Expand All@@ -256,11 +258,11 @@ def is_kernel_available(self, b: int, np: int, sq: int, sk: int) -> bool:
 return False

 def forward_fused_softmax(
 self, inp: torch.Tensor, mask: torch.Tensor
 self, inp: torch.Tensor, mask: torch.Tensor, scale: Optional[float] = None
 ) -> torch.Tensor:
 """Fused masked softmax kernel"""
 b, np, sq, sk = inp.size()
 scale = self.scale if self.scale is not None else 1.0
 scale = 1.0 if scale is None else scale

 if self.attn_mask_type == "causal":
 assert sq == sk, "causal mask is only for self attention"
Expand All@@ -275,14 +277,14 @@ def forward_fused_softmax(
 return ScaledSoftmax.apply(inp, scale)

 def forward_torch_softmax(
 self, inp: torch.Tensor, mask: torch.Tensor
 self, inp: torch.Tensor, mask: torch.Tensor, scale: Optional[float] = None
 ) -> torch.Tensor:
 """Framework softmax"""
 if self.input_in_float16 and self.softmax_in_fp32:
 inp = inp.float()

 if self.scale is not None:
 inp = inp * self.scale
 if scale is not None:
 inp = inp * scale

 if self.attn_mask_type == "causal":
 mask = _get_default_causal_mask(inp.size()[2])
Expand Down
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deprecate qk layer scaling and fp32 softmax args by ksivaman · Pull Request #90 · NVIDIA/TransformerEngine · GitHub
Skip to content
deprecate qk layer scaling and fp32 softmax args#90

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 Merged

ksivaman
merged 5 commits into

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Mar 11, 2023
 Merged


deprecate qk layer scaling and fp32 softmax args
#90
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145064e

deprecate qk layer scaling and fp32 softmax args


ksivaman Mar 10, 2023
2f3f263

Merge branch 'main' into deprecate_args


ksivaman Mar 10, 2023
ba85b46

apply QK layer scaling for fp16 training


ksivaman Mar 10, 2023
80edaa0

address review comments


ksivaman Mar 11, 2023
20932da

Merge branch 'main' into deprecate_args


ksivaman Mar 11, 2023

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18 changes: 2 additions & 16 deletions
tests/pytorch/test_onnx_export.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -756,16 +756,10 @@ def test_export_layernorm_mlp(
 (torch.float16, True, "padding"), # calls ScaledMaskedSoftmax
 (torch.float16, False, "padding"), # calls ScaledSoftmax
])
@pytest.mark.parametrize("attention_softmax_in_fp32",
 [True, False])
@pytest.mark.parametrize("apply_query_key_layer_scaling",
 [True, False])
def test_export_core_attention(
 precision: torch.dtype,
 use_mask: bool,
 attn_mask_type: str,
 attention_softmax_in_fp32: bool,
 apply_query_key_layer_scaling: bool,
):
 # Set dimensions (these are arbitrary).
 kv_channels = 64
Expand All@@ -784,11 +778,9 @@ def test_export_core_attention(
 input_names.append("attention_mask")
 inp = (query_layer, key_layer, value_layer, attention_mask)

 sm_prec_str = "_sm-fp32" if attention_softmax_in_fp32 else "_sm-fp16"
 qk_scaling_str = "_qk-scaling" if apply_query_key_layer_scaling else ""
 mask_str = get_attn_mask_str(use_mask, attn_mask_type)
 high_prec_str = dtype2str(precision)
 fname = f"te.core_attention{mask_str}{qk_scaling_str}{sm_prec_str}{high_prec_str}.onnx"
 fname = f"te.core_attention{mask_str}{high_prec_str}.onnx"

 if attn_mask_type is None:
 attn_mask_type = 'causal'
Expand All@@ -798,8 +790,6 @@ def test_export_core_attention(
 kv_channels=kv_channels,
 attention_dropout=0.5,
 attn_mask_type=attn_mask_type,
 attention_softmax_in_fp32=attention_softmax_in_fp32,
 apply_query_key_layer_scaling=apply_query_key_layer_scaling,
 ).to(device='cuda')
 do_export(model,
 inp,
Expand DownExpand Up@@ -911,7 +901,6 @@ def test_export_multihead_attention(
])
@pytest.mark.parametrize("precision", [torch.float32, torch.float16])
@pytest.mark.parametrize("fuse_qkv_params", [False, True])
@pytest.mark.parametrize("apply_query_key_layer_scaling", [True, False])
@pytest.mark.parametrize("zero_centered_gamma", [False, True])
def test_export_transformer_layer(
 use_fp8: bool,
Expand All@@ -920,7 +909,6 @@ def test_export_transformer_layer(
 output_layernorm: bool,
 precision: torch.dtype,
 fuse_qkv_params: bool,
 apply_query_key_layer_scaling: bool,
 zero_centered_gamma: bool
):
 # Skip FP8 tests on non-hopper devices
Expand All@@ -946,10 +934,9 @@ def test_export_transformer_layer(

 fp8_str = "_fp8" if use_fp8 else ""
 fuse_qkv_params_str = "_fused-qkv" if fuse_qkv_params else ""
 qk_scaling_str = "_qk-scaling" if apply_query_key_layer_scaling else ""
 high_prec_str = dtype2str(precision)
 attn_mask_str = get_attn_mask_str(use_mask, attn_mask_type)
 fname = f"te.transformer_layer{fp8_str}{attn_mask_str}{fuse_qkv_params_str}{qk_scaling_str}{high_prec_str}.onnx"
 fname = f"te.transformer_layer{fp8_str}{attn_mask_str}{fuse_qkv_params_str}{high_prec_str}.onnx"

 model = te.TransformerLayer(
 hidden_size,
Expand All@@ -959,7 +946,6 @@ def test_export_transformer_layer(
 output_layernorm=output_layernorm,
 params_dtype=precision,
 fuse_qkv_params=fuse_qkv_params,
 apply_query_key_layer_scaling=apply_query_key_layer_scaling,
 zero_centered_gamma=zero_centered_gamma).to(device='cuda')
 do_export(model, inp, fname, use_fp8)
 if not use_fp8:
Expand Down

36 changes: 19 additions & 17 deletions
transformer_engine/pytorch/softmax.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -4,7 +4,7 @@

"""Fused scaled masked softmax functions"""
import os
from typing import Callable, Tuple, Union
from typing import Callable, Tuple, Union, Optional
import torch
from torch import nn
import torch._C._onnx as _C_onnx
Expand DownExpand Up@@ -198,15 +198,13 @@ class FusedScaleMaskSoftmax(nn.Module):
 attn_mask_type: attention mask type (pad or causal)
 mask_func: mask function to be applied.
 softmax_in_fp32: if true, softmax in performed at fp32 precision.
 scale: scaling factor used in input tensor scaling.
 """

 def __init__(
 self,
 attn_mask_type: str,
 mask_func: Callable,
 softmax_in_fp32: bool,
 scale: float,
 softmax_in_fp32: bool = True,
Comment thread
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 ) -> None:
 super().__init__()
 self.attn_mask_type = attn_mask_type
Expand All@@ -215,23 +213,27 @@ def __init__(
 )
 self.mask_func = mask_func
 self.softmax_in_fp32 = softmax_in_fp32
 self.scale = scale

 assert (
 self.scale is None or softmax_in_fp32
 ), "softmax should be in fp32 when scaled"

 def forward(self, inp: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
 def forward(
 self,
 inp: torch.Tensor,
 mask: torch.Tensor,
 scale: Optional[float] = None,
 ) -> torch.Tensor:
 """FusedScaleMaskSoftmax fprop"""
 # [b, np, sq, sk]
 assert inp.dim() == 4
 self.input_in_fp16 = inp.dtype == torch.float16
 self.input_in_bf16 = inp.dtype == torch.bfloat16
 self.input_in_float16 = self.input_in_fp16 or self.input_in_bf16

 assert (
 scale is None or self.softmax_in_fp32
 ), "softmax should be in fp32 when scaled"

 if self.is_kernel_available(*inp.size()):
 return self.forward_fused_softmax(inp, mask)
 return self.forward_torch_softmax(inp, mask)
 return self.forward_fused_softmax(inp, mask, scale)
 return self.forward_torch_softmax(inp, mask, scale)

 def is_kernel_available(self, b: int, np: int, sq: int, sk: int) -> bool:
 """Check FusedScaleMaskSoftmax kernel availability based on size"""
Expand All@@ -256,11 +258,11 @@ def is_kernel_available(self, b: int, np: int, sq: int, sk: int) -> bool:
 return False

 def forward_fused_softmax(
 self, inp: torch.Tensor, mask: torch.Tensor
 self, inp: torch.Tensor, mask: torch.Tensor, scale: Optional[float] = None
 ) -> torch.Tensor:
 """Fused masked softmax kernel"""
 b, np, sq, sk = inp.size()
 scale = self.scale if self.scale is not None else 1.0
 scale = 1.0 if scale is None else scale

 if self.attn_mask_type == "causal":
 assert sq == sk, "causal mask is only for self attention"
Expand All@@ -275,14 +277,14 @@ def forward_fused_softmax(
 return ScaledSoftmax.apply(inp, scale)

 def forward_torch_softmax(
 self, inp: torch.Tensor, mask: torch.Tensor
 self, inp: torch.Tensor, mask: torch.Tensor, scale: Optional[float] = None
 ) -> torch.Tensor:
 """Framework softmax"""
 if self.input_in_float16 and self.softmax_in_fp32:
 inp = inp.float()

 if self.scale is not None:
 inp = inp * self.scale
 if scale is not None:
 inp = inp * scale

 if self.attn_mask_type == "causal":
 mask = _get_default_causal_mask(inp.size()[2])
Expand Down
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