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Fix deprecation warnings #1382

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Fix deprecation warnings #1382

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6 changes: 3 additions & 3 deletions flash_attn/fused_softmax.py
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Original file line number Diff line number Diff line change
Expand Up @@ -58,12 +58,12 @@ def scaled_upper_triang_masked_softmax(inputs, _, scale):
# Reshaping input to 3D tensor (attn_batches, sq, sk)
inputs = inputs.view(-1, sq, sk)
args = _cast_if_autocast_enabled(inputs, scale)
with torch.cuda.amp.autocast(enabled=False):
with torch.amp.autocast(device_type="cuda", enabled=False):
probs = ScaledUpperTriangMaskedSoftmax.apply(*args)
return probs.view(b, np, sq, sk)


# NOTE (mkozuki): `ScaledMaskedSoftmax` somehow doesn't work well with `torch.cuda.amp.custom_fwd`.
# NOTE (mkozuki): `ScaledMaskedSoftmax` somehow doesn't work well with `torch.amp.custom_fwd`.
# Without `cast_inputs` kwarg, somehow inputs are not cast to dtype used in the autocast context.
# So I needed to manually write two `torch.autograd.Function` inheritances.
# Fused operation which performs following three operations in sequence
Expand All @@ -88,7 +88,7 @@ def backward(ctx, output_grads):
def scaled_masked_softmax(inputs, mask, scale):
# input is 4D tensor (b, np, sq, sk)
args = _cast_if_autocast_enabled(inputs, mask, scale)
with torch.cuda.amp.autocast(enabled=False):
with torch.amp.autocast(device_type="cuda", enabled=False):
return ScaledMaskedSoftmax.apply(*args)


Expand Down
10 changes: 5 additions & 5 deletions flash_attn/ops/fused_dense.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from torch.cuda.amp import custom_bwd, custom_fwd
from torch.amp import custom_bwd, custom_fwd
from torch.distributed import ProcessGroup

from flash_attn.ops.activations import gelu_bwd, relu_bwd, sqrelu_bwd, sqrelu_fwd
Expand All @@ -26,7 +26,7 @@

class FusedDenseFunc(torch.autograd.Function):
@staticmethod
@custom_fwd
@custom_fwd(device_type="cuda")
def forward(
ctx, x, weight, bias, return_residual=False, process_group=None, sequence_parallel=True
):
Expand Down Expand Up @@ -67,7 +67,7 @@ def forward(
return output if not return_residual else (output, x)

@staticmethod
@custom_bwd
@custom_bwd(device_type="cuda")
def backward(ctx, grad_output, *args):
grad_output = grad_output.contiguous()
if ctx.return_residual:
Expand Down Expand Up @@ -248,7 +248,7 @@ def forward(self, x):

class FusedMLPFunc(torch.autograd.Function):
@staticmethod
@custom_fwd
@custom_fwd(device_type="cuda")
def forward(
ctx,
x,
Expand Down Expand Up @@ -345,7 +345,7 @@ def forward(
return output2 if not return_residual else (output2, x)

@staticmethod
@custom_bwd
@custom_bwd(device_type="cuda")
def backward(ctx, grad_output, *args):
grad_output = grad_output.contiguous()
checkpoint_lvl = ctx.checkpoint_lvl
Expand Down
6 changes: 3 additions & 3 deletions flash_attn/ops/triton/layer_norm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@

import torch
import torch.nn.functional as F
from torch.cuda.amp import custom_fwd, custom_bwd
from torch.amp import custom_fwd, custom_bwd

import triton
import triton.language as tl
Expand Down Expand Up @@ -981,7 +981,7 @@ def forward(self, x, residual=None, prenorm=False, residual_in_fp32=False):

class LayerNormLinearFn(torch.autograd.Function):
@staticmethod
@custom_fwd
@custom_fwd(device_type="cuda")
def forward(
ctx,
x,
Expand Down Expand Up @@ -1040,7 +1040,7 @@ def forward(
return out if not prenorm else (out, residual_out.reshape(x_shape_og))

@staticmethod
@custom_bwd
@custom_bwd(device_type="cuda")
def backward(ctx, dout, *args):
x, norm_weight, norm_bias, linear_weight, mean, rstd = ctx.saved_tensors
dout = dout.reshape(-1, dout.shape[-1])
Expand Down
6 changes: 3 additions & 3 deletions flash_attn/ops/triton/mlp.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,15 +4,15 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.cuda.amp import custom_bwd, custom_fwd
from torch.amp import custom_bwd, custom_fwd

from flash_attn.ops.activations import sqrelu_bwd, sqrelu_fwd
from flash_attn.ops.triton.linear import triton_dgrad_act, triton_linear_act


class FusedDenseSqreluDenseFunc(torch.autograd.Function):
@staticmethod
@custom_fwd
@custom_fwd(device_type="cuda")
def forward(ctx, x, weight1, bias1, weight2, bias2, checkpoint_lvl=0):
"""checkpoint_lvl:
0: no recomputation in the bwd
Expand Down Expand Up @@ -62,7 +62,7 @@ def forward(ctx, x, weight1, bias1, weight2, bias2, checkpoint_lvl=0):
return output2.reshape(*batch_shape, output2.shape[-1])

@staticmethod
@custom_bwd
@custom_bwd(device_type="cuda")
def backward(ctx, grad_output):
grad_output = grad_output.contiguous()
checkpoint_lvl = ctx.checkpoint_lvl
Expand Down