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fast_self_multihead_attn_func.py
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fast_self_multihead_attn_func.py
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import torch
import fast_multihead_attn
class FastSelfAttnFunc(torch.autograd.Function):
@staticmethod
def forward(
ctx,
use_time_mask,
is_training,
heads,
inputs,
input_weights,
output_weights,
input_biases,
output_biases,
pad_mask,
mask_additive,
dropout_prob,
):
use_biases_t = torch.tensor([input_biases is not None])
heads_t = torch.tensor([heads])
dropout_prob_t = torch.tensor([dropout_prob])
null_tensor = torch.tensor([])
use_mask = pad_mask is not None
mask_additive_t = torch.tensor([mask_additive])
if use_biases_t[0]:
if not mask_additive:
(
input_lin_results,
softmax_results,
dropout_results,
dropout_mask,
matmul2_results,
outputs,
) = fast_multihead_attn.self_attn_bias_forward(
use_mask,
use_time_mask,
is_training,
heads,
inputs,
input_weights,
output_weights,
input_biases,
output_biases,
pad_mask if use_mask else null_tensor,
dropout_prob,
)
# fast_self_multihead_attn_bias.forward() \
ctx.save_for_backward(
use_biases_t,
heads_t,
matmul2_results,
dropout_results,
softmax_results,
null_tensor,
null_tensor,
mask_additive_t,
input_lin_results,
inputs,
input_weights,
output_weights,
dropout_mask,
dropout_prob_t,
)
else:
(
input_lin_results,
bmm1_results,
dropout_results,
dropout_mask,
matmul2_results,
outputs,
) = fast_multihead_attn.self_attn_bias_additive_mask_forward(
use_mask,
use_time_mask,
is_training,
heads,
inputs,
input_weights,
output_weights,
input_biases,
output_biases,
pad_mask if use_mask else null_tensor,
dropout_prob,
)
# fast_self_multihead_attn_bias_additive_mask.forward( \
ctx.save_for_backward(
use_biases_t,
heads_t,
matmul2_results,
dropout_results,
null_tensor,
bmm1_results,
pad_mask,
mask_additive_t,
input_lin_results,
inputs,
input_weights,
output_weights,
dropout_mask,
dropout_prob_t,
)
else:
(
input_lin_results,
softmax_results,
dropout_results,
dropout_mask,
matmul2_results,
outputs,
) = fast_multihead_attn.self_attn_forward(
use_mask,
use_time_mask,
is_training,
heads,
inputs,
input_weights,
output_weights,
pad_mask if use_mask else null_tensor,
dropout_prob,
)
# fast_self_multihead_attn.forward( \
ctx.save_for_backward(
use_biases_t,
heads_t,
matmul2_results,
dropout_results,
softmax_results,
null_tensor,
null_tensor,
mask_additive_t,
input_lin_results,
inputs,
input_weights,
output_weights,
dropout_mask,
dropout_prob_t,
)
return outputs.detach()
@staticmethod
def backward(ctx, output_grads):
(
use_biases_t,
heads_t,
matmul2_results,
dropout_results,
softmax_results,
bmm1_results,
pad_mask,
mask_additive_t,
input_lin_results,
inputs,
input_weights,
output_weights,
dropout_mask,
dropout_prob_t,
) = ctx.saved_tensors
if use_biases_t[0]:
if not mask_additive_t[0]:
(
input_grads,
input_weight_grads,
output_weight_grads,
input_bias_grads,
output_bias_grads,
) = fast_multihead_attn.self_attn_bias_backward(
heads_t[0],
output_grads,
matmul2_results,
dropout_results,
softmax_results,
input_lin_results,
inputs,
input_weights,
output_weights,
dropout_mask,
dropout_prob_t[0],
)
# fast_self_multihead_attn_bias.backward( \
else:
(
input_grads,
input_weight_grads,
output_weight_grads,
input_bias_grads,
output_bias_grads,
) = fast_multihead_attn.self_attn_bias_additive_mask_backward(
heads_t[0],
output_grads,
matmul2_results,
dropout_results,
bmm1_results,
pad_mask,
input_lin_results,
inputs,
input_weights,
output_weights,
dropout_mask,
dropout_prob_t[0],
)
# fast_self_multihead_attn_bias_additive_mask.backward( \
else:
input_bias_grads = None
output_bias_grads = None
input_grads, input_weight_grads, output_weight_grads = fast_multihead_attn.self_attn_backward(
heads_t[0],
output_grads,
matmul2_results,
dropout_results,
softmax_results,
input_lin_results,
inputs,
input_weights,
output_weights,
dropout_mask,
dropout_prob_t[0],
)
# fast_self_multihead_attn.backward( \
return (
None,
None,
None,
input_grads,
input_weight_grads,
output_weight_grads,
input_bias_grads,
output_bias_grads,
None,
None,
None,
)
fast_self_attn_func = FastSelfAttnFunc.apply