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bmm1.py
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bmm1.py
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import torch
import mhalib
###########################################################################################
class Bmm1Function(torch.autograd.Function):
@staticmethod
def forward(ctx, batch1, batch2, seqlen, batch, maxseqlen, heads, embed, scale, stream, sync):
ctx.save_for_backward(batch1, batch2, seqlen)
ctx.batch = batch
ctx.maxseqlen = maxseqlen
ctx.heads = heads
ctx.embed = embed
ctx.scale = scale
ctx.sync = sync
ctx.stream = stream
ntokens = seqlen.sum().item()
ctx.ntokens = ntokens
ntokens2 = 0
for i in range(batch):
ntokens2 += seqlen[i]*seqlen[i]
output = torch.empty(ntokens2*heads, device="cuda", dtype=torch.float16)
mhalib.FastBmm1Fprop(batch2.flatten().contiguous(), batch1.flatten().contiguous(), output.flatten().contiguous(), batch, seqlen, heads, embed, scale, False, stream, sync)
return output[:ntokens2*heads]
@staticmethod
def backward(ctx, grad_output):
batch1, batch2, seqlen = ctx.saved_tensors
batch = ctx.batch
maxseqlen = ctx.maxseqlen
heads = ctx.heads
embed = ctx.embed
ntokens = ctx.ntokens
grad_batch1 = torch.empty(ntokens,heads*embed, device="cuda", dtype=torch.float16)
grad_batch2 = torch.empty(ntokens,heads*embed, device="cuda", dtype=torch.float16)
mhalib.FastBmm1Dgrad2(batch2.flatten().contiguous(), grad_output.flatten().contiguous(), grad_batch1.flatten().contiguous(), batch, seqlen, heads, embed, ctx.scale, False, ctx.stream, ctx.sync)
mhalib.FastBmm1Dgrad1(batch1.flatten().contiguous(), grad_output.flatten().contiguous(), grad_batch2.flatten().contiguous(), batch, seqlen, heads, embed, ctx.scale, False, ctx.stream, ctx.sync)
return grad_batch1[:ntokens], grad_batch2[:ntokens], None, None, None, None, None, None, None, None
class Bmm1(torch.nn.Module):
def __init__(self, batch, seqlen, heads, embed, scale=False, stream=True, sync=True):
super(Bmm1, self).__init__()
self.heads = heads
self.embed = embed
self.maxseqlen = seqlen
self.scale = scale
self.sync = sync
self.stream = stream
def forward(self, batch1, batch2, batch, seqlen):
return Bmm1Function.apply(batch1, batch2, seqlen, batch, self.maxseqlen, self.heads, self.embed, self.scale, self.stream, self.sync)
##########################################################################################
class Bmm1StridedFunction(torch.autograd.Function):
@staticmethod
def forward(ctx, mixed, seqlen, batch, maxseqlen, heads, embed, scale, stream, sync, timers):
ctx.save_for_backward(mixed, seqlen)
ctx.batch = batch
ctx.maxseqlen = maxseqlen
ctx.heads = heads
ctx.embed = embed
ctx.scale = scale
ctx.sync = sync
ctx.stream = stream
ctx.timers = timers
ntokens = seqlen.sum().item()
ctx.ntokens = ntokens
ntokens2 = 0
for i in range(batch):
ntokens2 += seqlen[i]*seqlen[i]
output = torch.empty(ntokens2*heads, device="cuda", dtype=torch.float16)
if timers: timers['start_fprop'].record()
mhalib.FastBmm1Fprop(mixed, mixed, output, batch, seqlen, heads, embed, scale, True, stream, sync)
if timers: timers['stop_fprop'].record()
return output[:ntokens2*heads], mixed
@staticmethod
#def backward(ctx, grad_output):
def backward(ctx, grad_output, grad_mixed):
mixed, seqlen = ctx.saved_tensors
batch = ctx.batch
maxseqlen = ctx.maxseqlen
heads = ctx.heads
embed = ctx.embed
ntokens = ctx.ntokens
#grad_mixed = torch.empty([ntokens,heads*3*embed], device="cuda", dtype=torch.float16)
if ctx.timers: ctx.timers['start_dgrad'].record()
mhalib.FastBmm1Dgrad2(mixed, grad_output, grad_mixed, batch, seqlen, heads, embed, ctx.scale, True, ctx.stream, ctx.sync)
if ctx.timers: ctx.timers['stop_dgrad'].record()
if ctx.timers: ctx.timers['start_wgrad'].record()
mhalib.FastBmm1Dgrad1(mixed, grad_output, grad_mixed, batch, seqlen, heads, embed, ctx.scale, True, ctx.stream, ctx.sync)
if ctx.timers: ctx.timers['stop_wgrad'].record()
#return grad_mixed[:ntokens], None, None, None, None, None, None, None, None, None
return grad_mixed[:ntokens], grad_mixed, None, None, None, None, None, None, None, None, None
class Bmm1Strided(torch.nn.Module):
def __init__(self, batch, seqlen, heads, embed, scale=True, stream=True, sync=True, timer=False):
super(Bmm1Strided, self).__init__()
self.heads = heads
self.embed = embed
self.maxseqlen = seqlen
self.scale = scale
self.sync = sync
self.stream = stream
if timer:
self.timers = {'start_fprop':torch.cuda.Event(enable_timing=True),
'start_dgrad':torch.cuda.Event(enable_timing=True),
'start_wgrad':torch.cuda.Event(enable_timing=True),
'stop_fprop':torch.cuda.Event(enable_timing=True),
'stop_dgrad':torch.cuda.Event(enable_timing=True),
'stop_wgrad':torch.cuda.Event(enable_timing=True)}
else:
self.timers = None
def forward(self, mixed, batch, seqlen):
return Bmm1StridedFunction.apply(mixed, seqlen, batch, self.maxseqlen, self.heads, self.embed, self.scale, self.stream, self.sync, self.timers)
###########################################################################################