mirror of https://github.com/hpcaitech/ColossalAI
[NFC] polish colossalai/nn/layer/parallel_sequence/_operation.py code style (#1266)
Geng Zhang
Frank Lee
parent
c95e18cdb9
commit
0e06f62160
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@ -19,24 +19,17 @@ class RingQK(torch.autograd.Function):
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@staticmethod
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@custom_fwd
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def forward(ctx,
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sub_q,
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sub_k,
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batch_size,
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num_attention_heads,
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sub_seq_length):
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def forward(ctx, sub_q, sub_k, batch_size, num_attention_heads, sub_seq_length):
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# save tensor for backward
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ctx.save_for_backward(sub_q, sub_k)
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ctx.sub_seq_length = sub_seq_length
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# create local segment of attention score
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attention_score = torch.empty(
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batch_size * num_attention_heads,
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sub_seq_length,
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sub_seq_length * gpc.get_world_size(ParallelMode.SEQUENCE),
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dtype=sub_q.dtype,
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device=get_current_device()
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)
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attention_score = torch.empty(batch_size * num_attention_heads,
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sub_seq_length,
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sub_seq_length * gpc.get_world_size(ParallelMode.SEQUENCE),
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dtype=sub_q.dtype,
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device=get_current_device())
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# compute local QK^T
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part_a = torch.matmul(sub_q, sub_k.transpose(2, 1))
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@ -44,7 +37,7 @@ class RingQK(torch.autograd.Function):
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local_world_size = gpc.get_world_size(ParallelMode.SEQUENCE)
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start_idx = local_rank * sub_seq_length
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end_idx = (local_rank + 1) * sub_seq_length
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attention_score[:, :, start_idx: end_idx] = part_a
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attention_score[:, :, start_idx:end_idx] = part_a
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# compute QK^T in ring-all-reduce style
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for i in range(local_world_size - 1):
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@ -63,19 +56,18 @@ class RingQK(torch.autograd.Function):
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local_world_size = gpc.get_world_size(ParallelMode.SEQUENCE)
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# calculate gradient of sub_k
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grad_k = torch.matmul(
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grad_output.transpose(2, 1),
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sub_q
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)
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grad_k = torch.matmul(grad_output.transpose(2, 1), sub_q)
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dist.all_reduce(grad_k, group=gpc.get_group(ParallelMode.SEQUENCE))
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grad_k = grad_k[:, local_rank * ctx.sub_seq_length: (local_rank + 1) * ctx.sub_seq_length]
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grad_k = grad_k[:, local_rank * ctx.sub_seq_length:(local_rank + 1) * ctx.sub_seq_length]
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grad_k /= local_world_size
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# calculate gradient for sub_q
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grad_q = torch.zeros_like(sub_q,
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dtype=sub_q.dtype,
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device=get_current_device(), )
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grad_q = torch.zeros_like(
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sub_q,
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dtype=sub_q.dtype,
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device=get_current_device(),
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)
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# compute with local sub_k
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start_idx, end_idx = _calc_current_device_range(local_rank, ctx.sub_seq_length)
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@ -85,7 +77,7 @@ class RingQK(torch.autograd.Function):
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for i in range(local_world_size - 1):
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sub_k = ring_forward(sub_k, ParallelMode.SEQUENCE)
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start_idx, end_idx = _calc_incoming_device_range(i, local_rank, local_world_size, ctx.sub_seq_length)
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grad_q += torch.matmul(grad_output[:, :, start_idx: end_idx], sub_k)
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grad_q += torch.matmul(grad_output[:, :, start_idx:end_idx], sub_k)
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grad_q /= local_world_size
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@ -99,23 +91,16 @@ class RingAV(torch.autograd.Function):
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@staticmethod
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@custom_fwd
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def forward(ctx,
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attention_score,
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sub_v,
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batch_size,
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num_attention_heads,
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attention_head_size,
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sub_seq_length):
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def forward(ctx, attention_score, sub_v, batch_size, num_attention_heads, attention_head_size, sub_seq_length):
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local_rank = gpc.get_local_rank(ParallelMode.SEQUENCE)
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local_world_size = gpc.get_world_size(ParallelMode.SEQUENCE)
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local_start_idx, local_end_idx = _calc_current_device_range(local_rank, sub_seq_length)
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sub_attention_result = torch.zeros(
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batch_size * num_attention_heads,
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sub_seq_length,
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attention_head_size,
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device=get_current_device(),
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dtype=attention_score.dtype)
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sub_attention_result = torch.zeros(batch_size * num_attention_heads,
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sub_seq_length,
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attention_head_size,
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device=get_current_device(),
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dtype=attention_score.dtype)
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# save tensors for backward
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ctx.save_for_backward(attention_score, sub_v)
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@ -144,23 +129,16 @@ class RingAV(torch.autograd.Function):
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attention_scores, sub_v = ctx.saved_tensors
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# calculate gradient of v
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grad_v = torch.matmul(
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attention_scores.transpose(2, 1),
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grad_output
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)
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grad_v = torch.matmul(attention_scores.transpose(2, 1), grad_output)
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dist.all_reduce(grad_v, group=gpc.get_group(ParallelMode.SEQUENCE))
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grad_v = grad_v[:, local_start_idx:local_end_idx]
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grad_v /= local_world_size
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# calculate gradient for attention score
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grad_attention_score = torch.zeros_like(attention_scores,
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dtype=grad_output.dtype,
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device=get_current_device())
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grad_attention_score = torch.zeros_like(attention_scores, dtype=grad_output.dtype, device=get_current_device())
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# compute with local sub_k
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grad_attention_score[:, :, local_start_idx:local_end_idx] += torch.matmul(
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grad_output,
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sub_v.transpose(2, 1))
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grad_attention_score[:, :, local_start_idx:local_end_idx] += torch.matmul(grad_output, sub_v.transpose(2, 1))
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# compute QK^T in ring-all-reduce style
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for i in range(local_world_size - 1):
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@ -168,8 +146,6 @@ class RingAV(torch.autograd.Function):
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start_idx, end_idx = _calc_incoming_device_range(i, local_rank, local_world_size, ctx.sub_seq_length)
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# compute grad_q
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grad_attention_score[:, :, start_idx:end_idx] += torch.matmul(
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grad_output,
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sub_v.transpose(2, 1))
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grad_attention_score[:, :, start_idx:end_idx] += torch.matmul(grad_output, sub_v.transpose(2, 1))
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return grad_attention_score, grad_v, None, None, None, None
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