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support multiple input-output in transformerblocklist
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,126 @@ | ||
| from utils import * | ||
|
|
||
| import bmtrain as bmt | ||
| import torch | ||
| import random | ||
| from bmtrain import config | ||
| from bmtrain.block_layer import CheckpointBlock, TransformerBlockList | ||
| from bmtrain.pipe_layer import PipelineTransformerBlockList | ||
| import torch.nn.functional as F | ||
|
|
||
| class MultiInputReturn(torch.nn.Module): | ||
| def __init__(self): | ||
| super().__init__() | ||
|
|
||
| def forward(self, a, b, c, d, e): | ||
| return a*2, b+d, c*4+e*5 | ||
|
|
||
| class Model_ZERO(torch.nn.Module): | ||
| def __init__(self, ms) -> None: | ||
| super().__init__() | ||
| self.ms = TransformerBlockList([ | ||
| CheckpointBlock(m) | ||
| for m in ms | ||
| ], num_hidden=3) | ||
|
|
||
| def forward(self, x): | ||
| y = self.ms(*x) | ||
| return y | ||
|
|
||
| class Model_PIPE(torch.nn.Module): | ||
| def __init__(self, ms) -> None: | ||
| super().__init__() | ||
| self.ms = PipelineTransformerBlockList([ | ||
| CheckpointBlock(m) | ||
| for m in ms | ||
| ], num_hidden=3) | ||
|
|
||
| def forward(self, x): | ||
| y = self.ms(*x) | ||
| return y | ||
|
|
||
| class Model_BLOCK(torch.nn.Module): | ||
| def __init__(self, ms) -> None: | ||
| super().__init__() | ||
| self.ms = torch.nn.ModuleList([ | ||
| CheckpointBlock(m) | ||
| for m in ms | ||
| ]) | ||
|
|
||
| def forward(self, x): | ||
| y = x[:3] | ||
| other = x[3:] | ||
| for m in self.ms: | ||
| y = m(*y, *other) | ||
| return y | ||
|
|
||
| class Model_NORMAL(torch.nn.Module): | ||
| def __init__(self, ms) -> None: | ||
| super().__init__() | ||
| self.ms = torch.nn.ModuleList(ms) | ||
|
|
||
| def forward(self, x): | ||
| y = x[:3] | ||
| other = x[3:] | ||
| for m in self.ms: | ||
| y = m(*y, *other) | ||
| return y | ||
|
|
||
| def manual_seed(seed=33): | ||
| torch.manual_seed(seed) | ||
| random.seed(seed) | ||
| try: | ||
| import numpy as np | ||
| np.random.seed(seed) | ||
| except ModuleNotFoundError: | ||
| pass | ||
|
|
||
| def run(name, cls, num_layer=4, dim=4096): | ||
| manual_seed() | ||
|
|
||
| ms = [MultiInputReturn() for i in range(num_layer)] | ||
|
|
||
| inps = ( | ||
| torch.randn((dim,)).cuda(), | ||
| torch.randn((dim,)).cuda(), | ||
| torch.randn((dim,)).cuda(), | ||
| torch.randn((dim,)).cuda(), | ||
| torch.randn((dim,)).cuda(), | ||
| ) | ||
| last_weights = ( | ||
| torch.randn((dim,)).cuda(), | ||
| torch.randn((dim,)).cuda(), | ||
| torch.randn((dim,)).cuda(), | ||
| ) | ||
|
|
||
| for inp in inps: | ||
| inp.requires_grad_(True) | ||
| m = cls(ms) | ||
|
|
||
| ret = "" | ||
| logits = m(inps) | ||
| loss = (logits[0]*last_weights[0] + logits[1]*last_weights[1] + logits[2]*last_weights[2]).sum() | ||
| loss.backward() | ||
| return list(logits) + [ | ||
| inp.grad | ||
| for inp in inps | ||
| ] | ||
|
|
||
| def test_main(): | ||
| ret = {} | ||
| ret["normal"] = run("normal", Model_NORMAL) | ||
| ret["block"] = run("block", Model_BLOCK) | ||
| ret["zero"] = run("zero", Model_ZERO) | ||
| # ret["pipe"] = run("pipe", Model_PIPE) # TODO pipeline not support multiple input-output yet | ||
| for k, r in ret.items(): | ||
| bmt.print_rank(f"============={k}============") | ||
| bmt.print_rank(r) | ||
| for r in ret.values(): | ||
| for r2 in ret.values(): | ||
| for i in range(len(r)): | ||
| assert_lt((r[i]-r2[i]).abs().max(), 1e-5) | ||
|
|
||
| if __name__ == "__main__": | ||
| bmt.init_distributed(pipe_size=2) | ||
|
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||
| test_main() |