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Fixing non divisible embeddings. (#1476) 

# What does this PR do?

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Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
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guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
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      to it if that's the case.
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This commit is contained in:
Nicolas Patry 2024-01-24 13:08:41 +01:00 committed by GitHub
parent 82f87ada6f
commit 7e542d4d05
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
5 changed files with 199 additions and 140 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

55
integration-tests/models/__snapshots__/test_idefics/test_idefics.json
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214
integration-tests/models/__snapshots__/test_idefics/test_idefics_load.json
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64
server/tests/utils/test_layers.py Normal file
View File

@ -0,0 +1,64 @@
import torch
from text_generation_server.utils.layers import (
TensorParallelEmbedding,
)
class ProcessGroup:
def __init__(self, rank: int, world_size: int):
self._rank = rank
self.world_size = world_size
def size(self)->int:
return self.world_size
def rank(self)->int:
return self._rank
class Weights:
def __init__(self, rank: int, world_size: int, vocab_size: int, hidden_dim: int):
self.weight = torch.arange(vocab_size*hidden_dim).float().view(vocab_size, hidden_dim)
self.process_group = ProcessGroup(rank, world_size)
def get_partial_sharded(self, name:str, dim: int):
assert dim == 0
rank = self.process_group.rank()
world_size = self.process_group.size()
size = self.weight.shape[dim]
block_size = (size + world_size - 1) // world_size
start = rank * block_size
stop = (rank + 1) * block_size
return self.weight[start:stop]
def get_shape(self, name: str):
return self.weight.shape
def test_weight_hub_files_offline_error():
vocab_size= 17
weights = Weights(rank=0, world_size=1, vocab_size = vocab_size,hidden_dim = 256)
embeddings = TensorParallelEmbedding("", weights)
input_ids = torch.arange(vocab_size)
output = embeddings.forward(input_ids)
assert embeddings.min_id == 0
assert embeddings.max_id == 17
torch.testing.assert_close(output, torch.arange(256 * 17).float().view(17, 256))
weights_0_2 = Weights(rank=0, world_size=2, vocab_size = vocab_size,hidden_dim = 256)
weights_1_2 = Weights(rank=1, world_size=2, vocab_size = vocab_size,hidden_dim = 256)
embeddings_0_2 = TensorParallelEmbedding("", weights_0_2, reduce=False)
assert embeddings_0_2.min_id == 0
assert embeddings_0_2.max_id == 9
torch.testing.assert_close(embeddings_0_2.weight , torch.cat([torch.arange(9 * 256), torch.zeros(256)], dim=0).view(10, 256).float())
embeddings_1_2 = TensorParallelEmbedding("", weights_1_2, reduce=False)
assert embeddings_1_2.min_id == 9
assert embeddings_1_2.max_id == 17
torch.testing.assert_close(embeddings_1_2.weight , torch.cat([torch.arange(8 * 256) + 9 * 256, torch.zeros(256)], dim=0).view(9, 256).float())
output_tp_0 = embeddings_0_2.forward(input_ids)
output_tp_1 = embeddings_1_2.forward(input_ids)
torch.testing.assert_close(output, output_tp_0 + output_tp_1)

4
server/text_generation_server/utils/layers.py
View File

@ -507,10 +507,10 @@ class TensorParallelEmbedding(nn.Module):
world_size = process_group.size()
rank = process_group.rank()
block_size = num_embeddings // world_size
block_size = (num_embeddings + world_size - 1) // world_size
self.min_id = rank * block_size
self.max_id = min(num_embeddings, (rank + 1) * block_size)
self.null_idx = block_size
self.null_idx = weight.shape[0] # Usually block_size, might be less in non even vocab_size.
self.process_group = weights.process_group
self.reduce = reduce

2
server/text_generation_server/utils/weights.py
View File

@ -92,7 +92,7 @@ class Weights:
rank = self.process_group.rank()
size = slice_.get_shape()[dim]
block_size = size // world_size
block_size = (size + world_size - 1) // world_size
start = rank * block_size
stop = (rank + 1) * block_size