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[misc] update pre-commit and run all files (#4752)

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* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format
This commit is contained in:
Hongxin Liu
2023-09-19 14:20:26 +08:00
committed by GitHub
parent 3c6b831c26
commit 079bf3cb26
1268 changed files with 50037 additions and 38444 deletions
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24
examples/language/gpt/experiments/auto_parallel/gpt_modules.py
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@@ -8,7 +8,6 @@ from transformers.pytorch_utils import Conv1D
class GPT2MLP(nn.Module):
def __init__(self, intermediate_size, config):
super().__init__()
embed_dim = config.hidden_size
@@ -30,15 +29,15 @@ class GPT2MLP(nn.Module):
# 2. The order of split and view op has been changed in the customized GPT2Attention class, the new
# order is same as megatron-lm gpt model.
class GPT2Attention(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
max_positions = config.max_position_embeddings
self.register_buffer(
"bias",
torch.tril(torch.ones((max_positions, max_positions),
dtype=torch.uint8)).view(1, 1, max_positions, max_positions),
torch.tril(torch.ones((max_positions, max_positions), dtype=torch.uint8)).view(
1, 1, max_positions, max_positions
),
)
self.register_buffer("masked_bias", torch.tensor(-1e4))
@@ -64,7 +63,7 @@ class GPT2Attention(nn.Module):
attn_weights = torch.matmul(query, key.transpose(-1, -2))
if self.scale_attn_weights:
attn_weights = attn_weights / (value.size(-1)**0.5)
attn_weights = attn_weights / (value.size(-1) ** 0.5)
# Layer-wise attention scaling
if self.scale_attn_by_inverse_layer_idx:
@@ -72,7 +71,7 @@ class GPT2Attention(nn.Module):
# if only "normal" attention layer implements causal mask
query_length, key_length = query.size(-2), key.size(-2)
causal_mask = self.bias[:, :, key_length - query_length:key_length, :key_length].to(torch.bool)
causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length].to(torch.bool)
attn_weights = torch.where(causal_mask, attn_weights, self.masked_bias.to(attn_weights.dtype))
if attention_mask is not None:
@@ -93,7 +92,7 @@ class GPT2Attention(nn.Module):
def _split_heads(self, tensor, num_heads, attn_head_size):
new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
tensor = tensor.view(new_shape)
return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features)
return tensor.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features)
def _merge_heads(self, tensor, num_heads, attn_head_size):
tensor = tensor.permute(0, 2, 1, 3).contiguous()
@@ -106,10 +105,9 @@ class GPT2Attention(nn.Module):
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
) -> Tuple[Union[torch.Tensor, Tuple[torch.Tensor]], ...]:
qkv = self.c_attn(hidden_states)
query, key, value = self._split_heads(qkv, self.num_heads, 3 * self.head_dim).split(self.head_dim, dim=3)
present = (key, value)
(key, value)
attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
attn_output = self._merge_heads(attn_output, self.num_heads, self.head_dim)
attn_output = self.c_proj(attn_output)
@@ -117,7 +115,6 @@ class GPT2Attention(nn.Module):
class GPT2Block(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
hidden_size = config.hidden_size
@@ -152,7 +149,6 @@ class GPT2Block(nn.Module):
class GPT2Model(GPT2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
@@ -189,11 +185,9 @@ class GPT2Model(GPT2PreTrainedModel):
# GPT2Attention mask.
attention_mask = attention_mask.view(batch_size, -1)
attention_mask = attention_mask[:, None, None, :]
attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * -10000.0
encoder_attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
@@ -217,7 +211,6 @@ class GPT2Model(GPT2PreTrainedModel):
class GPT2LMHeadModel(GPT2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.transformer = GPT2Model(config)
@@ -241,7 +234,6 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
class GPTLMLoss(nn.Module):
def __init__(self):
super().__init__()
self.loss_fn = nn.CrossEntropyLoss()