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log tensors #27

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log tensors #27

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38 changes: 38 additions & 0 deletions src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,6 +47,34 @@
# See all GPTBigCode models at https://huggingface.co/models?filter=gpt_bigcode
]

DEBUG_DIR = "/app/dataset/debug_logs/2/"
DEBUG_LEVEL = 1000

def log_tensor(tensor, name):
mu = tensor.mean().item()
std = tensor.std().item()
mini = tensor.min().item()
maxi = tensor.max().item()
# Get sample
target_samples = 2 ** (DEBUG_LEVEL - 3)
step = max(tensor.numel() // target_samples, 1)
while step > 1 and any(step % s == 0 and s > 1 for s in tuple(tensor.shape)):
step -= 1
samples = tensor.flatten()[: target_samples * step : step].cpu()

# print(f"{name}: shape {tensor.shape} mu: {mu:.5f} std: {std:.5f} min: {mini:.5f} max: {maxi:.5f}")
torch.save({
'name': name,
'shape': tensor.shape,
'dtype': tensor.dtype,
'device': tensor.device,
'mu': mu,
'std': std,
'min': mini,
'max': maxi,
'samples': samples,
'step': step
}, f"{DEBUG_DIR}/{name}.pt")

# Fused kernels
# Use separate functions for each case because conditionals prevent kernel fusion.
Expand Down Expand Up @@ -286,8 +314,12 @@ def forward(
query = _apply_rotary_embeddings(query, rotary_embedding_frequencies_q)
key = _apply_rotary_embeddings(key, rotary_embedding_frequencies_k)

log_tensor(query, f"layer {self.layer_idx} attn query")
log_tensor(key, f"layer {self.layer_idx} attn key")
log_tensor(value, f"layer {self.layer_idx} attn value")
attn_output, attn_weights = self._attn(query, key.transpose(-1, -2), value, attention_mask, head_mask)

log_tensor(attn_output, f"layer {self.layer_idx} attn context")
attn_output = self.c_proj(attn_output)
attn_output = self.resid_dropout(attn_output)

Expand Down Expand Up @@ -325,6 +357,7 @@ def forward(self, hidden_states: Optional[Tuple[torch.FloatTensor]]) -> torch.Fl
class GPTBigCodeBlock(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
self.layer_idx = layer_idx
hidden_size = config.hidden_size
self.inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size

Expand Down Expand Up @@ -357,6 +390,7 @@ def forward(
]:
residual = hidden_states
hidden_states = self.ln_1(hidden_states)
log_tensor(hidden_states, f"layer {self.layer_idx} Layer norm 1")
attn_outputs = self.attn(
hidden_states,
layer_past=layer_past,
Expand All @@ -369,8 +403,10 @@ def forward(
)
attn_output = attn_outputs[0] # output_attn: a, present, (attentions)
outputs = attn_outputs[1:]
log_tensor(attn_output, f"layer {self.layer_idx} Attn output")
# residual connection
hidden_states = attn_output + residual
log_tensor(hidden_states, f"layer {self.layer_idx} Attn residual")

if encoder_hidden_states is not None:
# add one self-attention block for cross-attention
Expand Down Expand Up @@ -398,7 +434,9 @@ def forward(

residual = hidden_states
hidden_states = self.ln_2(hidden_states)
log_tensor(hidden_states, f"layer {self.layer_idx} Layer norm 2")
feed_forward_hidden_states = self.mlp(hidden_states)
log_tensor(feed_forward_hidden_states, f"layer {self.layer_idx} MLP output")
# residual connection
hidden_states = residual + feed_forward_hidden_states

Expand Down