| | |
|---|
| | | |
|---|
| | | if self.batch_type != "example": |
|---|
| | | b, t = outputs["input_ids"].shape |
|---|
| | | if b * t > self.batch_size * self.batch_size_scale_ratio_max: |
|---|
| | | beg = torch.randint(0, 2, ()).item() |
|---|
| | | if b < 2: |
|---|
| | | beg = 0 |
|---|
| | | if b > 1 and b * t > self.batch_size * self.batch_size_scale_ratio_max: |
|---|
| | | # beg = torch.randint(0, 2, ()).item() |
|---|
| | | # if b < 2: |
|---|
| | | # beg = 0 |
|---|
| | | logging.info( |
|---|
| | | f"Warning, b * t: {b * t} > {self.batch_size_scale_ratio_max} * {self.batch_size}, b: {b}, t: {t}, drop half data {idx}th, beg:{beg}" |
|---|
| | | f"Warning, b*t: {b}*{t}={b * t} > batch_size*relax: {self.batch_size_scale_ratio_max}*{self.batch_size}={self.batch_size_scale_ratio_max*self.batch_size}, drop half data {idx}th, beg:{beg}" |
|---|
| | | ) |
|---|
| | | samples = samples[beg : beg + b : 2] |
|---|
| | | # samples = samples[beg : beg + b : 2] |
|---|
| | | samples = samples[:-1] |
|---|
| | | continue |
|---|
| | | |
|---|
| | | break |
|---|
