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#!/usr/bin/env python3
from transformers import SpeechEncoderDecoderModel, FlaxSpeechEncoderDecoderModel
import tempfile
import random
import numpy as np
import torch
import optax
import jax
from flax.training.common_utils import onehot
from flax.traverse_util import flatten_dict
def ids_tensor(shape, vocab_size, rng=None):
"""Creates a random int32 tensor of the shape within the vocab size."""
if rng is None:
rng = random.Random()
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.randint(0, vocab_size - 1))
output = np.array(values).reshape(shape)
return output
def random_attention_mask(shape, rng=None):
attn_mask = ids_tensor(shape, vocab_size=2, rng=rng)
# make sure that at least one token is attended to for each batch
attn_mask[:, -1] = 1
return attn_mask
def floats_tensor(shape, scale=1.0, rng=None):
"""Creates a random float32 tensor"""
if rng is None:
rng = random.Random()
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.random() * scale)
return np.array(values, dtype=np.float32).reshape(shape)
def shift_tokens_right(input_ids: np.array, pad_token_id: int, decoder_start_token_id: int) -> np.ndarray:
"""
Shift input ids one token to the right.
"""
shifted_input_ids = np.zeros_like(input_ids)
shifted_input_ids[:, 1:] = input_ids[:, :-1]
shifted_input_ids[:, 0] = decoder_start_token_id
shifted_input_ids = np.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids)
return shifted_input_ids
def assert_almost_equals(a: np.ndarray, b: np.ndarray, tol: float = 4e-2):
diff = np.abs((a - b)).max()
if diff < tol:
print(f"✅ Difference between Flax and PyTorch is {diff} (< {tol})")
else:
print(f"❌ Difference between Flax and PyTorch is {diff} (>= {tol})")
def assert_dict_equal(a: dict, b: dict, tol: float = 4e-2):
if a.keys() != b.keys():
print("❌ Dictionary keys for PyTorch and Flax do not match")
for k in a:
diff = np.abs((a[k] - b[k])).max()
if diff < tol:
print(f"✅ Layer {k} diff is {diff} < {tol}).")
else:
print(f"❌ Layer {k} diff is {diff} (>= {tol}).")
def main():
encoder_id = "hf-internal-testing/tiny-random-wav2vec2"
decoder_id = "hf-internal-testing/tiny-random-bart"
use_decoder_attention_mask = False
freeze_feature_encoder = False
pt_model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(encoder_id, decoder_id,
encoder_add_adapter=True)
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
fx_model = FlaxSpeechEncoderDecoderModel.from_pretrained(tmpdirname, from_pt=True)
batch_size = 13
input_values = floats_tensor([batch_size, 512], fx_model.config.encoder.vocab_size)
attention_mask = random_attention_mask([batch_size, 512])
label_ids = ids_tensor([batch_size, 4], fx_model.config.decoder.vocab_size)
decoder_input_ids = shift_tokens_right(input_ids=label_ids, pad_token_id=fx_model.config.decoder.pad_token_id,
decoder_start_token_id=fx_model.config.decoder.decoder_start_token_id)
decoder_attention_mask = random_attention_mask([batch_size, 4])
fx_inputs = {
"inputs": input_values,
"attention_mask": attention_mask,
"decoder_input_ids": decoder_input_ids,
}
if use_decoder_attention_mask:
fx_inputs["decoder_attention_mask"] = decoder_attention_mask
pt_inputs = {k: torch.tensor(v.tolist()) for k, v in fx_inputs.items()}
pt_inputs["labels"] = torch.tensor(label_ids.tolist())
fx_outputs = fx_model(**fx_inputs)
fx_logits = fx_outputs.logits
if freeze_feature_encoder:
pt_model.freeze_feature_encoder()
pt_outputs = pt_model(**pt_inputs)
pt_logits = pt_outputs.logits
pt_loss = pt_outputs.loss
print("--------------------------Checking logits match--------------------------")
print(f"Flax logits shape: {fx_logits.shape}, PyTorch logits shape: {pt_logits.shape}")
assert_almost_equals(fx_logits, pt_logits.detach().numpy())
def fx_train_step(fx_model, batch, freeze_feature_encoder=False):
def compute_loss(params):
label_ids = batch.pop('label_ids')
logits = fx_model(**batch, params=params,
freeze_feature_encoder=freeze_feature_encoder).logits
vocab_size = logits.shape[-1]
targets = onehot(label_ids, vocab_size)
loss = optax.softmax_cross_entropy(logits, targets)
return loss.mean()
grad_fn = jax.value_and_grad(compute_loss)
loss, grad = grad_fn(fx_model.params)
return loss, grad
fx_inputs["label_ids"] = label_ids
fx_loss, fx_grad = fx_train_step(fx_model, fx_inputs, freeze_feature_encoder=freeze_feature_encoder)
print("--------------------------Checking losses match--------------------------")
print(f"Flax loss: {fx_loss}, PyTorch loss: {pt_loss}")
assert_almost_equals(fx_loss, pt_loss.detach().numpy())
pt_loss.backward()
pt_grad_dict = {k: v.grad if v.grad is not None else torch.zeros_like(v) for k, v in pt_model.named_parameters()}
for k in pt_model.state_dict():
if k not in pt_grad_dict:
# set any unused parameters to zero in the grad-dict
# these won't be compared to the Flax model, but required for loading the PT model from state-dict
pt_grad_dict[k] = torch.zeros_like(pt_model.state_dict()[k])
pt_model.state_dict()[k] = pt_grad_dict[k]
pt_model.load_state_dict(pt_grad_dict)
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(tmpdirname)
pt_grad_model_to_fx = FlaxSpeechEncoderDecoderModel.from_pretrained(tmpdirname, from_pt=True)
pt_grad_to_fx = pt_grad_model_to_fx.params
fx_grad = flatten_dict(fx_grad)
pt_grad_to_fx = flatten_dict(pt_grad_to_fx)
print("--------------------------Checking gradients match--------------------------")
assert_dict_equal(fx_grad, pt_grad_to_fx)
if __name__ == "__main__":
main()
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