mirror of
https://github.com/amazon-science/chronos-forecasting
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223e576e2e
*Description of changes:* This splits `input_transform` into `context_input_transform` and `label_input_transform`. Previously, `input_transform` was being used for both context and label during training which would lead to incorrect results where `prediction_length` > `context_length`. TODO: - [x] Update docstrings - [x] Test the training script By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. --------- Co-authored-by: Abdul Fatir Ansari <ansarnd@amazon.com>
246 lines
7.6 KiB
Python
246 lines
7.6 KiB
Python
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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# SPDX-License-Identifier: Apache-2.0
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from pathlib import Path
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from typing import Tuple
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import torch
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import pytest
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from chronos import ChronosConfig, ChronosPipeline, MeanScaleUniformBins
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@pytest.mark.parametrize("n_numerical_tokens", [5, 10, 27])
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@pytest.mark.parametrize("n_special_tokens", [2, 5, 13])
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def test_tokenizer_consistency(n_numerical_tokens: int, n_special_tokens: int):
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n_tokens = n_numerical_tokens + n_special_tokens
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config = ChronosConfig(
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tokenizer_class="MeanScaleUniformBins",
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tokenizer_kwargs=dict(low_limit=-1.0, high_limit=1.0),
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n_tokens=n_tokens,
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n_special_tokens=n_special_tokens,
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pad_token_id=0,
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eos_token_id=1,
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use_eos_token=True,
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model_type="seq2seq",
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context_length=512,
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prediction_length=64,
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num_samples=20,
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temperature=1.0,
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top_k=50,
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top_p=1.0,
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)
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tokenizer = config.create_tokenizer()
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assert isinstance(tokenizer, MeanScaleUniformBins)
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context = tokenizer.centers.unsqueeze(0) # add batch dimension
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scale = torch.ones((1,)) # fix the scale to one to turn off scaling
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token_ids, _, _ = tokenizer._input_transform(context, scale=scale)
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samples = tokenizer.output_transform(
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token_ids.unsqueeze(1), # add sample dimension
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scale=scale,
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)
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assert (samples[0, 0, :] == context).all()
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@pytest.mark.xfail
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@pytest.mark.parametrize("n_numerical_tokens", [5, 10, 27])
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@pytest.mark.parametrize("n_special_tokens", [2, 5, 13])
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@pytest.mark.parametrize("use_eos_token", [False, True])
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def test_tokenizer_fixed_data(
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n_numerical_tokens: int, n_special_tokens: int, use_eos_token: bool
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):
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n_tokens = n_numerical_tokens + n_special_tokens
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context_length = 3
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config = ChronosConfig(
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tokenizer_class="MeanScaleUniformBins",
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tokenizer_kwargs=dict(low_limit=-1.0, high_limit=1.0),
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n_tokens=n_tokens,
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n_special_tokens=n_special_tokens,
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pad_token_id=0,
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eos_token_id=1,
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use_eos_token=use_eos_token,
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model_type="seq2seq",
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context_length=512,
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prediction_length=64,
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num_samples=20,
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temperature=1.0,
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top_k=50,
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top_p=1.0,
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)
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tokenizer = config.create_tokenizer()
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context = torch.tensor(
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[
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[-3.7, 3.7],
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[-42.0, 42.0],
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]
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)
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batch_size, _ = context.shape
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token_ids, attention_mask, scale = tokenizer.context_input_transform(context)
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assert token_ids.shape == (batch_size, context_length + 1 * use_eos_token)
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assert all(token_ids[:, 0] == torch.tensor([0]).repeat(batch_size))
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assert all(token_ids[:, 1] == torch.tensor([n_special_tokens]).repeat(batch_size))
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assert all(token_ids[:, 2] == torch.tensor([n_tokens - 1]).repeat(batch_size))
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if use_eos_token:
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assert all(token_ids[:, 3] == torch.tensor([1]).repeat(batch_size))
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samples = tokenizer.output_transform(
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torch.arange(n_special_tokens, n_tokens).unsqueeze(0).repeat(batch_size, 1, 1),
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tokenizer_state=scale,
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)
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assert (samples[:, 0, [0, -1]] == context).all()
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@pytest.mark.xfail
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@pytest.mark.parametrize("use_eos_token", [False, True])
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def test_tokenizer_random_data(use_eos_token: bool):
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context_length = 8
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n_tokens = 256
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n_special_tokens = 2
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config = ChronosConfig(
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tokenizer_class="MeanScaleUniformBins",
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tokenizer_kwargs=dict(low_limit=-1.0, high_limit=1.0),
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n_tokens=n_tokens,
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n_special_tokens=n_special_tokens,
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pad_token_id=0,
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eos_token_id=1,
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use_eos_token=use_eos_token,
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model_type="seq2seq",
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context_length=context_length,
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prediction_length=64,
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num_samples=20,
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temperature=1.0,
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top_k=50,
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top_p=1.0,
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)
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tokenizer = config.create_tokenizer()
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context = torch.tensor(
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[
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[torch.nan, torch.nan, 1.0, 1.1, torch.nan, 2.0],
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[3.0, torch.nan, 3.9, 4.0, 4.1, 4.9],
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]
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)
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token_ids, attention_mask, scale = tokenizer.context_input_transform(context)
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assert token_ids.shape == (
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*context.shape[:-1],
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context_length + 1 * use_eos_token,
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)
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assert attention_mask.shape == (
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*context.shape[:-1],
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context_length + 1 * use_eos_token,
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)
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assert scale.shape == context.shape[:1]
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sample_ids = torch.randint(low=n_special_tokens, high=n_tokens, size=(2, 10, 4))
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sample_ids[0, 0, 0] = n_special_tokens
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sample_ids[-1, -1, -1] = n_tokens - 1
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samples = tokenizer.output_transform(sample_ids, scale)
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assert samples.shape == (2, 10, 4)
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def validate_tensor(samples: torch.Tensor, shape: Tuple[int, ...]) -> None:
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assert isinstance(samples, torch.Tensor)
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assert samples.shape == shape
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@pytest.mark.parametrize("torch_dtype", [torch.float32, torch.bfloat16])
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def test_pipeline_predict(torch_dtype: str):
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pipeline = ChronosPipeline.from_pretrained(
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Path(__file__).parent / "dummy-chronos-model",
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device_map="cpu",
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torch_dtype=torch_dtype,
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)
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context = 10 * torch.rand(size=(4, 16)) + 10
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# input: tensor of shape (batch_size, context_length)
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samples = pipeline.predict(context, num_samples=12, prediction_length=3)
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validate_tensor(samples, (4, 12, 3))
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with pytest.raises(ValueError):
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samples = pipeline.predict(context, num_samples=7, prediction_length=65)
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samples = pipeline.predict(
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context, num_samples=7, prediction_length=65, limit_prediction_length=False
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)
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validate_tensor(samples, (4, 7, 65))
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# input: batch_size-long list of tensors of shape (context_length,)
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samples = pipeline.predict(list(context), num_samples=12, prediction_length=3)
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validate_tensor(samples, (4, 12, 3))
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with pytest.raises(ValueError):
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samples = pipeline.predict(list(context), num_samples=7, prediction_length=65)
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samples = pipeline.predict(
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list(context),
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num_samples=7,
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prediction_length=65,
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limit_prediction_length=False,
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)
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validate_tensor(samples, (4, 7, 65))
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# input: tensor of shape (context_length,)
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samples = pipeline.predict(context[0, ...], num_samples=12, prediction_length=3)
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validate_tensor(samples, (1, 12, 3))
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with pytest.raises(ValueError):
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samples = pipeline.predict(context[0, ...], num_samples=7, prediction_length=65)
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samples = pipeline.predict(
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context[0, ...],
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num_samples=7,
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prediction_length=65,
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limit_prediction_length=False,
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)
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validate_tensor(samples, (1, 7, 65))
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@pytest.mark.parametrize("torch_dtype", [torch.float32, torch.bfloat16])
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def test_pipeline_embed(torch_dtype: str):
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pipeline = ChronosPipeline.from_pretrained(
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Path(__file__).parent / "dummy-chronos-model",
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device_map="cpu",
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torch_dtype=torch_dtype,
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)
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d_model = pipeline.model.model.config.d_model
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context = 10 * torch.rand(size=(4, 16)) + 10
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expected_embed_length = 16 + (1 if pipeline.model.config.use_eos_token else 0)
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# input: tensor of shape (batch_size, context_length)
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embedding, scale = pipeline.embed(context)
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validate_tensor(embedding, (4, expected_embed_length, d_model))
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validate_tensor(scale, (4,))
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# input: batch_size-long list of tensors of shape (context_length,)
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embedding, scale = pipeline.embed(list(context))
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validate_tensor(embedding, (4, expected_embed_length, d_model))
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validate_tensor(scale, (4,))
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# input: tensor of shape (context_length,)
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embedding, scale = pipeline.embed(context[0, ...])
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validate_tensor(embedding, (1, expected_embed_length, d_model))
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validate_tensor(scale, (1,))
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