import pytest import torch from copy import copy from transformers import AutoTokenizer from text_generation_server.pb import generate_pb2 from text_generation_server.models.seq2seq_lm import Seq2SeqLM, Seq2SeqLMBatch @pytest.fixture(scope="session") def mt0_small_tokenizer(): tokenizer = AutoTokenizer.from_pretrained( "bigscience/mt0-small", padding_side="left" ) tokenizer.bos_token_id = 0 return tokenizer @pytest.fixture(scope="session") def default_seq2seq_lm(): return Seq2SeqLM("bigscience/mt0-small") @pytest.fixture def default_pb_request(default_pb_parameters, default_pb_stop_parameters): return generate_pb2.Request( id=0, inputs="Test", input_chunks=generate_pb2.Input(chunks=[generate_pb2.InputChunk(text="Test")]), prefill_logprobs=True, truncate=100, parameters=default_pb_parameters, stopping_parameters=default_pb_stop_parameters, ) @pytest.fixture def default_pb_batch(default_pb_request): return generate_pb2.Batch(id=0, requests=[default_pb_request], size=1) @pytest.fixture def default_seq2seq_lm_batch(default_pb_batch, mt0_small_tokenizer): return Seq2SeqLMBatch.from_pb( default_pb_batch, mt0_small_tokenizer, torch.float32, torch.device("cpu") ) @pytest.fixture def default_multi_requests_seq2seq_lm_batch(default_pb_request, mt0_small_tokenizer): req_0 = copy(default_pb_request) req_0.id = 1 req_1 = default_pb_request req_1.id = 2 req_1.stopping_parameters.max_new_tokens = 5 batch_pb = generate_pb2.Batch(id=0, requests=[req_0, req_1], size=2) return Seq2SeqLMBatch.from_pb( batch_pb, mt0_small_tokenizer, torch.float32, torch.device("cpu") ) def test_batch_from_pb(default_pb_batch, default_seq2seq_lm_batch): batch = default_seq2seq_lm_batch sequence_length = len(default_seq2seq_lm_batch.input_ids[0]) assert batch.batch_id == default_pb_batch.id assert batch.requests == default_pb_batch.requests assert batch.input_ids.shape == (default_pb_batch.size, sequence_length) assert batch.input_ids[0][-2] == 4268 assert batch.input_ids[0][-1] == 1 assert torch.all(batch.input_ids[0][:-2] == 0) assert torch.all(batch.attention_mask[0][-2:] == 1) assert torch.all(batch.attention_mask[0][:-2] == 0) assert len(batch.decoder_input_ids) == default_pb_batch.size assert batch.decoder_attention_mask is None assert batch.encoder_last_hidden_state is None assert batch.past_key_values is None assert batch.input_lengths == [2] assert batch.decoder_input_lengths == [1] assert len(batch) == default_pb_batch.size assert len(batch.next_token_choosers) == len(batch.stopping_criterias) == len(batch) assert batch.max_input_length == batch.input_lengths[0] assert batch.max_decoder_input_length == batch.decoder_input_lengths[0] def test_batch_concatenate_no_prefill(default_seq2seq_lm_batch): with pytest.raises(ValueError): Seq2SeqLMBatch.concatenate([default_seq2seq_lm_batch, default_seq2seq_lm_batch]) def test_seq2seq_lm_batch_type(default_seq2seq_lm): assert default_seq2seq_lm.batch_type == Seq2SeqLMBatch def test_seq2seq_lm_generate_token(default_seq2seq_lm, default_seq2seq_lm_batch): sequence_length = len(default_seq2seq_lm_batch.input_ids[0]) generations, next_batch, _ = default_seq2seq_lm.generate_token( default_seq2seq_lm_batch ) assert len(generations) == len(next_batch) assert isinstance(next_batch, Seq2SeqLMBatch) assert next_batch.input_ids is None assert torch.equal( next_batch.attention_mask, default_seq2seq_lm_batch.attention_mask ) assert next_batch.input_lengths == default_seq2seq_lm_batch.input_lengths assert next_batch.max_input_length == default_seq2seq_lm_batch.max_input_length assert ( next_batch.next_token_choosers == default_seq2seq_lm_batch.next_token_choosers ) assert next_batch.stopping_criterias == default_seq2seq_lm_batch.stopping_criterias assert len(next_batch.decoder_input_ids) == len(next_batch) assert next_batch.all_decoder_input_ids[0][0] == 0 assert next_batch.all_decoder_input_ids[0][1] == 259 assert next_batch.decoder_attention_mask is None assert next_batch.encoder_last_hidden_state.shape == (1, sequence_length, 512) assert next_batch.decoder_input_lengths == [2] assert next_batch.max_decoder_input_length == 2 assert next_batch.past_key_values is not None assert all( [p[0].shape == (len(next_batch), 6, 1, 64) for p in next_batch.past_key_values] ) assert all( [p[1].shape == (len(next_batch), 6, 1, 64) for p in next_batch.past_key_values] ) assert all( [ p[2].shape == (len(next_batch), 6, sequence_length, 64) for p in next_batch.past_key_values ] ) assert all( [ p[3].shape == (len(next_batch), 6, sequence_length, 64) for p in next_batch.past_key_values ] ) assert all([generation.generated_text is None for generation in generations]) assert all([len(generation.prefill_tokens) == 1 for generation in generations]) assert all( [ token_id.item() == 259 for generation in generations for token_id in generation.tokens.token_ids ] ) assert all( [ token_text == " " for generation in generations for token_text in generation.tokens.texts ] ) assert generations[0].request_id == 0 def test_seq2seq_lm_generate_token_completion( default_seq2seq_lm, default_seq2seq_lm_batch ): next_batch = default_seq2seq_lm_batch for _ in range(6): generations, next_batch, _ = default_seq2seq_lm.generate_token(next_batch) assert len(generations) == len(next_batch) generations, next_batch, _ = default_seq2seq_lm.generate_token(next_batch) assert next_batch is None assert len(generations) == 1 assert generations[0].generated_text.text == "a few weeks" assert generations[0].request_id == default_seq2seq_lm_batch.requests[0].id assert generations[0].generated_text.generated_tokens == 7 def test_seq2seq_lm_generate_token_completion_multi( default_seq2seq_lm, default_multi_requests_seq2seq_lm_batch ): next_batch = default_multi_requests_seq2seq_lm_batch for i in range(4): generations, next_batch, _ = default_seq2seq_lm.generate_token(next_batch) assert len(generations) == len(next_batch) generations, next_batch, _ = default_seq2seq_lm.generate_token(next_batch) assert next_batch is not None assert len(generations) == 2 assert generations[1].generated_text.text == "a few " assert ( generations[1].request_id == default_multi_requests_seq2seq_lm_batch.requests[1].id ) assert generations[1].generated_text.generated_tokens == 5 next_batch = next_batch.filter([next_batch.requests[0].id]) generations, next_batch, _ = default_seq2seq_lm.generate_token(next_batch) assert len(generations) == len(next_batch) generations, next_batch, _ = default_seq2seq_lm.generate_token(next_batch) assert next_batch is None assert len(generations) == 1 assert generations[0].generated_text.text == "a few weeks" assert ( generations[0].request_id == default_multi_requests_seq2seq_lm_batch.requests[0].id ) assert generations[0].generated_text.generated_tokens == 7 def test_batch_concatenate( default_seq2seq_lm, default_seq2seq_lm_batch, default_multi_requests_seq2seq_lm_batch, ): next_batch_0 = default_seq2seq_lm_batch _, next_batch_0, _ = default_seq2seq_lm.generate_token(next_batch_0) _, next_batch_0, _ = default_seq2seq_lm.generate_token(next_batch_0) next_batch_1 = default_multi_requests_seq2seq_lm_batch _, next_batch_1, _ = default_seq2seq_lm.generate_token(next_batch_1) # Copy hidden state because it is removed from the concatenated branches next_batch_0_encoder_last_hidden_state = next_batch_0.encoder_last_hidden_state next_batch_1_encoder_last_hidden_state = next_batch_1.encoder_last_hidden_state # Clone past_key_values before concatenating to compare after, # because they are removed from the concatenated batches next_batch_0_past_key_values = [ [t.clone() for t in layer] for layer in next_batch_0.past_key_values ] next_batch_1_past_key_values = [ [t.clone() for t in layer] for layer in next_batch_1.past_key_values ] next_batch = Seq2SeqLMBatch.concatenate([next_batch_0, next_batch_1]) assert next_batch.batch_id == 0 assert torch.equal( next_batch.decoder_input_ids[0], next_batch_0.decoder_input_ids[0] ) assert next_batch.all_decoder_input_ids[1][0] == 0 assert next_batch.all_decoder_input_ids[2][0] == 0 assert torch.equal( next_batch.decoder_input_ids[1:, -2:], next_batch_1.decoder_input_ids ) assert torch.all(next_batch.decoder_attention_mask[0, :3] == 1) assert torch.all(next_batch.decoder_attention_mask[0, 3:] == 0) assert torch.all(next_batch.decoder_attention_mask[1:, 0] == 0) assert torch.all(next_batch.decoder_attention_mask[1:, 1:3] == 1) assert torch.equal( next_batch.encoder_last_hidden_state[0], next_batch_0_encoder_last_hidden_state[0, -2:], ) assert torch.equal( next_batch.encoder_last_hidden_state[1:], next_batch_1_encoder_last_hidden_state[:, -2:], ) assert next_batch.input_lengths == [2, 2, 2] assert next_batch.decoder_input_lengths == [3, 2, 2] assert next_batch.max_input_length == 2 assert next_batch.max_decoder_input_length == 3 assert next_batch.requests[0] == next_batch_0.requests[0] assert next_batch.requests[1:] == next_batch_1.requests assert next_batch.next_token_choosers[0] == next_batch_0.next_token_choosers[0] assert next_batch.next_token_choosers[1:] == next_batch_1.next_token_choosers assert next_batch.stopping_criterias[0] == next_batch_0.stopping_criterias[0] assert next_batch.stopping_criterias[1:] == next_batch_1.stopping_criterias assert next_batch.past_key_values is not None assert all( [p[0].shape == (len(next_batch), 6, 2, 64) for p in next_batch.past_key_values] ) assert all( [p[1].shape == (len(next_batch), 6, 2, 64) for p in next_batch.past_key_values] ) assert all( [p[2].shape == (len(next_batch), 6, 2, 64) for p in next_batch.past_key_values] ) assert all( [p[3].shape == (len(next_batch), 6, 2, 64) for p in next_batch.past_key_values] ) for i, past in enumerate(next_batch.past_key_values): assert torch.equal(next_batch_0_past_key_values[i][0][0, :, -2:, :], past[0][0]) assert torch.equal( next_batch_1_past_key_values[i][0][:, :, -1:, :], past[0][1:, :, -1:, :] ) assert torch.equal(next_batch_0_past_key_values[i][1][0, :, -2:, :], past[1][0]) assert torch.equal( next_batch_1_past_key_values[i][1][:, :, -1:, :], past[1][1:, :, -1:, :] ) assert torch.equal(next_batch_0_past_key_values[i][2][0, :, -2:, :], past[2][0]) assert torch.equal( next_batch_1_past_key_values[i][2][:, :, -2:, :], past[2][1:] ) assert torch.equal(next_batch_0_past_key_values[i][3][0, :, -2:, :], past[3][0]) assert torch.equal( next_batch_1_past_key_values[i][3][:, :, -2:, :], past[3][1:] ) for _ in range(3): generations, next_batch, _ = default_seq2seq_lm.generate_token(next_batch) assert len(generations) == len(next_batch) generations, next_batch, _ = default_seq2seq_lm.generate_token(next_batch) assert next_batch is not None assert len(generations) == 3 assert generations[2].generated_text.text == "a few " assert ( generations[2].request_id == default_multi_requests_seq2seq_lm_batch.requests[1].id ) assert generations[2].generated_text.generated_tokens == 5 next_batch = next_batch.filter( [next_batch.requests[0].id, next_batch.requests[1].id] ) generations, next_batch, _ = default_seq2seq_lm.generate_token(next_batch) assert next_batch is not None assert len(generations) == 2 assert generations[0].generated_text.text == "a few weeks" assert generations[0].request_id == default_seq2seq_lm_batch.requests[0].id assert generations[0].generated_text.generated_tokens == 7 next_batch = next_batch.filter([next_batch.requests[1].id]) generations, next_batch, _ = default_seq2seq_lm.generate_token(next_batch) assert next_batch is None assert len(generations) == 1 assert generations[0].generated_text.text == "a few weeks" assert ( generations[0].request_id == default_multi_requests_seq2seq_lm_batch.requests[0].id ) assert generations[0].generated_text.generated_tokens == 7