mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-09-11 12:24:53 +00:00
Upgrade tests (still missing load tests for some reason).
This commit is contained in:
Nicolas Patry
parent
ccbfc05db5
commit
99771cfad5
@ -0,0 +1,65 @@
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{
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"details": {
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"best_of_sequences": null,
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"finish_reason": "stop_sequence",
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"generated_tokens": 6,
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"prefill": [
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{
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"id": 1,
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"logprob": null,
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"text": "<s>"
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},
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{
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"id": 3735,
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"logprob": -10.5,
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"text": "Test"
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},
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{
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"id": 2159,
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"logprob": -12.140625,
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"text": "request"
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}
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],
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"seed": 0,
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"tokens": [
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{
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"id": 13,
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"logprob": -1.0654297,
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"special": false,
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"text": "\n"
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},
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{
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"id": 1014,
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"logprob": -2.7460938,
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"special": false,
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"text": "The"
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},
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{
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"id": 6032,
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"logprob": -1.359375,
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"special": false,
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"text": " purpose"
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},
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{
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"id": 302,
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"logprob": 0.0,
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"special": false,
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"text": " of"
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},
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{
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"id": 456,
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"logprob": 0.0,
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"special": false,
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"text": " this"
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},
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{
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"id": 1369,
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"logprob": -0.40063477,
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"special": false,
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"text": " test"
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}
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],
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"top_tokens": null
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},
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"generated_text": "Test request\nThe purpose of this test"
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}
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@ -0,0 +1,73 @@
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{
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"details": {
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"best_of_sequences": null,
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"finish_reason": "length",
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"generated_tokens": 10,
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"prefill": [],
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"seed": null,
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"tokens": [
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{
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"id": 13,
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"logprob": -0.00756073,
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"special": false,
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"text": "\n"
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},
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{
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"id": 13,
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"logprob": -0.20117188,
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"special": false,
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"text": "\n"
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},
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{
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"id": 16114,
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"logprob": -1.2597656,
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"special": false,
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"text": "Once"
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},
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{
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"id": 3714,
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"logprob": -0.20825195,
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"special": false,
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"text": " upon"
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},
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{
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"id": 264,
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"logprob": -0.00178051,
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"special": false,
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"text": " a"
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},
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{
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"id": 727,
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"logprob": -0.011955261,
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"special": false,
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"text": " time"
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},
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{
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"id": 28725,
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"logprob": -0.17541504,
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"special": false,
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"text": ","
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},
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{
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"id": 736,
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"logprob": -0.91308594,
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"special": false,
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"text": " there"
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},
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{
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"id": 403,
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"logprob": -0.058410645,
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"special": false,
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"text": " was"
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},
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{
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"id": 264,
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"logprob": -0.009689331,
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"special": false,
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"text": " a"
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}
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],
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"top_tokens": null
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},
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"generated_text": "\n\nOnce upon a time, there was a"
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}
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@ -33,7 +33,9 @@ async def test_idefics(idefics, response_snapshot):
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)
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assert response.details.generated_tokens == 10
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assert response.generated_text == "\n\nDeep learning is a new type of machine"
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assert (
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response.generated_text == " \nAssistant: A rooster stands"
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), f"{repr(response.generated_text)}"
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assert response == response_snapshot
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@ -49,7 +51,9 @@ async def test_idefics_load(idefics, generate_load, response_snapshot):
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generated_texts = [r.generated_text for r in responses]
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assert generated_texts[0] == "\n\nDeep learning is a new type of machine"
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assert (
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generated_texts[0] == " \nAssistant: A rooster stands"
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), f"{response.generated_text}"
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assert len(generated_texts) == 4
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assert generated_texts, all(
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[text == generated_texts[0] for text in generated_texts]
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@ -13,7 +13,7 @@ def get_chicken():
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def flash_llava_next_handle(launcher):
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with launcher(
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"llava-hf/llava-v1.6-mistral-7b-hf",
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num_shard=4,
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num_shard=1,
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max_input_length=4000,
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max_total_tokens=4096,
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) as handle:
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@ -34,7 +34,9 @@ async def test_flash_llava_next_simple(flash_llava_next, response_snapshot):
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f"User:Can you tell me a very short story based on the image?",
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max_new_tokens=10,
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)
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assert response.generated_text == "toto"
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assert (
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response.generated_text == "\n\nOnce upon a time, there was a"
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), f"{repr(response.generated_text)}"
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assert response.details.generated_tokens == 10
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assert response == response_snapshot
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@ -58,7 +60,7 @@ async def test_flash_llava_next_all_params(flash_llava_next, response_snapshot):
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seed=0,
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)
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assert response.details.generated_tokens == 5
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assert response.details.generated_tokens == 6
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assert response == response_snapshot
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@ -75,7 +77,7 @@ async def test_flash_llava_next_load(
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n=4,
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)
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generated_texts = [r.generated_text for r in responses]
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assert generated_texts[0] == "\n\nDeep learning is a new type of machine"
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assert generated_texts[0] == "\n\nOnce upon a time, there was a"
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assert len(generated_texts) == 4
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assert all([r.generated_text == generated_texts[0] for r in responses])
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@ -413,7 +413,10 @@ class FlashMistralForCausalLM(torch.nn.Module):
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super().__init__()
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self.embed_tokens = TensorParallelEmbedding(
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prefix=f"{prefix}.model.embed_tokens", weights=weights
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prefix=(
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"model.embed_tokens" if not prefix else f"{prefix}.model.embed_tokens"
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),
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weights=weights,
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)
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self.model = MistralModel(
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prefix="model" if not prefix else f"{prefix}.model",
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@ -1047,12 +1047,7 @@ class FlashCausalLM(Model):
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batch.input_ids = next_input_ids[accepted_ids.cumsum(dim=-1) - 1]
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batch.speculative_ids = speculative_ids
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batch.position_ids = next_position_ids + accepted_ids
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try:
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batch.input_lengths_tensor += accepted_ids
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except Exception:
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import ipdb
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ipdb.set_trace()
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batch.input_lengths_tensor += accepted_ids
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batch.slot_indices += accepted_ids
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if prefill and prefill_logprobs:
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@ -82,7 +82,7 @@ class IDEFICSSharded(IdeficsCausalLM):
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model = IdeficsForVisionText2Text(config, weights)
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torch.distributed.barrier(group=self.process_group)
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super(VlmCausalLM, self).__init__(
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super(IdeficsCausalLM, self).__init__(
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model=model,
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tokenizer=tokenizer,
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requires_padding=True,
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@ -81,850 +81,10 @@ class IdeficsCausalLMBatch(Batch):
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cls,
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pb: generate_pb2.Batch,
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tokenizer: PreTrainedTokenizerBase,
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processor: ProcessorMixin, # Hack
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dtype: torch.dtype,
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device: torch.device,
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) -> "IdeficsCausalLMBatch":
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inputs = []
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next_token_choosers = []
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stopping_criterias = []
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prefix_offsets = []
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read_offsets = []
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requests_idx_mapping = {}
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# Parse batch
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max_truncation = 0
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padding_right_offset = 0
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max_decode_tokens = 0
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for i, r in enumerate(pb.requests):
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requests_idx_mapping[r.id] = i
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inputs.append(r.inputs)
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next_token_choosers.append(
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NextTokenChooser.from_pb(r.parameters, device, tokenizer)
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)
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stopping_criteria = StoppingCriteria.from_pb(
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r.stopping_parameters, tokenizer
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)
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stopping_criterias.append(stopping_criteria)
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max_truncation = max(max_truncation, r.truncate)
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max_decode_tokens += stopping_criteria.max_new_tokens
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padding_right_offset = max(
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padding_right_offset, stopping_criteria.max_new_tokens
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)
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prompts = []
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for inp in inputs:
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# Each input is encoded into a list, where each element of this input list is either a string or a URL
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prompts.append(split(inp))
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# The processor replaces the call to tokenizer, and
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# a/ takes care of fetching images from the URL
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# b/ generate the correct input_ids, attention_mask, pixel_values, image_attention_mask to feed to the model
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tokenized_inputs = processor(
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prompts,
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return_tensors="pt",
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padding=True,
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truncation=True,
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max_length=max_truncation,
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add_end_of_utterance_token=False, # Already taken care of inside the prompts, so bypassing the processor's handling of this token
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).to(device)
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for _ in pb.requests:
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input_len = tokenized_inputs["input_ids"].shape[1]
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prefix_offsets.append(
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input_len - 5
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) # To decode without potential fallbacks errors
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read_offsets.append(
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input_len
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) # To decode without potential fallbacks errors
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input_lengths = tokenized_inputs["attention_mask"].sum(1)
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max_input_length = input_lengths.max()
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input_ids = tokenized_inputs["input_ids"]
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pixel_values = tokenized_inputs.get("pixel_values", None)
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image_hidden_states = None
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# Allocate maximum attention_mask
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attention_mask = input_ids.new_zeros(
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(pb.size, max_input_length + padding_right_offset)
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)
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# Copy tokenizer attention_mask into fully allocated attention_mask
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attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"]
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# Do the same for image_attention_mask
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if pixel_values is None:
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image_attention_mask = None
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else:
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image_attention_mask = input_ids.new_zeros(
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(
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pb.size,
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max_input_length + padding_right_offset,
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pixel_values.size(1),
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)
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)
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image_attention_mask[:, :max_input_length, :] = tokenized_inputs[
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"image_attention_mask"
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]
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position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
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position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
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all_input_ids = tokenized_inputs["input_ids"].T.split(
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1, dim=1
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) # It's input_ids but splitted into a tuple of tensors where each tensor is (seq_len, 1) size. It is then transformed into a list
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max_tokens = len(inputs) * (max_input_length + max_decode_tokens)
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return cls(
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batch_id=pb.id,
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requests=pb.requests,
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requests_idx_mapping=requests_idx_mapping,
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input_ids=input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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pixel_values=pixel_values,
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image_hidden_states=image_hidden_states,
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image_attention_mask=image_attention_mask,
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past_key_values=None,
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all_input_ids=list(all_input_ids),
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input_lengths=input_lengths.tolist(),
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prefix_offsets=prefix_offsets,
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read_offsets=read_offsets,
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next_token_choosers=next_token_choosers,
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stopping_criterias=stopping_criterias,
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max_input_length=max_input_length.item(),
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padding_right_offset=padding_right_offset,
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max_tokens=max_tokens,
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)
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@tracer.start_as_current_span("filter")
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def filter(self, request_ids: List[int]) -> Optional["IdeficsCausalLMBatch"]:
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# It deletes requests from the batch. For instance when client lost connection
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if len(request_ids) == 0:
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raise ValueError("Batch must have at least one request")
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if len(request_ids) == len(self):
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return self
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keep_indices = []
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# New values after filtering
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requests_idx_mapping = {}
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requests = []
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input_lengths = []
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prefix_offsets = []
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read_offsets = []
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all_input_ids = []
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max_input_length = 0
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next_token_choosers = []
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stopping_criterias = []
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total_remaining_decode_tokens = 0
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new_padding_right_offset = 0
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for i, request_id in enumerate(request_ids):
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idx = self.requests_idx_mapping[request_id]
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requests_idx_mapping[request_id] = i
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keep_indices.append(idx)
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requests.append(self.requests[idx])
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prefix_offsets.append(self.prefix_offsets[idx])
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read_offsets.append(self.read_offsets[idx])
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all_input_ids.append(self.all_input_ids[idx])
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request_input_length = self.input_lengths[idx]
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input_lengths.append(request_input_length)
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max_input_length = max(max_input_length, request_input_length)
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next_token_choosers.append(self.next_token_choosers[idx])
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stopping_criteria = self.stopping_criterias[idx]
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stopping_criterias.append(stopping_criteria)
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remaining_decode_tokens = (
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stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
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)
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total_remaining_decode_tokens += remaining_decode_tokens
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new_padding_right_offset = max(
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new_padding_right_offset, remaining_decode_tokens
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)
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# Apply indices to input_ids, attention mask, past key values and other items that need to be cached
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input_ids = self.input_ids[keep_indices]
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position_ids = self.position_ids[keep_indices]
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self.attention_mask = self.attention_mask[
|
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keep_indices,
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-(self.padding_right_offset + max_input_length) : (
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self.attention_mask.shape[1] - self.padding_right_offset
|
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)
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+ new_padding_right_offset,
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]
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# Do the same for pixel_values and image_attention_mask
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pixel_values = self.pixel_values[keep_indices]
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self.image_attention_mask = self.image_attention_mask[
|
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keep_indices,
|
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-(self.padding_right_offset + max_input_length) : (
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self.image_attention_mask.shape[1] - self.padding_right_offset
|
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)
|
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+ new_padding_right_offset,
|
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:,
|
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]
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if self.image_hidden_states is None:
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image_hidden_states = None
|
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else:
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image_hidden_states = self.image_hidden_states[keep_indices]
|
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# Ensure that past_key_values tensors can be updated in-place
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if type(self.past_key_values[0]) == tuple:
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self.past_key_values = [list(layer) for layer in self.past_key_values]
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|
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# Update tensors in-place to allow incremental garbage collection
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past_kv_length = max_input_length - 1
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for layer in self.past_key_values:
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past_keys, past_values = layer
|
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if len(past_keys.shape) == 3:
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# Force past to be of dim [self_size, num_heads, ...] for easy indexing
|
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past_keys = past_keys.view(len(self), -1, *past_keys.shape[-2:])
|
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past_values = past_values.view(len(self), -1, *past_values.shape[-2:])
|
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if self.keys_head_dim_last:
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layer[0] = past_keys[keep_indices, :, -past_kv_length:, :]
|
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else:
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layer[0] = past_keys[keep_indices, :, :, -past_kv_length:]
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del past_keys
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layer[1] = past_values[keep_indices, :, -past_kv_length:, :]
|
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del past_values
|
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|
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max_tokens = len(request_ids) * max_input_length + total_remaining_decode_tokens
|
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|
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self.requests = requests
|
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self.requests_idx_mapping = requests_idx_mapping
|
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self.input_ids = input_ids
|
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self.pixel_values = pixel_values
|
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self.image_hidden_states = image_hidden_states
|
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self.position_ids = position_ids
|
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self.all_input_ids = all_input_ids
|
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self.input_lengths = input_lengths
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self.prefix_offsets = prefix_offsets
|
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self.read_offsets = read_offsets
|
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self.next_token_choosers = next_token_choosers
|
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self.stopping_criterias = stopping_criterias
|
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self.max_input_length = max_input_length
|
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self.padding_right_offset = new_padding_right_offset
|
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self.max_tokens = max_tokens
|
||||
|
||||
return self
|
||||
|
||||
@classmethod
|
||||
@tracer.start_as_current_span("concatenate")
|
||||
def concatenate(
|
||||
cls, batches: List["IdeficsCausalLMBatch"]
|
||||
) -> "IdeficsCausalLMBatch":
|
||||
# It adds new requests to the batch
|
||||
# Used for padding
|
||||
total_batch_size = 0
|
||||
max_input_length = 0
|
||||
max_num_images = 0
|
||||
padding_right_offset = 0
|
||||
for batch in batches:
|
||||
total_batch_size += len(batch)
|
||||
max_input_length = max(max_input_length, batch.max_input_length)
|
||||
max_num_images = max(max_num_images, batch.pixel_values.size(1))
|
||||
padding_right_offset = max(padding_right_offset, batch.padding_right_offset)
|
||||
|
||||
# Batch attributes
|
||||
requests = []
|
||||
requests_idx_mapping = {}
|
||||
input_lengths = []
|
||||
prefix_offsets = []
|
||||
read_offsets = []
|
||||
all_input_ids = []
|
||||
next_token_choosers = []
|
||||
stopping_criterias = []
|
||||
max_tokens = 0
|
||||
|
||||
# Batch tensors
|
||||
input_ids = None
|
||||
attention_mask = None
|
||||
position_ids = None
|
||||
pixel_values = None
|
||||
image_hidden_states = None
|
||||
image_attention_mask = None
|
||||
past_key_values = []
|
||||
|
||||
# Used for slicing correctly inside the tensors
|
||||
# Equivalent to a cumsum on batch sizes
|
||||
start_index = 0
|
||||
for i, batch in enumerate(batches):
|
||||
requests.extend(batch.requests)
|
||||
input_lengths.extend(batch.input_lengths)
|
||||
prefix_offsets.extend(batch.prefix_offsets)
|
||||
read_offsets.extend(batch.read_offsets)
|
||||
all_input_ids.extend(batch.all_input_ids)
|
||||
next_token_choosers.extend(batch.next_token_choosers)
|
||||
stopping_criterias.extend(batch.stopping_criterias)
|
||||
|
||||
if i == 0:
|
||||
requests_idx_mapping = batch.requests_idx_mapping
|
||||
else:
|
||||
# We need to offset the mapping for each batch by the cumulative batch size
|
||||
for k, v in batch.requests_idx_mapping.items():
|
||||
requests_idx_mapping[k] = v + start_index
|
||||
|
||||
# Slicing end index for this batch
|
||||
end_index = start_index + len(batch)
|
||||
|
||||
# We only concatenate batches that did at least one step
|
||||
if batch.past_key_values is None:
|
||||
raise ValueError("only concatenate prefilled batches")
|
||||
|
||||
# Create empty tensor
|
||||
# input_ids is always of shape [batch_size, 1]
|
||||
# We do not need to pad it
|
||||
if input_ids is None:
|
||||
input_ids = batch.input_ids.new_empty((total_batch_size, 1))
|
||||
# Copy to correct indices
|
||||
input_ids[start_index:end_index] = batch.input_ids
|
||||
|
||||
# Create padded tensor
|
||||
if attention_mask is None:
|
||||
attention_mask = batch.attention_mask.new_zeros(
|
||||
(total_batch_size, max_input_length + padding_right_offset),
|
||||
)
|
||||
|
||||
curr_batch_max_num_images = batch.pixel_values.size(1)
|
||||
if pixel_values is None:
|
||||
pixel_values = batch.pixel_values.new_zeros(
|
||||
(total_batch_size, max_num_images, 3, 224, 224)
|
||||
)
|
||||
pixel_values[start_index:end_index, :curr_batch_max_num_images] = (
|
||||
batch.pixel_values
|
||||
)
|
||||
|
||||
if image_attention_mask is None:
|
||||
image_attention_mask = batch.image_attention_mask.new_zeros(
|
||||
(
|
||||
total_batch_size,
|
||||
max_input_length + padding_right_offset,
|
||||
max_num_images,
|
||||
)
|
||||
)
|
||||
|
||||
# We need to slice the attention mask to remove padding from previous steps
|
||||
# and to remove unused allocated space
|
||||
left_offset = max_input_length - batch.max_input_length
|
||||
batch_left_offset = (
|
||||
batch.attention_mask.shape[1]
|
||||
- batch.max_input_length
|
||||
- batch.padding_right_offset
|
||||
)
|
||||
attention_mask[
|
||||
start_index:end_index,
|
||||
left_offset:-padding_right_offset,
|
||||
] = batch.attention_mask[
|
||||
:,
|
||||
batch_left_offset : -batch.padding_right_offset,
|
||||
]
|
||||
image_attention_mask[
|
||||
start_index:end_index,
|
||||
left_offset:-padding_right_offset,
|
||||
:curr_batch_max_num_images,
|
||||
] = batch.image_attention_mask[
|
||||
:, batch_left_offset : -batch.padding_right_offset, :
|
||||
]
|
||||
|
||||
# Create empty tensor
|
||||
# position_ids is always of shape [batch_size, 1]
|
||||
if position_ids is None:
|
||||
position_ids = batch.position_ids.new_empty((total_batch_size, 1))
|
||||
position_ids[start_index:end_index] = batch.position_ids
|
||||
|
||||
# Shenanigans to get dimensions because BLOOM outputs a past with a different shape
|
||||
# BLOOM Keys: [batch_size * num_heads, head_dim, seq_length]
|
||||
# BLOOM Values: [batch_size * num_heads, seq_length, head_dim]
|
||||
# And ensure that we can update tensors in-place
|
||||
if type(batch.past_key_values[0]) == tuple:
|
||||
batch.past_key_values = [
|
||||
[t.view(len(batch), -1, *t.shape[-2:]) for t in layer]
|
||||
for layer in batch.past_key_values
|
||||
]
|
||||
elif len(batch.past_key_values[0][0].shape) == 3:
|
||||
for layer in batch.past_key_values:
|
||||
for k, t in enumerate(layer):
|
||||
layer[k] = t.view(len(batch), -1, *t.shape[-2:])
|
||||
|
||||
# Add eventual padding tokens that were added while concatenating
|
||||
max_tokens += batch.max_tokens + (
|
||||
max_input_length - batch.max_input_length
|
||||
) * len(batch)
|
||||
|
||||
start_index = end_index
|
||||
|
||||
first_past_kvs = batches[0].past_key_values
|
||||
_, num_heads, padded_sequence_length, head_dim = first_past_kvs[0][1].shape
|
||||
|
||||
padded_past_values_shape = (
|
||||
total_batch_size,
|
||||
num_heads,
|
||||
max_input_length - 1,
|
||||
head_dim,
|
||||
)
|
||||
|
||||
if batches[0].keys_head_dim_last:
|
||||
padded_past_keys_shape = padded_past_values_shape
|
||||
else:
|
||||
# seq_length is last for BLOOM
|
||||
padded_past_keys_shape = (
|
||||
total_batch_size,
|
||||
num_heads,
|
||||
head_dim,
|
||||
max_input_length - 1,
|
||||
)
|
||||
|
||||
# Iterate over attention layers
|
||||
# Concatenate past key values layer by layer to allow incremental garbage collection
|
||||
for j in range(len(first_past_kvs)):
|
||||
padded_past_keys = first_past_kvs[j][0].new_zeros(padded_past_keys_shape)
|
||||
start_index = 0
|
||||
for batch in batches:
|
||||
past_keys = batch.past_key_values[j][0]
|
||||
# Clear reference to the original tensor
|
||||
batch.past_key_values[j][0] = None
|
||||
|
||||
# Slicing end index for this batch
|
||||
end_index = start_index + len(batch)
|
||||
# We slice the keys to remove the padding from previous batches
|
||||
past_seq_len = batch.max_input_length - 1
|
||||
if batch.keys_head_dim_last:
|
||||
padded_past_keys[start_index:end_index, :, -past_seq_len:, :] = (
|
||||
past_keys[:, :, -past_seq_len:, :]
|
||||
)
|
||||
else:
|
||||
# BLOOM case
|
||||
padded_past_keys[start_index:end_index, :, :, -past_seq_len:] = (
|
||||
past_keys[:, :, :, -past_seq_len:]
|
||||
)
|
||||
del past_keys
|
||||
|
||||
start_index = end_index
|
||||
|
||||
padded_past_values = first_past_kvs[j][1].new_zeros(
|
||||
padded_past_values_shape
|
||||
)
|
||||
start_index = 0
|
||||
for batch in batches:
|
||||
past_values = batch.past_key_values[j][1]
|
||||
# Clear reference to the original tensor
|
||||
batch.past_key_values[j][1] = None
|
||||
|
||||
# Slicing end index for this batch
|
||||
end_index = start_index + len(batch)
|
||||
# We slice the past values to remove the padding from previous batches
|
||||
past_seq_len = batch.max_input_length - 1
|
||||
padded_past_values[start_index:end_index, :, -past_seq_len:, :] = (
|
||||
past_values[:, :, -past_seq_len:, :]
|
||||
)
|
||||
del past_values
|
||||
|
||||
# Update values
|
||||
start_index = end_index
|
||||
|
||||
past_key_values.append([padded_past_keys, padded_past_values])
|
||||
|
||||
return cls(
|
||||
batch_id=batches[0].batch_id,
|
||||
requests=requests,
|
||||
requests_idx_mapping=requests_idx_mapping,
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
pixel_values=pixel_values,
|
||||
image_hidden_states=image_hidden_states,
|
||||
image_attention_mask=image_attention_mask,
|
||||
past_key_values=past_key_values,
|
||||
all_input_ids=all_input_ids,
|
||||
input_lengths=input_lengths,
|
||||
prefix_offsets=prefix_offsets,
|
||||
read_offsets=read_offsets,
|
||||
next_token_choosers=next_token_choosers,
|
||||
stopping_criterias=stopping_criterias,
|
||||
max_input_length=max_input_length,
|
||||
padding_right_offset=padding_right_offset,
|
||||
keys_head_dim_last=batches[0].keys_head_dim_last,
|
||||
max_tokens=max_tokens,
|
||||
)
|
||||
|
||||
def __len__(self):
|
||||
return len(self.requests)
|
||||
|
||||
|
||||
class IdeficsCausalLM(Model):
|
||||
def __init__(
|
||||
self,
|
||||
model_id: str,
|
||||
revision: Optional[str] = None,
|
||||
quantize: Optional[str] = None,
|
||||
dtype: Optional[torch.dtype] = None,
|
||||
trust_remote_code: bool = False,
|
||||
):
|
||||
from text_generation_server.models.custom_modeling.idefics_modeling import (
|
||||
IdeficsForVisionText2Text,
|
||||
)
|
||||
|
||||
if torch.cuda.is_available():
|
||||
device = torch.device("cuda")
|
||||
dtype = torch.bfloat16 if dtype is None else dtype
|
||||
else:
|
||||
if quantize:
|
||||
raise ValueError("quantization is not available on CPU")
|
||||
|
||||
device = torch.device("cpu")
|
||||
dtype = torch.float32 if dtype is None else dtype
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
padding_side="left",
|
||||
truncation_side="left",
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
self.processor = AutoProcessor.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
padding_side="left",
|
||||
truncation_side="left",
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
model = IdeficsForVisionText2Text.from_pretrained(
|
||||
model_id,
|
||||
revision=revision,
|
||||
torch_dtype=dtype,
|
||||
device_map=(
|
||||
"auto"
|
||||
if torch.cuda.is_available() and torch.cuda.device_count() > 1
|
||||
else None
|
||||
),
|
||||
load_in_8bit=quantize == "bitsandbytes",
|
||||
trust_remote_code=trust_remote_code,
|
||||
)
|
||||
if torch.cuda.is_available() and torch.cuda.device_count() == 1:
|
||||
model = model.cuda()
|
||||
|
||||
if tokenizer.pad_token_id is None:
|
||||
if model.config.pad_token_id is not None:
|
||||
tokenizer.pad_token_id = model.config.pad_token_id
|
||||
elif model.config.eos_token_id is not None:
|
||||
tokenizer.pad_token_id = model.config.eos_token_id
|
||||
elif tokenizer.eos_token_id is not None:
|
||||
tokenizer.pad_token_id = tokenizer.eos_token_id
|
||||
else:
|
||||
tokenizer.add_special_tokens({"pad_token": "<unk>"})
|
||||
|
||||
super(IdeficsCausalLM, self).__init__(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
requires_padding=True,
|
||||
dtype=dtype,
|
||||
device=device,
|
||||
)
|
||||
|
||||
@property
|
||||
def batch_type(self) -> Type[IdeficsCausalLMBatch]:
|
||||
return IdeficsCausalLMBatch
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids,
|
||||
attention_mask,
|
||||
position_ids,
|
||||
pixel_values,
|
||||
image_hidden_states,
|
||||
image_attention_mask,
|
||||
past_key_values: Optional = None,
|
||||
) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
|
||||
# Model Forward
|
||||
kwargs = {
|
||||
"input_ids": input_ids,
|
||||
"attention_mask": attention_mask,
|
||||
"pixel_values": pixel_values,
|
||||
"image_hidden_states": image_hidden_states,
|
||||
"image_attention_mask": image_attention_mask,
|
||||
"past_key_values": past_key_values,
|
||||
"use_cache": True,
|
||||
"return_dict": True,
|
||||
}
|
||||
if self.has_position_ids:
|
||||
kwargs["position_ids"] = position_ids
|
||||
|
||||
outputs, speculative_logits = self.model.forward(**kwargs)
|
||||
return (
|
||||
outputs.logits,
|
||||
speculative_logits,
|
||||
outputs.past_key_values,
|
||||
outputs.image_hidden_states,
|
||||
)
|
||||
|
||||
@tracer.start_as_current_span("generate_token")
|
||||
def generate_token(
|
||||
self, batch: IdeficsCausalLMBatch
|
||||
) -> Tuple[List[Generation], Optional[IdeficsCausalLMBatch], Tuple[int, int]]:
|
||||
start = time.time_ns()
|
||||
# slice the attention mask to the correct shape
|
||||
attention_mask = batch.attention_mask[:, : -batch.padding_right_offset]
|
||||
if batch.image_attention_mask is None:
|
||||
image_attention_mask = None
|
||||
else:
|
||||
if batch.input_ids.size(1) == 1:
|
||||
# THIS is a hack: when calling idefics.generate, the first time, we need the whole image_attention_mask (size bs x max_seq_len x max_num_images),
|
||||
# but the subsequent times, we only need the last attention mask along the `max_seq_len` dimension
|
||||
# this is due to the nature IDEFICS: it's an encoder decoder, and so when decoding, only the currently generated
|
||||
# token need to attend to the encoder hidden states (i.e. the vision encoder)
|
||||
# Also see seq2seq_lm.Seq2SeqLM.generate_token which has roughly the same logic
|
||||
image_attention_mask = batch.image_attention_mask[
|
||||
:, -(batch.padding_right_offset + 1)
|
||||
].unsqueeze(1)
|
||||
else:
|
||||
image_attention_mask = batch.image_attention_mask[
|
||||
:, : -batch.padding_right_offset
|
||||
]
|
||||
|
||||
logits, speculative_logits, past, image_hidden_states = self.forward(
|
||||
input_ids=batch.input_ids,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=batch.position_ids,
|
||||
pixel_values=batch.pixel_values,
|
||||
image_hidden_states=batch.image_hidden_states,
|
||||
image_attention_mask=image_attention_mask,
|
||||
past_key_values=batch.past_key_values,
|
||||
)
|
||||
# Hardcoded remove image tokens
|
||||
logits[:, 32000:32001] = torch.finfo(logits.dtype).min
|
||||
|
||||
start_decode = time.time_ns()
|
||||
|
||||
# Results
|
||||
generations: List[Generation] = []
|
||||
stopped = True
|
||||
|
||||
# Zipped iterator
|
||||
iterator = zip(
|
||||
batch.requests,
|
||||
batch.input_lengths,
|
||||
batch.prefix_offsets,
|
||||
batch.read_offsets,
|
||||
logits,
|
||||
batch.next_token_choosers,
|
||||
batch.stopping_criterias,
|
||||
batch.all_input_ids,
|
||||
)
|
||||
|
||||
# For each member of the batch
|
||||
for i, (
|
||||
request,
|
||||
input_length,
|
||||
prefix_offset,
|
||||
read_offset,
|
||||
logits,
|
||||
next_token_chooser,
|
||||
stopping_criteria,
|
||||
all_input_ids,
|
||||
) in enumerate(iterator):
|
||||
# Select next token
|
||||
next_token_id, logprobs = next_token_chooser(
|
||||
all_input_ids.view(1, -1), logits[-1:, :]
|
||||
)
|
||||
|
||||
# Append next token to all tokens
|
||||
all_input_ids = torch.cat([all_input_ids, next_token_id])
|
||||
new_input_length = input_length + 1
|
||||
|
||||
# Generated token
|
||||
next_token_logprob = logprobs[-1, next_token_id]
|
||||
next_token_id_squeezed = next_token_id.squeeze()
|
||||
next_token_text, prefix_offset, read_offset = self.decode_token(
|
||||
all_input_ids[:, 0], prefix_offset, read_offset
|
||||
)
|
||||
|
||||
# Evaluate stopping criteria
|
||||
stop, reason = stopping_criteria(
|
||||
next_token_id_squeezed,
|
||||
next_token_text,
|
||||
)
|
||||
|
||||
if not stop:
|
||||
stopped = False
|
||||
|
||||
# Shard generations
|
||||
# All generations will be appended in the rust sharded client
|
||||
if i % self.world_size == self.rank:
|
||||
if stop:
|
||||
# Decode generated tokens
|
||||
output_text, _, _ = self.decode_token(
|
||||
all_input_ids[:, 0],
|
||||
prefix_offset=len(all_input_ids)
|
||||
- stopping_criteria.current_tokens
|
||||
- 1,
|
||||
read_offset=len(all_input_ids)
|
||||
- stopping_criteria.current_tokens,
|
||||
skip_special_tokens=True,
|
||||
)
|
||||
# Get seed
|
||||
if isinstance(next_token_chooser.choice, Sampling):
|
||||
seed = next_token_chooser.choice.seed
|
||||
else:
|
||||
seed = None
|
||||
|
||||
generated_text = GeneratedText(
|
||||
output_text, stopping_criteria.current_tokens, reason, seed
|
||||
)
|
||||
else:
|
||||
generated_text = None
|
||||
|
||||
# Prefill
|
||||
if stopping_criteria.current_tokens == 1 and request.prefill_logprobs:
|
||||
# Remove generated token to only have prefill and add nan for first prompt token
|
||||
prefill_logprobs = [float("nan")] + torch.log_softmax(
|
||||
logits, -1
|
||||
).gather(1, all_input_ids[1:]).squeeze(1)[
|
||||
-new_input_length:-1
|
||||
].tolist()
|
||||
prefill_token_ids = all_input_ids[-new_input_length:-1]
|
||||
prefill_texts = self.tokenizer.batch_decode(
|
||||
prefill_token_ids,
|
||||
clean_up_tokenization_spaces=False,
|
||||
skip_special_tokens=False,
|
||||
)
|
||||
prefill_tokens = Tokens(
|
||||
prefill_token_ids,
|
||||
prefill_logprobs,
|
||||
prefill_texts,
|
||||
is_special=[],
|
||||
)
|
||||
else:
|
||||
prefill_tokens = None
|
||||
|
||||
top_tokens = None
|
||||
|
||||
generation = Generation(
|
||||
request.id,
|
||||
prefill_tokens,
|
||||
Tokens(
|
||||
[next_token_id_squeezed],
|
||||
[next_token_logprob],
|
||||
[next_token_text],
|
||||
[next_token_id_squeezed.item() in self.all_special_ids],
|
||||
),
|
||||
generated_text,
|
||||
top_tokens,
|
||||
)
|
||||
|
||||
generations.append(generation)
|
||||
|
||||
# Update values
|
||||
batch.next_token_choosers[i] = batch.next_token_choosers[i].advance_grammar(
|
||||
next_token_id_squeezed.item()
|
||||
)
|
||||
batch.input_ids[i, 0] = next_token_id
|
||||
batch.all_input_ids[i] = all_input_ids
|
||||
batch.input_lengths[i] = new_input_length
|
||||
batch.prefix_offsets[i] = prefix_offset
|
||||
batch.read_offsets[i] = read_offset
|
||||
batch.max_input_length = max(batch.max_input_length, new_input_length)
|
||||
|
||||
# We finished all generations in the batch; there is no next batch
|
||||
if stopped:
|
||||
forward_ns = start_decode - start
|
||||
decode_ns = time.time_ns() - start_decode
|
||||
return generations, None, (forward_ns, decode_ns)
|
||||
|
||||
# Slice unused values from prefill
|
||||
batch.input_ids = batch.input_ids[:, :1]
|
||||
|
||||
# Update attention_mask as we added a new token to input_ids
|
||||
batch.attention_mask[:, -batch.padding_right_offset] = 1
|
||||
batch.image_attention_mask[:, -batch.padding_right_offset, :] = (
|
||||
batch.image_attention_mask[:, -(batch.padding_right_offset + 1), :]
|
||||
)
|
||||
# Decrease right offset
|
||||
batch.padding_right_offset -= 1
|
||||
|
||||
# Update position_ids
|
||||
batch.position_ids = batch.position_ids[:, -1:] + 1
|
||||
|
||||
# Update past key values
|
||||
batch.past_key_values = past
|
||||
batch.image_hidden_states = image_hidden_states
|
||||
|
||||
forward_ns = start_decode - start
|
||||
decode_ns = time.time_ns() - start_decode
|
||||
return generations, batch, (forward_ns, decode_ns)
|
||||
|
||||
|
||||
import time
|
||||
|
||||
from dataclasses import dataclass
|
||||
from opentelemetry import trace
|
||||
from transformers import (
|
||||
AutoProcessor,
|
||||
AutoTokenizer,
|
||||
PreTrainedTokenizerBase,
|
||||
ProcessorMixin,
|
||||
)
|
||||
from typing import Optional, Tuple, List, Type, Dict
|
||||
|
||||
from text_generation_server.models import Model
|
||||
from text_generation_server.models.types import (
|
||||
Batch,
|
||||
Tokens,
|
||||
Generation,
|
||||
GeneratedText,
|
||||
)
|
||||
from text_generation_server.pb import generate_pb2
|
||||
from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
|
||||
|
||||
import re
|
||||
|
||||
tracer = trace.get_tracer(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class IdeficsCausalLMBatch(Batch):
|
||||
batch_id: int
|
||||
requests: List[generate_pb2.Request]
|
||||
requests_idx_mapping: Dict[int, int]
|
||||
|
||||
# Decoder values
|
||||
input_ids: torch.Tensor
|
||||
attention_mask: torch.Tensor
|
||||
position_ids: torch.Tensor
|
||||
pixel_values: Optional[torch.Tensor]
|
||||
image_hidden_states: Optional[torch.Tensor]
|
||||
image_attention_mask: Optional[torch.Tensor]
|
||||
past_key_values: Optional[List[Tuple]]
|
||||
|
||||
# All tokens
|
||||
all_input_ids: List[torch.Tensor]
|
||||
|
||||
# Lengths of all generations present in the batch
|
||||
input_lengths: List[int]
|
||||
prefix_offsets: List[int]
|
||||
read_offsets: List[int]
|
||||
|
||||
# Generation helpers
|
||||
next_token_choosers: List[NextTokenChooser]
|
||||
stopping_criterias: List[StoppingCriteria]
|
||||
|
||||
# Metadata used for padding
|
||||
max_input_length: int
|
||||
padding_right_offset: int
|
||||
|
||||
# Maximum number of tokens this batch will grow to
|
||||
max_tokens: int
|
||||
|
||||
# Past metadata
|
||||
keys_head_dim_last: bool = True
|
||||
|
||||
def to_pb(self) -> generate_pb2.CachedBatch:
|
||||
return generate_pb2.CachedBatch(
|
||||
id=self.batch_id,
|
||||
request_ids=[r.id for r in self.requests],
|
||||
size=len(self),
|
||||
max_tokens=self.max_tokens,
|
||||
)
|
||||
raise NotImplementedError
|
||||
|
||||
@classmethod
|
||||
def from_pb_processor(
|
||||
@ -932,6 +92,7 @@ class IdeficsCausalLMBatch(Batch):
|
||||
pb: generate_pb2.Batch,
|
||||
tokenizer: PreTrainedTokenizerBase,
|
||||
processor: ProcessorMixin, # Hack
|
||||
config,
|
||||
dtype: torch.dtype,
|
||||
device: torch.device,
|
||||
) -> "IdeficsCausalLMBatch":
|
||||
@ -966,7 +127,10 @@ class IdeficsCausalLMBatch(Batch):
|
||||
prompts = []
|
||||
for inp in inputs:
|
||||
# Each input is encoded into a list, where each element of this input list is either a string or a URL
|
||||
prompts.append(split(inp))
|
||||
prompt = []
|
||||
for chunk in split(inp):
|
||||
prompt.append(chunk["content"])
|
||||
prompts.append(prompt)
|
||||
|
||||
# The processor replaces the call to tokenizer, and
|
||||
# a/ takes care of fetching images from the URL
|
||||
|
||||
@ -1,5 +1,8 @@
|
||||
import re
|
||||
import torch
|
||||
from PIL import Image
|
||||
from io import BytesIO
|
||||
import base64
|
||||
|
||||
from opentelemetry import trace
|
||||
from typing import Optional, Tuple, List, Type, Dict
|
||||
@ -92,6 +95,13 @@ def get_number_of_features(height: int, width: int, config) -> int:
|
||||
return 2634
|
||||
|
||||
|
||||
def load_data_uri(image_uri: str) -> Image.Image:
|
||||
image_uri = image_uri.split(",")[-1]
|
||||
content = base64.b64decode(image_uri)
|
||||
image = Image.open(BytesIO(content))
|
||||
return image
|
||||
|
||||
|
||||
# assert get_number_of_features(889, 1024) == 2634, f"{get_number_of_features(889, 1024)}"
|
||||
# assert get_number_of_features(640, 640) == 2928
|
||||
|
||||
@ -100,6 +110,21 @@ class VlmCausalLMBatch(FlashMistralBatch):
|
||||
pixel_values: Optional[List[torch.Tensor]]
|
||||
image_sizes: Optional[List[Tuple[int, int]]]
|
||||
|
||||
@classmethod
|
||||
@tracer.start_as_current_span("concatenate")
|
||||
def concatenate(cls, batches):
|
||||
batch = super(VlmCausalLMBatch, cls).concatenate(batches)
|
||||
batch.pixel_values = None
|
||||
batch.image_sizes = None
|
||||
return batch
|
||||
|
||||
@tracer.start_as_current_span("filter")
|
||||
def filter(self, request_ids: List[int]):
|
||||
batch = super().filter(request_ids)
|
||||
batch.pixel_values = None
|
||||
batch.image_sizes = None
|
||||
return batch
|
||||
|
||||
@classmethod
|
||||
def batch_tokenized_inputs(cls, requests, tokenizer, processor, config):
|
||||
batch_inputs = []
|
||||
@ -115,6 +140,12 @@ class VlmCausalLMBatch(FlashMistralBatch):
|
||||
image = chunk["content"]
|
||||
if image.startswith("https://") or image.startswith("http://"):
|
||||
image = processor.image_processor.fetch_images(image)
|
||||
elif image.startswith("data:"):
|
||||
image = load_data_uri(image)
|
||||
else:
|
||||
raise RuntimeError(
|
||||
"Cannot process input image not starting with http(s):// nor data:"
|
||||
)
|
||||
image_input = processor.image_processor(image, return_tensors="pt")
|
||||
height, width = image_input["image_sizes"][0]
|
||||
num_features = get_number_of_features(height, width, config)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user