Adding ctranslate2 quantization and inference: moving the contribution (#1)
* rebaseing the commit on preemo fork. * reformatting and changes. * update dockerfile * update changes for dockerfile * adapt path * rebaseing the commit on preemo fork. * reformatting and changes. * update dockerfile * update changes for dockerfile * adapt path --------- Co-authored-by: michaelfeil <me@michaelfeil.eu>
This commit is contained in:
parent
012c917b6f
commit
ff703cb867
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@ -188,7 +188,7 @@ COPY server/Makefile server/Makefile
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RUN cd server && \
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RUN cd server && \
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make gen-server && \
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make gen-server && \
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pip install -r requirements.txt && \
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pip install -r requirements.txt && \
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pip install ".[bnb, accelerate, quantize]" --no-cache-dir
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pip install ".[bnb, accelerate, quantize, ct2]" --no-cache-dir
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# Install benchmarker
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# Install benchmarker
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COPY --from=builder /usr/src/target/release/text-generation-benchmark /usr/local/bin/text-generation-benchmark
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COPY --from=builder /usr/src/target/release/text-generation-benchmark /usr/local/bin/text-generation-benchmark
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@ -25,6 +25,7 @@ enum Quantization {
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BitsandbytesNF4,
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BitsandbytesNF4,
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BitsandbytesFP4,
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BitsandbytesFP4,
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Gptq,
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Gptq,
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Ct2,
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}
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}
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impl std::fmt::Display for Quantization {
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impl std::fmt::Display for Quantization {
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@ -43,6 +44,9 @@ impl std::fmt::Display for Quantization {
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Quantization::Gptq => {
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Quantization::Gptq => {
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write!(f, "gptq")
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write!(f, "gptq")
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}
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}
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Quantization::Ct2 => {
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write!(f, "ct2")
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}
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}
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}
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}
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}
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}
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}
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@ -104,7 +108,7 @@ struct Args {
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num_shard: Option<usize>,
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num_shard: Option<usize>,
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/// Whether you want the model to be quantized. This will use `bitsandbytes` for
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/// Whether you want the model to be quantized. This will use `bitsandbytes` for
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/// quantization on the fly, or `gptq`. 4bit quantization is available through
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/// quantization on the fly, `bnb` or `gptq`, or `ctranslate2`. 4bit quantization is available through
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/// `bitsandbytes` by providing the `bitsandbytes-fp4` or `bitsandbytes-nf4` options.
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/// `bitsandbytes` by providing the `bitsandbytes-fp4` or `bitsandbytes-nf4` options.
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#[clap(long, env, value_enum)]
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#[clap(long, env, value_enum)]
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quantize: Option<Quantization>,
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quantize: Option<Quantization>,
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@ -21,7 +21,7 @@ install-torch:
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install: gen-server install-torch
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install: gen-server install-torch
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pip install pip --upgrade
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pip install pip --upgrade
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pip install -r requirements.txt
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pip install -r requirements.txt
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pip install -e ".[bnb, accelerate]"
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pip install -e ".[bnb, accelerate, quantize, ct2]"
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run-dev:
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run-dev:
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SAFETENSORS_FAST_GPU=1 python -m torch.distributed.run --nproc_per_node=2 text_generation_server/cli.py serve bigscience/bloom-560m --sharded
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SAFETENSORS_FAST_GPU=1 python -m torch.distributed.run --nproc_per_node=2 text_generation_server/cli.py serve bigscience/bloom-560m --sharded
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@ -16,6 +16,7 @@ grpcio-reflection = "^1.51.1"
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grpc-interceptor = "^0.15.0"
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grpc-interceptor = "^0.15.0"
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typer = "^0.6.1"
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typer = "^0.6.1"
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accelerate = { version = "^0.19.0", optional = true }
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accelerate = { version = "^0.19.0", optional = true }
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ctranslate2 = { version = "^3.20.0", optional = true }
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bitsandbytes = { version = "^0.40.0", optional = true }
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bitsandbytes = { version = "^0.40.0", optional = true }
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safetensors = "0.3.1"
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safetensors = "0.3.1"
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loguru = "^0.6.0"
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loguru = "^0.6.0"
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@ -35,6 +36,7 @@ datasets = { version = "^2.14.0", optional = true }
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accelerate = ["accelerate"]
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accelerate = ["accelerate"]
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bnb = ["bitsandbytes"]
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bnb = ["bitsandbytes"]
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quantize = ["texttable", "datasets", "accelerate"]
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quantize = ["texttable", "datasets", "accelerate"]
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ct2 = ["ctranslate2"]
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[tool.poetry.group.dev.dependencies]
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[tool.poetry.group.dev.dependencies]
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grpcio-tools = "^1.51.1"
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grpcio-tools = "^1.51.1"
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@ -0,0 +1,99 @@
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import pytest
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import torch
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from text_generation_server.pb import generate_pb2
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from text_generation_server.models.causal_lm import CausalLMBatch
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from text_generation_server.models.ct2_causal_lm import CT2CausalLM
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@pytest.fixture(scope="session")
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def default_santacoder():
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return CT2CausalLM("bigcode/gpt_bigcode-santacoder", dtype=torch.float16)
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@pytest.fixture
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def default_pb_request(default_pb_parameters, default_pb_stop_parameters):
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return generate_pb2.Request(
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id=0,
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inputs="def",
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prefill_logprobs=True,
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truncate=100,
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parameters=default_pb_parameters,
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stopping_parameters=default_pb_stop_parameters,
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)
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@pytest.fixture
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def default_pb_batch(default_pb_request):
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return generate_pb2.Batch(id=0, requests=[default_pb_request], size=1)
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@pytest.fixture
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def default_fim_pb_request(default_pb_parameters, default_pb_stop_parameters):
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return generate_pb2.Request(
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id=0,
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inputs="<fim-prefix>def<fim-suffix>world<fim-middle>",
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prefill_logprobs=True,
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truncate=100,
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parameters=default_pb_parameters,
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stopping_parameters=default_pb_stop_parameters,
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)
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@pytest.fixture
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def default_fim_pb_batch(default_fim_pb_request):
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return generate_pb2.Batch(id=0, requests=[default_fim_pb_request], size=1)
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def test_ct2santa_generate_token_completion(default_santacoder, default_pb_batch):
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batch = CausalLMBatch.from_pb(
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default_pb_batch,
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default_santacoder.tokenizer,
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default_santacoder.dtype,
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default_santacoder.device,
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)
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next_batch = batch
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for _ in range(batch.stopping_criterias[0].max_new_tokens - 1):
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generations, next_batch = default_santacoder.generate_token(next_batch)
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assert len(generations) == len(next_batch)
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generations, next_batch = default_santacoder.generate_token(next_batch)
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assert next_batch is None
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assert len(generations) == 1
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assert generations[0].generated_text.text in (" test_get_all_users_with_", ' test_get_all_users(client):')
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assert generations[0].request_id == batch.requests[0].id
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assert (
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generations[0].generated_text.generated_tokens
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== batch.stopping_criterias[0].max_new_tokens
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)
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def test_fim_ct2santacoder_generate_token_completion(
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default_santacoder, default_fim_pb_batch
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):
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batch = CausalLMBatch.from_pb(
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default_fim_pb_batch,
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default_santacoder.tokenizer,
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default_santacoder.dtype,
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default_santacoder.device,
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)
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next_batch = batch
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for _ in range(batch.stopping_criterias[0].max_new_tokens - 1):
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generations, next_batch = default_santacoder.generate_token(next_batch)
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assert len(generations) == len(next_batch)
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generations, next_batch = default_santacoder.generate_token(next_batch)
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assert next_batch is None
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assert len(generations) == 1
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assert (
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generations[0].generated_text.text
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== """ineProperty(exports, "__esModule", { value"""
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)
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assert generations[0].request_id == batch.requests[0].id
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assert (
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generations[0].generated_text.generated_tokens
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== batch.stopping_criterias[0].max_new_tokens
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)
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@ -16,6 +16,7 @@ class Quantization(str, Enum):
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bitsandbytes_nf4 = "bitsandbytes-nf4"
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bitsandbytes_nf4 = "bitsandbytes-nf4"
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bitsandbytes_fp4 = "bitsandbytes-fp4"
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bitsandbytes_fp4 = "bitsandbytes-fp4"
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gptq = "gptq"
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gptq = "gptq"
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ct2 = "ct2"
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class Dtype(str, Enum):
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class Dtype(str, Enum):
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@ -73,7 +74,7 @@ def serve(
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# Downgrade enum into str for easier management later on
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# Downgrade enum into str for easier management later on
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quantize = None if quantize is None else quantize.value
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quantize = None if quantize is None else quantize.value
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dtype = None if dtype is None else dtype.value
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dtype = None if dtype is None else dtype.value
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if dtype is not None and quantize is not None:
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if dtype is not None and quantize is not None and quantize != Quantization.ct2:
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raise RuntimeError(
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raise RuntimeError(
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"Only 1 can be set between `dtype` and `quantize`, as they both decide how goes the final model."
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"Only 1 can be set between `dtype` and `quantize`, as they both decide how goes the final model."
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)
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)
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@ -90,6 +91,7 @@ def download_weights(
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auto_convert: bool = True,
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auto_convert: bool = True,
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logger_level: str = "INFO",
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logger_level: str = "INFO",
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json_output: bool = False,
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json_output: bool = False,
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trust_remote_code: bool = False
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):
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):
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# Remove default handler
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# Remove default handler
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logger.remove()
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logger.remove()
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@ -169,6 +171,7 @@ def download_weights(
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config = AutoConfig.from_pretrained(
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config = AutoConfig.from_pretrained(
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model_id,
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model_id,
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revision=revision,
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revision=revision,
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trust_remote_code=trust_remote_code
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)
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)
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architecture = config.architectures[0]
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architecture = config.architectures[0]
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@ -18,6 +18,7 @@ from text_generation_server.models.galactica import GalacticaSharded
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from text_generation_server.models.santacoder import SantaCoder
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from text_generation_server.models.santacoder import SantaCoder
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from text_generation_server.models.t5 import T5Sharded
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from text_generation_server.models.t5 import T5Sharded
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from text_generation_server.models.gpt_neox import GPTNeoxSharded
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from text_generation_server.models.gpt_neox import GPTNeoxSharded
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from text_generation_server.models.ct2_causal_lm import CT2CausalLM
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# The flag below controls whether to allow TF32 on matmul. This flag defaults to False
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# The flag below controls whether to allow TF32 on matmul. This flag defaults to False
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# in PyTorch 1.12 and later.
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# in PyTorch 1.12 and later.
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@ -75,6 +76,7 @@ def get_model(
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dtype: Optional[str],
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dtype: Optional[str],
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trust_remote_code: bool,
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trust_remote_code: bool,
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) -> Model:
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) -> Model:
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dtype_ct2 = dtype
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if dtype is None:
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if dtype is None:
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dtype = torch.float16
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dtype = torch.float16
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elif dtype == "float16":
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elif dtype == "float16":
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@ -84,6 +86,15 @@ def get_model(
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else:
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else:
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raise RuntimeError(f"Unknown dtype {dtype}")
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raise RuntimeError(f"Unknown dtype {dtype}")
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if quantize is not None and "ct2" in quantize:
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return CT2CausalLM(
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model_id,
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revision,
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quantize=quantize,
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dtype=dtype_ct2,
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trust_remote_code=trust_remote_code,
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)
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if "facebook/galactica" in model_id:
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if "facebook/galactica" in model_id:
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return GalacticaSharded(
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return GalacticaSharded(
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model_id,
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model_id,
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@ -0,0 +1,359 @@
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# coding=utf-8
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# Copyright 2023 Michael Feil.
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#
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# This code is loosely based on Huggingface text-generation-inference v0.9.3's causal_lm.py implementation.
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# While it remains licensed under Apache License, Version 2.0,
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# text-generation-inference itself on 7/28/2023 has changed its license.
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# This code remains unaffected by this change.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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|
# You may obtain a copy of the License at
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|
#
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|
# http://www.apache.org/licenses/LICENSE-2.0
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|
#
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|
# Unless required by applicable law or agreed to in writing, software
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|
# distributed under the License is distributed on an "AS IS" BASIS,
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|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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|
# See the License for the specific language governing permissions and
|
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|
# limitations under the License.
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|
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|
import torch
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import numpy as np
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import os
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import multiprocessing
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from pathlib import Path
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|
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from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE
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from opentelemetry import trace
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from transformers import (
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AutoTokenizer,
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AutoConfig,
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)
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from typing import Optional, Tuple, List, Type, Dict
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|
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from text_generation_server.models import Model
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from text_generation_server.models.types import (
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PrefillTokens,
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|
Generation,
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GeneratedText,
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)
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|
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from text_generation_server.utils import Sampling
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from text_generation_server.models.causal_lm import CausalLMBatch
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|
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|
try:
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import ctranslate2
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|
except ImportError:
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ctranslate2 = None
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|
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|
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|
tracer = trace.get_tracer(__name__)
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|
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|
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|
class CT2CausalLM(Model):
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|
def __init__(
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|
self,
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|
model_id: str,
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|
revision: Optional[str] = None,
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|
quantize: Optional[str] = None,
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|
dtype: Optional[torch.dtype] = None,
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|
trust_remote_code: bool = False,
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|
):
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|
if ctranslate2 is None:
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|
raise ValueError(
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|
"for quantization with ct2, the installation requires the pip package ctranslate2. "
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|
"install via `text-generation-server[ct2]` or `pip install ctranslate2` is required.",
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|
)
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|
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|
tokenizer = AutoTokenizer.from_pretrained(
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|
model_id,
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|
revision=revision,
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|
padding_side="left",
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|
truncation_side="left",
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|
trust_remote_code=trust_remote_code,
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|
)
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|
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|
# Start CT2
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|
ct2_generator_kwargs = {
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|
"inter_threads": int(os.environ.get("TGI_CT2_INTER_THREADS", 1))
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|
}
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|
if torch.cuda.is_available():
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|
self.ct2_device = "cuda"
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|
ct2_generator_kwargs["intra_threads"] = int(
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|
os.environ.get("TGI_CT2_INTRA_THREADS", 1)
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|
)
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|
else:
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|
self.ct2_device = "cpu"
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|
ct2_generator_kwargs["intra_threads"] = int(
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|
os.environ.get(
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|
"TGI_CT2_INTRA_THREADS", multiprocessing.cpu_count() // 2
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|
)
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|
)
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|
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|
if dtype == torch.float16 and self.ct2_device == "cuda":
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|
ct2_compute_type = "float16"
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|
elif dtype == torch.bfloat16 and self.ct2_device == "cuda":
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|
ct2_compute_type = "bfloat16"
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|
elif self.ct2_device == "cpu" and dtype in [torch.float16, torch.bfloat16]:
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|
# float16 is not available on CPU
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|
# and int16 has no stable implementation
|
||||||
|
ct2_compute_type = "float32"
|
||||||
|
else:
|
||||||
|
# default, int8 quantization.
|
||||||
|
|
||||||
|
if "cuda" in self.ct2_device:
|
||||||
|
# int8 for int8 layers, float16 for non-quantized layers
|
||||||
|
ct2_compute_type = "int8_float16"
|
||||||
|
else:
|
||||||
|
# int8 for int8 layers, float32 for non-quantized layers
|
||||||
|
ct2_compute_type = "int8"
|
||||||
|
|
||||||
|
# Start CT2 - conversion
|
||||||
|
out_dir = (
|
||||||
|
Path(HUGGINGFACE_HUB_CACHE)
|
||||||
|
/ "ct2models" / f"{model_id.replace('/','--')}--{ct2_compute_type}"
|
||||||
|
)
|
||||||
|
|
||||||
|
if not os.path.exists(out_dir / "model.bin"):
|
||||||
|
try:
|
||||||
|
converter = ctranslate2.converters.TransformersConverter(
|
||||||
|
model_id,
|
||||||
|
activation_scales=None,
|
||||||
|
load_as_float16=ct2_compute_type != "bfloat16",
|
||||||
|
revision=revision,
|
||||||
|
low_cpu_mem_usage=True,
|
||||||
|
trust_remote_code=trust_remote_code,
|
||||||
|
)
|
||||||
|
converter.convert(
|
||||||
|
output_dir=out_dir,
|
||||||
|
vmap=None,
|
||||||
|
quantization=ct2_compute_type,
|
||||||
|
force=True,
|
||||||
|
)
|
||||||
|
except Exception as ex:
|
||||||
|
raise ValueError(
|
||||||
|
f"conversion with ctranslate2 for {model_id} failed : Error {ex}"
|
||||||
|
)
|
||||||
|
if not os.path.exists(out_dir / "model.bin"):
|
||||||
|
raise ValueError(
|
||||||
|
f"no ctranslate2 model for {model_id} found after conversion in {out_dir}"
|
||||||
|
)
|
||||||
|
|
||||||
|
# Start CT2
|
||||||
|
self.ct2_model = ctranslate2.Generator(
|
||||||
|
str(out_dir),
|
||||||
|
device=self.ct2_device,
|
||||||
|
compute_type=ct2_compute_type,
|
||||||
|
**ct2_generator_kwargs,
|
||||||
|
)
|
||||||
|
|
||||||
|
class DummyModel(torch.nn.Module):
|
||||||
|
def __init__(self, *args, **kwargs) -> None:
|
||||||
|
super().__init__(*args, **kwargs)
|
||||||
|
self.config = AutoConfig.from_pretrained(
|
||||||
|
model_id, revision=revision, trust_remote_code=trust_remote_code
|
||||||
|
)
|
||||||
|
|
||||||
|
model = DummyModel()
|
||||||
|
|
||||||
|
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": "[PAD]"})
|
||||||
|
|
||||||
|
super().__init__(
|
||||||
|
model=model,
|
||||||
|
tokenizer=tokenizer,
|
||||||
|
requires_padding=True,
|
||||||
|
dtype=torch.int8 if "int8" in ct2_compute_type else torch.float16,
|
||||||
|
device=torch.device(self.ct2_device),
|
||||||
|
)
|
||||||
|
|
||||||
|
@property
|
||||||
|
def batch_type(self) -> Type[CausalLMBatch]:
|
||||||
|
return CausalLMBatch
|
||||||
|
|
||||||
|
def decode(self, generated_ids: List[int]) -> str:
|
||||||
|
return self.tokenizer.decode(
|
||||||
|
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
|
||||||
|
)
|
||||||
|
|
||||||
|
def forward_ct2(
|
||||||
|
self,
|
||||||
|
all_input_ids,
|
||||||
|
input_lengths,
|
||||||
|
) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
|
||||||
|
# CT2 forward requires a list of list of input tokens ids and lengths
|
||||||
|
ids_input = (
|
||||||
|
torch.nested.to_padded_tensor(
|
||||||
|
torch.nested.nested_tensor(all_input_ids), 1234567
|
||||||
|
)
|
||||||
|
.flatten(1)
|
||||||
|
.to(torch.int32)
|
||||||
|
)
|
||||||
|
# lengths of the padded ids_input, i.e. how often not pad=1234567 is used.
|
||||||
|
lengths = np.array(input_lengths, dtype=np.int32)
|
||||||
|
|
||||||
|
if self.ct2_device == "cuda":
|
||||||
|
lengths = torch.from_numpy(lengths).to(self.ct2_device)
|
||||||
|
elif self.ct2_device == "cpu":
|
||||||
|
ids_input = ids_input.numpy()
|
||||||
|
|
||||||
|
ids_input = ctranslate2.StorageView.from_array(ids_input)
|
||||||
|
lengths = ctranslate2.StorageView.from_array(lengths)
|
||||||
|
# now, forward through the network
|
||||||
|
logits = self.ct2_model.forward_batch(ids_input, lengths)
|
||||||
|
|
||||||
|
# continue with logits as torch tensor
|
||||||
|
if self.ct2_device == "cpu":
|
||||||
|
# logits is a float32 torch cpu tensor
|
||||||
|
logits = torch.from_numpy(np.asarray(logits))
|
||||||
|
else:
|
||||||
|
# logits is a float16 torch cuda tensor
|
||||||
|
logits = torch.as_tensor(logits, device=self.ct2_device)
|
||||||
|
return logits, None
|
||||||
|
|
||||||
|
@tracer.start_as_current_span("generate_token")
|
||||||
|
def generate_token(
|
||||||
|
self, batch: CausalLMBatch
|
||||||
|
) -> Tuple[List[Generation], Optional[CausalLMBatch]]:
|
||||||
|
logits, past = self.forward_ct2(batch.all_input_ids, batch.input_lengths)
|
||||||
|
|
||||||
|
# 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(
|
||||||
|
all_input_ids[-stopping_criteria.current_tokens :, 0]
|
||||||
|
)
|
||||||
|
# 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 = PrefillTokens(
|
||||||
|
prefill_token_ids, prefill_logprobs, prefill_texts
|
||||||
|
)
|
||||||
|
else:
|
||||||
|
prefill_tokens = None
|
||||||
|
|
||||||
|
generation = Generation(
|
||||||
|
request.id,
|
||||||
|
prefill_tokens,
|
||||||
|
next_token_id_squeezed,
|
||||||
|
next_token_logprob,
|
||||||
|
next_token_text,
|
||||||
|
next_token_id_squeezed.item() in self.all_special_ids,
|
||||||
|
generated_text,
|
||||||
|
)
|
||||||
|
|
||||||
|
generations.append(generation)
|
||||||
|
|
||||||
|
# Update values
|
||||||
|
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:
|
||||||
|
return generations, None
|
||||||
|
|
||||||
|
# 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
|
||||||
|
# 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
|
||||||
|
|
||||||
|
return generations, batch
|
|
@ -42,7 +42,7 @@ class StaticWarper:
|
||||||
self.static_next_logprob = None
|
self.static_next_logprob = None
|
||||||
|
|
||||||
def __call__(self, scores):
|
def __call__(self, scores):
|
||||||
if torch.cuda.is_available():
|
if scores.device.type == "cuda":
|
||||||
if self.cuda_graph is None:
|
if self.cuda_graph is None:
|
||||||
self.static_scores = scores
|
self.static_scores = scores
|
||||||
self.cuda_graph = torch.cuda.CUDAGraph()
|
self.cuda_graph = torch.cuda.CUDAGraph()
|
||||||
|
|
Loading…
Reference in New Issue