mirror of
https://github.com/huggingface/transformers.git
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221 lines
8.3 KiB
Python
221 lines
8.3 KiB
Python
# Copyright 2024 The HuggingFace Team. All rights reserved.
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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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# Run the test: CUDA_VISIBLE_DEVICES=0,1 RUN_SLOW=1 pytest -sv tests/tensor_parallel/test_tensor_parallel.py
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import os
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import tempfile
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import textwrap
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from transformers import is_torch_available
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from transformers.integrations.tensor_parallel import get_packed_weights, repack_weights
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from transformers.testing_utils import (
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TestCasePlus,
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backend_device_count,
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require_huggingface_hub_greater_or_equal,
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require_torch_multi_accelerator,
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torch_device,
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torchrun,
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)
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if is_torch_available():
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import torch
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class TestTensorParallelUtils(TestCasePlus):
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def test_packed_unpacked_conversion(self):
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WORLD_SIZE = 2
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PACKED_BLOCK_SIZE = 800
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SHARDING_DIM = 2
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NUM_BLOCKS = 2
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original_packed_weights = torch.randn(4, 512, 2 * PACKED_BLOCK_SIZE)
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original_packed_weights.get_dtype = lambda: "F32" # get_packed_weights expects PySlice object
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empty_param = torch.empty(4, 512, 2 * PACKED_BLOCK_SIZE)
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class MockDeviceMesh:
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def size(self):
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return WORLD_SIZE
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mock_mesh = (
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MockDeviceMesh()
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) # get_packed_weights only calls `.size()`, do this to avoid doing actual distributed run
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packed_weights_0 = get_packed_weights(original_packed_weights, empty_param, mock_mesh, 0, SHARDING_DIM)
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packed_weights_1 = get_packed_weights(original_packed_weights, empty_param, mock_mesh, 1, SHARDING_DIM)
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# simulate all gather of sharded weights
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packed_weights = torch.cat([packed_weights_0, packed_weights_1], dim=SHARDING_DIM)
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unpacked_weights = repack_weights(packed_weights, SHARDING_DIM, WORLD_SIZE, NUM_BLOCKS)
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assert torch.allclose(unpacked_weights, original_packed_weights)
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class TestTensorParallel(TestCasePlus):
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nproc_per_node = 2
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def test_model_forward(self):
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script_to_run = textwrap.dedent(
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"""
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import torch
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import os
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "JackFram/llama-68m"
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rank = int(os.environ["RANK"])
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world_size = int(os.environ["WORLD_SIZE"])
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", tp_plan="auto")
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torch.distributed.barrier()
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has_dtensor = 0
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for name, parameter in model.named_parameters():
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if isinstance(parameter.data, torch.distributed.tensor.DTensor):
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has_dtensor = 1
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break
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assert has_dtensor == 1, "TP model must has DTensor"
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tokenizer = AutoTokenizer.from_pretrained(model_id, legacy=False)
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prompt = "Can I help"
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inputs = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
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outputs = model(inputs)
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next_token_logits = outputs[0][:, -1, :]
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next_token = torch.argmax(next_token_logits, dim=-1)
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response = tokenizer.decode(next_token)
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assert response == "with"
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torch.distributed.barrier()
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torch.distributed.destroy_process_group()
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"""
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)
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torchrun(script_to_run, self.nproc_per_node, env=self.get_env())
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def test_model_backward_pass(self):
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script_to_run = textwrap.dedent(
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"""
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import torch
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import os
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from transformers import AutoModelForCausalLM
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from torch import nn
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model_id = "JackFram/llama-68m"
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32, tp_plan="auto")
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torch.distributed.barrier()
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# Dummy forward and backward pass
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# Note that loss.backward() will fail if there is a bug in the TP implementation
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inputs = torch.randint(0, model.config.vocab_size, (2, 10), device=model.device)
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labels = torch.randint(0, model.config.vocab_size, (2, 10), device=model.device)
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loss = model(inputs, labels=labels).loss
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loss.backward()
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torch.distributed.barrier()
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torch.distributed.destroy_process_group()
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"""
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)
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torchrun(script_to_run, self.nproc_per_node, env=self.get_env())
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def test_model_generate(self):
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script_to_run = textwrap.dedent(
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"""
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import torch
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import os
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "JackFram/llama-68m"
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rank = int(os.environ["RANK"])
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world_size = int(os.environ["WORLD_SIZE"])
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", tp_plan="auto")
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torch.distributed.barrier()
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model.forward = torch.compile(model.forward)
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has_dtensor = 0
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for name, parameter in model.named_parameters():
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if isinstance(parameter.data, torch.distributed.tensor.DTensor):
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has_dtensor = 1
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break
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assert has_dtensor == 1, "TP model must has DTensor"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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prompt = "Can I help"
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inputs = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
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outputs = model.generate(inputs, max_new_tokens=10, cache_implementation="static")
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output_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
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assert output_text[0].startswith(prompt), f"Expected output to start with '{prompt}', got '{output_text[0]}'"
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torch.distributed.barrier()
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torch.distributed.destroy_process_group()
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"""
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)
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torchrun(script_to_run, self.nproc_per_node, env=self.get_env())
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@require_huggingface_hub_greater_or_equal("0.31.4")
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def test_model_save(self):
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from safetensors import safe_open
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with tempfile.TemporaryDirectory() as tmp_dir:
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for is_torchrun in [True, False]:
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script_to_run = textwrap.dedent(
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f"""
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import torch
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import os
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from transformers import AutoModelForCausalLM
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model_id = "JackFram/llama-68m"
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kwargs = dict()
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if os.environ.get("RANK", None) is not None:
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kwargs["tp_plan"] = "auto"
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result_dir = "{tmp_dir}/tp"
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else:
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result_dir = "{tmp_dir}/nontp"
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model = AutoModelForCausalLM.from_pretrained(model_id, **kwargs)
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model.save_pretrained(result_dir)
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"""
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)
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torchrun(script_to_run, self.nproc_per_node, is_torchrun=is_torchrun, env=self.get_env())
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non_tp_model_path = os.path.join(tmp_dir, "nontp")
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tp_model_path = os.path.join(tmp_dir, "tp")
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for filename in os.listdir(non_tp_model_path):
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if not filename.endswith(".safetensors"):
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continue
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non_tp_model = safe_open(os.path.join(non_tp_model_path, filename), device="cpu", framework="pt")
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tp_model = safe_open(os.path.join(tp_model_path, filename), device="cpu", framework="pt")
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for non_tp_key in non_tp_model.keys():
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non_tp_tensor = non_tp_model.get_tensor(non_tp_key)
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tp_tensor = tp_model.get_tensor(non_tp_key)
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assert torch.allclose(non_tp_tensor, tp_tensor), f"Tensor with key: {non_tp_key} does not match"
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del non_tp_tensor, tp_tensor
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@require_torch_multi_accelerator
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class TestTensorParallelAccelerator(TestTensorParallel):
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nproc_per_node = backend_device_count(torch_device)
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