Adding API server

examples/run_text_generation_server_345M.sh

+#!/bin/bash
+# This example will start serving the 345M model.
+DISTRIBUTED_ARGS="--nproc_per_node 1 \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr localhost \
+                  --master_port 6000"
+
+CHECKPOINT=<Path to checkpoint (e.g /345m)>
+VOCAB_FILE=<Path to vocab.json (e.g. /gpt2-vocab.json)>
+MERGE_FILE=<Path to merges.txt (e.g. /gpt2-merges.txt)>
+
+pip install flask-restful
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS tools/run_text_generation_server.py   /
+       --tensor-model-parallel-size 1  /
+       --pipeline-model-parallel-size 1  /
+       --num-layers 24  /
+       --hidden-size 1024  /
+       --load ${CHECKPOINT}  /
+       --num-attention-heads 16  /
+       --max-position-embeddings 1024  /
+       --tokenizer-type GPT2BPETokenizer  /
+       --fp16  /
+       --micro-batch-size 1  /
+       --seq-length 1024  /
+       --out-seq-length 1024  /
+       --temperature 1.0  /
+       --vocab-file $VOCAB_FILE  /
+       --merge-file $MERGE_FILE  /
+       --top_p 0.9  /
+       --seed 42

examples/run_text_generation_server_345M_8_tensor_parallel.sh

+#!/bin/bash
+# This example will start serving the 345M model that is partitioned 8 way tensor parallel
+DISTRIBUTED_ARGS="--nproc_per_node 8 \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr localhost \
+                  --master_port 6000"
+
+CHECKPOINT=<Path to checkpoint (e.g /345m)>
+VOCAB_FILE=<Path to vocab.json (e.g. /gpt2-vocab.json)>
+MERGE_FILE=<Path to merges.txt (e.g. /gpt2-merges.txt)>
+
+pip install flask-restful
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS tools/run_text_generation_server.py   /
+       --tensor-model-parallel-size 8  /
+       --pipeline-model-parallel-size 1  /
+       --num-layers 24  /
+       --hidden-size 1024  /
+       --load ${CHECKPOINT}  /
+       --num-attention-heads 16  /
+       --max-position-embeddings 1024  /
+       --tokenizer-type GPT2BPETokenizer  /
+       --fp16  /
+       --micro-batch-size 1  /
+       --seq-length 1024  /
+       --out-seq-length 1024  /
+       --temperature 1.0  /
+       --vocab-file $VOCAB_FILE  /
+       --merge-file $MERGE_FILE  /
+       --top_p 0.9  /
+       --seed 42

megatron/initialize.py

@@ -21,6 +21,7 @@ import time
 
 import numpy as np
 import torch
+from datetime import timedelta
 
 from megatron import fused_kernels
 from megatron import get_adlr_autoresume
@@ -175,8 +176,8 @@ def _initialize_distributed():
         # Call the init process
         torch.distributed.init_process_group(
             backend=args.distributed_backend,
-            world_size=args.world_size, rank=args.rank)
-
+            world_size=args.world_size, rank=args.rank,
+            timeout=timedelta(days=7))
 
     # Set the tensor model-parallel, pipeline model-parallel, and
     # data-parallel communicators.

megatron/text_generation_server.py

+# coding=utf-8
+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import torch
+from flask import Flask, request, jsonify, current_app
+from flask_restful import Resource, Api
+
+from megatron import get_args
+from megatron import mpu
+from megatron.text_generation_utils import generate
+
+GENERATE_NUM = 0
+
+class MegatronGenerate(Resource):
+    def __init__(self, model):
+        self.model = model
+    
+    @staticmethod
+    def send_do_generate():
+        choice = torch.cuda.LongTensor([GENERATE_NUM])
+        torch.distributed.broadcast(choice,
+                                    mpu.get_tensor_model_parallel_src_rank(),
+                                    group=mpu.get_tensor_model_parallel_group())
+     
+    def put(self):
+        args = get_args()
+        sentences = request.get_json()["sentences"]
+        if len(sentences) > 128:
+            return "Maximum number of sentences is 128", 400
+
+        max_len = 64  # Choosing hopefully sane default.  Full sequence is slow
+        if "max_len" in request.get_json():
+            max_len = request.get_json()["max_len"]
+            if not isinstance(max_len, int):
+                return "max_len must be an integer greater than 0"
+            if max_len < 1:
+                return "max_len must be an integer greater than 0"
+
+        MegatronGenerate.send_do_generate()  # Tell other ranks we're doing generate
+        resp_sentences = generate(self.model, sentences, max_len) 
+        return jsonify({"sentences": resp_sentences})
+
+
+def index():
+    return current_app.send_static_file('index.html')
+
+class MegatronServer(object):
+    def __init__(self, model):
+        self.app = Flask(__name__)
+        self.app.add_url_rule('/', 'index', index)
+        api = Api(self.app)
+        api.add_resource(MegatronGenerate, '/generate', resource_class_args=[model])
+
+    def run(self, url):
+        self.app.run(url, threaded=False, debug=False)

tools/run_text_generation_server.py

+# coding=utf-8
+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Sample Generate GPT"""
+import os
+import sys
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
+                                             os.path.pardir)))
+import socket
+from megatron import get_args
+from megatron import print_rank_0
+from megatron import mpu
+from megatron.checkpointing import load_checkpoint
+from megatron.initialize import initialize_megatron
+from megatron.model import GPTModel
+from megatron.training import get_model
+from megatron.text_generation_server import MegatronServer
+from megatron.text_generation_utils import generate
+import torch
+
+def model_provider(pre_process=True, post_process=True):
+    """Build the model."""
+
+    print_rank_0('building GPT model ...')
+    model = GPTModel(num_tokentypes=0, parallel_output=False, pre_process=pre_process, post_process=post_process)
+
+    return model
+
+def add_text_generate_args(parser):
+    group = parser.add_argument_group(title='text generation')
+
+    group.add_argument("--temperature", type=float, default=1.0,
+                       help='Sampling temperature.')
+    group.add_argument("--greedy", action='store_true', default=False,
+                       help='Use greedy sampling.')
+    group.add_argument("--top_p", type=float, default=0.0,
+                       help='Top p sampling.')
+    group.add_argument("--top_k", type=int, default=0,
+                       help='Top k sampling.')
+    group.add_argument("--out-seq-length", type=int, default=1024,
+                       help='Size of the output generated text.')
+    return parser
+
+
+if __name__ == "__main__":
+    initialize_megatron(extra_args_provider=add_text_generate_args,
+                        args_defaults={'tokenizer_type': 'GPT2BPETokenizer',
+                                       'no_load_rng': True,
+                                       'no_load_optim': True})
+
+    args = get_args()
+    if args.num_layers_per_virtual_pipeline_stage is not None:
+        print("Interleaved pipeline schedule is not yet supported for text generation.")
+        exit()
+    # Set up model and load checkpoint
+    model = get_model(model_provider)
+
+    if args.load is not None:
+        _ = load_checkpoint(model, None, None)
+
+    assert len(model) == 1, "Above condition should have caught this"
+    model = model[0]
+    if mpu.is_pipeline_first_stage() and mpu.get_tensor_model_parallel_rank() == 0:
+        server = MegatronServer(model)
+        server.run("0.0.0.0")
+
+    while True:
+        choice = torch.cuda.LongTensor(1)
+        torch.distributed.broadcast(choice,
+                                    mpu.get_tensor_model_parallel_src_rank(),
+                                    group=mpu.get_tensor_model_parallel_group())
+        if choice[0].item() == 0:
+            generate(model)

tools/text_generation_cli.py

+# coding=utf-8
+# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import json
+import sys
+import urllib2
+class PutRequest(urllib2.Request):
+    '''class to handling putting with urllib2'''
+
+    def get_method(self, *args, **kwargs):
+        return 'PUT'
+
+if __name__ == "__main__":
+    url = sys.argv[1]
+    while True:
+        sentence = raw_input("Enter prompt: ")
+        max_len = int(input("Enter number tokens output: "))
+        data = json.dumps({"sentences": [sentence], "max_len":max_len})
+        req = PutRequest(url, data, {'Content-Type': 'application/json'})
+        response = urllib2.urlopen(req)
+        resp_sentences = json.load(response)
+        print("Megatron Response: ")
+        print(resp_sentences["sentences"][0])