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)

megatron/text_generation_utils.py

@@ -40,7 +40,8 @@ def get_batch(context_tokens):
     tokenizer = get_tokenizer()
 
     # Move to GPU.
-    tokens = context_tokens.view(args.micro_batch_size, -1).contiguous().cuda()
+    tokens = context_tokens.contiguous().cuda()
+    
     # Get the attention mask and postition ids.
     attention_mask, _, position_ids = get_ltor_masks_and_position_ids(
         tokens,
@@ -84,301 +85,7 @@ def top_k_logits(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
 
     return logits
 
-
-def generate_samples_input_from_file(model):
-
-    args = get_args()
-    tokenizer = get_tokenizer()
-
-    # Read the sample file and open the output file.
-    assert args.sample_input_file is not None, \
-        'sample input file is not provided.'
-    if mpu.is_pipeline_first_stage() and mpu.get_tensor_model_parallel_rank() == 0:
-        fname = open(args.sample_input_file, "r")
-        all_raw_text = fname.readlines()
-        input_count = len(all_raw_text)
-        input_pos = 0
-        if args.sample_output_file is None:
-            sample_output_file = args.sample_input_file + ".out"
-            print('`sample-output-file` not specified, setting '
-                  'it to {}'.format(sample_output_file))
-        else:
-            sample_output_file = args.sample_output_file
-        fname_out = open(sample_output_file, "w+")
-
-    context_count = 0
-    model.eval()
-    with torch.no_grad():
-        while True:
-            terminate_runs = 0
-            raw_text_len = 0
-
-            if mpu.is_pipeline_first_stage() \
-               and mpu.get_tensor_model_parallel_rank() == 0:
-                raw_text = all_raw_text[input_pos]
-                input_pos += 1
-                if input_pos == input_count:
-                    raw_text = "stop"
-                raw_text_len = len(raw_text)
-
-                if "stop" in raw_text:
-                    terminate_runs = 1
-                else:
-                    context_tokens = tokenizer.tokenize(raw_text)
-                    context_length = len(context_tokens)
-
-                    if context_length >= (args.seq_length // 2):
-                        print("\nContext length", context_length,
-                              "\nPlease give smaller context (half of the "
-                              "sequence length)!", flush=True)
-                        continue
-            else:
-                context_tokens = tokenizer.tokenize("EMPTY TEXT")
-                context_length = 0
-
-            input_info = [terminate_runs, raw_text_len, context_length]
-            input_info_tensor = torch.cuda.LongTensor(input_info)
-            torch.distributed.all_reduce(input_info_tensor,
-                                         group=mpu.get_model_parallel_group())
-            terminate_runs = input_info_tensor[0].item()
-            raw_text_len = input_info_tensor[1].item()
-            context_length = input_info_tensor[2].item()
-
-            if terminate_runs == 1:
-                return
-
-            # For pipeline parallel we send context tokens to other stages
-            # so they get the lengths correct
-            if mpu.get_tensor_model_parallel_rank() == 0 \
-               and args.pipeline_model_parallel_size > 1:
-                if mpu.is_pipeline_first_stage():
-                    src = mpu.get_pipeline_model_parallel_first_rank()
-                    group = mpu.get_pipeline_model_parallel_group()
-                    context_tokens_tensor = torch.cuda.LongTensor(context_tokens)
-                    torch.distributed.broadcast(context_tokens_tensor, src, group)
-                else:
-                    src = mpu.get_pipeline_model_parallel_first_rank()
-                    group = mpu.get_pipeline_model_parallel_group()
-                    context_tokens_tensor = torch.empty(context_length,
-                                                        dtype=torch.int64,
-                                                        device=torch.device("cuda"))
-                    torch.distributed.broadcast(context_tokens_tensor, src, group)
-                    context_tokens = context_tokens_tensor.cpu().numpy().tolist()
-
-            token_stream = get_token_stream(model, [context_tokens])
-            for _, decode_tokens in enumerate(token_stream):
-                pass
-
-            if mpu.get_tensor_model_parallel_rank() == 0:
-                if mpu.is_pipeline_first_stage():
-                    os.system('clear')
-                    print("\nContext:", raw_text, flush=True)
-
-                    fname_out.write("\nContext:")
-                    fname_out.write(raw_text)
-
-                    decode_tokens, _ = decode_tokens
-                    decode_tokens = decode_tokens[0].cpu().numpy().tolist()
-                    trim_decode_tokens = tokenizer.detokenize(
-                        decode_tokens)[raw_text_len:]
-                    print("\nMegatron-LM:", trim_decode_tokens, flush=True)
-
-                    fname_out.write("\n\nMegatron-LM:")
-                    fname_out.write(trim_decode_tokens)
-                    fname_out.write("\n")
-
-            raw_text = None
-            context_count += 1
-
-# We added this function to support the tasks evaluation such as squad
-# and drop in the https://github.com/EleutherAI/lm-evaluation-harness 
-# codebase. The lm-evaluation-harness code can now call this function
-# similar to their current generate function call used for gpt style models.
-def generate_samples_eval(model, context, max_gen_length, eos_token_id):
-    # Generate samples for lm evaluation
-    # NEED TO THINK ABOUT eos token
-
-    args = get_args()
-    tokenizer = get_tokenizer()
-
-    raw_text_len = len(context)
-    model.eval()
-
-    context_tokens = tokenizer.tokenize(context)
-    args.out_seq_length = max_gen_length + len(context_tokens)
-    args.eos_id = eos_token_id
-
-    with torch.no_grad():
-        token_stream = get_token_stream(model, [context_tokens])
-        for counter, decode_tokens in enumerate(token_stream):
-            if counter == args.out_seq_length:
-                break
-
-    decode_tokens, _ = decode_tokens
-    decode_tokens = decode_tokens[0].cpu().numpy().tolist()
-    trim_decode_tokens = tokenizer.detokenize(
-        decode_tokens)[raw_text_len:]
- 
-    return trim_decode_tokens
-
-
-def generate_samples_interactive(model, print_frequency=24):
-
-    args = get_args()
-    tokenizer = get_tokenizer()
-
-    context_count = 0
-    model.eval()
-    with torch.no_grad():
-        while True:
-            terminate_runs = 0
-            raw_text_len = 0
-
-            if mpu.is_pipeline_first_stage() \
-               and mpu.get_tensor_model_parallel_rank() == 0:
-                os.system('clear')
-                raw_text = input("\nContext prompt (stop to exit) >>> ")
-                while not raw_text:
-                    print('Prompt should not be empty!')
-                    raw_text = input("\nContext prompt (stop to exit) >>> ")
-                raw_text_len = len(raw_text)
-
-                if "stop" in raw_text:
-                    terminate_runs = 1
-                else:
-                    context_tokens = tokenizer.tokenize(raw_text)
-                    context_length = len(context_tokens)
-
-                    if context_length >= (args.seq_length // 2):
-                        print("\nContext length", context_length,
-                              "\nPlease give smaller context (half of the "
-                              "sequence length)!", flush=True)
-                        continue
-            else:
-                context_tokens = tokenizer.tokenize("EMPTY TEXT")
-                context_length = 0
-
-            input_info = [terminate_runs, raw_text_len, context_length]
-            input_info_tensor = torch.cuda.LongTensor(input_info)
-            torch.distributed.all_reduce(input_info_tensor,
-                                         group=mpu.get_model_parallel_group())
-            terminate_runs = input_info_tensor[0].item()
-            raw_text_len = input_info_tensor[1].item()
-            context_length = input_info_tensor[2].item()
-
-            if terminate_runs == 1:
-                return
-
-            # For pipeline parallel we send context tokens to other stages
-            # so they get the lengths correct
-            if mpu.get_tensor_model_parallel_rank() == 0 \
-               and args.pipeline_model_parallel_size > 1:
-                if mpu.is_pipeline_first_stage():
-                    src = mpu.get_pipeline_model_parallel_first_rank()
-                    group = mpu.get_pipeline_model_parallel_group()
-                    context_tokens_tensor = torch.cuda.LongTensor(context_tokens)
-                    torch.distributed.broadcast(context_tokens_tensor, src, group)
-                else:
-                    src = mpu.get_pipeline_model_parallel_first_rank()
-                    group = mpu.get_pipeline_model_parallel_group()
-                    context_tokens_tensor = torch.empty(context_length,
-                                                        dtype=torch.int64,
-                                                        device=torch.device("cuda"))
-                    torch.distributed.broadcast(context_tokens_tensor, src, group)
-                    context_tokens = context_tokens_tensor.cpu().numpy().tolist()
-
-            token_stream = get_token_stream(model, [context_tokens])
-
-            for counter, decode_tokens in enumerate(token_stream):
-                if counter % print_frequency != 0 \
-                   or mpu.get_tensor_model_parallel_rank() != 0 \
-                   or not mpu.is_pipeline_first_stage():
-                    continue
-
-                os.system('clear')
-                print("\nContext:", raw_text, flush=True)
-
-                decode_tokens, _ = decode_tokens
-                decode_tokens = decode_tokens[0].cpu().numpy().tolist()
-                trim_decode_tokens = tokenizer.detokenize(
-                    decode_tokens)[raw_text_len:]
-                print("\nMegatron-LM:", trim_decode_tokens, flush=True)
-
-            if mpu.is_pipeline_first_stage() \
-               and mpu.get_tensor_model_parallel_rank() == 0:
-                os.system('clear')
-                print("\nContext:", raw_text, flush=True)
-
-                if not isinstance(decode_tokens, list):
-                    decode_tokens, _ = decode_tokens
-                    decode_tokens = decode_tokens[0].cpu().numpy().tolist()
-                trim_decode_tokens = tokenizer.detokenize(
-                    decode_tokens)[raw_text_len:]
-                print("\nMegatron-LM:", trim_decode_tokens, flush=True)
-
-                input("\nPress Enter to continue >>>")
-
-            raw_text = None
-            context_count += 1
-
-
-
-def generate_samples_unconditional(model):
-
-    args = get_args()
-    tokenizer = get_tokenizer()
-
-    num_samples = args.num_samples
-    context_tokens = [[tokenizer.eod]
-                      for _ in range(args.micro_batch_size)]
-    ctr = 0
-    while True:
-        start_time = time.time()
-        for token_stream in get_token_stream(model,
-                                             copy.deepcopy(context_tokens)):
-            pass
-        if mpu.is_pipeline_last_stage() and \
-           mpu.get_tensor_model_parallel_rank() == 0:
-            if ctr % args.log_interval == 0:
-                print('Avg s/batch:',
-                      (time.time() - start_time) / min(args.log_interval, ctr + 1))
-                start_time = time.time()
-            length = len(token_stream)
-            token_batch = token_stream[0].cpu().numpy().tolist()
-            length_batch = token_stream[1].cpu().numpy().tolist()
-            assert len(length_batch) == args.micro_batch_size
-            for tokens, length in zip(token_batch, length_batch):
-                tokens = tokens[1:length - 1]
-                text = tokenizer.detokenize(tokens)
-                is_finished = length < args.seq_length - 1
-                datum = {'text': text, 'length': length - 1, 'finished': is_finished}
-                yield datum
-                ctr += 1
-                if ctr >= num_samples:
-                    break
-        else:
-            for _ in range(args.micro_batch_size):
-                yield None
-                ctr += 1
-                if ctr >= num_samples:
-                    break
-        if ctr >= num_samples:
-            break
-
-
-def generate_and_write_samples_unconditional(model):
-
-    args = get_args()
-    assert args.genfile is not None
-    with open(args.genfile, 'w') as f:
-        for datum in generate_samples_unconditional(model):
-            if mpu.is_pipeline_last_stage() and \
-               mpu.get_tensor_model_parallel_rank() == 0:
-                f.write(json.dumps(datum) + '\n')
-
-
 def pad_batch(batch, pad_id, args):
-
     context_lengths = []
     for tokens in batch:
         context_length = len(tokens)
@@ -387,41 +94,94 @@ def pad_batch(batch, pad_id, args):
         context_lengths.append(context_length)
     return batch, context_lengths
 
-
-def get_token_stream(model, context_tokens):
-
+def tokenize_batch(sentences):
     args = get_args()
     tokenizer = get_tokenizer()
-
+    context_tokens = [tokenizer.tokenize(s) for s in sentences]
     context_tokens, context_lengths = pad_batch(context_tokens,
                                                 tokenizer.eod, args)
-
     context_tokens_tensor = torch.cuda.LongTensor(context_tokens)
     context_length_tensor = torch.cuda.LongTensor(context_lengths)
+    return context_tokens_tensor, context_length_tensor 
+
+def send_generate_info(context_tokens_tensor, context_length_tensor, max_len):
+    """
+    Needs to be synced up with receive_generate_info
+    """
+    # Send the sizes of the tensors
+    input_info = [context_tokens_tensor.size(0), context_tokens_tensor.size(1), max_len]
+    input_info_tensor = torch.cuda.LongTensor(input_info)
+    torch.distributed.broadcast(input_info_tensor, 0)
+
+    # Send variables to all ranks 
+    torch.distributed.broadcast(context_length_tensor, 0)
+    torch.distributed.broadcast(context_tokens_tensor, 0)
+
+def receive_generate_info():
+    """
+    Needs to be synced up with send_generate_info
+    """
+    input_info_tensor = torch.empty(3, dtype=torch.int64, device=torch.cuda.current_device())
+    torch.distributed.broadcast(input_info_tensor, 0)
+    batch_size = input_info_tensor[0].item()
+    seq_len = input_info_tensor[1].item()
+    max_len = input_info_tensor[2].item()
     
-    torch.distributed.broadcast(context_length_tensor,
-                                mpu.get_tensor_model_parallel_src_rank(),
-                                group=mpu.get_tensor_model_parallel_group())
-    torch.distributed.broadcast(context_tokens_tensor,
-                                mpu.get_tensor_model_parallel_src_rank(),
-                                group=mpu.get_tensor_model_parallel_group())
+    context_length_tensor = torch.empty(batch_size, dtype=torch.int64, device=torch.cuda.current_device())
+    context_tokens_tensor = torch.empty(batch_size, seq_len, dtype=torch.int64, device=torch.cuda.current_device())
     
+    # Send variables to all ranks 
+    torch.distributed.broadcast(context_length_tensor, 0)
+    torch.distributed.broadcast(context_tokens_tensor, 0)
+    
+    return context_length_tensor, context_tokens_tensor, max_len
+
+def synced_generate(model, context_tokens_tensor, context_length_tensor, max_len):
     context_length = context_length_tensor.min().item()
     tokens, attention_mask, position_ids = get_batch(context_tokens_tensor)
 
     batch_token_iterator = sample_sequence_batch(model, context_tokens_tensor,
                                                  context_length_tensor,
-                                                 attention_mask, position_ids)
+                                                 attention_mask, position_ids,
+                                                 max_len)
     for tokens, lengths in batch_token_iterator:
         context_length += 1
+    
     if tokens is not None:
-            yield tokens[:, :context_length], lengths
+        return tokens[:, :context_length]
+
+def generate(model, sentences=None, max_len=0):
+    model.eval()
+    if torch.distributed.get_rank() == 0:
+        context_tokens_tensor, context_length_tensor = tokenize_batch(sentences)
+        send_generate_info(context_tokens_tensor, context_length_tensor, max_len)
     else:
-            yield None, None
+        context_length_tensor, context_tokens_tensor, max_len = receive_generate_info()
     
+    decode_tokens = synced_generate(model, context_tokens_tensor, context_length_tensor, max_len)
     
-def switch(val1, val2, boolean):
+    if torch.distributed.get_rank() == 0:
+        args = get_args()
+        tokenizer = get_tokenizer()
+        resp_sentences = []
+        for i in range(decode_tokens.size(0)):
+            decode_token = decode_tokens[i,:].cpu().numpy().tolist()
+            resp_sentences.append(tokenizer.detokenize(decode_token))
+        return resp_sentences
 
+def generate_samples_eval(model, context, max_gen_length, eos_token_id):
+    """
+    This function is here to provide an a matching API for a legacy task
+    This implementation hasn't been tested yet to make sure it matches
+    """
+    assert False, "Implementation untested"
+    args = get_args()
+    args.eos_id = eos_token_id
+    raw_text_len = len(context)
+    resp_sentences = generate(model, [context], max_gen_length)
+    return resp_sentences[0][raw_text_len:]
+
+def switch(val1, val2, boolean):
     boolean = boolean.type_as(val1)
     return (1 - boolean) * val1 + boolean * val2
 
@@ -435,6 +195,7 @@ def forward_step(model, tokens, position_ids, attention_mask, tokentype_ids,
     args = get_args()
     orig_seq_length = args.seq_length
     args.seq_length = tokens.shape[1]
+    args.micro_batch_size = tokens.shape[0]
 
     input_tensor = recv_forward()
 
@@ -462,7 +223,6 @@ def forward_step(model, tokens, position_ids, attention_mask, tokentype_ids,
 def sample_sequence_batch(model, context_tokens, context_lengths,
                           attention_mask, position_ids,
                           maxlen=None, type_ids=None):
-
     args = get_args()
     tokenizer = get_tokenizer()
 
@@ -486,22 +246,15 @@ def sample_sequence_batch(model, context_tokens, context_lengths,
         tokens = context_tokens
         if maxlen is None:
             maxlen = args.seq_length - 1
+        
+        maxlen = maxlen + org_context_length
+        
         if maxlen > (org_context_length + args.out_seq_length):
             maxlen = org_context_length + args.out_seq_length
         
         lengths = torch.ones([batch_size]).long().cuda() * maxlen
 
-        while context_length <= (maxlen):
-            if args.recompute:
-                output = forward_step(model, tokens,
-                                      position_ids,
-                                      attention_mask,
-                                      tokentype_ids=type_ids,
-                                      forward_method_parallel_output=False)
-                if mpu.is_pipeline_last_stage():
-                    assert output is not None
-                    logits = output[:, context_length - 1, :]
-            else:
+        while context_length < maxlen:
             types2use = None
             if counter == 0:
                 tokens2use = tokens[:, :context_length]

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])