Working single sentence version

megatron/api_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
+from flask_restful import Resource, Api
+
+from megatron import get_args
+from megatron import get_tokenizer
+from megatron import mpu
+from megatron.text_generation_utils import pad_batch
+from megatron.text_generation_utils import get_token_stream2
+
+GENERATE_NUM = 0
+
+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 
+
+
+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())
+    
+    @staticmethod
+    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,
+                                    mpu.get_tensor_model_parallel_src_rank(),
+                                    group=mpu.get_tensor_model_parallel_group())
+
+        # Now send tensors
+        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())
+
+    @staticmethod
+    def receive_generate_info():
+        """
+        Needs to be synced up with send_generate_info
+        """
+        input_info_tensor = torch.empty(3, dtype=torch.int64, device=torch.device("cuda"))
+        torch.distributed.broadcast(input_info_tensor,
+                                    mpu.get_tensor_model_parallel_src_rank(),
+                                    group=mpu.get_tensor_model_parallel_group())
+        batch_size = input_info_tensor[0].item()
+        seq_len = input_info_tensor[1].item()
+        max_len = input_info_tensor[2].item()
+        
+        context_length_tensor = torch.empty(batch_size, dtype=torch.int64, device=torch.device("cuda"))
+        context_tokens_tensor = torch.empty(batch_size, seq_len, dtype=torch.int64, device=torch.device("cuda"))
+        
+        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())
+        return context_length_tensor, context_tokens_tensor, max_len
+    
+    @staticmethod
+    def do_generate(model, context_length_tensor, context_tokens_tensor, max_len):
+        token_stream = get_token_stream2(model, context_tokens_tensor, context_length_tensor)
+        for i, decode_tokens in enumerate(token_stream):
+            if i == max_len-1:
+                break
+            pass
+        return decode_tokens
+    
+    def put(self):
+        sentences = request.get_json()["sentences"]
+        max_len = 1024  # TODO (rprenger) this should not be hardcoded
+        if "max_len" in request.get_json():
+            max_len = request.get_json()["max_len"]
+
+        context_tokens_tensor, context_length_tensor = tokenize_batch(sentences)
+        MegatronGenerate.send_do_generate()  # Tell other ranks we're doing generate
+        MegatronGenerate.send_generate_info(context_tokens_tensor, context_length_tensor, max_len)  # Send them info
+        decode_tokens = MegatronGenerate.do_generate(self.model, context_length_tensor, context_tokens_tensor, max_len)  # Do stuff
+        
+        args = get_args()
+        tokenizer = get_tokenizer()
+        decode_tokens, _ = decode_tokens
+        decode_tokens = decode_tokens[0].cpu().numpy().tolist()
+        trim_decode_tokens = tokenizer.detokenize(decode_tokens)
+        return jsonify({"sentences": [trim_decode_tokens]})
+    
+
+class MegatronServer(object):
+    def __init__(self, model):
+        self.app = Flask(__name__)
+        api = Api(self.app)
+        api.add_resource(MegatronGenerate, '/generate', resource_class_args=[model])
+
+    def run(self, url):
+        self.app.run(url, debug=False)

megatron/text_generation_utils.py

@@ -387,6 +387,19 @@ def pad_batch(batch, pad_id, args):
         context_lengths.append(context_length)
     return batch, context_lengths
 
+def get_token_stream2(model, context_tokens_tensor, context_length_tensor):
+    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)
+    for tokens, lengths in batch_token_iterator:
+        context_length += 1
+        if tokens is not None:
+            yield tokens[:, :context_length], lengths
+        else:
+            yield None, None
 
 def get_token_stream(model, context_tokens):
 
@@ -406,18 +419,7 @@ def get_token_stream(model, context_tokens):
                                 mpu.get_tensor_model_parallel_src_rank(),
                                 group=mpu.get_tensor_model_parallel_group())
 
-    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)
-    for tokens, lengths in batch_token_iterator:
-        context_length += 1
-        if tokens is not None:
-            yield tokens[:, :context_length], lengths
-        else:
-            yield None, None
+    return get_token_stream2(model, context_tokens_tensor, context_length_tensor)
 
 
 def switch(val1, val2, boolean):

tools/run_api_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 get_tokenizer
+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_utils import generate_samples_interactive
+from megatron.api_server import MegatronServer
+from megatron.api_server import MegatronGenerate
+import torch
+
+def do_generate(model):
+    context_length_tensor, context_tokens_tensor, max_len = MegatronGenerate.receive_generate_info()
+    MegatronGenerate.do_generate(model, context_length_tensor, context_tokens_tensor, max_len) 
+
+
+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):
+    """Text generation arguments."""
+    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.')
+    group.add_argument("--sample-input-file", type=str, default=None,
+                       help='Get input from file instead of interactive mode, '
+                       'each line is an input.')
+    group.add_argument("--sample-output-file", type=str, default=None,
+                       help='Output file got from --sample-input-file')
+    group.add_argument("--num-samples", type=int, default=0,
+                       help='Number of samples to generate unconditionally, '
+                       'defaults to 0 and interactive conditional sampling')
+    group.add_argument("--genfile", type=str,
+                       help='Output file when generating unconditionally')
+    group.add_argument("--recompute", action='store_true',
+                       help='During generation recompute all attention '
+                       'instead of using previously computed keys/values.')
+
+    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())
+        print("got: "+str(choice[0].item()))
+        if choice[0].item() == 0:
+            do_generate(model)