working

megatron/inference/api.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.
+
+"""Inference API."""
+
+
+import torch
+
+from .communication import broadcast_float_list
+from .generation import generate_tokens_probs_and_return_on_first_stage
+from .tokenization import tokenize_prompts
+
+
+def generate(model,
+             prompts=None,
+             tokens_to_generate=0,
+             return_output_log_probs=False,
+             return_all_log_probs=False,
+             temperature=1.0):
+    """TO DO ..."""
+
+    # Make sure input params are avaialble to all ranks.
+    values = [tokens_to_generate, return_output_log_probs,
+              return_all_log_probs, temperature]
+    values_float_tensor = broadcast_float_list(4, float_list=values)
+    tokens_to_generate = int(values_float_tensor[0].item())
+    return_output_log_probs = bool(values_float_tensor[1].item())
+    return_all_log_probs = bool(values_float_tensor[2].item())
+    temperature = values_float_tensor[2].item()
+
+    # Tokenize prompts and get the batch.
+    # Note that these tensors are broadcaseted to all ranks.
+    if torch.distributed.get_rank() == 0:
+        assert prompts is not None
+    context_tokens_tensor, context_length_tensor = tokenize_prompts(
+        prompts=prompts, tokens_to_generate=tokens_to_generate)
+
+    # Main inference function.
+    # Note that the outputs are available on the first stage.
+    return generate_tokens_probs_and_return_on_first_stage(
+        model, context_tokens_tensor, context_length_tensor,
+        return_output_log_probs=return_output_log_probs,
+        return_all_log_probs=return_all_log_probs,
+        temperature=temperature)

megatron/inference/communication.py

@@ -40,6 +40,33 @@ def broadcast_from_last_pipeline_stage(size, dtype, tensor=None):
     return tensor
 
 
+
+def broadcast_from_last_to_first_pipeline_stage(size, dtype, tensor=None):
+    """Broadcast tensor values from last stage into the first stage."""
+
+    # Only first and last stage pipeline stages need to be involved.
+    is_last_stage = mpu.is_pipeline_last_stage()
+    is_first_stage = mpu.is_pipeline_first_stage()
+    if is_last_stage or is_first_stage:
+        if is_last_stage:
+            assert tensor is not None
+            assert tensor.is_cuda
+            assert tensor.is_contiguous()
+        else:
+            tensor = torch.empty(size,
+                                 dtype=dtype,
+                                 device=torch.cuda.current_device())
+        src = mpu.get_pipeline_model_parallel_last_rank()
+        group = mpu.get_embedding_group()
+        # Broadcast from last stage into the first stage.
+        torch.distributed.broadcast(tensor, src, group)
+    else:
+        tensor = None
+
+    return tensor
+
+
+
 def copy_from_last_to_first_pipeline_stage(size, dtype, tensor=None):
     """Copy tensor values from last stage into the first stage.
     Note that the input tensor is updated in place."""
@@ -48,11 +75,15 @@ def copy_from_last_to_first_pipeline_stage(size, dtype, tensor=None):
     is_last_stage = mpu.is_pipeline_last_stage()
     is_first_stage = mpu.is_pipeline_first_stage()
     if is_last_stage or is_first_stage:
+        assert tensor is not None
+        assert tensor.is_cuda
+        is_contiguous = tensor.is_contiguous()
         src = mpu.get_pipeline_model_parallel_last_rank()
         group = mpu.get_embedding_group()
+        if is_contiguous:
+            tensor_ = tensor
+        else:
             if is_last_stage:
-            assert tensor is not None
-            assert tensor.is_cuda
                 tensor_ = tensor.contiguous()
             else:
                 tensor_ = torch.empty(size,
@@ -61,7 +92,7 @@ def copy_from_last_to_first_pipeline_stage(size, dtype, tensor=None):
         # Broadcast from last stage into the first stage.
         torch.distributed.broadcast(tensor_, src, group)
         # Update the first stage tensor
-        if is_first_stage:
+        if is_first_stage and not is_contiguous:
             tensor[...] = tensor_
 
 

megatron/inference/generation.py

@@ -19,19 +19,44 @@
 import torch
 import torch.nn.functional as F
 
-from megatron import get_args, get_tokenizer
-from megatron import mpu
+from megatron import get_args, get_tokenizer, mpu
 from megatron.utils import get_ltor_masks_and_position_ids
 from .communication import (
     copy_from_last_to_first_pipeline_stage,
-    broadcast_from_last_pipeline_stage)
+    broadcast_from_last_pipeline_stage,
+    broadcast_from_last_to_first_pipeline_stage)
 from .forward_step import forward_step
 from .sampling import sample
 
 
-def generate_tokens(model, tokens, lengths, return_all_probs=False,
+def generate_tokens_probs_and_return_on_first_stage(
+        model, tokens, lengths,
+        return_output_log_probs=False,
+        return_all_log_probs=False,
         temperature=1.0):
-    """Main token generation function."""
+    """Main token generation function.
+    Arguments:
+        model: XXX
+        tokens: prompt tokens extended to be of size [b, max-sequence-length]
+        lengths: original prompt length, size: [b]
+        return_output_log_probs: flag to calculate the log probability of
+            the generated tokens. Note that the log probability is the one
+            after logits are modifed for sampling.
+        return_all_log_probs: flag to calculate the log probability of across
+            all the tokens (vocab size). Note that the log probability is the
+            one after logits are modifed for sampling.
+        temperature: sampling temperature.
+    Note: Outside of model, other parameters only need to be available on
+          rank 0.
+    Outputs: Note that is size is adjusted to a lower value than
+             max-sequence-length if generation is terminated early.
+        tokens: prompt and generated tokens. size: [b, :]
+        generated_sequence_lengths: total length (including prompt) of
+            the generated sequence. size: [b]
+        output_log_probs: log probability of the selected tokens. size: [b, s]
+        all_log_probs: log probability of all the tokens.
+            size: [b, s, vocab-size]
+    """
 
     args = get_args()
     tokenizer = get_tokenizer()
@@ -52,11 +77,24 @@ def generate_tokens(model, tokens, lengths, return_all_probs=False,
     # Pre-allocate memory
     # ===================
 
-    # Log probability of the sequence (prompt + generated tokens)
-    output_log_probs = torch.empty(batch_size, max_sequence_length - 1,
+    # Log probability of the sequence (prompt + generated tokens).
+    output_log_probs = None
+    output_log_probs_size = (batch_size, max_sequence_length - 1)
+    # Log probability of all tokens for the sequence.
+    all_log_probs = None
+    all_log_probs_size = (batch_size, max_sequence_length -1,
+                          args.padded_vocab_size)
+    # Lengths of generated seuquence including including prompts.
+    generated_sequence_lengths = None
+    if mpu.is_pipeline_last_stage():
+        if return_output_log_probs:
+            output_log_probs = torch.empty(output_log_probs_size,
+                                           dtype=torch.float32,
+                                           device=torch.cuda.current_device())
+        if return_all_log_probs:
+            all_log_probs = torch.empty(all_log_probs_size,
                                         dtype=torch.float32,
                                         device=torch.cuda.current_device())
-    # Lengths of generated seuquence including including prompts.
         generated_sequence_lengths = torch.ones(
             batch_size, dtype=torch.int64,
             device=torch.cuda.current_device()) * max_sequence_length
@@ -64,6 +102,10 @@ def generate_tokens(model, tokens, lengths, return_all_probs=False,
     is_generation_done = torch.zeros(batch_size, dtype=torch.uint8,
                                      device=torch.cuda.current_device())
 
+    # =============
+    # Run infernece
+    # =============
+
     attention_mask, position_ids = _build_attention_mask_and_position_ids(
         tokens)
 
@@ -114,14 +156,24 @@ def generate_tokens(model, tokens, lengths, return_all_probs=False,
                 tokens[started, context_length] = new_sample[started]
 
                 # Calculate the log probabilities.
+                if return_output_log_probs or return_all_log_probs:
                     log_probs = F.log_softmax(logits, dim=2)
-                # Pick the tokens that we need to get the log probabilities for.
-                # Note that next input token is the token which we selected in
-                # the current logits, so shift by 1.
+                    if return_all_log_probs:
+                        all_log_probs[:,
+                                      prev_context_length:context_length,
+                                      :] = log_probs
+                    if return_output_log_probs:
+                        # Pick the tokens that we need to get the log
+                        # probabilities for. Note that next input token is
+                        # the token which we selected in the current logits,
+                        # so shift by 1.
                         indices = torch.unsqueeze(
-                    tokens[:, (prev_context_length + 1):(context_length + 1)],
+                            tokens[
+                                :,
+                                (prev_context_length + 1):(context_length + 1)],
                             2)
-                output_log_probs[:, prev_context_length:context_length] = \
+                        output_log_probs[:,
+                                         prev_context_length:context_length] = \
                             torch.gather(log_probs, 2, indices).squeeze(2)
 
             # Update the tokens on the first stage so the next input to
@@ -147,17 +199,36 @@ def generate_tokens(model, tokens, lengths, return_all_probs=False,
             if done:
                 break
 
+    # ===================================================
+    # Update the length of based on max generated length.
+    # ===================================================
+
+    tokens = tokens[:, :(context_length + 1)]
     if mpu.is_pipeline_last_stage():
-            if return_all_probs:
-                full_logits = None
-                return tokens, generated_sequence_lengths, output_log_probs, \
-                    full_logits, context_length + 1
+        if return_output_log_probs:
+            output_log_probs = output_log_probs[:, :context_length]
+        if return_all_log_probs:
+            all_log_probs = all_log_probs[:, :context_length, :]
+
+    # ======================================
+    # Broadcast to the first pipeline stage.
+    # ======================================
+
+    generated_sequence_lengths = broadcast_from_last_to_first_pipeline_stage(
+        batch_size, torch.int64, generated_sequence_lengths)
+    if return_output_log_probs:
+        output_log_probs_size = (batch_size, context_length)
+        output_log_probs = broadcast_from_last_to_first_pipeline_stage(
+            output_log_probs_size, torch.float32, output_log_probs)
+    if return_all_log_probs:
+        all_log_probs_size = (batch_size, context_length,
+                              args.padded_vocab_size)
+        all_log_probs = broadcast_from_last_to_first_pipeline_stage(
+            all_log_probs_size, torch.float32, all_log_probs)
+
     return tokens, generated_sequence_lengths, output_log_probs, \
-                None, context_length + 1
+        all_log_probs
 
-        if mpu.is_pipeline_first_stage():
-            return tokens, None, None, None, context_length + 1
-        return None, None, None, None, context_length + 1
 
 
 def _build_attention_mask_and_position_ids(tokens):

megatron/inference/tokenization.py

@@ -23,6 +23,39 @@ from megatron import get_tokenizer
 from .communication import broadcast_int_list, broadcast_tensor
 
 
+def detokenize_generations(tokens_gpu_tensor,
+                           lengths_gpu_tensor,
+                           return_segments):
+    """Detokenize the generated tokens."""
+
+    tokenizer = get_tokenizer()
+
+    prompts_plus_generations = []
+    if return_segments:
+        prompts_plus_generations_segments = []
+
+    tokens = tokens_gpu_tensor.cpu().numpy().tolist()
+    lengths = lengths_gpu_tensor.cpu().numpy().tolist()
+    for sequence_tokens, length in zip(tokens, lengths):
+        sequence_tokens = sequence_tokens[:length]
+        prompts_plus_generations.append(
+            tokenizer.detokenize(sequence_tokens))
+        if return_segments:
+            words = []
+            for token in sequence_tokens:
+                word = tokenizer.tokenizer.decoder[token]
+                word = bytearray(
+                    [tokenizer.tokenizer.byte_decoder[c] for c in word]).decode(
+                        'utf-8', errors='replace')
+                words.append(word)
+            prompts_plus_generations_segments.append(words)
+
+    if return_segments:
+        return tokens, prompts_plus_generations, \
+            prompts_plus_generations_segments
+
+    return tokens, prompts_plus_generations
+
 
 def tokenize_prompts(prompts=None, tokens_to_generate=None, rank=0):
     """Tokenize prompts and make them avaiable on all ranks."""

megatron/text_generation_utils.py

@@ -153,8 +153,12 @@ def synced_generate(model, context_tokens_tensor, context_length_tensor, tokens_
     if mpu.is_pipeline_last_stage():
         src = mpu.get_pipeline_model_parallel_last_rank()
         group = mpu.get_embedding_group()
+        print('last rank output size {} {} | \n'.format(output_logits.size(0), output_logits.size(1)))
         torch.distributed.broadcast(output_logits, src, group)
         if all_probs:
+            print('last rank full size {} {} | \n'.format(full_logits.size(0),
+                                                        full_logits.size(1),
+                                                        full_logits.size(2)))
             src = mpu.get_pipeline_model_parallel_last_rank()
             group = mpu.get_embedding_group()
             torch.distributed.broadcast(full_logits, src, group)
@@ -164,13 +168,18 @@ def synced_generate(model, context_tokens_tensor, context_length_tensor, tokens_
             src = mpu.get_pipeline_model_parallel_last_rank()
             group = mpu.get_embedding_group()
             output_logits = torch.empty(tokens.size(0), context_length-1, dtype=torch.float32, device=torch.device("cuda"))
+            print('first rank output size {} {} | \n'.format(output_logits.size(0), output_logits.size(1)))
             torch.distributed.broadcast(output_logits, src, group)
             
             if all_probs:
                 args = get_args()
                 src = mpu.get_pipeline_model_parallel_last_rank()
                 group = mpu.get_embedding_group()
-                full_logits = torch.empty(tokens.size(0), context_length, args.padded_vocab_size, dtype=torch.float32, device=torch.device("cuda"))
+                full_logits = torch.empty(tokens.size(0), context_length-1, args.padded_vocab_size, dtype=torch.float32, device=torch.device("cuda"))
+                print('first rank full size {} {} | \n'.format(full_logits.size(0),
+                                                            full_logits.size(1),
+                                                            full_logits.size(2)))
+                
                 torch.distributed.broadcast(full_logits, src, group)
     if tokens is not None:
         return tokens[:, :context_length], output_logits, full_logits 
@@ -204,7 +213,7 @@ def generate(model, sentences=None, tokens_to_generate=0, all_probs=False, tempe
 
         output_logits = output_logits.cpu().numpy().tolist()
         if all_probs:
-            full_logits = full_logits.cpu().numpy().tolist()
+            full_logits = full_logits.cpu().numpy() #.tolist()
        
         return resp_sentences, resp_sentences_seg, output_logits, full_logits, decode_tokens