401 lines
15 KiB
Python
401 lines
15 KiB
Python
# Copyright (c) 2023, Tri Dao.
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# Adapted from https://github.com/NVIDIA/Megatron-LM/blob/0bb597b42c53355a567aba2a1357cc34b9d99ddd/megatron/text_generation/forward_step.py#L31
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import gc
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import time
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from collections import namedtuple
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from dataclasses import dataclass, field
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from typing import Callable, Optional, Sequence, Union
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import torch
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from einops import rearrange
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from torch import Tensor
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from torch.profiler import ProfilerActivity, profile, record_function
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from transformers.generation import GreedySearchDecoderOnlyOutput, SampleDecoderOnlyOutput
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@dataclass
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class InferenceParams:
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"""Inference parameters that are passed to the main model in order
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to efficienly calculate and store the context during inference."""
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max_sequence_len: int
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max_batch_size: int
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sequence_len_offset: int = 0
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batch_size_offset: int = 0
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key_value_memory_dict: dict = field(default_factory=dict)
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fused_ft_kernel: bool = False
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lengths_per_sample: Optional[Tensor] = None
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# https://github.com/NVIDIA/Megatron-LM/blob/0bb597b42c53355a567aba2a1357cc34b9d99ddd/megatron/text_generation/sampling.py
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# https://github.com/huggingface/transformers/blob/a44985b41cfa2de48a5e1de7f1f93b7483da25d1/src/transformers/generation/logits_process.py#L231
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def modify_logits_for_top_k_filtering(logits, top_k):
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"""Set the logits for none top-k values to -inf."""
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indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
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logits.masked_fill_(indices_to_remove, float("-Inf"))
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# https://github.com/NVIDIA/Megatron-LM/blob/0bb597b42c53355a567aba2a1357cc34b9d99ddd/megatron/text_generation/sampling.py
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# https://github.com/huggingface/transformers/blob/a44985b41cfa2de48a5e1de7f1f93b7483da25d1/src/transformers/generation/logits_process.py#L170
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def modify_logits_for_top_p_filtering(logits, top_p):
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"""Set the logits for none top-p values to -inf."""
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if top_p <= 0.0 or top_p >= 1.0:
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return
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# First sort and calculate cumulative sum of probabilities.
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sorted_logits, sorted_indices = torch.sort(logits, descending=False)
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cumulative_probs = sorted_logits.softmax(dim=-1).cumsum(dim=-1)
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# Remove tokens with cumulative top_p above the threshold (token with 0 are kept)
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sorted_indices_to_remove = cumulative_probs <= (1 - top_p)
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# scatter sorted tensors to original indexing
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indices_to_remove = sorted_indices_to_remove.scatter(
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1, sorted_indices, sorted_indices_to_remove
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)
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logits = logits.masked_fill(indices_to_remove, float("-inf"))
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def sample(logits, top_k=1, top_p=0.0, temperature=1.0):
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"""Sample from top-k logits.
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Arguments:
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logits: Tensor of shape (batch_size, vocab_size)
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"""
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if top_k == 1: # Short-circuit for greedy decoding
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return logits.argmax(dim=-1)
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else:
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if top_p > 0.0:
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assert top_p <= 1.0, "top-p should be in (0, 1]."
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if top_k > 0:
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top_k = min(top_k, logits.size(-1)) # Safety check
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logits_top, indices = torch.topk(logits, top_k, dim=-1)
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if temperature != 1.0:
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logits_top /= temperature
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modify_logits_for_top_p_filtering(logits_top, top_p)
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return indices[
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torch.arange(indices.shape[0], device=indices.device),
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torch.multinomial(torch.softmax(logits_top, dim=-1), num_samples=1).squeeze(dim=-1),
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]
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else:
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# Clone so that when we modify for top_p we don't change the original logits
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logits_top = logits / temperature if temperature != 1.0 else logits.clone()
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modify_logits_for_top_p_filtering(logits_top, top_p)
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return torch.multinomial(torch.softmax(logits_top, dim=-1), num_samples=1).squeeze(
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dim=-1
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)
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def decode(
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input_ids,
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model,
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max_length,
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top_k=1,
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top_p=0.0,
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temperature=1.0,
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eos_token_id=None,
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teacher_outputs=None,
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vocab_size=None,
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tensor_parallel=1,
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fused_ft_kernel=False,
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cg=False,
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timing=False,
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):
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"""Decoding, either greedy or with top-k or top-p sampling.
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If top-k = 0, don't limit the number of candidates (pure sampling).
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Top-k and top-p can be used together. If top_k > 0 and top_p > 0, then top-k is applied first,
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then top-p.
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We assume that all sequences in the same batch have the same length.
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Arguments:
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input_ids: (batch, seq_len)
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max_length: int
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teacher_outputs (optional): (batch, seq_len). If provided, instead of sampling from the
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logits, the next token is taken from the teacher_outputs. Useful for testing.
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Returns: GreedySearchDecoderOnlyOutput or SampleDecoderOnlyOutput, with the following fields:
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sequences: (batch, max_length)
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scores: tuples of (batch, vocab_size)
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"""
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batch_size, seqlen_og = input_ids.shape
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teacher_output_len = teacher_outputs.shape[1] if teacher_outputs is not None else 0
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if cg:
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assert fused_ft_kernel
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if not hasattr(model, "_decoding_cache"):
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model._decoding_cache = None
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model._decoding_cache = update_graph_cache(
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model,
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model._decoding_cache,
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batch_size,
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seqlen_og,
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max_length,
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tensor_parallel=tensor_parallel,
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)
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inference_params = model._decoding_cache.inference_params
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inference_params.max_sequence_len = max_length
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inference_params.max_batch_size = batch_size
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inference_params.sequence_len_offset = 0
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else:
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inference_params = InferenceParams(
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max_sequence_len=max_length, max_batch_size=batch_size, fused_ft_kernel=fused_ft_kernel
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)
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def logits_forward_fn(input_ids, position_ids, inference_params):
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if not cg:
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return model(
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input_ids,
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position_ids=position_ids,
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inference_params=inference_params,
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num_last_tokens=1,
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).logits.squeeze(dim=1)
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else:
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return model._decoding_cache.run(
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input_ids, position_ids, inference_params.sequence_len_offset
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).clone()
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logits_postprocess_fn = (
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lambda logits: logits[..., :vocab_size] if vocab_size is not None else logits
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)
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scores = []
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with torch.inference_mode():
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if timing:
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if tensor_parallel > 1:
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torch.distributed.barrier()
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torch.cuda.synchronize()
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start = time.time()
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logits = model(
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input_ids, inference_params=inference_params, num_last_tokens=1
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).logits.squeeze(dim=1)
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logits = logits_postprocess_fn(logits)
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scores.append(logits if not cg else logits.clone())
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if teacher_outputs is None or teacher_output_len <= seqlen_og:
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next_token = sample(logits, top_k=top_k, top_p=top_p, temperature=temperature)
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else:
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next_token = teacher_outputs[:, seqlen_og]
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sequences = [next_token]
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inference_params.sequence_len_offset = seqlen_og
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while True:
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position_ids = torch.full(
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(batch_size, 1),
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inference_params.sequence_len_offset,
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dtype=torch.long,
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device=input_ids.device,
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)
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logits = logits_postprocess_fn(
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logits_forward_fn(rearrange(next_token, "b -> b 1"), position_ids, inference_params)
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)
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scores.append(logits)
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if (
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teacher_outputs is None
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or teacher_output_len <= inference_params.sequence_len_offset + 1
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):
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next_token = sample(logits, top_k=top_k, top_p=top_p, temperature=temperature)
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else:
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next_token = teacher_outputs[:, inference_params.sequence_len_offset + 1]
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sequences.append(next_token)
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inference_params.sequence_len_offset += 1
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if eos_token_id is not None and (next_token == eos_token_id).all():
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break
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if inference_params.sequence_len_offset >= max_length - 1:
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break
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if timing:
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if tensor_parallel > 1:
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torch.distributed.barrier()
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torch.cuda.synchronize()
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print(f"Prompt processing + decoding time: {(time.time() - start) * 1000:.0f}ms")
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output_cls = GreedySearchDecoderOnlyOutput if top_k == 1 else SampleDecoderOnlyOutput
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return output_cls(
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sequences=torch.cat([input_ids, torch.stack(sequences, dim=1)], dim=1), scores=tuple(scores)
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)
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class GenerationMixin:
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def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):
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raise NotImplementedError
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def generate(
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self,
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input_ids,
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max_length,
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top_k=1,
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top_p=0.0,
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temperature=1.0,
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return_dict_in_generate=False,
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output_scores=False,
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**kwargs,
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):
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output = decode(
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input_ids, self, max_length, top_k=top_k, top_p=top_p, temperature=temperature, **kwargs
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)
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if not output_scores:
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output.scores = None
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return output if return_dict_in_generate else output.sequences
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def allocate_inference_cache(
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max_batch_size,
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max_seqlen,
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nheads,
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headdim,
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layers: Union[int, Sequence],
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device,
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dtype=torch.float16,
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):
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assert dtype in [torch.float16, torch.bfloat16, torch.float32]
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packsize = 4 if dtype == torch.float32 else 8
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assert headdim % packsize == 0
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k_cache_shape = (max_batch_size, nheads, headdim // packsize, max_seqlen, packsize)
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v_cache_shape = (max_batch_size, nheads, max_seqlen, headdim)
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if isinstance(layers, int):
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layers = range(layers)
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return {
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i: (
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torch.empty(k_cache_shape, device=device, dtype=dtype),
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torch.empty(v_cache_shape, device=device, dtype=dtype),
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)
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for i in layers
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}
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def seqlen_to_seqlen_type(seqlen: int) -> int:
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"""Convert sequence length to a seqlen_type.
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This is used to determine which cuda graph to use.
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Arguments:
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seqlen: int
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"""
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return 0 if seqlen < 32 else (1 if seqlen < 2048 else 2)
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def seqlen_type_to_max_seqlen(seqlen_type: int) -> int:
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assert seqlen_type in [0, 1, 2]
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return 32 if seqlen_type == 0 else (2048 if seqlen_type == 1 else 2**32)
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@dataclass
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class DecodingCGCache:
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max_batch_size: int = 0
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max_seqlen: int = 0
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device = None
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dtype = None
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callables: dict = field(default_factory=dict)
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mempool = None
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inference_params: Optional[InferenceParams] = None
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run: Optional[Callable] = None
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@torch.inference_mode()
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def update_graph_cache(
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model, cache, batch_size, seqlen_og, max_seqlen, tensor_parallel=1, dtype=None, n_warmups=2
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):
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if cache is None:
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cache = DecodingCGCache()
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param_example = next(iter(model.parameters()))
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device = param_example.device
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if dtype is None:
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dtype = param_example.dtype
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if (
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(device, dtype) != (cache.device, cache.dtype)
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or batch_size > cache.max_batch_size
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or max_seqlen > cache.max_seqlen
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): # Invalidate the cache
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cache.callables = {}
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cache.mempool = None
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cache.inference_params = None
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gc.collect()
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cache.device, cache.dtype = device, dtype
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cache.max_batch_size, cache.max_seqlen = batch_size, max_seqlen
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if hasattr(model, "allocate_inference_cache"):
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inf_cache = model.allocate_inference_cache(batch_size, max_seqlen, dtype)
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else:
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headdim = getattr(
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model.config,
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"head_dim",
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model.config.hidden_size // model.config.num_attention_heads,
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)
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inf_cache = allocate_inference_cache(
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batch_size,
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max_seqlen,
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model.config.num_attention_heads // tensor_parallel,
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headdim,
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model.config.num_hidden_layers,
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device,
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dtype,
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)
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lengths_per_sample = torch.full((batch_size,), seqlen_og, dtype=torch.int32, device=device)
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cache.inference_params = InferenceParams(
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max_sequence_len=max_seqlen,
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max_batch_size=batch_size,
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sequence_len_offset=seqlen_og,
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key_value_memory_dict=inf_cache,
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fused_ft_kernel=True,
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lengths_per_sample=lengths_per_sample,
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)
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cache.mempool = torch.cuda.graphs.graph_pool_handle()
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for s_type in range(seqlen_to_seqlen_type(seqlen_og), seqlen_to_seqlen_type(max_seqlen) + 1):
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if (batch_size, s_type) not in cache.callables:
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max_seqlen_ = min(max(seqlen_og, seqlen_type_to_max_seqlen(s_type)), max_seqlen)
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cache.callables[batch_size, s_type] = capture_graph(
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model,
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cache.inference_params,
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batch_size,
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max_seqlen_,
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mempool=cache.mempool,
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n_warmups=n_warmups,
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)
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def dispatch(input_ids, position_ids, seqlen):
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batch_size = input_ids.shape[0]
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return cache.callables[batch_size, seqlen_to_seqlen_type(seqlen)](
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input_ids, position_ids, seqlen
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)
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cache.run = dispatch
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cache.inference_params.sequence_len_offset = 0 # Reset so it's not confusing
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return cache
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def capture_graph(model, inference_params, batch_size, max_seqlen, mempool=None, n_warmups=2):
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device = next(iter(model.parameters())).device
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input_ids = torch.full((batch_size, 1), 0, dtype=torch.long, device=device)
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position_ids = torch.full((batch_size, 1), 0, dtype=torch.long, device=device)
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sequence_len_offset_og = inference_params.sequence_len_offset
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# TD [2023-04-14]: important for correctness of the FT's attention kernel, as seqlen_cpu is
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# used to determine the size of smem. Hence seqlen_cpu must be >= lengths_per_sample.
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inference_params.sequence_len_offset = max_seqlen - 1
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inference_params.lengths_per_sample[:] = max_seqlen - 1
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# Warmup before capture
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s = torch.cuda.Stream()
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s.wait_stream(torch.cuda.current_stream())
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with torch.cuda.stream(s):
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for _ in range(n_warmups):
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logits = model(
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input_ids,
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position_ids=position_ids,
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inference_params=inference_params,
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num_last_tokens=1,
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).logits
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s.synchronize()
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# This might be needed for correctness if we run with NCCL_GRAPH_MIXING_SUPPORT=0,
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# which requires that graph launch and non-captured launch to not overlap (I think,
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# that's how I interpret the documentation). I'm not sure if this is required.
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if torch.distributed.is_initialized():
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torch.distributed.barrier()
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torch.cuda.current_stream().wait_stream(s)
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# Captures the graph
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# To allow capture, automatically sets a side stream as the current stream in the context
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graph = torch.cuda.CUDAGraph()
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with torch.cuda.graph(graph, pool=mempool):
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logits = model(
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input_ids,
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position_ids=position_ids,
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inference_params=inference_params,
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num_last_tokens=1,
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).logits.squeeze(dim=1)
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def run(new_input_ids, new_position_ids, seqlen):
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inference_params.lengths_per_sample[:] = seqlen
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input_ids.copy_(new_input_ids)
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position_ids.copy_(new_position_ids)
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graph.replay()
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return logits
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inference_params.sequence_len_offset = sequence_len_offset_og
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return run
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