wehub-resource-sync
265 lines
8.7 KiB
Python
265 lines
8.7 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import torch
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from vllm.triton_utils import HAS_TRITON, tl, tldevice, triton
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# Smallest positive value produced by Triton's fp32 `tl.rand`. Used to clamp
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# zero draws before the flipped Gumbel transform below.
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#
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# Triton requires globals accessed from `@triton.jit` functions to be wrapped
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# in `tl.constexpr(...)`. We can only do that when Triton is actually
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# available — on the CPU worker path `tl` is a placeholder whose `constexpr`
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# attribute is `None`, and `tl.constexpr(...)` would crash at import time.
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_TL_RAND_MIN = tl.constexpr(4.6566127342e-10) if HAS_TRITON else 4.6566127342e-10
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@triton.jit
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def _temperature_kernel(
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logits_ptr,
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logits_stride,
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expanded_idx_mapping_ptr,
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temperature_ptr,
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vocab_size,
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BLOCK_SIZE: tl.constexpr,
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):
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token_idx = tl.program_id(0).to(tl.int64)
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req_state_idx = tl.load(expanded_idx_mapping_ptr + token_idx)
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temperature = tl.load(temperature_ptr + req_state_idx).to(tl.float32)
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if temperature == 0.0 or temperature == 1.0:
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# Early return to avoid loading logits.
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return
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block_idx = tl.program_id(1)
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block = block_idx * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
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mask = block < vocab_size
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logits = tl.load(logits_ptr + token_idx * logits_stride + block, mask=mask)
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logits = logits.to(tl.float32)
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logits = logits / temperature
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tl.store(logits_ptr + token_idx * logits_stride + block, logits, mask=mask)
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def apply_temperature(
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logits: torch.Tensor,
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expanded_idx_mapping: torch.Tensor,
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temperature: torch.Tensor,
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) -> None:
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num_tokens, vocab_size = logits.shape
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BLOCK_SIZE = 8192
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num_blocks = triton.cdiv(vocab_size, BLOCK_SIZE)
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_temperature_kernel[(num_tokens, num_blocks)](
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logits,
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logits.stride(0),
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expanded_idx_mapping,
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temperature,
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vocab_size,
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BLOCK_SIZE=BLOCK_SIZE,
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)
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@triton.jit
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def tl_rand64(seed, offset, includes_zero: tl.constexpr):
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lo, hi, _, _ = tl.randint4x(seed, offset)
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lo = lo.to(tl.uint32, bitcast=True).to(tl.uint64)
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hi = hi.to(tl.uint32, bitcast=True).to(tl.uint64)
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r = (hi << 32) | lo
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# 1 / 2**64
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scale = 5.421010862427522170037e-20
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u = r.to(tl.float64) * scale
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if not includes_zero:
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u = tl.maximum(u, 2.2250738585072014e-308) # float64 tiny
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return u
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@triton.jit
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def tl_rand32(seed, offset, includes_zero: tl.constexpr):
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u = tl.rand(seed, offset)
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if not includes_zero:
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u = tl.maximum(u, _TL_RAND_MIN)
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return u
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@triton.jit
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def gumbel_block_argmax(
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logits,
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block,
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mask,
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token_idx,
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expanded_idx_mapping_ptr,
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temp_ptr,
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seeds_ptr,
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pos_ptr,
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processed_logits_ptr,
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processed_logits_stride,
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processed_logits_col_ptr,
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vocab_size,
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APPLY_TEMPERATURE: tl.constexpr,
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USE_FP64: tl.constexpr,
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PER_TOKEN_COL: tl.constexpr = False,
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):
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req_state_idx = tl.load(expanded_idx_mapping_ptr + token_idx).to(tl.int64)
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is_valid_req = req_state_idx >= 0
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temp = tl.load(temp_ptr + req_state_idx, mask=is_valid_req, other=0.0).to(
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tl.float32
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)
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if temp != 0.0 and APPLY_TEMPERATURE:
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# Apply temperature.
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# NOTE(woosuk): Match the behavior of _temperature_kernel.
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# E.g., if the kernel uses tl.div_rn, we should use tl.div_rn here too.
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logits = logits / temp
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if processed_logits_ptr is not None:
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# Store the temperature-applied logits.
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if processed_logits_col_ptr is not None:
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if PER_TOKEN_COL:
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col = tl.load(processed_logits_col_ptr + token_idx)
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else:
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col = tl.load(processed_logits_col_ptr)
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else:
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col = 0
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tl.store(
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processed_logits_ptr
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+ req_state_idx * processed_logits_stride
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+ col * vocab_size
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+ block,
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logits,
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mask=mask & is_valid_req,
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)
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# fp32 is the default reduction dtype; fp64 is ~1/32–1/64x the throughput
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# on H100/Ada/Blackwell and empirically indistinguishable for Gumbel-max.
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if USE_FP64:
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logits = logits.to(tl.float64)
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if temp != 0.0:
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# Calculate the seed for gumbel noise.
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seed = tl.load(seeds_ptr + req_state_idx, mask=is_valid_req, other=0)
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pos = tl.load(pos_ptr + token_idx)
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gumbel_seed = tl.randint(seed, pos)
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if USE_FP64:
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u = tl_rand64(gumbel_seed, block, includes_zero=False)
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gumbel_noise = -tl.log(-tl.log(u))
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else:
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u = tl_rand32(gumbel_seed, block, includes_zero=False)
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# Draw the large-noise tail (which decides the argmax winner) from u -> 0,
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# where fp32 has fine resolution, instead of u -> 1, where fp32 spacing is
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# ~2**-24. The naive `-log(-log(u))` puts the winning tail at u -> 1,
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# hard-capping the noise at ~16.6 and coarsely quantizing it; using
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# `log1p(-u)` == `log(1 - u)` keeps the tail in the well-resolved region.
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# Note `1 - u` would lose precision for small u, so `log1p` is required.
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gumbel_noise = -tl.log(-tldevice.log1p(-u))
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# Apply gumbel noise.
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logits = tl.where(mask, logits + gumbel_noise, float("-inf"))
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value, idx = tl.max(logits, axis=0, return_indices=True)
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return value, idx
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@triton.jit
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def _gumbel_sample_kernel(
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local_argmax_ptr,
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local_argmax_stride,
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local_max_ptr,
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local_max_stride,
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processed_logits_ptr,
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processed_logits_stride,
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processed_logits_col_ptr,
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logits_ptr,
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logits_stride,
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expanded_idx_mapping_ptr,
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seeds_ptr,
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pos_ptr,
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temp_ptr,
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vocab_size,
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BLOCK_SIZE: tl.constexpr,
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APPLY_TEMPERATURE: tl.constexpr,
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USE_FP64: tl.constexpr,
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PER_TOKEN_COL: tl.constexpr,
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):
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token_idx = tl.program_id(0).to(tl.int64)
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block_idx = tl.program_id(1)
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block = block_idx * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
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mask = block < vocab_size
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logits = tl.load(
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logits_ptr + token_idx * logits_stride + block,
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mask=mask,
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other=float("-inf"),
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)
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logits = logits.to(tl.float32)
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value, idx = gumbel_block_argmax(
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logits,
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block,
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mask,
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token_idx,
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expanded_idx_mapping_ptr,
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temp_ptr,
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seeds_ptr,
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pos_ptr,
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processed_logits_ptr,
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processed_logits_stride,
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processed_logits_col_ptr,
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vocab_size,
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APPLY_TEMPERATURE=APPLY_TEMPERATURE,
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USE_FP64=USE_FP64,
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PER_TOKEN_COL=PER_TOKEN_COL,
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)
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token_id = block_idx * BLOCK_SIZE + idx
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tl.store(local_argmax_ptr + token_idx * local_argmax_stride + block_idx, token_id)
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tl.store(local_max_ptr + token_idx * local_max_stride + block_idx, value)
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def gumbel_sample(
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logits: torch.Tensor, # [num_tokens, vocab_size]
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expanded_idx_mapping: torch.Tensor, # [num_tokens]
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temperature: torch.Tensor, # [max_num_reqs]
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seed: torch.Tensor, # [max_num_reqs]
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pos: torch.Tensor, # [num_tokens]
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apply_temperature: bool,
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output_processed_logits: torch.Tensor | None = None,
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output_processed_logits_col: torch.Tensor | None = None,
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use_fp64: bool = False,
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) -> torch.Tensor:
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# Enforce contiguity on non-strided input tensors
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expanded_idx_mapping = expanded_idx_mapping.contiguous()
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pos = pos.contiguous()
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if output_processed_logits_col is not None:
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output_processed_logits_col = output_processed_logits_col.contiguous()
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num_tokens, vocab_size = logits.shape
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BLOCK_SIZE = 1024
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num_blocks = triton.cdiv(vocab_size, BLOCK_SIZE)
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local_argmax = logits.new_empty(num_tokens, num_blocks, dtype=torch.int64)
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local_max_dtype = torch.float64 if use_fp64 else torch.float32
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local_max = logits.new_empty(num_tokens, num_blocks, dtype=local_max_dtype)
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per_token_col = (
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output_processed_logits_col is not None
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and output_processed_logits_col.dim() > 0
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)
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_gumbel_sample_kernel[(num_tokens, num_blocks)](
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local_argmax,
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local_argmax.stride(0),
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local_max,
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local_max.stride(0),
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output_processed_logits,
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output_processed_logits.stride(0) if output_processed_logits is not None else 0,
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output_processed_logits_col,
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logits,
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logits.stride(0),
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expanded_idx_mapping,
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seed,
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pos,
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temperature,
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vocab_size,
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BLOCK_SIZE=BLOCK_SIZE,
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APPLY_TEMPERATURE=apply_temperature,
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USE_FP64=use_fp64,
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PER_TOKEN_COL=per_token_col,
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)
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# NOTE(woosuk): Use int64 for later indexing.
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max_block_idx = local_max.argmax(dim=-1, keepdim=True)
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sampled = local_argmax.gather(dim=-1, index=max_block_idx).view(-1)
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return sampled
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