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dmoe.py
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dmoe.py
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from megablocks.layers import common
from megablocks.layers import moe
from megablocks.layers import dmlp_registry
from megablocks.layers import mpu
from megablocks.layers import router
from megablocks.layers.arguments import Arguments
import megablocks.ops as ops
import numpy as np
import stk
import torch
def promote_scalar(x):
return x.view(1) if not len(x.size()) else x
class ParallelDroplessMLP(moe.ParallelMLP):
def __init__(self, args : Arguments):
super(ParallelDroplessMLP, self).__init__(args)
self.hidden_size = args.hidden_size
self.ffn_hidden_size = mpu.features_per_rank(args)
self.blocking = 128
self.mlp = dmlp_registry.get(args)
# Calculate the number of bits needed to represent the column indices
# in the intermediate sparse matrix.
max_column_index = (
(self.ffn_hidden_size * self.num_experts) // self.blocking)
self.transpose_sort_end_bit = max(
int(np.ceil(np.log2(max_column_index))), 1)
def sparse_transpose(self, size, row_indices, column_indices, offsets):
block_columns = size[1] // self.blocking
# Sort row indices by column indices to get the transposed matrix's
# column indices.
#
# NOTE: Our sort operation uses the same width indices as the input values.
# To avoid overflow when we have large activation matrices we cast to
# 32-bit before sorting.
_, gather_indices = ops.sort(
column_indices.int(), self.transpose_sort_end_bit)
# There are a constant number of blocks in every row of the sparse matrix.
# A blocks offset is:
#
# row_index * blocks_per_row + column_index % blocks_per_row
#
# Once we have the block offsets ordered for transposition we can divide
# by blocks_per_row to get the transposed column indices.
column_indices_t = row_indices.gather(0, gather_indices.long())
block_offsets_t = gather_indices.int()
zero = torch.zeros((1,), dtype=torch.int32, device=row_indices.device)
nnz_per_column = ops.histogram(column_indices, block_columns)
nnz_per_column = ops.inclusive_cumsum(nnz_per_column, 0)
offsets_t = torch.cat([zero, nnz_per_column])
return column_indices_t, offsets_t, block_offsets_t
def topology(self, x, padded_bins):
padded_tokens, _ = x.size()
assert padded_tokens % self.blocking == 0
assert self.ffn_hidden_size % self.blocking == 0
# Offsets for the sparse matrix. All rows have the
# same number of nonzero blocks dictated by the
# dimensionality of a single expert.
block_rows = padded_tokens // self.blocking
blocks_per_row = self.ffn_hidden_size // self.blocking
offsets = torch.arange(
0,
block_rows * blocks_per_row + 1,
blocks_per_row,
dtype=torch.int32,
device=x.device)
# Indices for the sparse matrix. The indices for
# the intermediate matrix are dynamic depending
# on the mapping of tokens to experts.
column_indices = ops.topology(padded_bins,
self.blocking,
block_rows,
blocks_per_row)
# TODO(tgale): This is unused. Remove the need for this in stk.
# For now, use meta init to save the device memory.
data = torch.empty(
column_indices.numel(),
self.blocking,
self.blocking,
dtype=common.dtype(self.args),
device='meta')
shape = (
padded_tokens,
self.ffn_hidden_size * mpu.experts_per_rank(self.args)
)
row_indices = stk.ops.row_indices(
shape, data, offsets, column_indices)
column_indices_t, offsets_t, block_offsets_t = self.sparse_transpose(
shape, row_indices, column_indices, offsets)
return stk.Matrix(shape, data, row_indices, column_indices, offsets,
column_indices_t, offsets_t, block_offsets_t)
def indices_and_padded_bins(self, top_experts):
# Sort the expert ids to produce the scatter/gather
# indices for the permutation.
top_experts = top_experts.int()
bin_ids, indices = ops.sort(top_experts, self.sort_end_bit)
# Histogram the expert ids to identify the number of
# tokens routed to each expert.
tokens_per_expert = ops.histogram(top_experts, self.num_experts)
# Round the token counts up to the block size used in
# the matrix muliplications. Caculate the starting
# position of each bin.
padded_tokens_per_expert = ops.round_up(
tokens_per_expert, self.blocking)
padded_bins = ops.inclusive_cumsum(padded_tokens_per_expert, 0)
padded_bins = promote_scalar(padded_bins)
# Calculate the bin bounds for the sorted tokens.
bins = ops.inclusive_cumsum(tokens_per_expert, 0)
bins = promote_scalar(bins)
return indices, bin_ids, bins, padded_bins, tokens_per_expert
def sparse_forward_once(self, x, expert_weights, top_experts):
# x: [sl, bs, hs]
# expert_weights: [sl * bs, top-k]
# top_experts: [sl * bs, top-k]
expert_weights = expert_weights.flatten()
top_experts = top_experts.flatten()
with torch.no_grad():
indices, bin_ids, bins, padded_bins, tokens_per_expert = (
self.indices_and_padded_bins(top_experts))
# Route the tokens for MoE computation.
x = x.view(-1, x.shape[-1])
x = ops.padded_gather(
x,
indices,
bin_ids,
bins,
padded_bins,
self.top_k)
# Create the sparse matrix topology.
with torch.no_grad():
topo = self.topology(x, padded_bins)
# Perform the expert computation.
x = self.mlp(x, topo)
# Un-route the data for the MoE output.
x = ops.padded_scatter(
x,
indices,
bin_ids,
expert_weights,
bins,
padded_bins,
self.top_k,
self.args.quantize_scatter_num_bits)
return x, tokens_per_expert
# For use in the base-class parallel_forward_once.
def sparse_permute_and_compute(
self,
x,
tokens_per_expert,
indices,
bin_ids,
expert_weights,
bins,
expert_capactiy, # unused
top_k):
# Round the token counts up to the block size used in the matrix
# multiplication. Calculate the starting position of each bin.
padded_tokens_per_expert = ops.round_up(
tokens_per_expert, self.blocking)
padded_bins = ops.inclusive_cumsum(padded_tokens_per_expert, 0)
padded_bins = promote_scalar(padded_bins)
# Route the tokens for MoE computation.
x = x.view(-1, x.shape[-1])
x = ops.padded_gather(
x,
indices,
bin_ids,
bins,
padded_bins,
top_k)
# Create the sparse matrix topology.
with torch.no_grad():
topo = self.topology(x, padded_bins)
# Perform the expert computation.
x = self.mlp(x, topo)
# Un-route the data for the MoE output.
return ops.padded_scatter(
x,
indices,
bin_ids,
expert_weights,
bins,
padded_bins,
top_k)
def grouped_forward_once(self, x, expert_weights, top_experts):
# x: [sl, bs, hs]
# expert_weights: [sl * bs, top-k]
# top_experts: [sl * bs, top-k]
expert_weights = expert_weights.flatten()
top_experts = top_experts.flatten()
with torch.no_grad():
indices, bin_ids, bins, tokens_per_expert = (
self.indices_and_bins(top_experts))
out = self.grouped_permute_and_compute(
x,
tokens_per_expert,
indices,
bin_ids,
expert_weights,
bins,
-1, # unused
self.args.moe_top_k)
return out, tokens_per_expert
def grouped_permute_and_compute(
self,
x,
tokens_per_expert,
indices,
bin_ids,
expert_weights,
bins,
expert_capactiy, # unused
top_k):
# Route the tokens for MoE computation.
x = x.view(-1, x.shape[-1])
x = ops.gather(
x,
indices,
bin_ids,
bins,
top_k)
# Perform the expert computation.
x = self.mlp(x, tokens_per_expert)
# Un-route the data for the MoE output.
return ops.scatter(
x,
indices,
bin_ids,
expert_weights,
bins,
top_k,
self.args.quantize_scatter_num_bits)
def forward_once(self, x, expert_weights, top_experts):
if self.args.grouped_mlp:
return self.grouped_forward_once(
x, expert_weights, top_experts)
return self.sparse_forward_once(
x, expert_weights, top_experts)
def permute_and_compute(
self,
x,
tokens_per_expert,
indices,
bin_ids,
expert_weights,
bins,
expert_capactiy,
top_k):
if self.args.grouped_mlp:
return self.grouped_permute_and_compute(
x,
tokens_per_expert,
indices,
bin_ids,
expert_weights,
bins,
expert_capactiy,
top_k)
return self.sparse_permute_and_compute(
x,
tokens_per_expert,
indices,
bin_ids,
expert_weights,
bins,
expert_capactiy,
top_k)
class dMoE(torch.nn.Module):
def __init__(self, args : Arguments):
super(dMoE, self).__init__()
# Token router.
self.router = router.LearnedRouter(args)
# Expert computation helper.
self.experts = ParallelDroplessMLP(args)
def forward(self, x):
# NOTE: If we're going to cast the activations to lower precision
# do it before we permute the tokens to save bandwidth.
x = common.cast_if_autocast_enabled(x)
# Compute the expert scores and assignments.
scores, expert_weights, top_experts = self.router(x)
# Compute the experts.
return self.experts(x, scores, expert_weights, top_experts)