Enabling MOE Quantization using linear decomposition [WIP]

Summary: This PR is a first step at optimizing moe inference using
torchAO. The goal for this step is to enable existing quantization
kernels and workflows to work for moe quantization by decomposing the
group gemm into a sequence of unbalanced linear ops that can use the
existing quantized kernels. To enable this we had to add support for
quantizing these 3D tensors as well as slicing and indexing.

current tests are running locally but will be added once working.

currently int8wo and int8dq are working for multi and single token moe
inference while int4wo is being finished up.

TODO move test set into ao, move quantizable moe module code to ao test
on hf model definition.

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Tags:

testing

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Author: HDCharles

torchao/_models/mixtral-moe/generate.py

@@ -0,0 +1,360 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+from torch.nn import functional as F
+
+
+def find_multiple(n: int, k: int) -> int:
+    if n % k == 0:
+        return n
+    return n + k - (n % k)
+
+@dataclass
+class ModelArgs:
+    block_size: int = 2048
+    vocab_size: int = 32000
+    n_layer: int = 32
+    n_head: int = 32
+    dim: int = 4096
+    intermediate_size: int = None
+    n_local_heads: int = -1
+    head_dim: int = 64
+    rope_base: float = 10000
+    norm_eps: float = 1e-5
+    num_experts: int = 8
+    num_activated_experts: int = 2
+
+    def __post_init__(self):
+        if self.n_local_heads == -1:
+            self.n_local_heads = self.n_head
+        if self.intermediate_size is None:
+            hidden_dim = 4 * self.dim
+            n_hidden = int(2 * hidden_dim / 3)
+            self.intermediate_size = find_multiple(n_hidden, 256)
+        self.head_dim = self.dim // self.n_head
+
+    @classmethod
+    def from_name(cls, name: str):
+        if name in transformer_configs:
+            return cls(**transformer_configs[name])
+        # fuzzy search
+        config = [config for config in transformer_configs if config in str(name).upper() or config in str(name)]
+        assert len(config) == 1, name
+        return cls(**transformer_configs[config[0]])
+
+
+transformer_configs = {
+    "Mixtral-8x7B-Instruct-v0.1": dict(block_size=32768, n_layer=32, n_head=32, n_local_heads=8, dim=4096, intermediate_size=14336, rope_base=1000000.0, num_experts=8, num_activated_experts=2),
+}
+
+class KVCache(nn.Module):
+    def __init__(self, max_batch_size, max_seq_length, n_heads, head_dim, dtype=torch.bfloat16):
+        super().__init__()
+        cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim)
+        self.register_buffer('k_cache', torch.zeros(cache_shape, dtype=dtype))
+        self.register_buffer('v_cache', torch.zeros(cache_shape, dtype=dtype))
+
+    def update(self, input_pos, k_val, v_val):
+        # input_pos: [S], k_val: [B, H, S, D]
+        assert input_pos.shape[0] == k_val.shape[2]
+
+        k_out = self.k_cache
+        v_out = self.v_cache
+        k_out[:, :, input_pos] = k_val
+        v_out[:, :, input_pos] = v_val
+
+        return k_out, v_out
+
+class Transformer(nn.Module):
+    def __init__(self, config: ModelArgs) -> None:
+        super().__init__()
+        self.config = config
+
+        self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
+        self.layers = nn.ModuleList(TransformerBlock(config) for _ in range(config.n_layer))
+        self.norm = RMSNorm(config.dim, eps=config.norm_eps)
+        self.output = nn.Linear(config.dim, config.vocab_size, bias=False)
+
+        self.freqs_cis: Optional[Tensor] = None
+        self.mask_cache: Optional[Tensor] = None
+        self.max_batch_size = -1
+        self.max_seq_length = -1
+
+    def setup_caches(self, max_batch_size, max_seq_length):
+        if self.max_seq_length >= max_seq_length and self.max_batch_size >= max_batch_size:
+            return
+        head_dim = self.config.dim // self.config.n_head
+        max_seq_length = find_multiple(max_seq_length, 8)
+        self.max_seq_length = max_seq_length
+        self.max_batch_size = max_batch_size
+        for b in self.layers:
+            b.attention.kv_cache = KVCache(max_batch_size, max_seq_length, self.config.n_local_heads, head_dim)
+
+        self.freqs_cis = precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base)
+        self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))
+
+    def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+        assert self.freqs_cis is not None, "Caches must be initialized first"
+        mask = self.causal_mask[None, None, input_pos]
+        freqs_cis = self.freqs_cis[input_pos]
+        x = self.tok_embeddings(idx)
+
+        for i, layer in enumerate(self.layers):
+            x = layer(x, input_pos, freqs_cis, mask)
+        x = self.norm(x)
+        logits = self.output(x)
+        return logits
+
+    @classmethod
+    def from_name(cls, name: str):
+        return cls(ModelArgs.from_name(name))
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, config: ModelArgs) -> None:
+        super().__init__()
+        self.attention = Attention(config)
+        self.block_sparse_moe = MOEFeedForwardAOQuantizable(config)
+        self.ffn_norm = RMSNorm(config.dim, config.norm_eps)
+        self.attention_norm = RMSNorm(config.dim, config.norm_eps)
+
+    def forward(self, x: Tensor, input_pos: Tensor, freqs_cis: Tensor, mask: Tensor) -> Tensor:
+        h = x + self.attention(self.attention_norm(x), freqs_cis, mask, input_pos)
+        out = h + self.block_sparse_moe(self.ffn_norm(h))
+        return out
+
+
+class Attention(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        assert config.dim % config.n_head == 0
+
+        total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim
+        # key, query, value projections for all heads, but in a batch
+        self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False)
+        self.wo = nn.Linear(config.dim, config.dim, bias=False)
+        self.kv_cache = None
+
+        self.n_head = config.n_head
+        self.head_dim = config.head_dim
+        self.n_local_heads = config.n_local_heads
+        self.dim = config.dim
+        self._register_load_state_dict_pre_hook(self.load_hook)
+
+    def load_hook(self, state_dict, prefix, *args):
+        if prefix + "wq.weight" in state_dict:
+            wq = state_dict.pop(prefix + "wq.weight")
+            wk = state_dict.pop(prefix + "wk.weight")
+            wv = state_dict.pop(prefix + "wv.weight")
+            state_dict[prefix + "wqkv.weight"] = torch.cat([wq, wk, wv])
+
+    def forward(self, x: Tensor, freqs_cis: Tensor, mask: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
+        bsz, seqlen, _ = x.shape
+
+        kv_size = self.n_local_heads * self.head_dim
+        q, k, v = self.wqkv(x).split([self.dim, kv_size, kv_size], dim=-1)
+
+        q = q.view(bsz, seqlen, self.n_head, self.head_dim)
+        k = k.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+        v = v.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+
+        q = apply_rotary_emb(q, freqs_cis)
+        k = apply_rotary_emb(k, freqs_cis)
+
+        q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))
+
+        if self.kv_cache is not None:
+            k, v = self.kv_cache.update(input_pos, k, v)
+
+        k = k.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
+        v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
+        y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)
+
+        y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)
+
+        y = self.wo(y)
+        return y
+
+
+class ConditionalFeedForward(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.w1 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim))
+        self.w2 = nn.Parameter(torch.empty(config.num_experts, config.dim, config.intermediate_size))
+        self.w3 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim))
+
+    def forward(self, x: Tensor, expert_indices: Tensor) -> Tensor:
+        w1_weights = self.w1[expert_indices] # [T, A, D, D]
+        w3_weights = self.w3[expert_indices] # [T, A, D, D]
+        w2_weights = self.w2[expert_indices]  # [T, A, D, D]
+        x1 = F.silu(torch.einsum('ti,taoi -> tao', x, w1_weights))
+        x3 = torch.einsum('ti, taoi -> tao', x, w3_weights)
+        expert_outs =  torch.einsum('tao, taio -> tai', (x1 * x3), w2_weights)
+        return expert_outs
+
+
+class MOEFeedForward(nn.Module):
+    def __init__(self, config) -> None:
+        super().__init__()
+        self.gate = nn.Linear(config.dim, config.num_experts, bias=False)
+        self.cond_ffn = ConditionalFeedForward(config)
+        self.dim = config.dim
+        self.num_activated_experts = config.num_activated_experts
+    def forward(self, x: Tensor) -> Tensor:
+        x = x.view(-1, self.dim)
+        # T = num_tokens, E = num_experts, D = hidden dim, A = activated experts
+        # x: [T, D]
+        scores = self.gate(x) # [T, E]
+        expert_weights = F.softmax(scores, dim=-1)
+        expert_weights, expert_indices = torch.topk(expert_weights, self.num_activated_experts, dim=-1) # [T, A], [T, A]
+        expert_weights /= expert_weights.sum(dim=-1, keepdim=True) # [T, A]
+        expert_outs = self.cond_ffn(x, expert_indices)
+        return torch.einsum('tai,ta -> ti', expert_outs, expert_weights)
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-5):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
+
+    def forward(self, x: Tensor) -> Tensor:
+        output = self._norm(x.float()).type_as(x)
+        return output * self.weight
+
+
+def precompute_freqs_cis(
+    seq_len: int, n_elem: int, base: int = 10000
+) -> Tensor:
+    freqs = 1.0 / (base ** (torch.arange(0, n_elem, 2)[: (n_elem // 2)].float() / n_elem))
+    t = torch.arange(seq_len, device=freqs.device)
+    freqs = torch.outer(t, freqs)
+    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
+    cache = torch.stack([freqs_cis.real, freqs_cis.imag], dim=-1)
+    return cache.to(dtype=torch.bfloat16)
+
+
+def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor:
+    xshaped = x.float().reshape(*x.shape[:-1], -1, 2)
+    freqs_cis = freqs_cis.view(1, xshaped.size(1), 1, xshaped.size(3), 2)
+    x_out2 = torch.stack(
+        [
+            xshaped[..., 0] * freqs_cis[..., 0] - xshaped[..., 1] * freqs_cis[..., 1],
+            xshaped[..., 1] * freqs_cis[..., 0] + xshaped[..., 0] * freqs_cis[..., 1],
+        ],
+        -1,
+    )
+
+    x_out2 = x_out2.flatten(3)
+    return x_out2.type_as(x)
+
+
+# T tokens
+# E experts
+# D dim
+# I intermediate dim
+# A activated experts
+# T'(e) tokens for expert e
+
+class MOEFeedForwardAOQuantizable(nn.Module):
+    def __init__(self, config) -> None:
+        super().__init__()
+        self.gate = nn.Linear(config.dim, config.num_experts, bias=False)
+        self.cond_ffn = ConditionalFeedForwardAOQuantizable(config)
+        self.dim = config.dim
+        self.num_activated_experts = config.num_activated_experts
+    def forward(self, x: Tensor) -> Tensor:
+        batch_size = x.shape[0]
+        x = x.view(-1, self.dim) # x: [T, D]
+        scores = self.gate(x) # [T, E]
+        expert_weights = F.softmax(scores, dim=-1)
+        expert_weights, expert_indices = torch.topk(expert_weights, self.num_activated_experts, dim=-1) # [T, A], [T, A]
+        expert_weights /= expert_weights.sum(dim=-1, keepdim=True).to(x.dtype) # [T, A]
+        out = self.cond_ffn(x, expert_indices, expert_weights, self.num_activated_experts)
+        return out.reshape(batch_size, -1, self.dim)
+
+
+class ConditionalFeedForwardAOQuantizable(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.w1 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim)) # E, I, D
+        self.w2 = nn.Parameter(torch.empty(config.num_experts, config.dim, config.intermediate_size)) # E, D, I
+        self.w3 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim)) # E, I, D
+        self.num_experts = config.num_experts
+    def forward(
+        self, x: Tensor,        # T, D
+        expert_indices: Tensor, # T, A
+        expert_weights: Tensor,  # T, A
+        num_activated_experts: int,
+        ) -> Tensor:
+        num_tokens, dim = x.shape
+        num_token_activations = num_tokens * num_activated_experts
+
+        if x.shape[0]==1: #only 1 token (can be done without graph breaks when compiled)
+            outs = []
+            expert_indices=expert_indices.squeeze()
+            # collect used experts
+            w1 = self.w1[expert_indices]
+            w2 = self.w2[expert_indices]
+            w3 = self.w3[expert_indices]
+
+            # run token through each expert
+            for index in range(num_activated_experts):
+                y1 = F.silu(F.linear(x, w1[index]))
+                y3 = F.linear(x, w3[index])
+                y2 = w2[index]
+                cur_out = F.linear( y1 * y3, y2)
+                outs.append(cur_out)
+
+            # combine outputs
+            final_out = (torch.cat(outs, dim=0) * expert_weights.view(-1,1)).sum(dim=0).unsqueeze(-1)
+            return final_out
+        else:
+            expert_list = [x for x in range(self.num_experts)]
+            
+            # shuffle tokens into groups for each expert
+            ordered_token_activations = expert_indices.view(-1).argsort(stable=True) # [A]
+            ordered_token_indices = ordered_token_activations.div(num_activated_experts).floor().to(torch.int64) #  [T]
+
+            num_tokens_per_expert = torch.histc(expert_indices, bins=self.num_experts+1, min=-1, max=self.num_experts) #  [E+1] (added leading 0 so can be used for indexing)
+            cum_tokens_per_expert = num_tokens_per_expert.cumsum(0).to(torch.int64)  #  [E+1]
+            
+            @torch._dynamo.disable()
+            def group_tokens_by_expert(ordered_token_indices, cum_tokens_per_expert, expert_list):
+                token_indices_per_expert = [ordered_token_indices[cum_tokens_per_expert[expert]:cum_tokens_per_expert[expert+1]] for expert in expert_list] # [T'(e1)], [T'(e2)] ...
+                return token_indices_per_expert
+            token_indices_per_expert = group_tokens_by_expert(ordered_token_indices, cum_tokens_per_expert, expert_list)
+            tokens_grouped_by_expert = [x[indices] for indices in token_indices_per_expert]
+
+            # calculate outputs for each expert
+            outs = []
+            for cur_x, expert in zip(tokens_grouped_by_expert,expert_list):
+
+                w1=self.w1[expert] # I, D
+                w2=self.w2[expert] # D, I
+                w3=self.w3[expert] # I, D
+
+                cur_out = F.linear( F.silu(F.linear(cur_x, w1)) * F.linear(cur_x, w3), w2) # [T'(e), D]
+                outs.append(cur_out)
+
+            # weigh outputs
+            ordered_outs = torch.cat(outs, dim=0) # [T*A, D]
+            ordered_token_activation_weights = expert_weights.view(-1,1)[ordered_token_activations].view(-1,1) # [T*A, 1]
+            weighted_ordered_outs = ordered_outs*ordered_token_activation_weights # [T*A, D]
+            
+            # sum weighted token-activation outputs together for each token
+            final_out = torch.zeros_like(x) #  [T, D]
+            final_out = final_out.scatter_add(dim=0, index=ordered_token_indices.unsqueeze(-1).expand(num_token_activations,dim).to(torch.int64), src=weighted_ordered_outs)
+        return final_out

torchao/_models/mixtral-moe/model.py

@@ -0,0 +1,6 @@
+export MODEL_REPO=mistralai/Mixtral-8x7B-Instruct-v0.1
+export CHECKPOINT_PATH=/data/users/cdhernandez/gpt-fast/checkpoints/
+
+
+
+python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --moe_quant int8wo-base --compile