Update grok 1 model (#1095)

benchmark/gsm8k/bench_sglang.py

@@ -88,6 +88,9 @@ def main(args):
     for i in range(len(states)):
         preds.append(get_answer_value(states[i]["answer"]))
 
+    # print(f"{preds=}")
+    # print(f"{labels=}")
+
     # Compute accuracy
     acc = np.mean(np.array(preds) == np.array(labels))
     invalid = np.mean(np.array(preds) == INVALID)

python/sglang/bench_latency.py

@@ -221,6 +221,7 @@ def correctness_test(
 
     # Prepare inputs
     input_ids, reqs = prepare_inputs_for_correctness_test(bench_args, tokenizer)
+    rank_print(f"{input_ids=}")
 
     if bench_args.cut_len > 0:
         # Prefill

python/sglang/srt/layers/activation.py

@@ -14,7 +14,6 @@ limitations under the License.
 """Fused operators for activation layers."""
 
 import torch
-import torch.nn as nn
 import torch.nn.functional as F
 from flashinfer.activation import silu_and_mul
 from vllm.model_executor.custom_op import CustomOp

python/sglang/srt/layers/fused_moe/__init__.py

+from sglang.srt.layers.fused_moe.layer import FusedMoE, FusedMoEMethodBase

python/sglang/srt/layers/fused_moe.py → python/sglang/srt/layers/fused_moe/fused_moe.py

-"""
-Copyright 2023-2024 SGLang Team
-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.
-"""
-
 # Adapted from
-# https://github.com/vllm-project/vllm/blob/c7f2cf2b7f67bce5842fedfdba508440fe257375/vllm/model_executor/layers/fused_moe/fused_moe.py#L1
+# https://github.com/vllm-project/vllm/tree/v0.5.4/vllm/model_executor/layers/fused_moe
 """Fused MoE kernel."""
 import functools
 import json
@@ -24,6 +9,7 @@ from typing import Any, Dict, Optional, Tuple
 import torch
 import triton
 import triton.language as tl
+import vllm.envs as envs
 from vllm import _custom_ops as ops
 from vllm.logger import init_logger
 
@@ -373,6 +359,31 @@ def get_default_config(
     return config
 
 
+def try_get_optimal_moe_config(
+    w1_shape: Tuple[int, ...],
+    w2_shape: Tuple[int, ...],
+    top_k: int,
+    dtype: Optional[str],
+    M: int,
+    override_config: Optional[Dict[str, Any]] = None,
+):
+    if override_config:
+        config = override_config
+    else:
+        # First try to load optimal config from the file
+        E, _, N = w2_shape
+        configs = get_moe_configs(E, N, dtype)
+
+        if configs:
+            # If an optimal configuration map has been found, look up the
+            # optimal config
+            config = configs[min(configs.keys(), key=lambda x: abs(x - M))]
+        else:
+            # Else use the default config
+            config = get_default_config(M, E, N, w1_shape[2], top_k, dtype)
+    return config
+
+
 def fused_topk(
     hidden_states: torch.Tensor,
     gating_output: torch.Tensor,
@@ -403,6 +414,41 @@ def fused_topk(
     return topk_weights, topk_ids
 
 
+# This is used by the Deepseek-V2 model
+def grouped_topk(
+    hidden_states: torch.Tensor,
+    gating_output: torch.Tensor,
+    topk: int,
+    renormalize: bool,
+    num_expert_group: int = 0,
+    topk_group: int = 0,
+):
+
+    assert hidden_states.shape[0] == gating_output.shape[0], "Number of tokens mismatch"
+
+    scores = torch.softmax(gating_output, dim=-1)
+    num_token = scores.shape[0]
+    group_scores = (
+        scores.view(num_token, num_expert_group, -1).max(dim=-1).values
+    )  # [n, n_group]
+    group_idx = torch.topk(group_scores, k=topk_group, dim=-1, sorted=False)[
+        1
+    ]  # [n, top_k_group]
+    group_mask = torch.zeros_like(group_scores)  # [n, n_group]
+    group_mask.scatter_(1, group_idx, 1)  # [n, n_group]
+    score_mask = (
+        group_mask.unsqueeze(-1)
+        .expand(num_token, num_expert_group, scores.shape[-1] // num_expert_group)
+        .reshape(num_token, -1)
+    )  # [n, e]
+    tmp_scores = scores.masked_fill(~score_mask.bool(), 0.0)  # [n, e]
+    topk_weights, topk_ids = torch.topk(tmp_scores, k=topk, dim=-1, sorted=False)
+
+    if renormalize:
+        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
+    return topk_weights, topk_ids
+
+
 def fused_experts(
     hidden_states: torch.Tensor,
     w1: torch.Tensor,
@@ -425,24 +471,23 @@ def fused_experts(
     assert w2.is_contiguous(), "Expert weights2 must be contiguous"
     assert hidden_states.dtype in [torch.float32, torch.float16, torch.bfloat16]
 
-    M, _ = hidden_states.shape
+    num_tokens, _ = hidden_states.shape
     E, N, _ = w1.shape
+    # We execute the fused_moe kernel in chunks to circumvent this issue:
+    # https://github.com/vllm-project/vllm/issues/5938
+    CHUNK_SIZE = envs.VLLM_FUSED_MOE_CHUNK_SIZE
+    M = min(num_tokens, CHUNK_SIZE)
+
+    get_config_func = functools.partial(
+        try_get_optimal_moe_config,
+        w1.shape,
+        w2.shape,
+        topk_ids.shape[1],
+        "float8" if use_fp8 else None,
+        override_config=override_config,
+    )
 
-    if override_config:
-        config = override_config
-    else:
-        # First try to load optimal config from the file
-        configs = get_moe_configs(E, w2.shape[2], "float8" if use_fp8 else None)
-
-        if configs:
-            # If an optimal configuration map has been found, look up the
-            # optimal config
-            config = configs[min(configs.keys(), key=lambda x: abs(x - M))]
-        else:
-            # Else use the default config
-            config = get_default_config(
-                M, E, N, w1.shape[2], topk_ids.shape[1], "float8" if use_fp8 else None
-            )
+    config = get_config_func(M)
 
     intermediate_cache1 = torch.empty(
         (M, topk_ids.shape[1], N),
@@ -460,56 +505,85 @@ def fused_experts(
         dtype=hidden_states.dtype,
     )
 
-    sorted_token_ids, expert_ids, num_tokens_post_padded = moe_align_block_size(
-        topk_ids, config["BLOCK_SIZE_M"], E
-    )
     compute_type = tl.bfloat16 if hidden_states.dtype == torch.bfloat16 else tl.float16
 
-    invoke_fused_moe_kernel(
-        hidden_states,
-        w1,
-        intermediate_cache1,
-        a1_scale,
-        w1_scale,
-        topk_weights,
-        topk_ids,
-        sorted_token_ids,
-        expert_ids,
-        num_tokens_post_padded,
-        False,
-        topk_ids.shape[1],
-        config,
-        compute_type=compute_type,
-        use_fp8=use_fp8,
-    )
+    if inplace:
+        out_hidden_states = hidden_states
+    else:
+        out_hidden_states = torch.empty_like(hidden_states)
 
-    ops.gelu_and_mul(intermediate_cache2, intermediate_cache1.view(-1, N))
+    for chunk in range((num_tokens // CHUNK_SIZE) + 1):
+        begin_chunk_idx, end_chunk_idx = (
+            chunk * CHUNK_SIZE,
+            min((chunk + 1) * CHUNK_SIZE, num_tokens),
+        )
+        curr_hidden_states = hidden_states[begin_chunk_idx:end_chunk_idx]
+        tokens_in_chunk, _ = curr_hidden_states.shape
+
+        if tokens_in_chunk == 0:
+            break
+
+        if tokens_in_chunk < CHUNK_SIZE and chunk > 0:
+            # Adjust the intermediate cache size and config for the last
+            # chunk. Note that in most cases we only have one chunk
+            # so the cache size and config are already set correctly and
+            # do not need to be adjusted.
+            intermediate_cache1 = intermediate_cache1[:tokens_in_chunk]
+            intermediate_cache2 = intermediate_cache2[:tokens_in_chunk]
+            intermediate_cache3 = intermediate_cache3[:tokens_in_chunk]
+            config = get_config_func(tokens_in_chunk)
+
+        curr_topk_ids = topk_ids[begin_chunk_idx:end_chunk_idx]
+        curr_topk_weights = topk_weights[begin_chunk_idx:end_chunk_idx]
+
+        sorted_token_ids, expert_ids, num_tokens_post_padded = moe_align_block_size(
+            curr_topk_ids, config["BLOCK_SIZE_M"], E
+        )
 
-    invoke_fused_moe_kernel(
-        intermediate_cache2,
-        w2,
-        intermediate_cache3,
-        a2_scale,
-        w2_scale,
-        topk_weights,
-        topk_ids,
-        sorted_token_ids,
-        expert_ids,
-        num_tokens_post_padded,
-        True,
-        1,
-        config,
-        compute_type=compute_type,
-        use_fp8=use_fp8,
-    )
+        invoke_fused_moe_kernel(
+            curr_hidden_states,
+            w1,
+            intermediate_cache1,
+            a1_scale,
+            w1_scale,
+            curr_topk_weights,
+            curr_topk_ids,
+            sorted_token_ids,
+            expert_ids,
+            num_tokens_post_padded,
+            False,
+            topk_ids.shape[1],
+            config,
+            compute_type=compute_type,
+            use_fp8=use_fp8,
+        )
 
-    if inplace:
-        return torch.sum(
+        ops.gelu_and_mul(intermediate_cache2, intermediate_cache1.view(-1, N))
+
+        invoke_fused_moe_kernel(
+            intermediate_cache2,
+            w2,
+            intermediate_cache3,
+            a2_scale,
+            w2_scale,
+            curr_topk_weights,
+            curr_topk_ids,
+            sorted_token_ids,
+            expert_ids,
+            num_tokens_post_padded,
+            True,
+            1,
+            config,
+            compute_type=compute_type,
+            use_fp8=use_fp8,
+        )
+
+        torch.sum(
             intermediate_cache3.view(*intermediate_cache3.shape),
             dim=1,
-            out=hidden_states,
+            out=out_hidden_states[begin_chunk_idx:end_chunk_idx],
         )
-    return torch.sum(intermediate_cache3.view(*intermediate_cache3.shape), dim=1)
+    return out_hidden_states
 
 
 def fused_moe(
@@ -521,6 +595,9 @@ def fused_moe(
     renormalize: bool,
     inplace: bool = False,
     override_config: Optional[Dict[str, Any]] = None,
+    use_grouped_topk: bool = False,
+    num_expert_group: Optional[int] = None,
+    topk_group: Optional[int] = None,
     use_fp8: bool = False,
     w1_scale: Optional[torch.Tensor] = None,
     w2_scale: Optional[torch.Tensor] = None,
@@ -543,6 +620,10 @@ def fused_moe(
         Defaults to False.
     - override_config (Optional[Dict[str, Any]]): Optional override
         for the kernel configuration.
+    - num_expert_group: Optional[int]: additional parameter for grouped_topk
+    - topk_group: Optional[int]: additional parameter for grouped_topk
+    - use_grouped_topk: If True, use grouped_topk instead of fused_topk
+        note: Deepseekv2 model uses grouped_topk
     - use_fp8 (bool): If True, use fp8 arithmetic to compute the inner
         products for w1 and w2. Defaults to False.
     - w1_scale (Optional[torch.Tensor]): Optional scale to be used for
@@ -556,12 +637,18 @@ def fused_moe(
     # Check constraints.
     assert gating_output.shape[1] == w1.shape[0], "Number of experts mismatch"
 
-    if hasattr(ops, "topk_softmax"):
-        topk_weights, topk_ids = fused_topk(
-            hidden_states, gating_output, topk, renormalize
+    if use_grouped_topk:
+        assert num_expert_group is not None and topk_group is not None
+        topk_weights, topk_ids = grouped_topk(
+            hidden_states,
+            gating_output,
+            topk,
+            renormalize,
+            num_expert_group,
+            topk_group,
         )
     else:
-        topk_weights, topk_ids = fused_topk_v0_4_3(
+        topk_weights, topk_ids = fused_topk(
             hidden_states, gating_output, topk, renormalize
         )
 
@@ -579,33 +666,3 @@ def fused_moe(
         a1_scale=a1_scale,
         a2_scale=a2_scale,
     )
-
-
-def fused_topk_v0_4_3(
-    hidden_states: torch.Tensor,
-    gating_output: torch.Tensor,
-    topk: int,
-    renormalize: bool,
-):
-    import vllm._moe_C as moe_kernels
-
-    M, _ = hidden_states.shape
-
-    topk_weights = torch.empty(
-        M, topk, dtype=torch.float32, device=hidden_states.device
-    )
-    topk_ids = torch.empty(M, topk, dtype=torch.int32, device=hidden_states.device)
-    token_expert_indicies = torch.empty(
-        M, topk, dtype=torch.int32, device=hidden_states.device
-    )
-    moe_kernels.topk_softmax(
-        topk_weights,
-        topk_ids,
-        token_expert_indicies,
-        gating_output.float(),  # TODO(woosuk): Optimize this.
-    )
-    del token_expert_indicies  # Not used. Will be used in the future.
-    if renormalize:
-        topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True)
-
-    return topk_weights, topk_ids

python/sglang/srt/layers/logits_processor.py

@@ -164,9 +164,9 @@ class LogitsProcessor(nn.Module):
         last_logits = last_logits[:, : self.config.vocab_size].float()
 
         if hasattr(self.config, "final_logit_softcapping"):
-            last_logits /= self.config.final_logit_softcapping
+            last_logits.div_(self.config.final_logit_softcapping)
             last_logits = torch.tanh(last_logits)
-            last_logits *= self.config.final_logit_softcapping
+            last_logits.mul_(self.config.final_logit_softcapping)
 
         # Return only last_logits if logprob is not requested
         if not logits_metadata.return_logprob:
@@ -209,9 +209,9 @@ class LogitsProcessor(nn.Module):
                 all_logits = all_logits[:, : self.config.vocab_size].float()
 
                 if hasattr(self.config, "final_logit_softcapping"):
-                    all_logits /= self.config.final_logit_softcapping
+                    all_logits.div_(self.config.final_logit_softcapping)
                     all_logits = torch.tanh(all_logits)
-                    all_logits *= self.config.final_logit_softcapping
+                    all_logits.mul_(self.config.final_logit_softcapping)
 
                 all_logprobs = all_logits
                 del all_logits, hidden_states

python/sglang/srt/model_executor/model_runner.py

@@ -53,7 +53,7 @@ from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import (
     get_available_gpu_memory,
     is_generation_model,
-    is_llama3_405b_fp8,
+    is_llama3_405b_fp8_head_16,
     is_multimodal_model,
     monkey_patch_vllm_dummy_weight_loader,
     monkey_patch_vllm_p2p_access_check,
@@ -158,7 +158,7 @@ class ModelRunner:
             skip_tokenizer_init=True,
         )
 
-        if is_llama3_405b_fp8(self.model_config) and self.tp_size <= 8:
+        if is_llama3_405b_fp8_head_16(self.model_config) and self.tp_size <= 8:
             # A temporary hack to fix the num_heads for meta-llama/Meta-Llama-3.1-405B-FP8 checkpoints
             self.model_config.hf_config.num_key_value_heads = 8
             vllm_model_config.hf_config.num_key_value_heads = 8

python/sglang/srt/models/mixtral.py

@@ -32,7 +32,6 @@ from vllm.model_executor.layers.linear import (
 from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.layers.vocab_parallel_embedding import (
-    DEFAULT_VOCAB_PADDING_SIZE,
     ParallelLMHead,
     VocabParallelEmbedding,
 )

python/sglang/srt/utils.py

@@ -35,7 +35,6 @@ import torch
 import torch.distributed as dist
 from fastapi.responses import JSONResponse
 from packaging import version as pkg_version
-from starlette.middleware.base import BaseHTTPMiddleware
 from torch.nn.parameter import Parameter
 from triton.runtime.cache import (
     FileCacheManager,
@@ -644,7 +643,7 @@ def set_ulimit(target_soft_limit=65535):
             logger.warn(f"Fail to set RLIMIT_NOFILE: {e}")
 
 
-def is_llama3_405b_fp8(model_config):
+def is_llama3_405b_fp8_head_16(model_config):
     """Return whether the model is meta-llama/Meta-Llama-3.1-405B-FP8 with 16 kv heads."""
     if (
         model_config.hf_config.architectures[0] == "LlamaForCausalLM"