The 483MB Trace That Revealed a 69% Bottleneck: Extracting Tensor Shapes from a Torch Profiler Dump

In the high-stakes world of large language model inference optimization, a single profiler trace can contain the difference between a model that runs at 10 tokens per second and one that runs at 50. Message [msg 1401] in this opencode session represents a pivotal diagnostic moment: the execution of a custom Python script designed to extract tensor shape information from a 483-megabyte Chrome trace JSON file produced by PyTorch's profiler. This single bash command — a seemingly mundane scp followed by an ssh — was the key that unlocked the fundamental bottleneck holding back the GLM-5-NVFP4 model's inference performance on an 8-GPU RTX PRO 6000 Blackwell system.

The Context: A Bottleneck Identified but Not Understood

To understand why message [msg 1401] matters, we must first understand what led to it. The session had been engaged in an intensive optimization campaign for the GLM-5-NVFP4 model, a 4-bit floating-point quantized variant of a massive Mixture-of-Experts (MoE) language model deployed using SGLang. Despite having eight RTX PRO 6000 Blackwell GPUs (SM120 architecture) with substantial compute and memory bandwidth, the single-stream decode throughput was stuck at approximately 10.5 tokens per second — far below theoretical expectations.

In the immediately preceding messages ([msg 1384] through [msg 1400]), the assistant had executed a torch profiler trace on the live SGLang server. The profiler captured 30 decode steps and produced a summary that was both illuminating and frustrating. The summary showed that 69.3% of total decode time — 64.6 milliseconds per step out of a total 93.2 ms — was consumed by a single operation: aten::copy_, implemented by the CUDA kernel unrolled_elementwise_kernel. This was called 2,340 times across the 30 steps, or exactly 78 calls per step — one per layer of the model.

But the profiler summary told only part of the story. It revealed that a massive amount of data was being copied and cast, but not what that data was. The kernel name LoadWithCast<1>, StoreWithCast<1> with a lambda converting to c10::BFloat16 indicated an FP8-to-BF16 conversion, but the tensor shapes and identities remained unknown. The assistant needed to drill into the raw trace data.

The Message: Uploading and Executing the Trace Parser

Message [msg 1401] consists of a single compound bash command:

scp /home/theuser/glm-kimi-sm120-rtx6000bw/extract_copy_info.py root@10.1.230.174:/tmp/ && ssh root@10.1.230.174 'python3 /tmp/extract_copy_info.py' 2>&1

This command does two things sequentially. First, it uses scp to copy a Python script (extract_copy_info.py) from the local machine to the remote inference server at IP 10.1.230.174. Second, it uses ssh to execute that script on the remote machine and capture its output (redirecting stderr to stdout with 2>&1).

The script itself had been written in the preceding message ([msg 1400]). The assistant's reasoning for writing it was explicit: "The trace JSON is 483MB so I can't download it all, but I can extract the relevant info on the container." This is a practical engineering decision — transferring half a gigabyte of JSON over the network to the local machine would be slow and wasteful when the relevant information could be extracted server-side with a targeted script.

The output of the command reveals the script's operation:

Loading trace from /tmp/decode_profile_trace.json...
Total events: 1423873

Found 65830 copy_/to_copy/unrolled events

--- First 20 copy_/_to_copy events with shapes ---

  Name: aten::_to_copy
  Duration: 31.016 us
  Concrete Inputs: ['', '15', '', '', '', 'False', '']
  Input Dims: [[1, 1, 32], [], [], [], [], [], []]
  Input type: ['float', 'Scalar', '', '', '', 'Scalar', '']

  Name: aten::copy_
  Duration: 17.295 us
  Concrete Inputs: ['', '', 'False']
  Input Dims: [[1, 1, 32], [1, 1, 32],...

The script loaded the full trace JSON (1,423,873 events), filtered to the 65,830 events related to copy_, _to_copy, and unrolled_elementwise_kernel operations, and began printing the first 20 entries with their tensor shapes, durations, and concrete input types.

The Critical Insight: Why Tensor Shapes Matter

The first two events printed show shapes of [1, 1, 32] — small tensors with durations of 31 and 17 microseconds respectively. These are clearly not the dominant bottleneck. The 2,340 large unrolled_elementwise_kernel calls (the ones consuming 69% of time) are not among the first 20 entries shown here.

But the script's output is truncated in the message — we see only the beginning. The critical discovery comes in the very next message ([msg 1402]), where the assistant continues the analysis and finds the kernel configuration for the large casts:

Shape: [495552, 1, 576] — this is the KV cache! 495,552 = the max number of KV cache tokens 576 = kv_lora_rank (512) + qk_rope (64) — the compressed MLA KV dimension It's casting the ENTIRE KV cache from FP8 to BF16 on EVERY LAYER!

This is the smoking gun. The KV cache — a preallocated buffer holding 495,552 token positions, each with 576 elements of compressed Multi-head Latent Attention (MLA) state — was being cast from FP8 (1 byte per element) to BF16 (2 bytes per element) on every single layer of every decode step. That's 285.6 million elements read and written per layer, totaling 857 MB of data movement per layer, or 66.8 GB per decode step across all 78 layers.

Assumptions and Knowledge Required

Understanding message [msg 1401] requires substantial background knowledge. The reader needs to know what a Chrome trace is (the JSON format used by PyTorch's profiler, compatible with Chrome's chrome://tracing viewer), how CUDA kernel launches are recorded in such traces, and how tensor shapes can be extracted from the profiler's event metadata. Knowledge of the GLM-5 model architecture is also essential — specifically that it uses 78 transformer layers with MoE, MLA attention with kv_lora_rank=512 and qk_rope_head=64, and that the KV cache is stored in FP8 format.

The assistant made several assumptions that proved correct. It assumed that the unrolled_elementwise_kernel was performing a dtype conversion (confirmed by the kernel name containing LoadWithCast and StoreWithCast). It assumed that the 2,340 calls across 30 steps corresponded to 78 calls per step, matching the model's layer count. And it assumed that the tensor shapes embedded in the trace could be extracted programmatically — which the script successfully did.

Output Knowledge Created

Message [msg 1401] itself produces only the partial output shown — the first 20 copy events with their shapes. But this output is the foundation for the breakthrough analysis that follows. The script establishes the methodology for extracting tensor shapes from the trace, which the assistant immediately builds upon in subsequent messages by writing more targeted scripts (trace_copy_parent.py in [msg 1402]) to correlate the large kernel launches with their CPU-level call sites and tensor identities.

The Thinking Process

The assistant's reasoning in this message is characteristic of a systematic debugger. Having identified a bottleneck at the aggregate level (69% of time in copy_), the next logical step is to identify what is being copied. The trace JSON is too large to transfer, so the assistant writes a targeted extraction script. The script is designed to filter the 1.4 million events down to the relevant 65,830 copy operations and print their tensor dimensions — the key metadata needed to identify the data source.

The choice to show only the first 20 events is a practical one: the full 65,830 events would overwhelm the conversation context. The assistant trusts that the pattern will be visible in the first few entries, and indeed the critical [495552, 1, 576] shape appears among them (visible in the subsequent analysis).

A Missed Opportunity

One subtle mistake in this message: the output is truncated by the conversation display, showing only the first two small events. The truly important entries — the ones with shape [495552, 1, 576] — are not visible in the message output as presented. The assistant compensates for this in the next message by writing a more sophisticated script that specifically searches for the large kernel launches and their parent CPU operations. But a more complete initial script might have sorted events by duration or filtered for the largest tensor sizes, surfacing the critical finding immediately rather than requiring an additional round trip.

Nevertheless, message [msg 1401] represents a textbook example of profiler-driven optimization: identify the hot spot at the aggregate level, then drill into the details with targeted instrumentation. The 483MB trace contained the answer; this message shows the moment of extraction.