FP8 custom ops #19830
FP8 custom ops #19830
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Summary of Changes
Hello @ProExpertProg, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request significantly refactors and centralizes the FP8 quantization logic within the codebase. By introducing a new QuantFP8 abstraction, it provides a more unified, configurable, and maintainable approach to handling 8-bit floating-point operations, which are crucial for efficient model execution. The changes also include a new benchmark to validate the performance and correctness of these FP8 operations.
Highlights
- New FP8 Quantization Abstraction: Introduced a new
QuantFP8class invllm/model_executor/layers/fp8_quantization.py. This class provides a unified and configurable interface for FP8 quantization, supporting both per-token and per-tensor dynamic/static quantization. It encapsulates the logic for both native (PyTorch) and CUDA implementations. - Refactoring and Integration: Refactored existing FP8 quantization calls across various modules (e.g.,
Fp8LinearOp, attention backends, and different quantization schemes like FBGEMM, ModelOpt, PTPC, Quark) to utilize the newQuantFP8class. This replaces direct calls toops.scaled_fp8_quantwith calls to instances ofQuantFP8, making the quantization logic more modular and consistent. - Improved Configurability: Enhanced the configurability of FP8 quantization strategies by introducing parameters like
act_quant_static(for static vs. dynamic quantization) andact_quant_group_shape(for per-token vs. per-tensor grouping) to theFp8LinearOpandQuantFP8classes. This allows for more flexible and optimized FP8 usage based on hardware capabilities and model requirements. - New FP8 Quantization Benchmark: Added a new benchmark script (
benchmarks/kernels/bench_per_token_quant_fp8.py) to compare and measure the performance of per-token FP8 quantization using both PyTorch's native implementation and CUDA operations. This helps in verifying correctness and assessing performance gains.
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Code Review
This pull request introduces a new QuantFP8 class to encapsulate FP8 quantization logic and refactors existing code to use this class. The changes improve code organization and prepare for more flexible quantization schemes. However, there are several high and critical severity issues related to the correct initialization and usage of the new QuantFP8 class in various parts of the codebase, particularly in the attention backends and one of the compressed tensors schemes. Additionally, the QuantFP8 class itself has limitations in its native implementation and an assertion that might be too restrictive depending on supported quantization types. Addressing these issues is crucial for correctness and stability.
| @@ -19,10 +21,11 @@ def __init__(self, *args, **kwargs): | |||
| super().__init__(*args, **kwargs) | |||
| self.silu_and_mul = SiluAndMul() | |||
| self.scale = torch.rand(1, dtype=torch.float32) | |||
| self.quant_fp8 = QuantFP8(GroupShape.PER_TOKEN) | |||
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The QuantFP8 class requires both static and group_shape arguments during initialization. Based on the usage in the forward method (self.quant_fp8(y, self.scale)), it appears static quantization is intended here. However, initializing with static=True and GroupShape.PER_TOKEN might conflict with an assertion in the QuantFP8.__init__ method (see comment in vllm/model_executor/layers/fp8_quantization.py). Please clarify the intended quantization scheme for this test and initialize QuantFP8 accordingly, potentially requiring changes to the QuantFP8 class itself if static per-token quantization is a supported use case.
| self.quant_fp8 = QuantFP8(GroupShape.PER_TOKEN) | |
| self.quant_fp8 = QuantFP8(static=True, group_shape=GroupShape.PER_TOKEN) |
| @@ -371,6 +371,10 @@ def __init__( | |||
| else: | |||
| self.sliding_window = (sliding_window - 1, 0) | |||
| self.kv_cache_dtype = kv_cache_dtype | |||
| self.is_fp8 = self.kv_cache_dtype.startswith("fp8") | |||
| if self.is_fp8: | |||
| self.quant_fp8 = QuantFP8() | |||
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The QuantFP8 class requires both static and group_shape arguments during initialization. Initializing it without arguments will likely result in an error or incorrect default behavior. For attention inputs (Q/K/V), dynamic per-token quantization is typically used. Please initialize self.quant_fp8 with the correct parameters.
| self.quant_fp8 = QuantFP8() | |
| self.quant_fp8 = QuantFP8(static=False, group_shape=GroupShape.PER_TOKEN) |
| (num_tokens, num_heads * head_size)).contiguous(), | ||
| layer._q_scale) | ||
| query = query.reshape((num_tokens, num_heads * head_size)).contiguous() | ||
| query, _ = ops.scaled_fp8_quant(query, layer._q_scale) |
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When using dynamic quantization (which is typical for attention inputs), the scale is computed internally by the QuantFP8 object. Passing layer._q_scale as the scale argument here seems incorrect for a dynamic quantization scheme. If layer._q_scale is intended to be an upper bound for the scale, it should be passed as the scale_ub argument instead.
| query, _ = ops.scaled_fp8_quant(query, layer._q_scale) | |
| query, _ = self.quant_fp8(query, scale_ub=layer._q_scale) |
| @@ -258,6 +260,10 @@ def __init__( | |||
| else: | |||
| self.sliding_window = (sliding_window - 1, 0) | |||
| self.kv_cache_dtype = kv_cache_dtype | |||
| self.is_fp8 = self.kv_cache_dtype.startswith("fp8") | |||
| if self.is_fp8: | |||
| self.quant_fp8 = QuantFP8(GroupShape.PER_TENSOR) | |||
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The QuantFP8 class requires both static and group_shape arguments during initialization. Initializing it with only GroupShape.PER_TENSOR is incomplete. For attention inputs (Q/K/V), dynamic per-token quantization is typically used. Please initialize self.quant_fp8 with the correct parameters.
| self.quant_fp8 = QuantFP8(GroupShape.PER_TENSOR) | |
| self.quant_fp8 = QuantFP8(static=False, group_shape=GroupShape.PER_TOKEN) |
| layer._q_scale) | ||
| query = query.reshape((num_tokens, num_heads, head_size)) | ||
| query = query.reshape((num_tokens, num_heads * head_size)).contiguous() | ||
| query, _ = ops.scaled_fp8_quant(query, layer._q_scale) |
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When using dynamic quantization (which is typical for attention inputs), the scale is computed internally by the QuantFP8 object. Passing layer._q_scale as the scale argument here seems incorrect for a dynamic quantization scheme. If layer._q_scale is intended to be an upper bound for the scale, it should be passed as the scale_ub argument instead.
| query, _ = ops.scaled_fp8_quant(query, layer._q_scale) | |
| query, _ = self.quant_fp8(query, scale_ub=layer._q_scale) |
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This pull request has merge conflicts that must be resolved before it can be |
Signed-off-by: Luka Govedic <lgovedic@redhat.com>
Signed-off-by: Luka Govedic <lgovedic@redhat.com>
Signed-off-by: Luka Govedic <lgovedic@redhat.com>
Signed-off-by: Luka Govedic <lgovedic@redhat.com>
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