diff --git a/pyproject.toml b/pyproject.toml index a2215844dd..41b4bb9760 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -136,7 +136,7 @@ aime2025 = { index = "primeintellect" } deepdive = { index = "primeintellect" } mini_swe_agent_plus = { index = "primeintellect" } deep-ep = { url = "https://github.com/samsja/flash-attn-builds/releases/download/v0.2/deep_ep-1.2.1+73b6ea4-cp312-cp312-linux_x86_64.whl" } -deep-gemm = { url = "https://github.com/samsja/flash-attn-builds/releases/download/v0.2/deep_gemm-2.3.0+477618c-cp312-cp312-linux_x86_64.whl" } +deep-gemm = { path = "tools/wheels/deep_gemm-2.3.0+d30fc36-cp312-cp312-linux_x86_64.whl" } nixl-cu12 = { url = "https://github.com/samsja/flash-attn-builds/releases/download/v0.2/nixl_cu12-0.10.1-cp312-cp312-linux_x86_64.whl" } flash-linear-attention = { git = "https://github.com/fla-org/flash-linear-attention" } diff --git a/src/prime_rl/configs/trainer.py b/src/prime_rl/configs/trainer.py index 1508a0a683..d06a1759fb 100644 --- a/src/prime_rl/configs/trainer.py +++ b/src/prime_rl/configs/trainer.py @@ -314,6 +314,14 @@ class ModelConfig(BaseModelConfig): ), ] = True + fp8: Annotated[ + bool, + Field( + description="Whether to use FP8 training via DeepGEMM. Replaces nn.Linear layers with FP8 blockwise linear " + "and uses FP8 grouped GEMM for MoE experts. Requires SM90 (Hopper) GPUs and model.impl='custom'.", + ), + ] = False + freeze_moe_router: Annotated[ bool, Field( @@ -401,6 +409,12 @@ def flash_attention_4_only_with_custom_impl(self): raise ValueError("Flash attention 4 is only supported with the custom implementation") return self + @model_validator(mode="after") + def fp8_only_with_custom_impl(self): + if self.fp8 and self.impl not in ("custom", "auto"): + raise ValueError("FP8 training is only supported with model.impl='custom' or 'auto'.") + return self + @model_validator(mode="after") def validate_ep_comm_backend(self): if self.ep_comm_backend == "torch": diff --git a/src/prime_rl/trainer/model.py b/src/prime_rl/trainer/model.py index 188b0f61f7..a76ee7fdc6 100644 --- a/src/prime_rl/trainer/model.py +++ b/src/prime_rl/trainer/model.py @@ -37,6 +37,7 @@ set_selective_activation_checkpointing, supports_selective_activation_checkpointing, ) +from prime_rl.trainer.models.layers.fp8_linear import replace_linear_with_fp8_blockwise_linear from prime_rl.trainer.models.layers.lm_head import inject_prime_lm_head from prime_rl.trainer.models.layers.moe import LatentMoE, MoE from prime_rl.trainer.parallel_dims import ParallelDims @@ -244,6 +245,7 @@ def get_model( if subconfig is not None and hasattr(subconfig, "use_cache"): subconfig.use_cache = False model_config.use_grouped_mm = config.moe_use_grouped_mm + model_config.fp8 = config.fp8 # Ensure pad_token_id is set (some models like Qwen3MoE don't have it). # In transformers v5, token IDs moved from PretrainedConfig to GenerationConfig. @@ -783,6 +785,9 @@ def setup_model( inject_prime_lm_head(model, chunk_size=lm_head_chunk_size, fused_cross_entropy=fused_cross_entropy) + if config.fp8: + replace_linear_with_fp8_blockwise_linear(model) + # Apply LoRA before FSDP setup if config.lora is not None: apply_lora_to_model(model, config.lora) diff --git a/src/prime_rl/trainer/models/afmoe/modeling_afmoe.py b/src/prime_rl/trainer/models/afmoe/modeling_afmoe.py index 136ad70306..384e0cff9d 100644 --- a/src/prime_rl/trainer/models/afmoe/modeling_afmoe.py +++ b/src/prime_rl/trainer/models/afmoe/modeling_afmoe.py @@ -352,6 +352,7 @@ def __init__(self, config: AfmoeConfig, layer_idx: int): top_k=config.num_experts_per_tok, use_grouped_mm=getattr(config, "use_grouped_mm", True), load_balance_coeff=getattr(config, "load_balance_coeff", None), + fp8=getattr(config, "fp8", False), ) if self.moe_enabled: self.mlp = MoE(moe_args, dim=config.hidden_size, hidden_dim=config.moe_intermediate_size) diff --git a/src/prime_rl/trainer/models/glm4_moe/modeling_glm4_moe.py b/src/prime_rl/trainer/models/glm4_moe/modeling_glm4_moe.py index 1263265a95..5bd880edd9 100644 --- a/src/prime_rl/trainer/models/glm4_moe/modeling_glm4_moe.py +++ b/src/prime_rl/trainer/models/glm4_moe/modeling_glm4_moe.py @@ -67,6 +67,7 @@ def __init__(self, config: Glm4MoeConfig, layer_idx: int): top_k=config.num_experts_per_tok, load_balance_coeff=1e-3, use_grouped_mm=config.use_grouped_mm, + fp8=getattr(config, "fp8", False), ) mlp_config = MLPConfig( hidden_size=config.hidden_size, diff --git a/src/prime_rl/trainer/models/glm_moe_dsa/modeling_glm_moe_dsa.py b/src/prime_rl/trainer/models/glm_moe_dsa/modeling_glm_moe_dsa.py index ecbe146e02..8fe56d7441 100644 --- a/src/prime_rl/trainer/models/glm_moe_dsa/modeling_glm_moe_dsa.py +++ b/src/prime_rl/trainer/models/glm_moe_dsa/modeling_glm_moe_dsa.py @@ -64,6 +64,7 @@ def __init__(self, config: GlmMoeDsaConfig, layer_idx: int): top_k=config.num_experts_per_tok, load_balance_coeff=1e-3, use_grouped_mm=config.use_grouped_mm, + fp8=getattr(config, "fp8", False), ) mlp_config = MLPConfig( hidden_size=config.hidden_size, diff --git a/src/prime_rl/trainer/models/kernels/fp8_utils.py b/src/prime_rl/trainer/models/kernels/fp8_utils.py new file mode 100644 index 0000000000..83dd81b52a --- /dev/null +++ b/src/prime_rl/trainer/models/kernels/fp8_utils.py @@ -0,0 +1,546 @@ +from __future__ import annotations + +from typing import Tuple + +import torch +import triton +import triton.language as tl + +FP8_MAX = 448.0 +MIN_SCALE = 1e-4 +GROUP_ALIGNMENT = 128 + + +def ceil_div(x: int, y: int) -> int: + return (x + y - 1) // y + + +def align(x: int, y: int) -> int: + return ceil_div(x, y) * y + + +# --------------------------------------------------------------------------- +# Layout building +# --------------------------------------------------------------------------- + + +def build_grouped_layout(offs: torch.Tensor, *, total_m: int | None = None): + assert offs.dim() == 1 + assert offs.dtype == torch.int32 + device = offs.device + total_m = (total_m if total_m is not None else int(offs[-1].item())) if offs.numel() else 0 + starts_tensor = torch.empty_like(offs) + if offs.numel() > 0: + starts_tensor[0] = 0 + if offs.numel() > 1: + starts_tensor[1:] = offs[:-1] + actual_ms_tensor = offs - starts_tensor + aligned_ms_tensor = ((actual_ms_tensor + GROUP_ALIGNMENT - 1) // GROUP_ALIGNMENT) * GROUP_ALIGNMENT + padded_ends = aligned_ms_tensor.cumsum(0) + block_starts_tensor = (padded_ends - aligned_ms_tensor) // GROUP_ALIGNMENT + ks_tensor = aligned_ms_tensor.contiguous() + padded_total_m = int(padded_ends[-1].item()) if offs.numel() else 0 + total_blocks = padded_total_m // GROUP_ALIGNMENT + grouped_layout = torch.empty((padded_total_m,), dtype=torch.int32, device=device) + block_to_group = torch.empty((total_blocks,), dtype=torch.int32, device=device) + if offs.numel(): + _build_grouped_layout_triton( + grouped_layout, + block_to_group, + starts_tensor, + actual_ms_tensor, + aligned_ms_tensor, + block_starts_tensor, + ) + return ( + total_m, + padded_total_m, + grouped_layout, + block_to_group, + ks_tensor, + starts_tensor, + actual_ms_tensor, + block_starts_tensor, + ) + + +def _build_grouped_layout_triton( + grouped_layout: torch.Tensor, + block_to_group: torch.Tensor, + starts_tensor: torch.Tensor, + actual_ms_tensor: torch.Tensor, + aligned_ms_tensor: torch.Tensor, + block_starts_tensor: torch.Tensor, +) -> None: + _build_grouped_layout_kernel[(actual_ms_tensor.numel(),)]( + grouped_layout, + block_to_group, + starts_tensor, + actual_ms_tensor, + aligned_ms_tensor, + block_starts_tensor, + BLOCK_M=128, + num_warps=4, + ) + + +# --------------------------------------------------------------------------- +# Triton kernels +# --------------------------------------------------------------------------- + + +@triton.jit +def _build_grouped_layout_kernel( + grouped_layout_ptr, + block_to_group_ptr, + starts_ptr, + actual_ms_ptr, + aligned_ms_ptr, + block_starts_ptr, + BLOCK_M: tl.constexpr, +): + pid_g = tl.program_id(axis=0) + actual_m = tl.load(actual_ms_ptr + pid_g) + aligned_m = tl.load(aligned_ms_ptr + pid_g) + block_start = tl.load(block_starts_ptr + pid_g) + dst_start = block_start * BLOCK_M + block_idx = 0 + while block_idx < (aligned_m // BLOCK_M): + row_offsets = block_idx * BLOCK_M + tl.arange(0, BLOCK_M) + values = tl.where(row_offsets < actual_m, pid_g, -1) + tl.store(grouped_layout_ptr + dst_start + row_offsets, values) + tl.store(block_to_group_ptr + block_start + block_idx, pid_g) + block_idx += 1 + + +@triton.jit +def _unpack_grouped_rows_kernel( + x_ptr, + block_to_group_ptr, + starts_ptr, + actual_ms_ptr, + block_starts_ptr, + out_ptr, + cols, + stride_xm, + stride_xn, + stride_ym, + stride_yn, + BLOCK_M: tl.constexpr, + BLOCK_N: tl.constexpr, + GROUP_BLOCK_M: tl.constexpr, +): + pid_blk = tl.program_id(axis=0) + pid_sub = tl.program_id(axis=1) + pid_n = tl.program_id(axis=2) + pid_g = tl.load(block_to_group_ptr + pid_blk) + block_start = tl.load(block_starts_ptr + pid_g) + dst_start = tl.load(starts_ptr + pid_g) + actual_m = tl.load(actual_ms_ptr + pid_g) + row_offsets = (pid_blk - block_start) * GROUP_BLOCK_M + pid_sub * BLOCK_M + tl.arange(0, BLOCK_M) + col_offsets = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + valid_rows = row_offsets < actual_m + valid_cols = col_offsets < cols + x = tl.load( + x_ptr + + (pid_blk * GROUP_BLOCK_M + pid_sub * BLOCK_M + tl.arange(0, BLOCK_M))[:, None] * stride_xm + + col_offsets[None, :] * stride_xn, + mask=valid_rows[:, None] & valid_cols[None, :], + other=0.0, + ) + tl.store( + out_ptr + (dst_start + row_offsets)[:, None] * stride_ym + col_offsets[None, :] * stride_yn, + x, + mask=valid_rows[:, None] & valid_cols[None, :], + ) + + +@triton.jit +def _per_token_fp8_kernel( + x_ptr, + out_ptr, + sf_ptr, + rows, + cols, + stride_xm, + stride_xn, + stride_ym, + stride_yn, + stride_sm, + stride_sk, + USE_UE8M0: tl.constexpr, + BLOCK_M: tl.constexpr, + BLOCK_K: tl.constexpr, +): + pid_m = tl.program_id(axis=0) + pid_k = tl.program_id(axis=1) + row_offsets = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + col_offsets = pid_k * BLOCK_K + tl.arange(0, BLOCK_K) + mask = (row_offsets[:, None] < rows) & (col_offsets[None, :] < cols) + x = tl.load( + x_ptr + row_offsets[:, None] * stride_xm + col_offsets[None, :] * stride_xn, + mask=mask, + other=0.0, + ).to(tl.float32) + amax = tl.max(tl.abs(x), axis=1) + scale = tl.maximum(amax / 448.0, 1e-4) + if USE_UE8M0: + scale = tl.exp2(tl.ceil(tl.log2(scale))) + y = x / scale[:, None] + tl.store( + out_ptr + row_offsets[:, None] * stride_ym + col_offsets[None, :] * stride_yn, + y.to(tl.float8e4nv), + mask=mask, + ) + tl.store(sf_ptr + row_offsets * stride_sm + pid_k * stride_sk, scale, mask=row_offsets < rows) + + +@triton.jit +def _grouped_per_token_fp8_kernel( + x_ptr, + block_to_group_ptr, + starts_ptr, + actual_ms_ptr, + block_starts_ptr, + out_ptr, + sf_ptr, + cols, + stride_xm, + stride_xn, + stride_ym, + stride_yn, + stride_sm, + stride_sk, + USE_UE8M0: tl.constexpr, + BLOCK_M: tl.constexpr, + BLOCK_K: tl.constexpr, + GROUP_BLOCK_M: tl.constexpr, +): + pid_blk = tl.program_id(axis=0) + pid_sub = tl.program_id(axis=1) + pid_k = tl.program_id(axis=2) + pid_g = tl.load(block_to_group_ptr + pid_blk) + src_start = tl.load(starts_ptr + pid_g) + actual_m = tl.load(actual_ms_ptr + pid_g) + block_start = tl.load(block_starts_ptr + pid_g) + local_block = pid_blk - block_start + row_offsets = local_block * GROUP_BLOCK_M + pid_sub * BLOCK_M + tl.arange(0, BLOCK_M) + col_offsets = pid_k * BLOCK_K + tl.arange(0, BLOCK_K) + valid_rows = row_offsets < actual_m + valid_cols = col_offsets < cols + src_rows = src_start + row_offsets + dst_rows = pid_blk * GROUP_BLOCK_M + pid_sub * BLOCK_M + tl.arange(0, BLOCK_M) + x = tl.load( + x_ptr + src_rows[:, None] * stride_xm + col_offsets[None, :] * stride_xn, + mask=valid_rows[:, None] & valid_cols[None, :], + other=0.0, + ).to(tl.float32) + amax = tl.max(tl.abs(x), axis=1) + scale = tl.maximum(amax / 448.0, 1e-4) + if USE_UE8M0: + scale = tl.exp2(tl.ceil(tl.log2(scale))) + y = x / scale[:, None] + tl.store( + out_ptr + dst_rows[:, None] * stride_ym + col_offsets[None, :] * stride_yn, + y.to(tl.float8e4nv), + mask=valid_rows[:, None] & valid_cols[None, :], + ) + tl.store(sf_ptr + dst_rows * stride_sm + pid_k * stride_sk, scale, mask=valid_rows) + + +@triton.jit +def _grouped_per_channel_fp8_sm90_kmajor_kernel( + x_ptr, + block_to_group_ptr, + starts_ptr, + actual_ms_ptr, + aligned_ms_ptr, + block_starts_ptr, + out_ptr, + sf_ptr, + cols, + stride_xm, + stride_xn, + stride_sf0, + stride_sf1, + USE_UE8M0: tl.constexpr, + BLOCK_K: tl.constexpr, + BLOCK_N: tl.constexpr, +): + pid_blk = tl.program_id(axis=0) + pid_n = tl.program_id(axis=1) + pid_g = tl.load(block_to_group_ptr + pid_blk) + src_start = tl.load(starts_ptr + pid_g) + actual_m = tl.load(actual_ms_ptr + pid_g) + aligned_m = tl.load(aligned_ms_ptr + pid_g) + block_start = tl.load(block_starts_ptr + pid_g) + local_block = pid_blk - block_start + row_offsets = local_block * BLOCK_K + tl.arange(0, BLOCK_K) + col_offsets = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + valid_rows = row_offsets < actual_m + valid_cols = col_offsets < cols + src_rows = src_start + row_offsets + x = tl.load( + x_ptr + src_rows[:, None] * stride_xm + col_offsets[None, :] * stride_xn, + mask=valid_rows[:, None] & valid_cols[None, :], + other=0.0, + ).to(tl.float32) + amax = tl.max(tl.abs(x), axis=0) + scale = tl.maximum(amax / 448.0, 1e-4) + if USE_UE8M0: + scale = tl.exp2(tl.ceil(tl.log2(scale))) + y = x / scale[None, :] + flat_base = block_start * BLOCK_K * cols + out_ptrs = out_ptr + flat_base + col_offsets[:, None] * aligned_m + row_offsets[None, :] + tl.store(out_ptrs, tl.trans(y).to(tl.float8e4nv), mask=valid_cols[:, None] & (row_offsets[None, :] < aligned_m)) + tl.store( + sf_ptr + pid_blk * stride_sf0 + col_offsets * stride_sf1, + scale, + mask=valid_cols, + ) + + +@triton.jit +def _grouped_per_block_fp8_kernel( + x_ptr, + out_ptr, + sf_ptr, + groups, + rows, + cols, + stride_xg, + stride_xm, + stride_xn, + stride_yg, + stride_ym, + stride_yn, + stride_sg, + stride_sm, + stride_sn, + USE_UE8M0: tl.constexpr, + BLOCK_M: tl.constexpr, + BLOCK_N: tl.constexpr, +): + pid_g = tl.program_id(axis=0) + pid_m = tl.program_id(axis=1) + pid_n = tl.program_id(axis=2) + row_offsets = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + col_offsets = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + mask = (pid_g < groups) & (row_offsets[:, None] < rows) & (col_offsets[None, :] < cols) + x = tl.load( + x_ptr + pid_g * stride_xg + row_offsets[:, None] * stride_xm + col_offsets[None, :] * stride_xn, + mask=mask, + other=0.0, + ).to(tl.float32) + amax = tl.max(tl.abs(x)) + scale = tl.maximum(amax / 448.0, 1e-4) + if USE_UE8M0: + scale = tl.exp2(tl.ceil(tl.log2(scale))) + y = x / scale + tl.store( + out_ptr + pid_g * stride_yg + row_offsets[:, None] * stride_ym + col_offsets[None, :] * stride_yn, + y.to(tl.float8e4nv), + mask=mask, + ) + tl.store(sf_ptr + pid_g * stride_sg + pid_m * stride_sm + pid_n * stride_sn, scale, mask=pid_g < groups) + + +# --------------------------------------------------------------------------- +# Public quantization functions +# --------------------------------------------------------------------------- + + +def unpack_rows_triton( + x: torch.Tensor, + total_m: int, + block_to_group: torch.Tensor, + starts_tensor: torch.Tensor, + actual_ms_tensor: torch.Tensor, + block_starts_tensor: torch.Tensor, +) -> torch.Tensor: + out = torch.empty((total_m, x.size(1)), device=x.device, dtype=x.dtype) + if total_m == 0: + return out + grid = (block_to_group.numel(), GROUP_ALIGNMENT // 32, ceil_div(x.size(1), 128)) + _unpack_grouped_rows_kernel[grid]( + x, + block_to_group, + starts_tensor, + actual_ms_tensor, + block_starts_tensor, + out, + x.size(1), + x.stride(0), + x.stride(1), + out.stride(0), + out.stride(1), + BLOCK_M=32, + BLOCK_N=128, + GROUP_BLOCK_M=GROUP_ALIGNMENT, + num_warps=4, + ) + return out + + +def per_token_cast_to_fp8_triton( + x: torch.Tensor, use_ue8m0: bool, gran_k: int = GROUP_ALIGNMENT +) -> Tuple[torch.Tensor, torch.Tensor]: + assert x.dim() == 2 + assert gran_k == GROUP_ALIGNMENT + rows, cols = x.shape + out = torch.empty_like(x, dtype=torch.float8_e4m3fn) + sf = torch.empty((rows, ceil_div(cols, gran_k)), device=x.device, dtype=torch.float32) + grid = lambda meta: (ceil_div(rows, meta["BLOCK_M"]), ceil_div(cols, meta["BLOCK_K"])) + _per_token_fp8_kernel[grid]( + x, + out, + sf, + rows, + cols, + x.stride(0), + x.stride(1), + out.stride(0), + out.stride(1), + sf.stride(0), + sf.stride(1), + USE_UE8M0=use_ue8m0, + BLOCK_M=8, + BLOCK_K=gran_k, + num_warps=4, + ) + return out, sf + + +def grouped_per_token_cast_to_fp8_triton( + x: torch.Tensor, + padded_total_m: int, + block_to_group: torch.Tensor, + starts_tensor: torch.Tensor, + actual_ms_tensor: torch.Tensor, + block_starts_tensor: torch.Tensor, + use_ue8m0: bool, + gran_k: int = GROUP_ALIGNMENT, +) -> Tuple[torch.Tensor, torch.Tensor]: + assert x.dim() == 2 + assert gran_k == GROUP_ALIGNMENT + out = torch.empty((padded_total_m, x.size(1)), device=x.device, dtype=torch.float8_e4m3fn) + sf = torch.empty( + (padded_total_m, ceil_div(x.size(1), gran_k)), + device=x.device, + dtype=torch.float32, + ) + if block_to_group.numel() == 0: + return out, sf + grid = (block_to_group.numel(), GROUP_ALIGNMENT // 8, ceil_div(x.size(1), gran_k)) + _grouped_per_token_fp8_kernel[grid]( + x, + block_to_group, + starts_tensor, + actual_ms_tensor, + block_starts_tensor, + out, + sf, + x.size(1), + x.stride(0), + x.stride(1), + out.stride(0), + out.stride(1), + sf.stride(0), + sf.stride(1), + USE_UE8M0=use_ue8m0, + BLOCK_M=8, + BLOCK_K=gran_k, + GROUP_BLOCK_M=GROUP_ALIGNMENT, + num_warps=4, + ) + return out, sf + + +def grouped_per_channel_cast_to_fp8_sm90_kmajor_triton( + x: torch.Tensor, + padded_total_m: int, + block_to_group: torch.Tensor, + starts_tensor: torch.Tensor, + actual_ms_tensor: torch.Tensor, + ks_tensor: torch.Tensor, + block_starts_tensor: torch.Tensor, + use_ue8m0: bool, + gran_k: int = GROUP_ALIGNMENT, +) -> Tuple[torch.Tensor, torch.Tensor]: + assert x.dim() == 2 + assert gran_k == GROUP_ALIGNMENT + out = torch.empty((padded_total_m * x.size(1),), device=x.device, dtype=torch.float8_e4m3fn) + total_blocks = padded_total_m // gran_k + sf = torch.empty((total_blocks, x.size(1)), device=x.device, dtype=torch.float32) + if block_to_group.numel() == 0: + return out, sf.T + grid = (block_to_group.numel(), ceil_div(x.size(1), 128)) + _grouped_per_channel_fp8_sm90_kmajor_kernel[grid]( + x, + block_to_group, + starts_tensor, + actual_ms_tensor, + ks_tensor, + block_starts_tensor, + out, + sf, + x.size(1), + x.stride(0), + x.stride(1), + sf.stride(0), + sf.stride(1), + USE_UE8M0=use_ue8m0, + BLOCK_K=gran_k, + BLOCK_N=128, + num_warps=4, + ) + return out, sf.T + + +def grouped_per_block_cast_to_fp8_triton( + x: torch.Tensor, use_ue8m0: bool, gran_k: int = GROUP_ALIGNMENT +) -> Tuple[torch.Tensor, torch.Tensor]: + assert x.dim() == 3 + assert gran_k == GROUP_ALIGNMENT + groups, rows, cols = x.shape + out = torch.empty((groups, rows, cols), device=x.device, dtype=torch.float8_e4m3fn) + sf = torch.empty( + (groups, ceil_div(rows, gran_k), ceil_div(cols, gran_k)), + device=x.device, + dtype=torch.float32, + ) + grid = (groups, ceil_div(rows, gran_k), ceil_div(cols, gran_k)) + _grouped_per_block_fp8_kernel[grid]( + x, + out, + sf, + groups, + rows, + cols, + x.stride(0), + x.stride(1), + x.stride(2), + out.stride(0), + out.stride(1), + out.stride(2), + sf.stride(0), + sf.stride(1), + sf.stride(2), + USE_UE8M0=use_ue8m0, + BLOCK_M=gran_k, + BLOCK_N=gran_k, + num_warps=8, + ) + return out, sf + + +def per_block_cast_to_fp8_triton( + x: torch.Tensor, use_ue8m0: bool, gran_k: int = GROUP_ALIGNMENT +) -> Tuple[torch.Tensor, torch.Tensor]: + assert x.dim() == 2 + out, sf = grouped_per_block_cast_to_fp8_triton( + x.unsqueeze(0), + use_ue8m0, + gran_k, + ) + return out[0], sf[0] diff --git a/src/prime_rl/trainer/models/kernels/sparse_mla_bwd.py b/src/prime_rl/trainer/models/kernels/sparse_mla_bwd.py index 67350332a8..2310b05395 100644 --- a/src/prime_rl/trainer/models/kernels/sparse_mla_bwd.py +++ b/src/prime_rl/trainer/models/kernels/sparse_mla_bwd.py @@ -1,5 +1,14 @@ # Vendored from tile-ai/tilelang (Apache 2.0), modified for dynamic shapes. +# TileLang ships a libcudart stub that proxies to the real CUDA runtime via +# dlsym(RTLD_DEFAULT, ...). If the stub's own symbols are the first ones found +# (because nothing loaded the real libcudart globally yet), the self-check fails +# and the stub calls abort(). Pre-loading the real library with RTLD_GLOBAL +# ensures dlsym finds it before the stub's own exports. +import ctypes as _ctypes + +_ctypes.CDLL("libcudart.so", mode=_ctypes.RTLD_GLOBAL) + import tilelang import torch from tilelang import language as T diff --git a/src/prime_rl/trainer/models/kernels/sparse_mla_fwd.py b/src/prime_rl/trainer/models/kernels/sparse_mla_fwd.py index 46af33f8e6..c93b8a3256 100644 --- a/src/prime_rl/trainer/models/kernels/sparse_mla_fwd.py +++ b/src/prime_rl/trainer/models/kernels/sparse_mla_fwd.py @@ -1,5 +1,14 @@ # Vendored from tile-ai/tilelang (Apache 2.0), modified for dynamic shapes. +# TileLang ships a libcudart stub that proxies to the real CUDA runtime via +# dlsym(RTLD_DEFAULT, ...). If the stub's own symbols are the first ones found +# (because nothing loaded the real libcudart globally yet), the self-check fails +# and the stub calls abort(). Pre-loading the real library with RTLD_GLOBAL +# ensures dlsym finds it before the stub's own exports. +import ctypes as _ctypes + +_ctypes.CDLL("libcudart.so", mode=_ctypes.RTLD_GLOBAL) + import tilelang from tilelang import language as T diff --git a/src/prime_rl/trainer/models/layers/fp8_grouped_gemm.py b/src/prime_rl/trainer/models/layers/fp8_grouped_gemm.py new file mode 100644 index 0000000000..f6217cdb11 --- /dev/null +++ b/src/prime_rl/trainer/models/layers/fp8_grouped_gemm.py @@ -0,0 +1,219 @@ +from __future__ import annotations + +import deep_gemm +import torch + +from prime_rl.trainer.models.kernels.fp8_utils import ( + GROUP_ALIGNMENT, + build_grouped_layout, + grouped_per_block_cast_to_fp8_triton, + grouped_per_channel_cast_to_fp8_sm90_kmajor_triton, + grouped_per_token_cast_to_fp8_triton, + unpack_rows_triton, +) + + +def _compute_grad_weight( + x: torch.Tensor, + grad_output: torch.Tensor, + weight_shape: torch.Size, + padded_total_m: int, + block_to_group: torch.Tensor, + ks_tensor: torch.Tensor, + starts_tensor: torch.Tensor, + actual_ms_tensor: torch.Tensor, + block_starts_tensor: torch.Tensor, + aligned_ms: list[int], +) -> torch.Tensor: + x_k_major = grouped_per_channel_cast_to_fp8_sm90_kmajor_triton( + x, + padded_total_m, + block_to_group, + starts_tensor, + actual_ms_tensor, + ks_tensor, + block_starts_tensor, + False, + GROUP_ALIGNMENT, + ) + dy_k_major = grouped_per_channel_cast_to_fp8_sm90_kmajor_triton( + grad_output, + padded_total_m, + block_to_group, + starts_tensor, + actual_ms_tensor, + ks_tensor, + block_starts_tensor, + False, + GROUP_ALIGNMENT, + ) + grad_weight = torch.zeros(weight_shape, device=x.device, dtype=torch.float32) + deep_gemm.k_grouped_fp8_gemm_nt_contiguous( + x_k_major, + dy_k_major, + grad_weight, + aligned_ms, + ks_tensor, + grad_weight, + ) + return grad_weight.to(torch.bfloat16) + + +class _GroupedFP8Gemm(torch.autograd.Function): + @staticmethod + def forward( + ctx, + x: torch.Tensor, + weight: torch.Tensor, + offs: torch.Tensor, + ) -> torch.Tensor: + ( + total_m, + padded_total_m, + grouped_layout, + block_to_group, + ks_tensor, + starts_tensor, + actual_ms_tensor, + block_starts_tensor, + ) = build_grouped_layout(offs, total_m=x.size(0)) + + x_fp8 = grouped_per_token_cast_to_fp8_triton( + x, + padded_total_m, + block_to_group, + starts_tensor, + actual_ms_tensor, + block_starts_tensor, + False, + GROUP_ALIGNMENT, + ) + weight_fp8 = grouped_per_block_cast_to_fp8_triton( + weight.transpose(1, 2), + False, + GROUP_ALIGNMENT, + ) + + out_padded = torch.empty( + (padded_total_m, weight.size(2)), + device=x.device, + dtype=x.dtype, + ) + deep_gemm.m_grouped_fp8_gemm_nt_contiguous( + x_fp8, + weight_fp8, + out_padded, + grouped_layout, + use_psum_layout=False, + ) + out = unpack_rows_triton( + out_padded, + total_m, + block_to_group, + starts_tensor, + actual_ms_tensor, + block_starts_tensor, + ) + + ctx.padded_total_m = padded_total_m + ctx.aligned_ms = ks_tensor.tolist() + ctx.save_for_backward( + x, + weight, + grouped_layout, + block_to_group, + ks_tensor, + starts_tensor, + actual_ms_tensor, + block_starts_tensor, + ) + return out + + @staticmethod + def backward(ctx, grad_output: torch.Tensor): + ( + x, + weight, + grouped_layout, + block_to_group, + ks_tensor, + starts_tensor, + actual_ms_tensor, + block_starts_tensor, + ) = ctx.saved_tensors + padded_total_m = ctx.padded_total_m + aligned_ms = ctx.aligned_ms + grad_output = grad_output.contiguous() + + grad_x = grad_weight = None + + if ctx.needs_input_grad[1]: + grad_weight = _compute_grad_weight( + x, + grad_output, + weight.shape, + padded_total_m, + block_to_group, + ks_tensor, + starts_tensor, + actual_ms_tensor, + block_starts_tensor, + aligned_ms, + ) + + if ctx.needs_input_grad[0]: + dy_fp8 = grouped_per_token_cast_to_fp8_triton( + grad_output, + padded_total_m, + block_to_group, + starts_tensor, + actual_ms_tensor, + block_starts_tensor, + False, + GROUP_ALIGNMENT, + ) + weight_dx_fp8 = grouped_per_block_cast_to_fp8_triton( + weight, + False, + GROUP_ALIGNMENT, + ) + grad_x_padded = torch.empty( + (padded_total_m, weight.size(1)), + device=grad_output.device, + dtype=grad_output.dtype, + ) + deep_gemm.m_grouped_fp8_gemm_nt_contiguous( + dy_fp8, + weight_dx_fp8, + grad_x_padded, + grouped_layout, + use_psum_layout=False, + ) + grad_x = unpack_rows_triton( + grad_x_padded, + x.size(0), + block_to_group, + starts_tensor, + actual_ms_tensor, + block_starts_tensor, + ) + + return grad_x, grad_weight, None + + +def grouped_fp8_gemm( + x: torch.Tensor, + weight: torch.Tensor, + offs: torch.Tensor, +) -> torch.Tensor: + """FP8 grouped GEMM, drop-in replacement for torch._grouped_mm. + + Args: + x: (M, K) concatenated token activations in bfloat16. + weight: (G, K, N) expert weights in bfloat16. + offs: (G,) int32 cumulative token counts per expert. + + Returns: + (M, N) output tensor in bfloat16. + """ + return _GroupedFP8Gemm.apply(x, weight, offs) diff --git a/src/prime_rl/trainer/models/layers/fp8_linear.py b/src/prime_rl/trainer/models/layers/fp8_linear.py new file mode 100644 index 0000000000..32101445fd --- /dev/null +++ b/src/prime_rl/trainer/models/layers/fp8_linear.py @@ -0,0 +1,114 @@ +from __future__ import annotations + +import re + +import deep_gemm +import torch +from torch import nn + +from prime_rl.trainer.models.kernels.fp8_utils import ( + per_block_cast_to_fp8_triton, + per_token_cast_to_fp8_triton, +) +from prime_rl.utils.logger import get_logger + + +class _FP8BlockwiseMM(torch.autograd.Function): + @staticmethod + def forward(ctx, x, weight, block_size, out_dtype=torch.bfloat16): + x_shape = x.shape + x_2d = x.reshape(-1, x_shape[-1]).contiguous() + x_fp8 = per_token_cast_to_fp8_triton(x_2d, False, block_size) + weight_fp8 = per_block_cast_to_fp8_triton(weight, False, block_size) + + out = torch.empty((x_2d.size(0), weight.size(0)), device=x.device, dtype=out_dtype) + deep_gemm.fp8_gemm_nt(x_fp8, weight_fp8, out) + + ctx.save_for_backward(x_2d, weight) + ctx.x_shape = x_shape + ctx.block_size = block_size + return out.reshape(*x_shape[:-1], out.size(-1)) + + @staticmethod + def backward(ctx, grad_output): + x_2d, weight = ctx.saved_tensors + block_size = ctx.block_size + grad_output_2d = grad_output.reshape(-1, grad_output.shape[-1]).contiguous() + + grad_x = grad_weight = None + if ctx.needs_input_grad[0]: + grad_output_fp8 = per_token_cast_to_fp8_triton(grad_output_2d, False, block_size) + weight_t = weight.transpose(0, 1).contiguous() + weight_dx_fp8 = per_block_cast_to_fp8_triton(weight_t, False, block_size) + grad_x_2d = torch.empty_like(x_2d) + deep_gemm.fp8_gemm_nt(grad_output_fp8, weight_dx_fp8, grad_x_2d) + grad_x = grad_x_2d.reshape(ctx.x_shape) + + if ctx.needs_input_grad[1]: + grad_output_t = grad_output_2d.transpose(0, 1).contiguous() + x_t = x_2d.transpose(0, 1).contiguous() + grad_output_t_fp8 = per_token_cast_to_fp8_triton(grad_output_t, False, block_size) + x_t_fp8 = per_token_cast_to_fp8_triton(x_t, False, block_size) + grad_weight_fp32 = torch.zeros_like(weight, dtype=torch.float32) + deep_gemm.fp8_gemm_nt( + grad_output_t_fp8, + x_t_fp8, + grad_weight_fp32, + c=grad_weight_fp32, + recipe=(1, 1, 128), + ) + grad_weight = grad_weight_fp32.to(weight.dtype) + + return grad_x, grad_weight, None, None + + +class Float8BlockwiseLinear(nn.Linear): + """nn.Linear replacement that uses FP8 blockwise matmul via DeepGEMM. + + Requires: + - SM90 (Hopper) GPU + - bfloat16 inputs/weights + - No bias + - in_features and out_features divisible by 128 + """ + + def __init__(self, *args, block_size: int = 128, dtype=torch.bfloat16, **kwargs): + super().__init__(*args, **kwargs) + self.block_size = block_size + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return _FP8BlockwiseMM.apply(x, self.weight, self.block_size, torch.bfloat16) + + @classmethod + def from_linear(cls, mod: nn.Linear) -> "Float8BlockwiseLinear": + """Convert an existing nn.Linear to Float8BlockwiseLinear.""" + with torch.device("meta"): + new_mod = cls( + mod.in_features, + mod.out_features, + bias=mod.bias is not None, + ) + new_mod.weight = mod.weight + new_mod.bias = mod.bias + return new_mod + + +def replace_linear_with_fp8_blockwise_linear( + model: nn.Module, ignore_modules: list[str] = ["lm_head", "model.layers.*.router.gate"] +) -> None: + logger = get_logger() + logger.info("Replacing linear layers with FP8 blockwise linear layers") + replaced_modules = [] + named_modules = dict(model.named_modules()) + for name, module in named_modules.items(): + if name in ignore_modules or any(re.match(pattern, name) for pattern in ignore_modules): + continue + if isinstance(module, nn.Linear): + parent_name, attr_name = name.rsplit(".", 1) if "." in name else ("", name) + parent = model.get_submodule(parent_name) if parent_name else model + setattr(parent, attr_name, Float8BlockwiseLinear.from_linear(module)) + replaced_modules.append(name) + + logger.info( + f"Replaced {len(replaced_modules)} linear layers with FP8 blockwise linear layers: {replaced_modules[:5]}..." + ) diff --git a/src/prime_rl/trainer/models/layers/moe.py b/src/prime_rl/trainer/models/layers/moe.py index f6f5649cce..f29f02b288 100644 --- a/src/prime_rl/trainer/models/layers/moe.py +++ b/src/prime_rl/trainer/models/layers/moe.py @@ -30,6 +30,7 @@ class MoEArgs: top_k: int = 1 use_grouped_mm: bool = True # grouped mm or for-loop for the experts computation load_balance_coeff: float | None = 1e-3 + fp8: bool = False # use FP8 grouped GEMM via DeepGEMM (requires SM90) # can be used as dense FFN layer or shared experts in MoE layers @@ -144,20 +145,39 @@ def _run_experts_grouped_mm( return _run_experts_grouped_mm_impl(w1, w2, w3, x, num_tokens_per_expert) +@expert_parallel +def _run_experts_fp8_grouped_mm( + w1: torch.Tensor, + w2: torch.Tensor, + w3: torch.Tensor, + x: torch.Tensor, + num_tokens_per_expert: torch.Tensor, +) -> torch.Tensor: + return _run_experts_grouped_mm_impl(w1, w2, w3, x, num_tokens_per_expert, fp8=True) + + def _run_experts_grouped_mm_impl( w1: torch.Tensor, w2: torch.Tensor, w3: torch.Tensor, x: torch.Tensor, num_tokens_per_expert: torch.Tensor, + fp8: bool = False, ) -> torch.Tensor: offsets = torch.cumsum(num_tokens_per_expert, dim=0, dtype=torch.int32) # grouped mm between a 2D tensor and a 3D tensor assert x.dim() == 2 - h = F.silu(torch._grouped_mm(x.bfloat16(), w1.bfloat16().transpose(-2, -1), offs=offsets)) - h = h * torch._grouped_mm(x.bfloat16(), w3.bfloat16().transpose(-2, -1), offs=offsets) - out = torch._grouped_mm(h, w2.bfloat16().transpose(-2, -1), offs=offsets).type_as(x) + if fp8: + from prime_rl.trainer.models.layers.fp8_grouped_gemm import grouped_fp8_gemm + + h = F.silu(grouped_fp8_gemm(x.bfloat16(), w1.bfloat16().transpose(-2, -1), offsets)) + h = h * grouped_fp8_gemm(x.bfloat16(), w3.bfloat16().transpose(-2, -1), offsets) + out = grouped_fp8_gemm(h, w2.bfloat16().transpose(-2, -1), offsets).type_as(x) + else: + h = F.silu(torch._grouped_mm(x.bfloat16(), w1.bfloat16().transpose(-2, -1), offs=offsets)) + h = h * torch._grouped_mm(x.bfloat16(), w3.bfloat16().transpose(-2, -1), offs=offsets) + out = torch._grouped_mm(h, w2.bfloat16().transpose(-2, -1), offs=offsets).type_as(x) return out @@ -169,6 +189,7 @@ def __init__( hidden_dim: int, num_experts: int, use_grouped_mm: bool, + fp8: bool = False, ): super().__init__() self.num_experts = num_experts @@ -176,6 +197,7 @@ def __init__( self.w2 = nn.Parameter(torch.empty(num_experts, dim, hidden_dim)) self.w3 = nn.Parameter(torch.empty(num_experts, hidden_dim, dim)) self.use_grouped_mm = use_grouped_mm + self.fp8 = fp8 self.ep_comm_backend: EPCommBackend = "torch" def set_ep_comm_backend(self, backend: EPCommBackend) -> None: @@ -186,7 +208,7 @@ def _forward_deepep(self, x: torch.Tensor, num_tokens_per_expert: torch.Tensor) w2 = self.w2.to_local() w3 = self.w3.to_local() if self.use_grouped_mm: - return _run_experts_grouped_mm_impl(w1, w2, w3, x, num_tokens_per_expert) + return _run_experts_grouped_mm_impl(w1, w2, w3, x, num_tokens_per_expert, fp8=self.fp8) return _run_experts_for_loop_impl(w1, w2, w3, x, num_tokens_per_expert) def forward( @@ -198,6 +220,8 @@ def forward( return self._forward_deepep(x, num_tokens_per_expert) if self.use_grouped_mm: + if self.fp8: + return _run_experts_fp8_grouped_mm(self.w1, self.w2, self.w3, x, num_tokens_per_expert) return _run_experts_grouped_mm(self.w1, self.w2, self.w3, x, num_tokens_per_expert) else: return _run_experts_for_loop(self.w1, self.w2, self.w3, x, num_tokens_per_expert) @@ -373,6 +397,7 @@ def __init__(self, moe_args: MoEArgs, dim: int, hidden_dim: int): hidden_dim=hidden_dim, num_experts=num_experts, use_grouped_mm=moe_args.use_grouped_mm, + fp8=moe_args.fp8, ) self.ep_comm_backend: EPCommBackend = "torch" self.experts.set_ep_comm_backend(self.ep_comm_backend) diff --git a/src/prime_rl/trainer/models/minimax_m2/modeling_minimax_m2.py b/src/prime_rl/trainer/models/minimax_m2/modeling_minimax_m2.py index 48acfca2d2..96b1eb7e22 100644 --- a/src/prime_rl/trainer/models/minimax_m2/modeling_minimax_m2.py +++ b/src/prime_rl/trainer/models/minimax_m2/modeling_minimax_m2.py @@ -54,6 +54,7 @@ def __init__(self, config: MiniMaxM2Config, layer_idx: int): top_k=config.num_experts_per_tok, use_grouped_mm=config.use_grouped_mm, load_balance_coeff=1e-3 if config.use_routing_bias else None, + fp8=getattr(config, "fp8", False), ) self.mlp = MoE(moe_args, dim=config.hidden_size, hidden_dim=config.intermediate_size) diff --git a/src/prime_rl/trainer/models/qwen3_5_moe/modeling_qwen3_5_moe.py b/src/prime_rl/trainer/models/qwen3_5_moe/modeling_qwen3_5_moe.py index d26ebc627d..b6dba415ad 100644 --- a/src/prime_rl/trainer/models/qwen3_5_moe/modeling_qwen3_5_moe.py +++ b/src/prime_rl/trainer/models/qwen3_5_moe/modeling_qwen3_5_moe.py @@ -582,6 +582,7 @@ def __init__(self, config: Qwen3_5MoeConfig, layer_idx: int): top_k=config.num_experts_per_tok, use_grouped_mm=config.use_grouped_mm, load_balance_coeff=config.load_balance_coeff, + fp8=getattr(config, "fp8", False), ) self.mlp = MoE(moe_args, dim=config.hidden_size, hidden_dim=config.moe_intermediate_size) diff --git a/src/prime_rl/trainer/models/qwen3_moe/modeling_qwen3_moe.py b/src/prime_rl/trainer/models/qwen3_moe/modeling_qwen3_moe.py index f09cf6b098..ccd95a1d4c 100644 --- a/src/prime_rl/trainer/models/qwen3_moe/modeling_qwen3_moe.py +++ b/src/prime_rl/trainer/models/qwen3_moe/modeling_qwen3_moe.py @@ -72,6 +72,7 @@ def __init__(self, config: Qwen3MoeConfig, layer_idx: int): top_k=config.num_experts_per_tok, use_grouped_mm=config.use_grouped_mm, load_balance_coeff=config.load_balance_coeff, + fp8=getattr(config, "fp8", False), ) mlp_config = MLPConfig( hidden_size=config.hidden_size, diff --git a/uv.lock b/uv.lock index 3ec8cb2104..3d0ffb409d 100644 --- a/uv.lock +++ b/uv.lock @@ -84,8 +84,16 @@ 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